Emulation-based laboratory instruction has become an indispensable component of computer networking education, providing a strong practical complement to theoretical coursework. However, existing network emulation platforms are constrained in both usability and scalability, struggling to sustain stable operation and scale effectively under multi-host deployments with classroom-scale, high-concurrency workloads. To address these challenges, we propose Klonet , a scalable network emulation platform tailored for educational settings. Klonet integrates a browser–server (B/S) architecture, an intuitive graphical user interface (GUI), and course-oriented templates to streamline cross-host configuration, automated deployment, and centralized management, thereby reducing the operational burden on instructors and students. To support scalability, Klonet introduces a resource-aware virtual network mapping method that jointly considers CPU and bandwidth constraints, partitions experimental topologies when needed, and maps virtual nodes and links across multiple hosts, where a MaxRemain-based selection criterion effectively avoids resource fragmentation. Comprehensive evaluations under static workloads and dynamic operation demonstrate that Klonet can stably support networks with up to thousands of nodes, maintain a high deployment success ratio and efficient resource utilization across a range of task intensities, and outperform comparable schemes. These results indicate that Klonet strikes a practical balance among usability, manageability, and scalability for modern networking courses.

The persistent disparity between the slow growth of wide-area network bandwidth and the escalating demand for high-speed bulk data transfer poses a significant challenge for inter-datacenter data transmission. Existing approaches typically rely on optimized algorithms for dedicated networks and often struggle to meet the stringent deadline requirements of modern applications cost-effectively. To overcome these limitations, we propose a novel cloud-accelerated transfer approach that is intrinsically deadline-aware. We present a Profit-driven RelAxation-based TransfEr Scheduling (PRATER) system that leverages cloud proxies and multipath transmission to accelerate bulk data transfer. Specifically, we tackle the problem of profit maximization by jointly optimizing cloud proxy deployment and traffic allocation under transmission deadlines, a problem formally modeled and identified as NP-hard. To efficiently derive near-optimal solutions, we decompose this complex problem into two interconnected subproblems: cost minimization through proxy deployment optimization and revenue maximization via traffic allocation optimization. Our efficient iterative algorithm resolves these subproblems by minimizing cloud proxy and bandwidth costs while maximizing timely data transfer completions, thereby enhancing overall system profitability. Extensive experimental results demonstrate that our approach achieves a significant profit improvement of approximately 2.4×-6.1× compared to state-of-the-art existing algorithms.

With the development of information technology, the originally closed industrial control system (ICS) protocol has become more public. Existing protocol fuzzing techniques can be directly applied to public ICS protocols to discover their vulnerabilities. However, they do not consider how to obtain the complete ICS protocol message syntax and perform effective state-guided fuzzing based on this syntax. This causes a large number of generated test cases to fail the syntax checking of the ICS protocol program and are eventually discarded. In this paper, we propose CtrlFuzz, a control field aware protocol fuzzer for public ICS protocols. We find that most ICS protocol programs perform different processing based on the value of the control field when processing received messages. These messages with different control field values together constitute the complete ICS protocol message syntax. Based on the above observations, we propose an expert system-based extracting strategy for complete ICS protocol message syntax. Specifically, CtrlFuzz uses an expert system to identify and infer the control fields in ICS protocol messages. To narrow the scope of inference, CtrlFuzz uses static analysis techniques to extract specific values in the program as optional values of the control fields. Moreover, we propose a state-guided fuzzing strategy based on the inferred message syntax. Our evaluation shows that compared with the state-of-the-art protocol fuzzers AFLNET, BooFuzz, StateAFL and ChatAFL, CtrlFuzz improves branch coverage by 6.8%–10.9% and state coverage by 23.7%–147.4% on average within 24 hours. Moreover, CtrlFuzz has exposed a total of 4 unknown vulnerabilities, and all have been confirmed and fixed, with one assigned a CVE number.

The Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication efficiency and reduced latency. However, traditional centralized networks struggle to meet these demands, prompting the exploration of decentralized solutions such as blockchain. Addressing blockchain's scalability challenges posed by the growing number of nodes and transactions calls for innovative solutions, among which sharding stands out as a pivotal approach to significantly enhance blockchain throughput. However, existing schemes still face challenges related to a) the impact of vehicle mobility on blockchain consensus, especially for cross-shard transaction; and b) the strict requirements of low latency consensus in a highly dynamic network. In this paper, we propose a DAG (Directed Acyclic Graph) consensus leveraging Robust Dynamic Sharding and Tree-broadcasting (DRDST) to address these challenges. Specifically, we first develop a standard for evaluating the network stability of nodes, combined with the nodes' trust values, to propose a novel robust sharding model that is solved through the design of the Genetic Sharding Algorithm (GSA). Then, we optimize the broadcast latency of the whole sharded network by improving the tree-broadcasting to minimize the maximum broadcast latency within each shard. On this basis, we also design a DAG consensus scheme based on an improved hashgraph protocol, which can efficiently handle crossshard transactions. Finally, the simulation proves the proposed scheme is superior to the comparison schemes in latency, throughput, consensus success rate, and node traffic load.

The Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication efficiency and reduced latency. However, traditional centralized networks struggle to meet these demands, prompting the exploration of decentralized solutions such as blockchain. The scalability of blockchain systems, particularly within the context of IoV, requires innovative approaches to manage the growing number of nodes and transactions. In this paper, we propose a DAG (Directed Acyclic Graph) consensus leveraging Robust Dynamic Sharding and Tree-broadcasting (DRDST) to address these challenges. Specifically, we initially develop a standard for evaluating the network stability of nodes, combined with the nodes' trust values, to propose a novel robust sharding model that is solved through the design of the Genetic Sharding Algorithm (GSA). Then, we optimize the broadcast latency of the whole sharded network by improving the tree-broadcasting to minimize the maximum broadcast latency within each shard. On this basis, we also design a DAG consensus scheme based on an improved hashgraph protocol, which can efficiently handle cross-shard transactions. Finally, the simulation proves the proposed scheme is superior to the comparison schemes in terms of latency, throughput, and consensus success rate.

Training deep learning (DL) models demands substantial computational resources, often relying on expensive GPUs in a distributed manner. To meet this demand, cloud providers deploy GPU clusters worldwide to offer users compute instance rental services. These GPU clusters are typically heterogeneous, comprising multiple GPU types with varying computational capabilities, and their prices vary significantly across both GPU instance types and geographic regions. Meanwhile, users often have specific deadlines for training their models. Most existing works focus solely on performance, overlooking price heterogeneity and failing to optimize costs effectively. Given the high cost and time demands of model training, focusing on performance alone is insufficient. In this paper, we aim to balance performance and cost through a DL broker service that maximizes the number of jobs completed within their deadlines under a given budget. We propose CADDS, which jointly optimizes cluster placement and dynamically adjusts GPU type and quantity during training to reduce rental costs. We formulate the scheduling problem as an integer nonlinear programming problem and propose an efficient online approach combining greedy and dynamic programming. Experimental results demonstrate that CADDS significantly outperforms existing approaches, improving the deadline satisfactory ratio within budget limits by$\mathbf{5 9. 4 \%}$to$\mathbf{8 0. 2 \%}$.

This paper presents the MAPLE framework, which harnesses large language models (LLMs) to facilitate multi-agent collaboration for fully automated deployment and management of large-scale networks. Within MAPLE, a supervisor agent interprets natural language instructions from users, orchestrates specialized agents to execute tasks, and validates outcomes through integration with a network simulation platform. Experimental findings show that MAPLE outperforms single-agent approaches in terms of success rates for topology deployment and service configuration. Moreover, experiments reveal that by adaptively employing LLMs with varying capabilities according to task requirements and inter-agent dependencies, the framework effectively balances task success rates with cost efficiency.

Training distributed large language models (LLMs) for diverse user intents is challenging due to a high-dimensional, hybrid strategy space and heterogeneous workloads. We present SG2, a simulation-guided strategy generation framework that tackles intent-aware distributed LLM training by unifying Bayesian Optimization with a simulation-based evaluation loop. By leveraging near-realistic performance and intent-satisfaction metrics in simulation, SG2iteratively refines candidate training strategies before real deployment, significantly reducing exploration cost and failure risk. Experiments on heterogeneous LLM training workloads show that SG2consistently achieves higher intent-satisfaction rates and better multi-objective trade-offs than baselines, demonstrating its effectiveness for intent-aware distributed LLM training.

Today’s cloud-centric Large Language Model (LLM) inference can raise privacy concerns, as raw user data is sent over public networks. Split LLM inference offers a promising alternative by handling sensitive stages, such as prefill and input/output decoding, locally at the edge while offloading heavy intermediate decoding layers to the cloud. This approach could enhance privacy, yet it introduces a key challenge: how to transfer large intermediate vectors with ultra-low latency and high reliability over dynamic networks. We explore a potential transmission framework combining (i) dynamic congestion control to adapt flow and reduce queueing/loss, and (ii) adaptive FEC and quantization to balance redundancy with compression. Preliminary results indicate that this approach can achieve lower per-token latency and higher throughput than TCP/QUIC, enabling privacy-preserving LLM services that remain highly responsive and aligned with real-time user experience demands.

Deploying agentic AI services, such as large language models (LLMs) training and inference, presents significant challenges due to their complex, interdependent design across multiple layers of strategy space(framework, system, transport, network). Addressing diverse user intents with limited cross disciplinary expertise further exacerbates this complexity. To overcome these hurdles, we introduce a novel simulation-guided closed-loop (SGCL) strategy orchestration and optimization framework that is inherently intent-aware. Our approach leverages high-fidelity simulators to evaluate candidate deployment strategies, employs a surrogate-based Bayesian optimization engine to guide the closed-loop process, and incorporates a layer-wise caching mechanism to minimize redundant simulations and reduce evaluation overhead. We demonstrate its efficacy in distributed LLM training. Compared to baselines, our method consistently achieves higher intent satisfaction ratio and significantly boosts orchestration efficiency, notably reducing time-to-target by up to 94.4%. These findings highlight SGCL as a practical and effective solution for reliable, cost- and time efficient deployment of agentic AI services.

In the Internet of Things (IoT), blockchain-based cross-domain authentication schemes can effectively establish trust and share data across different administrative domains. However, current blockchain-based cross-domain authentication solutions often overlook dynamic node participation challenges, which is crucial for the flexible IoT environment. In this study, we propose a blockchain-based cross-domain authentication scheme that supports dynamic node participation, allowing administrative domainss to freely join or leave under legal conditions. First, we introduce an efficient blockchain-based cross-domain authentication framework. Second, we propose a blockchain consensus algorithm that supports dynamic node participation to serve the aforementioned framework. Specifically, inspired by the transmission control protocol (TCP) protocol’s piggybacking strategy, this algorithm integrates the process of node joining and leaving into the regular consensus flow to enhance efficiency. To further improve the algorithm’s efficiency, we designed compressed block and parallel chain structures to increase bandwidth utilization and throughput. Detailed correctness proofs demonstrate the algorithm’s security. Extensive experiments have been conducted to show that our scheme increases throughput by approximately 8x compared to existing approaches.

Mobile immersive streaming applications, such as virtual reality (VR) and augmented reality (AR), impose stringent requirements on network transmission to deliver a seamless user experience. However, existing transmission control methods often fall short in dynamic mobile network environments. Traditional transport-layer protocols typically prioritize network-wide Quality of Service (QoS), overlooking the strict deadlines and varying priorities intrinsic to immersive streaming content, thereby adversely impacting application-specific Quality of Experience (QoE). Furthermore, heuristic-based congestion control methods lack adaptability to rapidly changing network environments, whereas machine learning (ML)-driven approaches, despite their adaptability potential, frequently prove computationally intensive and impractical for deployment on resource-constrained mobile devices. To address these drawbacks, we propose an intelligent transmission control approach that integrates a deadline- and priority-aware scheduler with a learning enhanced congestion control mechanism to optimize immersive streaming content delivery. The scheduling module dynamically prioritizes data packets based on their urgency and importance, timely delivering critical data within strict deadlines as much as possible. Complementing this, the hybrid congestion control module strategically combines lightweight heuristics for baseline performance efficiency with a selectively invoked ML model designed to adaptively adjust the sending rate, efficiently responding to real-time fluctuations in network conditions. Experimental results highlight the effectiveness of our approach, achieving QoE improvements of 15% to 45% compared to existing methods across a range of streaming applications and mobile network scenarios.

The explosive growth in training data and model sizes has spurred the adoption of distributed deep learning (DL) in heterogeneous computing clusters. Efficiently scheduling distributed training jobs in such heterogeneous environments while ensuring they meet user-specified deadlines remains a critical challenge. While most existing works focus on reducing job completion time in homogeneous clusters, they pay little attention to meeting job deadlines in heterogeneous clusters. To address this issue, we proposeDancer(Deadline-Aware dyNamiC GPU allocation approach for Efficient Resource utilization), a novel framework that dynamically adjusts not only the number but the type of GPUs assigned to each job throughout its training lifecycle.Danceraims to maximize the number of jobs meeting their deadlines in heterogeneous GPU clusters. It decouples job placement from resource allocation and formulates the scheduling optimization problem for maximizing the number of deadline-meeting jobs as an Integer Linear Programming (ILP) problem. To solve this ILP problem in real-time, we propose an online algorithm with a competitive ratio guarantee, leveraging primal-dual and dynamic programming techniques. Extensive trace-driven simulations based on real-world DL workloads demonstrate thatDancersignificantly outperforms state-of-the-art approaches, improving the deadline satisfactory ratio up to 58.9%–74.2%.

Mobile Edge Computing (MEC) reduces the computational burden on terminal devices by shortening the distance between these devices and computing nodes. Integrating Uncrewed Aerial Vehicles (UAVs) with enhanced MEC networks can leverage the high mobility of UAVs to flexibly adjust network topology, further expanding the applicability of MEC. However, in highly dynamic and complex real-world environments, it is crucial to balance task offloading effectiveness with algorithm performance. This paper investigates a multi-UAV communication network equipped with edge computing nodes to assist terminal users in task computation. Our goal is to reduce the task processing delay for users through the joint optimization of discrete computation modes, continuous 3D trajectories, and resource assignment. To address the challenges posed by the mixed action space, we propose a Multi-UAV Edge Computing Resource Scheduling (MUECRS) algorithm, which comprises two key components: 1) trajectory optimization, and 2) computation mode and resource management. Experimental results show that our method effectively plans 3D UAV trajectories and enables rapid user coverage. Compared to state-of-the-art baselines, our approach achieves at least 16.5% reduction in task delay, demonstrating superior adaptability and robustness.

With the proliferation of mobile terminals, the continuous upgrading of services, 4G LTE networks are showing signs of weakness. To enhance the capacity of wireless networks, millimeter waves are introduced to drive the evolution of networks towards multi-band 5G heterogeneous networks. The distinct propagation characteristics of mmWaves, microwaves, as well as the vastly different hardware configurations of heterogeneous base stations, make traditional access strategies no longer effective. Therefore, to narrowing the gap between theory, practice, we investigate the access strategy in multi-band 5G heterogeneous networks, taking into account the characteristics of mobile users, asynchronous switching between uplink, downlink of pico base stations, asymmetric service requirements, user communication continuity. We formulate the problem as integer nonlinear programming, prove its intractability. Thereby, we decouple it into three subproblems: user association, switch point selection, subchannel allocation, design an algorithm based on optimal matching, spectral clustering to solve it efficiently. The simulation results show that the proposed algorithm outperforms the comparison methods in terms of overall data rate, effective data rate, number of satisfied users.

Nowadays, cross-device federated learning (FL) is the key to achieving personalization services for mobile users and has been widely employed by companies like Google, Microsoft, and Alibaba in production. With the explosive growth in the number of participants, the central FL server, which acts as the manager and aggregator of cross-device model training, would get overloaded, becoming the system bottlenecks. Inspired by the emerging wave of edge computing, an interesting question arises:Could edge clouds help cross-device FL systems overcome the bottleneck?This article provides a cautiously optimistic answer by proposingINP, a FL-specific In-Network Processing framework to achieve the goal. As in-network processing has broken the end-to-end principle of the involved communication and lacks the support of transport protocols, the key is to design domain-specific transport protocols forINP. To fill the gap, we propose the novel Model Download Protocol ofmdpand Model Upload Protocol ofmup. Withmdpandmup, edge cloud nodes along the paths inINPcan easily eliminate duplicated model downloads and pre-aggregate associated gradient uploads for the central FL server, thus alleviating its bottleneck effect, and further accelerating the entire training progress significantly.

The rise of real-time applications, services has made Multi-access Edge Computing (MEC) essential for delivering low-latency, high-performance computing. The effectiveness of MEC, however, is largely contingent on the efficient pre-deployment of services. Despite its importance, efficient service pre-deployment is challenged by the inherent unpredictability of user mobility, the fluctuating conditions of network environments. Accurately predicting user locations, dynamically optimizing resource allocation across geographically distributed MEC servers are complex tasks that are essential to minimizing latency, maximizing data transmission efficiency. The variability in user movement patterns, network bandwidth further exacerbates these challenges, often leading to increased latency, diminished performance, which can negate the advantages offered by MEC. To address these challenges, this paper introduces a novel edge service pre-deployment scheme based on location prediction (ESPD-LP). The ESPD-LP scheme leverages historical user trajectory data to predict future locations, facilitating proactive, strategic resource allocation via a user-centric bidirectional matching algorithm across multiple MEC servers. By pre-deploying services in anticipation of user needs, this approach optimizes data transmission rates, reduces pre-deployment latency, significantly enhancing the overall performance of MEC systems. A comprehensive analysis reveals that the ESPD-LP scheme consistently outperforms similar approaches, with a 41% increase in data transmission rates, a 31% reduction in pre-deployment latency compared to the JO-CDSD, MEC-RDESN schemes, demonstrating consistently superior performance.

Although blockchain empowers the IoT trading market and presents new development opportunities for IoT, scalability issues of blockchain limit its application in this area. Payment Channel Networks (PCNs) have emerged as a promising solution to address the scalability issues. With the help of routing protocols, two users can utilize payment channels to conduct off-chain transactions. However, most Payment Channel Network (PCN) routing protocols overlook the scalability of PCNs, resulting in substantial storage, communication, and computational overhead for lightweight nodes, such as IoT devices. Additionally, frequent utilization of a payment channel can quickly exhaust the channel’s balance, leading to congestion and causing subsequent payments to fail. Channel congestion restricts the throughput of PCNs, yet most PCN routing protocols lack designs for channel congestion control. In this paper, we propose a Cluster-based scalable and high-throughput Routing Protocol (CRP), to enhance the scalability and throughput of PCNs. CRP organizes PCNs into clusters to reduce the average routing table size, thereby alleviating users’ storage, communication, and computational overhead. Furthermore, CRP aims to minimize maximum channel congestion when selecting payment routes, thereby improving throughput. Extensive simulations demonstrate that CRP achieves high scalability and throughput compared to state-of-the-art PCN routing protocols.

In the Internet of Things (IoT) ecosystems, securing interactions among myriad devices across diverse domains is paramount. Traditional centralized authentication systems, primarily based on Public Key Infrastructure (PKI), often suffer from significant drawbacks such as single points of failure, privacy invasion, and limited scalability. Addressing these concerns, this paper explores the application of blockchain technology, known for its decentralized, tamper-resistant, and transparent features, as a robust alternative. However, the integration of blockchain faces scalability challenges, particularly in storage management, as the number of devices and authentication demands escalate. This work introduces a novel blockchain data value concept and a storage optimization strategy that prioritizes critical authentication data to minimize storage demands without compromising security. We formalize this approach as an optimization problem and propose two heuristic algorithms aimed at efficient storage allocation. Our experimental results affirm the effectiveness of these strategies, significantly alleviating storage constraints and enhancing scalability in blockchain-based cross-domain authentication systems for IoT. The key contributions of this research include a pioneering storage strategy that could set a precedent for future blockchain implementations in IoT and potential broad implications for enhancing IoT security infrastructure globally. This work not only contributes to the existing body of knowledge by proposing practical solutions to recognized scalability issues but also paves the way for more resilient IoT ecosystems.

Time-Sensitive Networking (TSN) employs shaping mechanisms such as Time-Aware Shaping (TAS) and Cyclic Queuing and Forwarding (CQF), which depend heavily on precise time synchronization and complex Gate Control Lists (GCL) configurations, limiting their effectiveness in large-scale mixed traffic networks like those in vehicular systems. In response, IEEE 802.1Qcr protocol introduces the Asynchronous Traffic Shaping (ATS) mechanism, based on Urgency-Based Schedulers (UBS), to asynchronously address diverse traffic needs and ensure low and predictable latency. Nonetheless, no traffic scheduling algorithm exists that can be directly applied to ATS shapers in generic large-scale traffic scenarios to solve for fixed end-to-end (E2E) delay constraints and the number of priority queues.In this paper, we propose an urgency-based fast flow scheduling algorithm (UBFS) to address the issue. UBFS leverages domain-specific optimizing strategies with a focus on traffic delay urgency inspired by greedy algorithm for priority allocation across hops and flows, complemented by preprocessing for scenario solvability and dynamic verification to ensure scheduling feasibility. We benchmark UBFS against the method with both scalability and solution quality in typical network topology and demonstrate that UBFS achieves more rapid scheduling within seconds across linear, ring, and star topologies. Notably, UBFS significantly outperforms the baseline algorithm in scheduling efficiency in mixed and large-scale traffic environments, scheduling a larger number of flows. UBFS also reduces time costs by 2-10 times in delay-sensitive environments and by more than 10 times in large-scale scenarios, effectively balancing time efficiency, performance and scalability, thereby enhancing its applicability in real-world industrial settings.

LLM serving is shifting from cloud to edge due to privacy concerns over user interaction data. However, mobile devices struggle with very limited computing power and memory, requiring collaboration among multiple devices to run LLM apps. The mainstream solution, pipeline parallelism, is inefficient for such cases because mobile devices typically run only one inference task at a time. This article argues that tensor parallelism, despite its high communication cost, can better fit such scenarios. We introduce TPI-LLM, a compute and memory-efficient tensor parallel inference system designed to run 70B-scale LLMs on low-resource mobile devices. It keeps sensitive raw data local on users’ devices and employs a sliding window memory scheduler to dynamically manage layer weights. It overlaps disk I/O with computation and communication, enabling efficient operation of large models on memory-limited devices. Extensive experiments show that TPI-LLM reduces token latency by 80%–90% compared to Transformers, Accelerate, and Galaxy. It also cuts the peak memory footprint by 90%, requiring just 3.1 GiB of memory for 70B-scale models.

Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided that workers will eventually participate in the synchronizations,$i)$the training still converges, even if only$p$workers take part in each round of synchronization, and$ii)$a larger$p$generally leads to a faster rate of convergence. These findings shed light on eliminating the bottleneck effects of parameter synchronization in large-scale data-parallel distributed training and have motivated several optimization designs. In this paper, we focus on optimizing the parameter synchronization forpeer-to-peerdistributed learning, where workers broadcast or multicast their updated parameters to others for synchronization, and proposeSelMcast, a suite of expressive and efficient multicast receiver selection algorithms, to achieve the goal. Compared with the state-of-the-art (SOTA) design, which randomly selects exactly$p$receivers for each worker’s multicast in a bandwidth-agnostic way,SelMcastchooses receivers based on the global view of their available bandwidth and loads, yielding two advantages, i.e., accelerated parameter synchronization for higher utilization of computing resources and enlarged average$p$values for faster convergence. Comprehensive evaluations show thatSelMcastis efficient for both peer-to-peer Bulk Synchronous Parallel (BSP) and Stale Synchronous Parallel (SSP) distributed training, outperforming the SOTA solution significantly.

Container networks have become crucial to the overall performance and health of network systems due to the increasing adoption of container technologies. Passive per-packet and per-hop one-way delay (p4 h-OWD) measurement is essential for prompt and accurate detection and localization of network issues in container environments. Existing measurement methods for generic virtual networks require intrusive packet modification to uniquely match packets during p4 h-OWD measurements. However, the overhead and ensuing performance impact induced by high-frequency packet operations during the measurement process have been largely overlooked in the context of lightweight and weak-isolation container networks. This paper demonstrates that even state-of-the-art technologies leveraging the efficient extended Berkeley Packet Filter (eBPF) can introduce significant overhead, impacting both the networking and computing performance of container networks during p4 h-OWD measurements. To effectively reduce the p4 h-OWD measurement tax, which encompasses the measurement overhead and its consequent impact, we propose a non-intrusive method to obtain unique packet identifiers in container networks, thereby avoiding the significant operational overhead associated with intrusive packet matching. Building on this foundation, we present CNDMeas, an efficient eBPF -based technology designed for p4 h-OWD mea-surement within container networks. Evaluation results show that CNDMeas effectively limits the increase in CPU time dedicated to handling software interrupts to within 3 % and reduces the impact on both networking performance by up to 71 % in terms of delay, and computing performance during the measurement process compared to state-of-the-art technologies.

The Generation-Grid-Load-Storage (GGLS) integrated network was born in the context of carbon neutrality to deal with the unstable impact on power grid operation by renewable energy. The network contains many different entities that generate a large amount of power data along with power transmission, known as Power Big Data (PBD). It assists power companies in formulating marketing and pricing strategies, and also helps to develop power generation and dispatching. Nevertheless, the intensive PBD transaction poses privacy breaches, particularly erroneous transactions misleading the formulation of the above strategies, and causing significant economic losses. As a decentralized system, blockchain offers a trusted environment for PBD interactions. However, the intricate blockchain consensus mechanisms contribute to low processing speeds and scalability challenges, rendering it less suitable for effectively handling high-frequency and extensive PBD transactions. To address this limitation, this study proposes a multi-chain system tailored to the data sources and its parallel consensus mechanism, facilitating efficient processing of PBD transactions. Simulation outcomes demonstrate that our design can meet the transaction requirements with notable efficiency and scalability.

Existing strong leader consensus algorithms, such as the Raft algorithm and its prevote optimization, can encounter significant vote splitting rate and relatively long timeout periods during leader elections, resulting in prolonged election latency and extended service interruptions, especially in wide-area networks. This paper introduces SCRaft, a consensus algorithm that leverages the centralized advantages of a predecessor leader to reduce the latency associated with leader transitions in distributed systems. SCRaft is a re-invented Raft consensus algorithm that, by pre-designating a successive leader during the current leader’s term, which significantly speeds up the election process and minimizes service interruptions due to network packet loss or node failures. This approach not only minimizes disruptions due to network failures or node outages but also preserves the high throughput characteristic of the original Raft algorithm. We detail the algorithm design considerations and the critical aspects of its implementation. Theoretical proof formally demonstrates that SCRaft ensures log consistency, and empirical results confirm its efficacy in reducing election latency without compromising system throughput. Our algorithm provides a robust solution for enhancing the availability of distributed systems.

A large-scale system contains a huge configuration space because of its large number of configuration parameters. This leads to a combination explosion among configuration parameters when exploring the configuration space. Existing configuration testing techniques first use fuzzing to generate different configuration parameters, and then directly inject them into the program under test to find configuration-induced bugs. However, they do not fully consider the complexity of large-scale systems, resulting in low testing effectiveness. In this paper, we propose ECFuzz, an effective configuration fuzzer for large-scale systems. Our core approach consists of (i) Multi-dimensional configuration generation strategy. ECFuzz first designs different mutation strategies according to different dependencies and selects multiple configuration parameters from the candidate configuration parameters to effectively generate configuration parameters; (ii) Unit-testing-oriented configuration validation strategy. ECFuzz introduces unit testing into configuration testing techniques to filter out configuration parameters that are unlikely to yield errors before executing system testing, and effectively validate generated configuration parameters. We have conducted extensive experiments in real-world large-scale systems including HCommon, HDFS, HBase, ZooKeeper and Alluxio. Our evaluation shows that ECFuzz is effective in finding configuration-induced crash bugs. Compared with the state-of-the-art configuration testing tools including ConfTest, ConfErr and ConfDiagDetector, ECFuzz finds 60.3--67 more unexpected failures when the same 1000 testcases are injected into the system with an increase of 1.87x--2.63x. Moreover, ECFuzz has exposed 14 previously unknown bugs, and 5 of them have been confirmed.

The Internet of Vehicles (IoV) is an intricate ecosystem brimming with diverse data modalities, including visual streams from cameras, GPS-based location information, sensor-derived operational metrics, and auditory commands from users. These necessitate advanced multimodal learning capabilities. The Transformer architecture, a significant innovation in artificial intelligence, has demonstrated its proficiency in representing varied modalities, facilitating multimodal machine learning. However, its direct application within the IoV is hampered by concerns over user data privacy. Federated learning (FL), a distributed learning paradigm, offers a solution that upholds data privacy. We propose a novel multimodal Transformer-based federated learning framework that capitalizes on the Transformer's ability to effectively handle multimodal data, enabling collaborative learning across heterogeneous data sources. This framework aligns with stringent privacy regulations, enhancing the protection of user data privacy while also boosting learning efficiency. Our approach surpasses traditional models in both accuracy and efficiency, presenting a significant advance in the multimodal machine learning domain.

Existing blockchain technologies and consensus protocols are primarily designed for static, ideal peer-to-peer (P2P) networks, which don't suit the highly dynamic and diverse Internet of Things (IoT) environment. This results in issues such as increased node overhead, network data synchronization delays, block failures, and system crashes. To address the limitations posed by network dynamics and heterogeneity, this paper proposes a structured overlay protocol that can adapt to network dynamics, reduce data broadcast latency and bandwidth load, named DHBN. DHBN logically layers the network based on the survival time of nodes and communication resource heterogeneity, maintaining multiple Minimum Latency Broadcast Trees (MLBT) in a distributed manner in each layer. Nodes communicate with neighbors through tree connections and with other trees through random connections, balancing participant relay tasks. Simulation results demonstrate that compared to other structured broadcast protocols and gossip, DHBN reduces broadcast latency by 34%, bandwidth consumption by 25%, and increases the number of nodes participating in consensus by 14%, offering improved scalability and security.

With the widespread use of distributed machine learning (DML), many IT companies have established networks dedicated to DML. Different communication architectures of DML have different traffic patterns and different requirements on network performance, which is closely related to network topology . However, traditional network topologies usually pursue general goals and are agnostic to the special communication pattern of the applications. The mismatch between network topology and the applications will directly affect the training performance. Although some studies have analyzed the effect of topology on training performance, the topologies and communication architectures involved are not comprehensive, and it is still not known which topology is appropriate for which communication architecture. This survey investigates typical topologies and analyzes whether they meet the requirements of three commonly used communication architectures (i.e., Parameter Server (PS), Tree and Ring architectures) of DML. Specifically, the topology requirements of each communication architecture and two common topology requirements (i.e., high scalability and fault tolerance) for DML are studied firstly. Next, whether these topologies meet the topology requirements is analyzed. Then, this paper discusses potential technologies and approaches to construct the appropriate scheme for each topology requirement, and then presents DMLNet , a novel network topology that suits the three communication architectures. Finally, several potential directions for future research are outlined.

Payment channel networks (PCNs) have emerged as a leading solution to address the scalability problem of blockchain-based cryptocurrencies. Within a PCN, two parties can efficiently conduct path-based off-chain transactions using the funds held in payment channels. Intermediaries involved in transaction routing charge a fee for their services. However, since transaction routing leads to highly dynamic and irreversible changes in channel balances, the success of transaction routing is difficult to guarantee, especially in the presence of concurrent transactions. Existing solutions for PCN routing either overlook the routing of concurrent transactions or impose high fees. In this article, we propose a novel routing protocol Deter-Pay for concurrent PCNs, which improves the success rate of the transaction routing and minimizes transaction fees. Our protocol adopts the idea of the deterministic transaction request declaration and reserved balance feedback. By reserving a sufficient balance during the path probing process, we mitigate the risk of transaction failure caused by fluctuations in channel balances during transaction routing. Additionally, our protocol employs the cheapest path first strategy to select paths, thereby minimizing transaction fees. Extensive simulations show that Deter-Pay can significantly increase the successful transaction volume and reduce the transaction fee compared to the comparison algorithms.

Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine fault tolerance (PBFT) and RAFT are seen as the most applicable consensus mechanisms (CMs) in blockchain-enabled wireless networks. However, previous studies on PBFT and RAFT rarely consider the channel performance of the physical layer, such as path loss and channel fading, resulting in research results that are far from real networks. Additionally, 6G communications will widely deploy high-frequency signals, such as terahertz (THz) and millimeter wave (mmWave), while performances of PBFT and RAFT are still unknown when these signals are transmitted in wireless PBFT or RAFT networks. Therefore, it is urgent to study the performance of nonideal wireless PBFT and RAFT networks with THz and mmWave signals, to better make PBFT and RAFT play a role in the 6G era. In this article, we study and compare the performance of THz and mmWave signals in nonideal wireless PBFT and RAFT networks, considering rayleigh fading (RF) and close-in free space (FS) reference distance path loss. Performance is evaluated by five metrics: 1) consensus success rate; 2) latency; 3) throughput; 4) reliability gain; and 5) energy consumption. Meanwhile, we find and derive that there is a maximum distance between two nodes that can make CMs inevitably successful, and it is named the active distance of CMs. The results show that the two consensus networks have a lower consensus success rate, higher delay, lower throughput, and lower energy consumption in mmWave than THz. Compared with the wireless RAFT consensus, wireless PBFT consensus has a lower consensus success rate, higher delay, lower throughput, and higher energy consumption. The research results provide important references for the future transmission of THz and mmWave signals in PBFT and RAFT networks.

With the rapid emergence of Internet of Things (IoT) technology, cross-domain collaboration and information sharing among IoT devices have become increasingly critical. However, the security and privacy risks associated with cross-domain communication have also escalated. Establishing efficient and secure authentication mechanisms for cross-domain collaboration among IoT devices remains a complex challenge, particularly in the context of bandwidth-constrained wide-area networks. Most recent cross-domain authentication solutions rely heavily on emerging blockchain technology. However, in bandwidth-constrained scenarios, they may suffer from efficiency losses. Consequently, an effective resolution of cross-domain authentication issues in bandwidth-limited conditions has become a pressing concern. In this article, we introduce an efficient blockchain-assisted cross-domain authentication mechanism, named EBCA, designed for bandwidth-constrained wide area IoT networks. To enhance authentication efficiency, we design a highly efficient multiblockchain architecture, which optimally leverages the underlying network bandwidth, significantly improving throughput and reducing latency for cross-domain authentication. Additionally, threshold signatures are incorporated to minimize communication overhead, further enhancing authentication efficiency. Extensive experiments have been conducted to demonstrate the efficiency of our approach. Our scheme has been shown to increase throughput by approximately 34% when compared to existing methods.

With the development of the Internet of Vehicles (IoV), multiaccess edge computing (MEC) technology places computing resources closer to users at edge nodes, enabling faster, more reliable, and secure computing services. In the MEC-enabled IoV networks, task offloading scheduling, as an effective method to alleviate the computational burden on vehicles, is gaining increasing attention. However, with the intelligent and networked development of vehicles, the complex data dependency between in-vehicle tasks brings challenges to offloading scheduling. In contrast to many existing methods that solely address individual tasks, there is a growing need to tackle interrelated tasks within the IoV framework. This includes tasks like processing vehicle sensor data, gathering and analyzing road condition information, facilitating collaborative decision making among vehicles, and optimizing traffic signal systems. Our objective is to address the broader challenge of offloading dependent tasks, as this closely aligns with real-world scenes and requirements. In this article, we propose a priority-based task scheduling algorithm (PBTSA) to minimize processing delay when the tasks are interdependent. PBTSA proposes a method that can better measure the data transmission and calculation delay of the IoV networks. We first model dependent tasks as a directed acyclic graph (DAG) and then use the reverse breadth-first search (RBFS) algorithm to generate the priority of each subtask, and finally according to the priority with low complexity to offload subtasks greedily to minimize task processing delay. We compare the PBTSA with the other two existing algorithms through simulations. The results show that the PBTSA can effectively reduce the task processing delay and can reach close to 10%.

The hierarchical collaborative learning within Low Earth Orbit (LEO) satellite constellations, termed LEO-HCL, is gaining increasing popularity by integrating intra-orbit Inter-Satellite Links and orbital edge computing to alleviate the latency issues caused by intermittent satellite connectivity in satellite-ground training architectures. However, LEO-HCL systems are confronted with a triad of challenges: the variable topology induced by satellite mobility, limited onboard computing and communication resources, and stringent energy constraints. In response to these challenges, we propose an energy-efficient training algorithm called FedAAC, which adaptively optimizes both aggregation frequency and model compression ratio within the resource-constrained LEO network. We have conducted a theoretical analysis of model convergence and investigated the relationship between convergence, aggregation frequency, and model compression ratio. Building on this analysis, we offer an approximation algorithm that dynamically calculates the optimal aggregation frequency and compression ratio during the training process. Extensive simulations have demonstrated that FedAAC significantly outperforms existing methods, offering enhanced convergence speed and energy efficiency. Compared to prior solutions, FedAAC achieves a 60% reduction in energy consumption, a 70% decrease in training time, and a 52% lower communication overhead.

With the rapid growth of edge intelligence, the deployment of federated learning (FL) over wireless networks has garnered increasing attention, which is called Federated Edge Learning (FEEL). In FEEL, both mobile devices transmitting model parameters over noisy channels and collecting data in diverse environments pose challenges to the generalization of trained models. Moreover, devices can engage in decentralized FL via Device-to-Device communication while the communication topology of connected devices also impacts the generalization of models. Most recent theoretical studies overlook the incorporation of all these effects into FEEL when developing generalization analyses and ignore designing algorithms to enhance the generalization of models based on their analysis. In contrast, our work presents an information-theoretic generalization analysis for topology-aware FEEL with data heterogeneity and noisy channels. Additionally, we propose a novel regularization method called Federated Global Mutual Information Reduction (FedGMIR) to enhance the performance of models based on our analysis. Numerical results validate our theoretical findings and provide evidence for the effectiveness of the proposed method.

The development of the Internet of Vehicles (IoVs) has attracted much attention due to the increasing number of connected cars. IoV refers to the interconnection of vehicles with other devices through the internet to enable information sharing and interaction. The advent of 5G mobile communication technologies has provided high-speed, low-latency, and high-reliability communication services, which have gone a long way in solving the communication problems associated with IoV. Additionally, the Multi-Access Edge Computing (MEC) technology has placed computing resources on edge nodes closer to the users, thus enabling faster, more reliable, and more secure computing services to meet the vehicles’ computing resource requirements. However, task offloading and resource allocation issues of 5G-connected vehicles enabled by Mobile edge computing remain a significant challenge when it comes to computing tasks and data related to IoVs. Our study proposes a Lyapunov Based Profit Maximum (LBPM) task offloading algorithm, which utilizes the Lyapunov optimization theory to maximize the time-averaged profit as the optimization objective. The algorithm uses the drift plus penalty optimization framework to establish the Lyapunov function and transforms the optimization goal into making a reasonable offloading decision at each time slot to optimize the upper bound of the function. We also compare the LBPM algorithm with existing algorithms for simulation experiments and performance analysis. The experimental results indicate that the LBPM algorithm increases the time-averaged profit by over 15%.

The rapid advancement of networking and manufacturing technologies has facilitated the proliferation of Internet of Things (IoT) devices connecting to networks. Just like humans, these devices, spanning various domains worldwide, necessitate interconnection. Therefore, cross domain identity authentication becomes crucial to ensure secure communication and mitigate cyber threats. Blockchain, as an emerging decentralized ledger technology, has garnered widespread attention due to its ability to effectively address the issue of single point of failure in traditional cross-domain identity authentication solutions. However, most cross-domain identity authentication solutions based on blockchain are limited by their reliance on existing single-chain blockchain architectures, which significantly restricts the performance of cross-domain authentication. In this paper, we present a splitchain based efficient authentication mechanism for cross-domain IoT. Specifically, we design an innovative split-chain blockchain structure that enhances the efficiency of authentication through parallelization. Additionally, we have developed a consensus algorithm that guarantees the security and fairness of the authentication system. Extensive experiments have been conducted to demonstrate that our scheme increases throughput by approximately 40% compared to MCCA, one of the most recent existing cross-domain solutions.

UAV (unmanned aerial vehicle) is rapidly gaining traction in various human activities and has become an integral component of the satellite-air-ground-sea (SAGS) integrated network. As high-speed moving objects, UAVs not only have extremely strict requirements for communication delay, but also cannot be maliciously controlled as a weapon by the attacker. Therefore, it is necessary to design an efficient and secure communication mechanism (ESCM) for the UAV network (a mobile ad hoc network composed of multiple UAVs). For high efficiency, ESCM provides a routing protocol based on the artificial bee colony (ABC) algorithm to accelerate communications between UAVs. Meanwhile, we use blockchain to guarantee the security of UAV networks. However, blockchain has unstable links in high-mobility networks resulting in low consensus efficiency and high communication overhead. Consequently, ESCM introduces digital twin (DT), which transforms the UAV network into a static network by mapping UAVs from the physical world into Cyberspace. This virtual UAV network is called CyberUAV. Then, in CyberUAV, we design a blockchain consensus based on network coding, named Proof of Network Coding (PoNC). Analysis and simulation show that the above modules in ESCM have advantages over existing schemes. Through ablation studies, we demonstrate that these modules are indispensable for efficient and secure communication of UAV networks.

Network slicing endows 5G/B5G with differentiated and customized capabilities to cope with the proliferation of diversified services, whereas limited physical network resources may not be able to support all service requests. Slice admission control is regarded as an essential means to ensure service quality and service isolation when the network is under burden. Herein, the scenario where rational tenants coexist with partially competitive network slice providers is adopted. We aim to maximize the long-term average revenue of the network operators through slice admission control, with the feasibility of multidimensional resource requirements, the priority differences among heterogeneous slices, and the admission fairness within each slice taken into account concurrently. We prove the intractability of our problem by a reduction from the Multidimensional Knapsack Problem (MKP), and propose a two-stage algorithm called MPSAC to make a suboptimal solution efficiently. The principle of MPSAC is to split the original problem into two sub-problems; inter-slice decision-making and intra-slice quota allocation, which are solved using a heuristic method and a tailored auction mechanism respectively. Extensive simulations are carried out to demonstrate the efficacy of our algorithm, the results show that the long-term average revenue of ours is at least 9.6% higher than comparisons while maintaining better priority relations and achieving improved fairness performance.

Distributed machine learning is becoming increasingly popular for geo-distributed data analytics, facilitating the collaborative analysis of data scattered across data centers in different regions. This paradigm eliminates the need for centralizing sensitive raw data in one location but faces the significant challenge of high parameter synchronization delays, which stems from the constraints of bandwidth-limited, heterogeneous, and fluctuating wide-area networks. Prior research has focused on optimizing the synchronization topology, evolving from starlike to tree-based structures. However, these solutions typically depend on regular tree structures and lack an adequate topology metric, resulting in limited improvements. This paper proposes NetStorm, an adaptive and highly efficient communication scheduler designed to speed up parameter synchronization across geo-distributed data centers. First, it establishes an effective metric for optimizing a multi-root FAPT synchronization topology. Second, a network awareness module is developed to acquire network knowledge, aiding in topology decisions. Third, a multipath auxiliary transmission mechanism is introduced to enhance network awareness and facilitate multipath transmissions. Lastly, we design policy consistency protocols to guarantee seamless updates of transmission policies. Empirical results demonstrate that NetStorm significantly outperforms distributed training systems like MXNET, MLNET, and TSEngine, with a speedup of 6.5~9.2 times over MXNET.

The wireless blockchain network (WBN) concept, born from the blockchain deployed in wireless networks, has appealed to many network scenarios. Blockchain consensus mechanisms (CMs) are key to enabling nodes in a wireless network to achieve consistency without any trusted entity. However, consensus reliability will be seriously affected by the instability of communication links in wireless networks. Meanwhile, it is difficult for nodes in wireless scenarios to obtain a timely energy supply. Energy-intensive blockchain functions can quickly drain the power of nodes, thus degrading consensus performance. Fortunately, a symbiotic radio (SR) system enabled by cognitive backscatter communications can solve the above problems. In SR, the secondary transmitter (STx) transmits messages over the radio frequency (RF) signal emitted from a primary transmitter (PTx) with extremely low energy consumption, and the STx can provide multipath gain to the PTx in return. Such an approach is useful for almost all vote-based CMs, such as the Practical Byzantine Fault-tolerant (PBFT)-like and the RAFT-like CMs. This paper proposes symbiotic blockchain consensus (SBC) by transforming 6 PBFT-like and 4 RAFT-like state-of-the-art (SOTA) CMs to demonstrate universality. These new CMs will benefit from mutualistic transmission relationships in SR, making full use of the limited spectrum resources in WBN. Simulation results show that SBC can increase the consensus success rate of PBFT-like and RAFT- like by 54.1% and 5.8%, respectively, and reduce energy consumption by 9.2% and 23.7%, respectively.

Client-edge-cloud Federated Learning (CEC-FL) is emerging as an increasingly popular FL paradigm, alleviating the performance limitations of conventional cloud-centric Federated Learning (FL) by incorporating edge computing. However, improving training efficiency while retaining model convergence is not easy in CEC-FL. Although controlling aggregation frequency exhibits great promise in improving efficiency by reducing communication overhead, existing works still struggle to simultaneously achieve satisfactory training efficiency and model convergence performance in heterogeneous and dynamic environments. This paper proposes FedAda, a communication-efficient CEC-FL training method that aims to enhance training performance while ensuring model convergence through adaptive aggregation frequency adjustment. To this end, we theoretically analyze the model convergence under aggregation frequency control. Based on this analysis of the relationship between model convergence and aggregation frequencies, we propose an approximation algorithm to calculate aggregation frequencies, considering convergence and aligning with heterogeneous and dynamic node capabilities, ultimately achieving superior convergence accuracy and speed. Simulation results validate the effectiveness and efficiency of FedAda, demonstrating up to 4% improvement in test accuracy, 6.8× shorter training time and 3.3× less communication overhead compared to prior solutions.

Space-Air-Ground Integrated Networks (SAGIN) is considered as the key structure of the next generation network. The space satellites and air nodes are potential candidates to assist and offload the computing tasks. An Unmanned Aerial Vehicle (UAV) collects computing tasks from IoT devices and then makes online offloading decisions. However, UAVs belonging to different service providers compete for computing resources from ground base stations during task scheduling, resulting in extremely long queue delays and load imbalance. In this paper, we designed a task scheduling algorithm based on Proportional Fairness-Aware Auction with Proximal Policy Optimization (PFAPPO), which decouples the task scheduling process in competitive scenarios into two parts: resource allocation and task offloading decision-making. We first propose an auction algorithm to allocate computing resources reasonably to each UAV, after resource allocation is completed, the UAV learns its available computing resources at each offloading destination. Based on the heterogeneous characteristics of the tasks, the UAV makes intelligent offloading decisions using the distributed deep reinforcement learning PPO algorithm. The simulation results show that our proposed PFAPPO has obvious performance improvement compared with existing methods in terms of system profit, load balancing, and system fairness.

Wireless blockchain networks have played an important role in many network scenarios, among which wireless Practical Byzantine Fault Tolerance (PBFT) consensus is regarded as one of the most important consensus mechanisms. It enables nodes in wireless networks to reach consistency without any trusted entity. However, due to the instability of wireless communication links, the reliability of the PBFT consensus will be seriously affected. Meanwhile, it is difficult for nodes in wireless scenarios to obtain a timely energy supply. The high-energy-consumption blockchain functions will quickly consume the power of nodes, thus, affecting consensus performance. Fortunately, the symbiotic radio (SR) system enabled by cognitive backscatter communications can provide a solution to the above problems. In SR, the secondary transmitter (STx) transmits messages by modulating its information over the radio frequency (RF) signal of the primary transmitter (PTx) with extremely low energy consumption, and the STx can provide multipath gain to the PTx in return. In our paper, we propose the symbiotic PBFT (S-PBFT) consensus benefited from the mutualistic transmission in SR, which can increase the consensus security by 54.82 %, and save energy consumption by about 10%.

Distributed training (DT) has become an important and popular practice for collaborative training of high-quality machine learning (ML) models. The communication efficiency of gradient aggregation has been shown to be the primary performance bottleneck for distributed training today. Advanced programmable switches with in-network computing capabilities provide a promising direction for improving the communication efficiency of DT by offloading some gradient aggregations from the host to switches in the network. In this paper, we propose SPAR to optimize the performance of gradient aggregation under constrained in-network computing capabilities. To improve the aggregation throughput, SPAR jointly optimizes the deployment of in-network aggregation switches and the routing of aggregation requests from workers. We formulate this joint optimization problem as an integer nonlinear programming problem and design an efficient greedy algorithm to compute solutions quickly. The experimental results show that SPAR significantly outper-forms the other state-of-the-art solutions based on in-network aggregation, improving aggregation throughput by up to 3×.

The Payment Channel Network (PCN) has emerged as a prominent solution for addressing the scalability limitations of cryptocurrencies. A critical aspect of PCN transactions is the selection of appropriate routing paths between parties. However, the success rate of payment routing in PCNs is significantly influenced by channel capacity and other payments, making path selection in PCNs more challenging than in traditional networks. In this paper, we present a novel path selection algorithm for PCNs that jointly considers path delay, channel capacity, and inter-path impact to achieve high throughput. Our simulation results demonstrate that the proposed algorithm improves throughput by 24.2% compared to the traditional widest path algorithm, and by 5.3% compared to the traditional shortest path algorithm.

Due to privacy and cost reasons, distributed machine learning in Wide-Area Networks(DML-WAN) is becoming an emerging and popular collaborative learning paradigm. However, heterogeneity in computing power and data distribution among workers in different locations has a dramatic impact on training performance, including convergence speed and learning accuracy. Most of the existing works on distributed training mechanisms either focus on computing heterogeneity or data heterogeneity, and none of them can handle both well. In this paper, we propose FedGSync, a novel distributed training mechanism to improve the training performance for DML-WAN, where computing heterogeneity and data heterogeneity usually coexist. To speed up training and improve model accuracy, FedGSync clusters workers into groups according to the similarity of their data distribution and introduce group-based weak synchronization to minimize the synchronization delays waiting for slow workers and the accuracy loss by balancing the contributions of all data distributions. To preserve data privacy and improve efficiency, FedGSync only groups workers based on principal components of gradients and design an approximate grouping mechanism based on Kmeans. To further reduce synchronization time, FedGSync prioritizes packets and uses differential transmission for gradient packets between groups. Evaluation results demonstrate that FedGSync improves convergence speed and learning accuracy under the coexistence of computing heterogeneity and data heterogeneity compared with state-of-the-art distributed training mechanisms.

Federated learning is gaining popularity as a distributed machine learning method that can be used to deploy AI-dependent Internet of Things applications while protecting client data privacy and security. Due to the differences of clients, a single global model may not perform well on all clients, so the personalized federated learning method, which trains a personalized model for each client that better suits its individual needs, becomes a research hotspot. Most personalized federated learning research, however, focuses on data heterogeneity while ignoring the need for model architecture heterogeneity. Most existing federated learning methods uniformly set the model architecture of all clients participating in federated learning, which is inconvenient for each client’s individual model and local data distribution requirements, and also increases the risk of client model leakage. This article proposes a federated learning method based on co-training and generative adversarial networks (GANs) that allows each client to design its own model to participate in federated learning training independently without sharing any model architecture or parameter information with other clients or a center. In our experiments, the proposed method outperforms the existing methods in mean test accuracy by 42% when the client’s model architecture and data distribution vary significantly.

The development of Internet of Things (IoT) makes human life more intelligent, and the interconnection of all things has become a reality. However, the surge in the number of devices and centralized management brings severe challenges to IoT, such as single point of failure, poor security, privacy leakage, and low reliability. Due to the decentralization, verifiability, and privacy protection of blockchain federated learning (BFL), some BFL schemes have been proposed to solve these problems, but bring new challenges, such as device privacy leakage and heavy security computing load. In this article, we propose a new decentralized, secure and verifiable consortium BFL privacy protection scheme, named LPBFL, which realizes lightweight computing while ensuring the privacy of the local model and data set of the device. To achieve lightweight privacy protection, LPBFL adopts the Paillier encryption and the newly designed lightweight digital signature and batch verification algorithm. Additionally, considering that devices upload invalid or even toxic local models intentionally or unintentionally, we design a device reputation selection mechanism to make BFL more efficient. Finally, the theoretical analysis proves the security of LPBFL and verifies the unforgeability of the proposed digital signature. Comprehensive comparisons and extensive experiments demonstrate that our LPBFL has significant advantages in multiple aspects.

In recent years, edge computing, as an important pillar for future networks, has been developing rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed, and save energy. An efficient and feasible task offloading scheme can not only greatly improve the Quality of Experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the Industrial Internet of Things (IIoT), computing networks, etc. To achieve these goals, this article proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on optimizing the energy utilization ratio (EUR) and the processing latency. In the preimplementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service’s popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on task priority. The task priority is generated by corresponding service popularity and task offloading. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse. Even though many successful use cases have proved the feasibility of FL in theory, in the industrial practice of Metaverse, the problems of non-independent and identically distributed (non-i.i.d.) data, learning forgetting caused by streaming industrial data, and scarce communication bandwidth remain key barriers to realize practical FL. Facing the above three challenges simultaneously, this paper presents a high-performance and efficient system namedHFedMSfor incorporating practical FL into Industrial Metaverse.HFedMSreduces data heterogeneity through dynamic grouping and training mode conversion (Dynamic Sequential-to-Parallel Training, STP). Then, it compensates for the forgotten knowledge by fusing compressed historical data semantics and calibrates classifier parameters (Semantic Compression and Compensation, SCC). Finally, the network parameters of the feature extractor and classifier are synchronized in different frequencies (Layer-wise Alternative Synchronization Protocol, LASP) to reduce communication costs. These techniques make FL more adaptable to the heterogeneous streaming data continuously generated by industrial equipment, and are also more efficient in communication than traditional methods (e.g., Federated Averaging). Extensive experiments have been conducted on the streamed non-i.i.d. FEMNIST dataset using 368 simulated devices. Numerical results show thatHFedMSimproves the classification accuracy by at least 6.4% compared with 8 benchmarks and saves both the overall runtime and transfer bytes by up to 98%, proving its superiority in precision and efficiency.

Recently, the abstraction ofcoflowis introduced to capture the collective data transmission patterns among modern distributed data-parallel applications. During processing, coflows generally act as barriers; accordingly, time-sensitive applications prefer their coflows to complete within deadlines, and deadline-aware coflow scheduling becomes very crucial. Regarding these data-parallel applications, we notice that many of them, includinglarge-scale query systems,distributed iterative training, anderasure codes enabled storage, are able to tolerate loss-bounded incomplete inputs by design. This tolerance indeed brings a flexible design space for the schedule of their coflows: when getting overloaded, the network can trade coflow completeness for the timeliness, and balance the completeness of different coflows on demand. Unfortunately, existing coflow schedulers neglect this tolerance, resulting in inflexible and inefficient bandwidth allocations. In this paper, we explore this fundamental trade-off and design POCO, a POlicy-based COflow scheduler, along with a transport layer enhancement scheme, to achieve customizable selective coflow completion for emerging time-sensitive distributed applications. Internally, POCO employs a suite of novel designs along with admission controls to makeflexible,work-conserving, andperformance-guaranteedrate allocation to online coflow requests very efficiently. Extensive trace-based simulations indicate that POCO is highly flexible and achieves optimal coflow schedules respecting the requirements specified by applications.

Moderate-scale datacenters are increasingly deployed at the network edge to support low-latency and high-bandwidth internet of Things (IoT) and 5G applications. These applications usually have bulk data to transfer from one datacenter to several datacenters, which can cause a significant amount of bandwidth costs. Traffic engineering (TE) systems at the network edge must minimize the bandwidth costs of multicast transfers across edge datacenters. To avoid service quality degradation, TE should also guarantee transfer deadlines, which have received little attention in existing work. Due to the dynamic bandwidth pricing schemes, it is challenging to strike a good balance between minimizing costs and guaranteeing transfer deadlines. In this paper, we present CDScheduler a cost-efficient scheduling solution for multicast transfers with deadline guarantees. To reduce bandwidth costs, CDScheduler uses Steiner trees for forwarding and adaptive routing that considers bandwidth price variation and transfer demands. We formulate the cost-efficient multicast transfer scheduling problem and propose an algorithm based on linear program relaxation and randomized rounding to find a solution that guarantees timely completion and reduces cost. Extensive evaluations show that CDScheduler can reduce bandwidth cost significantly and outperforms the state-of-the-art solutions by cutting down up to 78% bandwidth cost.

Recently, due to privacy concerns, distributed machine learning in Wide-Area Networks (DML-WANs) attracts increasing attention and has been widely deployed to promote the widespread application of intelligence services that rely on geographically distributed data. DML-WANs is essentially performing collaboratively federated learning over a combination of servers at both edge and cloud on a large spatial scale. However, efficient model training is challenging for DML-WANs because it is blocked by the high overhead of model parameter synchronization between computing servers over WANs. The reason is that there has a sequential dependency between local model computing and global model synchronization of traditional DML-WANs training methods intrinsically producing a sequential blockage between them, e.g., FedAvg. When the computing heterogeneity and the low WAN bandwidth coexist, a long block of global model synchronization prolongs the training time and leads to low utilization of local computing. Despite many efforts on alleviating synchronization overhead with novel communication technologies and synchronization methods, they still use traditional training patterns with sequential dependency and thereby have very limited improvements, such as FedAsync and ESync. In this article, we propose NBSync, a novel training algorithm for DML-WANs, which greatly speeds up the model training by the parallelism of local computing and global synchronization. NBSync employs a well-designed pipelining scheme, which can properly relax the sequential dependency of local computing and global synchronization and process them in parallel so as to overlap their operating overhead in the time dimension. NBSync also realizes flexible, differentiated and dynamical local computing for workers to maximize the overlap ratio in dynamically heterogeneous training environments. Convergence analysis shows that the convergence rate of NBSync training process is asymptotically equal to that of SSGD, and NBSync has a better convergence efficiency. We implemented the prototype of NBSync based on a popular parameter server system, i.e., MXNET's PS-LITE library, and evaluate its performance on a DML-WANs testbed. Experimental results show that NBSync speeds up training about 1.43×–2.79× than state-of-the-art distributed training algorithms (DTAs) in DML-WANs scenarios where computing heterogeneity and low WAN bandwidth coexist.

The rapid development of deep learning (DL) technology has made deep learning libraries such as Tensor Flow widely used in practice. However, the complexity of DL libraries inevitably leads to multiple vulnerabilities. Recently, research on DL library testing generally designs a variety of mutation operators to generate new models from the perspective of model mutation. However, we find these research efforts do not consider that different mutation operators have different vulnerability mining efficiencies, thus treating all mutation operators equally can lead to inefficiency. Based on the above observation, we design a novel mutation operator scheduling strategy to improve the efficiency of vulnerability mining in the DL library, including the multi-staged mutation operator selection strategy and mutation operator energy allocation strategy. To evaluate the efficiency of our work, we implement a prototype called MMOS. The results show that MMOS finds six more crash bugs, two more NaN bugs and one more inconsistency bug in four widely used DL libraries including TensorFlow, Theano, CNTK and MXNet compared with the random strategy. Among them, MMOS finds 5 previously unknown vulnerabilities in MXNet and 1 in CNTK. Moreover, MMOS outperforms LEMON in vulnerability mining of DL libraries. In total, MMOS finds 13 more vulnerabilities than LEMON.

Measuring traffic metrics is indispensable in virtual networks as it is the basis for a wide range of applications, such as network diagnostics and performance evaluation of the network algorithms. However, existing measurement schemes fail to have all these excellent characteristics simultaneously: 1) fine-grained, i.e. to obtain per packet level information. 2) lightweight, namely low CPU and bandwidth overhead. 3) network-wide, which means obtaining metrics of the whole network, e.g. per packet path. 4) easy-to-deploy, which refers to deployment without additional modification of Maximum Transmission Units (MTUs). We design vNetRadar, a virtual network measurement system, which has these excellent characteristics simultaneously. Specifically, vNetRadar 1) identifies each packet without increasing the size of each packet, to obtain network-wide metrics without MTU modification, 2) allocates each packet an area in memory, called backpack, and carries metadata in it to largely reduce bandwidth overhead. vNetRadar is implemented based on the extended Berkeley Packet Filter (eBPF) and is mainly in kernel space, avoiding the CPU overhead of copying packets to user space when performing the fine-grained measurement. Evaluation results show that the easy-to-deploy vNetRadar can get fine-grained network-wide metrics with low CPU and bandwidth overhead.

Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided workers would participate in the synchronizations eventually, i) the training still converges, even if only p workers take part in each round of synchronization, and ii) a larger p generally leads to a faster rate of convergence. These findings shed light on eliminating the bottleneck effects of parameter synchronization in large-scale data-parallel distributed training, having motivated several optimization designs.In this paper, we focus on optimizing the parameter synchronization for peer-to-peer distributed learning, in which workers generally broadcast or multicast their updated parameters to others for synchronization, and propose SELMCAST, an expressive and Pareto-optimal multicast receiver selection algorithm, to achieve the goal. Compared with the state-of-the-art design that randomly selects exactly p receivers for each worker’s multicast in a bandwidth-agnostic way, SELMCAST chooses receivers based on the global view of their available bandwidth and loads, yielding two advantages. Firstly, it could optimize the bottleneck sending rate, thus cutting down the time cost of parameter synchronization. Secondly, when more than p receivers are with sufficient bandwidth, they would be selected as many as possible, bringing benefits to the convergence of training. Extensive evaluations show that SELMCAST is efficient and always achieves near-optimal performance.

Nowadays, cross-device federated learning (FL) is the key to achieving personalization services for mobile users and has been widely employed by companies like Google, Microsoft, and Alibaba in production. With the explosive increase of participants, the central FL server, which acts as the manager and aggregator of cross-device model training, would get overloaded, becoming the system bottlenecks. Inspired by the emerging wave of edge computing, an interesting question is: could edge clouds help cross-device FL systems overcome the bottleneck?This article provides a cautiously optimistic answer by proposing INP, an FL-specific In-Network Processing framework, along with the novel Model Download Protocol of MDP and Model Upload Protocol of MUP. With MDP and MUP, edge cloud nodes along the paths in INP can easily eliminate duplicated model downloads and pre-aggregate associated gradient uploads for the central FL server, thus alleviating its bottleneck effect, and further accelerating the entire training progress significantly.

The explosive growth of global distributed services has led to a massive increase in bulk multicast data transfers over the inter-datacenter Wide-Area Network. While many solutions have been proposed to improve the performance of inter-DC bulk data transfers, they are insufficient to optimize multicast transfers because they fail to explore the characteristics of multicast transfers and network topology. This paper presents FlexCast, a flexible and efficient solution to optimize the completion times for multicast transfers. FlexCast takes advantage of topological characteristics to divide network sites into groups, partition receivers into subsets, and construct load-adaptive Steiner trees for receiver partitions to reduce completion time. It also employs a flexible multicast model for parallel transmission. For better performance FlexCast uses multiple scheduling policies to handle offline request submission, and for greater efficiency it adopts a combination of small-scale optimization and fast heuristic to address online request submission quickly. Simulations on real-world topologies show that FlexCast improves the completion time for multicast receivers by up to 80% compared to prior solutions.

Federated Learning (FL) has received widespread attention in 5G mobile edge networks (MENs) due to its ability to facilitate collaborative learning of machine learning models without revealing user privacy data. However, FL training is both time and energy consuming. Constrained by the instability and limited resources of clients in MENs, it is challenging to optimize both learning time and energy consumption for FL. This paper studies the problem of client selection and resource allocation to minimize the energy consumption and learning time of multiple FL jobs competing for resources. Because minimizing learning time and minimizing energy consumption are conflicting objectives, we design a decoupling algorithm to optimize them separately and efficiently. Simulations based on popular models and learning datasets show the effectiveness of our approach, reducing up to 75.7% energy consumption and 38.5% learning time compared to prior work.

LEACH-like clustering protocols focus mainly on the low-power, low-rate, and low-wakeup network applications, and work in a multiround clustering strategy that causes frequent handovers of cluster heads (CHs), thus less support for real-time services that require stable cluster topologies. Besides, these protocols are faced with respective drawbacks, such as suboptimality of selected heads, costly node-base station (BS) energy overheads, lack of runtime cluster maintenance, etc. This article proposes RANCE, a randomly centralized and on-demand clustering protocol, aiming at prolonging nodes’ clustered time to support internodes collaboration while being energy efficient in mobile ad hoc networks. First, RANCE designs a randomly centralized CH selection mechanism in which every node in the local wireless network is eligible to initiate the centralized CH selection, so that the self-organizing characteristics of mobile ad hoc nodes can be utilized for head selection optimization. Second, taking into account the wireless volatility caused by changes of topology, obstacles, signal strength, etc., the fine-grained cluster relationships maintenance is provided by means of multilevel aliveness and adaptive bidirectional heartbeat packets. Third, RANCE works in an event-driven and on-demand manner instead of a time-triggered manner in LEACH-like protocols, to reduce the impact on continuous services caused by frequent CH handovers among all nodes. Simulation results show that RANCE provides longer clustered time (over 99% of nodes’ lifetime in networks more than 100 nodes) and good clustering scalability with high consistency at minimum energy cost, and exhibits good potentials in mobile wireless environments that are infrastructureless/poor for continuous missions.

Nowadays, the Industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate on decentralized devices in modern factories. To protect the confidentiality of industrial data, federated learning (FL) was introduced to collaboratively train shared machine learning (ML) models. However, the local data collected by different devices skew in class distribution and degrade industrial FL performance. This challenge has been widely studied at the mobile edge, but they ignored the rapidly changing streaming data and clustering nature of factory devices, and more seriously, they may threaten data security. In this article, we propose FED GS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-independent and identically distributed (non-j) data. Taking advantage of naturally clustered factory devices, FED GS uses a gradient-based binary permutation algorithm (GBP-CS) to select a subset of devices within each factory and build homogeneous super nodes participating in FL training. Then, we propose a compound-step synchronization protocol to coordinate the training process within and among these super nodes, which shows great robustness against data heterogeneity. The proposed methods are time-efficient and can adapt to dynamic environments, without exposing confidential industrial data in risky manipulation. We prove that FED GS has better convergence performance than FedAvg and give a relaxed condition under which FED GS is more communication efficient. The extensive experiments show that FED GS improves accuracy by 3.5% and reduces training rounds by 59% on average, confirming its superior effectiveness and efficiency on non-i.i.d. data.

As people pay more attention to environment protection, the number of electric vehicles (EVs) is gradually increasing. Energy trading management for EVs is becoming a challenge. However, existing research has not considered the problem of information sharing between energy traders and issues surrounding the protection of user privacy. Therefore, in this article, we propose a vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) electricity trading architecture based on blockchain. All energy transactions of EVs can be recorded on the blockchain ledger to ensure privacy and smart contracts work as agents for pricing and optimal energy allocation. Furthermore, we introduce a two-way auction mechanism based on the Bayesian game and design a new price adjustment strategy. Finally, we propose a bidirectional auction mechanism based on the Bayesian game approach. We use extensive simulations to evaluate the performance of our proposed algorithm. Simulation results show that the social welfare and cost performance of our algorithm can be improved by up to 102.8% and 319%, respectively.

An increasing number of cloud services are operated globally, where the service data are frequently replicated across geographically distributed datacenters to improve service quality and reliability. Such replication generates many one-to-many bulk data transfers over inter-datacenter networks from one datacenter to many receiver datacenters. To provide end-users with guaranteed services, these data transfers are usually required to be completed within designated deadlines. Despite the exponential growth in data demand, there has been little work on guaranteeing deadlines for one-to-many transfers, which is the subject of this paper. This paper proposes a centralized admission control coupled with a scheduling algorithm, named deAdline-Guaranteed transfEr (AGE), to guarantee the deadline of admitted data transfers and utilize the network capacity efficiently. The key idea is to flexibly select the source datacenter for receiver datacenters and allow the remaining receivers to obtain a replica from either the original source or the other receivers that have already received a copy. By jointly allocating the source for receivers and the bandwidth and routing paths for every data transfer, AGE maximizes the number of deadline-satisfied transfers. Our simulations show that compared to the state-of-the-art, AGE guarantees the deadline for up to 70 percent more transfers, achieves at least 2× higher network throughput, and reduces the completion time up to 80 percent.

Greybox fuzzing has been widely used in stateless programs and has achieved great success. However, most stateof- the-art greybox fuzzers have slow speed and shallow state depth coverage in fuzzing stateful network protocol programs, which are able to remember and store the details of interactions. The existing greybox fuzzers for network protocol programs first send a series of well-defined prefix sequences of input messages and then send mutated messages to test the target state of a stateful network protocol. This process leads to a high time cost. In this paper, we propose SNPSFuzzer, a fast greybox fuzzer for stateful network protocols using snapshots. SNPSFuzzer dumps the context information when the network protocol program is in a specific state and restores it when the state needs to be fuzzed. Furthermore, we design a message chain analysis algorithm to explore more and deeper network protocol states. Our evaluation shows that compared with the state-of-the-art network protocol greybox fuzzer AFLNET, SNPSFuzzer improves the message processing speed of network protocol fuzzing by 70.7% and increases the path coverage by 20.9% on average within 24 hours. Moreover, SNPSFuzzer exposes a previously unreported vulnerability in the program Tinydtls.

With the rapid development of the Internet of vehicles (IoV), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. Due to the features of the dynamic network structure, constraints of road topology and variable states of vehicle nodes, VANET routing protocols face many challenges, including intermittent connectivity, large delay and high communication overhead. Location-based geographic routing is the most suitable method for VANETs, and such routing performs well on paths with an appropriate vehicle density and network load. We propose an intersection-based V2X routing protocol that includes a learning routing strategy based on historical traffic flows via Q-learning and monitoring real-time network status. The hierarchical routing protocol consists of two parts: a multidimensional Q-table, which is established to select the optimal road segments for packet forwarding at intersections; and an improved greedy strategy, which is implemented to select the optimal relays on paths. The monitoring models can detect network load and adjust routing decisions in a timely manner to prevent network congestion. This method minimizes the communication overhead and latency and ensures reliable transmission of packets. We compare our algorithm with three benchmark algorithms in an extensive simulation. The results show that our algorithm outperforms the existing methods in terms of network performance, including packet delivery ratio, end-to-end delay, and communication overhead.

The vehicle-to-vehicle (V2V) communication driven by the fifth generation (5G) cellular mobile network with the features of ultra-high reliability and low latency provides promising solutions to various applications in the intelligent transportation system (ITS). To improve the resource utilization and guarantee the quality-of-service (QoS), users in 5G vehicular networks have to select appropriate communication modes and control their own transmission power. However, the highly dynamic network topology and channel status pose challenges to the mode selection. In this paper, we propose a scheme for joint mode selection and power adaptation based on the game theoretic approach with the objective of maximizing the overall system throughput. We consider the transmission requirements of multi-priority packets of different vehicular applications, where packets with higher priority have more stringent latency constraints. The segmented auction method with reserve price is performed to select modes for the vehicular users (VUEs) and the Stackelberg gaming model is introduced to solve the problem of cochannel interference. We compare our approach with three existing methods in extensive simulations. The results show that our approach outperforms the existing methods in terms of network performance, including the network throughput, resource utilization and QoS violation rate.

Training a neural network requires retraining the same model many times to search for the configuration of hyper-parameters with the best training result. It is common to launch multiple training jobs and evaluate them in stages. At the completion of each stage, jobs with unpromising configurations will be terminated and jobs with new configurations will start. Each job typically performs distributed training across multiple GPUs, and GPUs periodically synchronize their models over the network. However, model synchronizations of running jobs cause severe network congestion, significantly increasing the stage completion time (SCT) and thus the time to successfully search for the desired configuration. Existing flow schedulers are ineffective to reduce SCT since they are agnostic to training stages. In this paper, we propose a stage-aware coflow scheduling method to minimize the average SCT. In this method, an algorithm is designed to order coflows by considering stage information and then coflows are scheduled according to the order. Mathematical analysis shows that the method achieves the average SCT within 20/3 of the optimal. We implement the method in a real system called Beamer. Extensive testbed experiments and simulations show that Beamer significantly outperforms advanced network designs, such as Sincronia, FIFO-LM, and per-flow fair sharing.

Federated Learning (FL) serves privacy-preserving collaborative learning among multiple isolated parties, while retaining their privacy data locally. Cross-device and cross-silo FL have achieved great success in cross-domain applications, in which the scarce communication resource is the primary bottleneck. Driven by the need to combine heterogeneous machines from different parties to build a shared data center, we foundintra-domain FL, a new type of FL in which isolated parties collaborate in the shared data center, and strong computational heterogeneity becomes the primary bottleneck. To mitigate the training inefficiency caused by stragglers, this article proposes an efficient synchronization algorithmESync, which allows parties to train different iterations locally under the coordination of a novel schedulerState Server. We give the boundaries of weight divergence and optimality gap ofESync, and analyze the trade-off between convergence accuracy and communication efficiency. Extensive experiments are conducted to compareESyncwith SSGD, ASGD, DC-ASGD, FedAvg, FedAsync, TiFL, and FedDrop under strong computational heterogeneity. Numerical results show thatESyncachieves great speed up without loss of accuracy, and therefore demonstrate the effectiveness ofESyncin both training efficiency and converged accuracy.

In the last few years, distributed machine learning has been usually executed over heterogeneous networks such as a local area network within a multi-tenant cluster or a wide area network connecting data centers and edge clusters. In these heterogeneous networks, the link speeds among worker nodes vary significantly, making it challenging for state-of-the-art machine learning approaches to perform efficient training. Both centralized and decentralized training approaches suffer from low-speed links. In this paper, we propose a decentralized approach, namely NetMax, that enables worker nodes to communicate via high-speed links and, thus, significantly speed up the training process. NetMax possesses the following novel features. First, it consists of a novel consensus algorithm that allows worker nodes to train model copies on their local dataset asynchronously and exchange information via peer-to-peer communication to synchronize their local copies, instead of a central master node (i.e., parameter server). Second, each worker node selects one peer randomly with a fine-tuned probability to exchange information per iteration. In particular, peers with high-speed links are selected with high probability. Third, the probabilities of selecting peers are designed to minimize the total convergence time. Moreover, we mathematically prove the convergence of NetMax. We evaluate NetMax on heterogeneous cluster networks and show that it achieves speedups of 3.7×, 3.4×, and 1.9× in comparison with the state-of-the-art decentralized training approaches Prague, Allreduce-SGD, and AD-PSGD, respectively.

Since its emergence, blockchain technology has received great attention because of its advantages in terms of decentralization, transparency, traceability, and the ability to be tamper proof. These advantages help blockchain become a better option for fields, such as digital currency and information storage. Specifically, consortium blockchain is preferred by researchers because it provides a certain degree of access control and a supervisory mechanism. However, in real-world applications, blockchain platforms pervasively show bottlenecks, such as ultrahigh energy consumption, time inefficiency, low transaction throughput, vulnerability to targeted attacks, and poor fairness of user profits, which seriously influence the performance of this technology and thus hinder its development and adoption. In this article, we try to enhance the performance of blockchain platforms by optimizing the quality of its core module, known as the consensus algorithm. To do so, we introduce proof of assets and proof of reputation to design a voting-based decentralized consensus (VDC) algorithm for consortium blockchain. Combined with the verifiable random function (VRF), VDC realizes better fairness of user profits and time efficiency with acceptable energy consumption and without sacrificing security. The simulation results show that the proposed algorithm achieves a faster consensus process and better user fairness than existing algorithms while still maintaining a negligible energy cost and adequate security.

In view of the rapid development of the Internet of Vehicles (IoV) and wireless communication technology, intelligent transportation systems play an important role in improving urban road safety, promoting the behavioral interaction between users and networks, improving the service quality, and controlling the network cost. Based on the universality and real-time nature of the IoV, data collectors can cooperate with users to sense and collect relevant data within the driving range of vehicles. To achieve this goal, this article proposes a two-tier sensing scheme around the optimization of the sensing mechanism and data processing. In the routing layer, we build a weighted graph model based on vehicle fog, and we propose a new routing strategy to maximize each vehicle’s utilization. We consider that the sensing information collected by multiple vehicles could be repetitive, which would lead to many unnecessary communication flows in the network. Therefore, in the data processing layer, we consider resource consumption in the whole fog, assign different tasks to the sensing vehicles, and filter similar information on the relay nodes of the routing paths to reduce the waste of resources in IoV. Finally, we compare and analyze our two-tier scheme with related approaches, and the results show that our scheme has higher link utilization and lower resource consumption for a high-speed mobile network environment in IoV.

Blockchain is a promising emerging technology that is envisioned to play a key role in establishing secure and reliable Internet-of-Things (IoT) ecosystems without the involvement of any third party. Hyperledger Fabric, a permissioned blockchain system that can yield high throughput and low consensus delay, has shown its capability in enhancing security and privacy protection for delay-sensitive IoT services. The literature, however, has not considered the conflicting transaction problem which may substantially limit the system performance and degrade QoS for the end users. In this article, we propose CATP-Fabric, a new blockchain system to address the conflicting transaction problem by reducing the number of potentially conflicting transactions with less overhead. First, the transactions within a block are divided into different groups to facilitate parallel transaction processing. Then, CATP-Fabric filters stale transactions and prioritizes the read-only transactions in each group to eliminate unnecessary overhead. Finally, we formulate the selection of aborting transactions in CATP-Fabric as a binary integer-programming problem and develop a low-complexity optimization algorithm to minimize the number of aborted transactions. Illustrative results show that our proposed CATP-Fabric blockchain system achieves high throughput of successful transactions while maintaining a lower aborting transaction rate compared to the benchmark blockchain systems.

Deep neural networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this article presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression; 2) knowledge distillation (KD); and 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future directions.

Network function virtualization (NFV) in 6G can use standard virtualization techniques to enable network functions via software. Resource scheduling is one of the key research areas of NFV in 6G and is mainly used to deploy service function chains (SFCs) in substrate networks. However, determining how to utilize network resources efficiently has always been a difficult problem in SFC deployment. This article focuses on how to efficiently provision online SFC requests in NFV with 6G. We first establish a mathematical model for the problem of online SFC provisioning. Then, we propose an efficient online service function chain deployment (OSFCD) algorithm that selects the path to deploy that is close to the SFC length. Finally, we compare our proposed algorithm with three other existing algorithms by simulation experiments. The experimental results show that the OSFCD algorithm optimizes multiple performance indicators of online SFC deployment.

To meet the increasing traffic demands characterized by large bandwidth and high burstiness, more traffic has been moving to inter-datacenter elastic optical networks (inter-DC EONs) for processing. The integration of two emerging paradigms, network function virtualization (NFV) and software-defined networking (SDN), enables Internet service providers (ISPs) to deploy service function chains (SFCs) from users flexibly while reducing operational and capital expenditures. This article focuses on the problem of online SFC provisioning in inter-DC-EONs with the aim of maximizing ISP profits, where the challenge in jointly allocating IT and spectrum resources when deploying SFCs is balanced with the deployment costs of processing as many user requests as possible. We design two-phase time-efficient orchestration algorithms for online SFC requests and the strategy of SFC splitting is adopted to improve the utilization of spectrum resources on fiber links. Simulation results show that, compared with the existing algorithm, our proposed algorithms significantly shorten the deployment time, improve total profit of ISP by up to 40% and reduce the blocking probability by up to 35%.

In recent years, distributed machine learning in WANs (DML-WANs), i.e., collaboratively training a high-quality ML model cross geo-distributed micro-clouds or edge devices, has attracted attention and been widely applied. Compared with cloud-centric training, DML-WANs avoids the high cost of transferring large amounts of raw data to a central cloud and privacy concerns. However, performing DML-WANs still faces challenges. Model synchronization, an essential step of DML-WANs, is accompanied by a lot of model communication cross limited-bandwidth WANs, which generates high communication overhead. Moreover, the parameter server system, which has been widely used, performs model synchronization in a centralized manner, resulting in serious communication in-cast problem. Such communication in-cast further raises the communication overhead, leading to the low efficiency of DML-WANs. To alleviate the communication in-cast, existing researches attempt to build tree-based communication overlays over the parameter server and workers. However, we identify that these approaches can not adapt to the dynamic and heterogeneous network of DML-WANs, resulting in insufficient improvements. This paper proposes TSEngine, an adaptive communication scheduler for efficient communication overlay of the parameter server system in DML-WANs. Its core idea is to dynamically schedule the communication logic over the parameter server and workers based on the active network perception. Specifically, we propose novel communication scheduling protocols for model distribution and model aggregation, respectively. We have implemented TSEngine in a mainstream parameter server system and verified its effectiveness in DML-WANs testbeds.

Recently, the abstraction of coflow is introduced to capture the collective data transmission patterns among modern distributed data-parallel application. During processing, coflows generally act as barriers; accordingly, time-sensitive applications prefer their coflows to complete within deadlines and deadline-aware coflow scheduling becomes very crucial.

Many modern cloud applications frequently generate multicast traffic, which is becoming one of the primary communication patterns in datacenters. Emerging reconfigurable datacenter technologies enable interesting new opportunities to support such multicast traffic in the physical layer: novel circuit switches offer high-performance inter-rack multicast capabilities. However, not much is known today about the algorithmic challenges introduced by this new technology.This paper presents SplitCast, a preemptive multicast scheduling approach that fully exploits emerging physical-layer multicast capabilities to reduce flow times. SplitCast dynamically reconfigures the circuit switches to adapt to the multicast traffic, accounting for reconfiguration delays. In particular, SplitCast relies on simple single-hop routing and leverages flexibilities by supporting splittable multicast so that a transfer can already be delivered to just a subset of receivers when the circuit capacity is insufficient. Our evaluation results show that SplitCast can reduce flow times significantly compared to state-of-the-art solutions.

As the key infrastructure for emerging 5G and Internet-of-Things (IoT) applications, micro data centers would be widely deployed at network edges to provide high-bandwidth low-latency cloud service. In these systems, applications would deliver large-size data objects among servers for various purposes like service deployment, application scale-up, and data duplication on demand. Accordingly, reducing delivery time is crucial for the optimization of service delay and system utilization. To accelerate the delivery, this article proposes a multisource-aware adaptive data transmission solution, Parallel Push (PPUSH), by leveraging the fact that data objects in the cloud are generally replicated among servers by design. At the high level, PPUSH achieves efficient delivery of multisource data by launching multiple push flows in parallel; and at the low level, it decouples transfers from different sources by encoding data objects with rateless RaptorQ code, and further employing novel congestion controls to prioritize the bandwidth allocation of concurrent tasks respecting their remaining sizes. Fluid model analysis along with Mininet-based test and packet-level simulation shows that, unlike DCTCP and other proposals, push is robust to packet loss and achieves provable prioritized bandwidth allocation. Extensive simulation results imply that, with above advantages, PPUSH could achieve very efficient data delivery by making use of all available data sources: for instance, compared with the straightforward design of equal-size task split and fair bandwidth allocation, its adaptive task assignment and prioritized traffic scheduling reduce the average task completion time in a tested scenario by 1.495× and 1.329×, respectively, demonstrating a total improvement of 1.586×, when enabled at the same time.

To introduce the opportunities brought by plug-in hybrid electric vehicles (PHEVs) to the energy Internet, we propose a local vehicle-to-vehicle (V2V) energy trading architecture based on fog computing in social hotspots and model the social welfare maximization (SWM) problem to balance the interests of both charging and discharging PHEVs. Considering transaction security and privacy protection issues, we employ a consortium blockchain in our designed energy trading architecture, which is different from the traditional centralized power systems, to reduce the reliance on trusted third parties. Moreover, we improve the practical Byzantine fault tolerance (PBFT) algorithm and introduce it into a consensus algorithm, called the delegated proof of stake (DPOS) algorithm, to design a more efficient and promising consensus algorithm, called DPOSP, which greatly reduces resource consumption and enhances consensus efficiency. To encourage PHEVs to participate in V2V energy transactions, we design an energy iterative bidirectional auction (EIDA) mechanism to resolve the SWM problem and obtain optimal charging and discharging decisions and energy pricing. Finally, we conduct extensive simulations to verify the proposed DPOSP algorithm and provide numerical results for a comparison with the performance of the genetic algorithm and the Lagrange algorithm in achieving EIDA.

In the face of a massive number of vehicular users, a data collection paradigm based on vehicular crowdsensing requires an effective means of attracting participants. Thus, the incentive mechanisms play a key role in crowdsensing procedure design, inevitably leading to problems related to user privacy leakage. In this article, to reduce the risk of privacy leakage in the implementation of incentive mechanisms, we propose a fog computing-based crowdsensing architecture specialized for vehicular crowdsensing and corresponding privacy-preserving solutions for the processes of data reporting, reward issuing, and trust management. Authentication and encryption technologies, such as zero-knowledge verification, one-way hashing, partially blind signature authentication, and homomorphic encryption, are utilized to achieve our goals. Finally, efficiency improvements in both privacy preservation and network response are proven through analysis and simulations.

Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing.

The efficient deployment of virtual network functions (VNFs) for network service provisioning is key for achieving network function virtualization (NFV); however, most existing studies address only offline or one-off deployments of service function chains (SFCs) while neglecting the dynamic (i.e., online) deployment and expansion requirements. In particular, many methods of energy/resource cost reduction are achieved by merging VNFs. However, the energy waste and device wear for large-scale collections of servers (e.g., cloud networks and data centers) caused by sporadic request updating are ignored. To solve these problems, we propose an energy-aware routing and adaptive delayed shutdown (EAR-ADS) algorithm for dynamic SFC deployment, which includes the following features: 1) energy-aware routing (EAR): by considering a practical deployment environment, a flexible solution is developed based on reusing open servers and selecting paths with the aims of balancing energy and resources and minimizing the total cost and 2) adaptive delayed shutdown (ADS): the delayed shutdown time of the servers can be flexibly adjusted in accordance with the usage of each device in each time slot, thus eliminating the no-load wait time of the servers and frequent on/off switching. Therefore, the EAR-ADS can achieve dual-energy savings by both decreasing the number of open servers and reducing the idle/switching energy consumption of these servers. The simulation results show that EAR-ADS not only minimizes the cost of energy and resources but also achieves an excellent success rate and stability. Moreover, EAR-ADS is efficient compared with an improved Markov algorithm (SAMA), reducing the average deployment time by more than a factor of 40.

The increasing amount of data replication across datacenters introduces a need for efficient bulk data transfer protocols which provide certain guarantees, most notably timely transfer completion. We present DaRTree which leverages emerging optical reconfiguration technologies, to jointly optimize topology and multicast transfers in software-defined optical Wide-Area Networks (WANs), and thereby maximize throughput and acceptance ratio of transfer requests subject to transfer deadlines. DaRTree is based on a novel integer linear program relaxation and deterministic rounding scheme. To this end, DaRTree uses Steiner trees for forwarding and adaptive routing based on the current network load. DaRTree provides transfer completion guarantees without the need for rescheduling or preemption. Our evaluations show that DaRTree increases the network throughput and the number of accepted requests by up to 1.7×, especially for larger WANs. Moreover, DaRTree even outperforms state-of-the-art solutions when the traffic demands are only unicast transfers or when the WAN topology cannot be reconfigured. While DaRTree determines the rate and route to serve a request at the time of (online) admission control, we show that the acceptance ratio and throughput can be improved by up to 1.3× even further when DaRTree updates the rate and route of admitted transfers also at runtime.

In today's data center networks (DCN), cloud applications commonly disseminate files from a single source to a group of receivers for service deployment, data replication, software upgrade, etc. For these group communication tasks, recent advantages of software-defined networking (SDN) provide bandwidth-efficient ways-they enable DCN to establish and control a large number of explicit multicast trees on demand. Yet, the benefits of data center multicast are severely limited, since there does not exist a scheme that could prioritize multicast transfers respecting the performance metrics wanted by today's cloud applications, such as pursuing small mean completion times or meeting soft-time deadlines with high probability. To this end, we propose PAM (Priority-based Adaptive Multicast), a preemptive, decentralized, and ready-deployable rate control protocol for data center multicast. At the core, switches in PAM explicitly control the sending rates of concurrent multicast transfers based on their desired priorities and the available link bandwidth. With different policies of priority generation, PAM supports a range of scheduling goals. We not only prototype PAM upon the emerged P4-based programmable switch with novel approximation designs, but also evaluate its performance with ns3-based extensive simulations. Results imply that PAM is ready-deployable; it converges very fast, has negligible impacts on coexisting TCP traffic, and always performs near-optimal priority-based multicast scheduling.

Due to the characteristics of urban vehicular ad hoc networks (VANETs), many difficulties exist when designing routing protocols. In this paper, we focus on designing an efficient routing strategy for vehicle-to-vehicle (V2V) communication in urban VANETs. Because, the characteristics of urban VANET routing performance are affected mainly by intersections, traffic lights, and traffic conditions, we propose an intersection-based distributed routing (IDR) strategy. In view of the fact that traffic lights are used to cause vehicles to stop at intersections, we propose an intersection vehicle fog (IVF) model, in which waiting vehicles dynamically form a collection or fog of vehicles at an intersection. Acting as infrastructure components, the IVFs proactively establish multihop links with adjacent intersections and analyze the traffic conditions on adjacent road segments using fuzzy logic. This approach offloads a large part of the routing work. During routing, the IVFs adjust the routing direction based on the real-time position of the destination, thus avoiding rerouting. Each time an IVF makes a distributed routing decision, the IDR model employs the ant colony optimization (ACO) algorithm to identify an optimal routing path whose connectivity is based on the traffic conditions existing in the multihop links between intersections. Because of the high connectivity of the routing path, the model requires only packet forwarding and not carrying when transmitting along the routing path, which reduces the transmission delay and increases the transmission ratio. The presented mathematical analyses and simulation results demonstrate that our proposed routing strategy is feasible and that it achieves relatively high performance.

Training a high-accuracy model requires trying hundreds of configurations of hyperparameters to search for the optimal configuration. It is common to launch a group of training jobs (named cojob) with different configurations at the same time and stop the jobs performing worst every stage (i.e., a certain number of iterations). Thus deep learning requires minimizing stage completion time (SCT) to accelerate the searching. To quickly complete the stages, each job in the cojob typically uses multiple GPUs to perform distributed training. The GPUs exchange data per iteration to synchronize their models through the network. However, data transfers of DL jobs compete for network bandwidth since the GPU cluster hosts a number of cojobs from various users, resulting in network congestion and consequently a large SCT for cojobs. Existing flow schedulers aimed at reducing flow/coflow/job completion time mismatch the requirement of hyperparameter searching. In this paper, we implement a system Grouper to minimize average SCT for cojobs. Grouper adopts a well-designed algorithm to permute stages of cojobs and schedules flows from different stages in the order of the permutation. The extensive testbed experiments and simulations show that Grouper outperforms advanced network designs Baraat, Sincrona, and per-flow fair share.

The increasing amount of data replication across datacenters introduces a need for efficient bulk data transfer protocols which meet QoS guarantees, notably timely completion. We present DaRTree which leverages emerging optical reconfiguration technologies, to jointly optimize topology and multicast transfers, and thereby maximize throughput and acceptance ratio of transfer requests subject to deadlines. DaRTree is based on a novel integer linear program relaxation and deterministic rounding scheme. To this end, DaRTree uses multicast Steiner trees and adaptive routing based on the current network load. DaRTree provides its guarantees without need for rescheduling or preemption. Our evaluations show that DaRTree increases the network throughput and the number of accepted requests by up to 70%, especially for larger Wide-Area Networks (WANs). In fact, we also find that DaRTree even outperforms state-of-the-art solutions when the network scheduler is only capable of routing unicast transfers or when the WAN topology is bound to be non-reconfigurable.

The wireless body area network (WBAN) has attracted considerable attention. Two main problems exist in WBANs: 1) the quality of service (QoS) requirements and 2) the energy efficiency of data transmission. To solve the above problems, in this paper, we carefully design the sensor priority according to the importance degree, sampling rate, timeout condition, and remaining energy. Then, considering the priority of the node and the channel factors, a utility function is introduced to characterize the value of a node transmitting data frames in a specific time period. Next, we model the time slot allocation problem, where the objective is to maximize the total utility of the data transmission of all nodes in a specified period of time by adjusting the transmission time and the transmission duration of each node. Finally, according to the problem model, we propose a time slot allocation scheme based on a greedy strategy, which effectively reduces the time complexity of the direct solution to the problem. In this scheme, nodes with higher priority are arranged to transmit data frames in the time slots with better channel conditions. The experimental results show that the proposed scheme achieves substantial improvements in QoS and energy efficiency relative to the comparison schemes.

A large number of plug-in hybrid electric vehicles (PHEVs) have high mobility but a small battery capacity; thus, these vehicles urgently need to make charging and discharging decisions in real time. This paper proposes a new architecture based on fog computing for an Internet of Vehicles energy trading system, which we call a vehicle-mounted energy fog. This architecture includes a fog computing energy center (FCEC), which manages local energy trading and reduces the peak load energy trading for an external public energy company. We model the optimization problems for energy trading under two different types of FCECs: 1) a nonprofit-driven FCEC whose goal is solely to benefit the PHEV charging and discharging operations and 2) a profit-driven FCEC whose goal is to maximize its own profits while still guaranteeing that each PHEV achieves a non-negative utility. We also propose efficient algorithms for these two types of FCECs to seek optimal pricing and make supply demand decisions. Simulation results show that our proposed algorithms are superior to existing algorithms in terms of the convergence rate, the final objective value and the evenness of the Pareto solution set. Specifically, the evenness of the Pareto solution set is improved by 23% compared to the results of the existing algorithm.

Many modern cloud services are operated across geographically distributed datacenters, and they are usually associated with a demand of transferring bulk data among datacenters for achieving a high performance and reliability. These transfers (e.g., data replications and synchronizations) may require the inter-datacenter networks to deliver data from one Point (or datacenter) to Multiple Points (or datacenters) and impose a deadline on the transfer for providing a guaranteed service to end users. However, very little work has been done to manage an efficient point-to- multipoint bulk transfer while considering the deadline requirements. In this paper, we investigate the deadline- aware point-to-multipoint (P2MP) transfer problem and propose a centralized deAdline- Guaranteed transfEr (AGE) approach that can guarantee the deadline for P2MP transfers while efficiently utilizing the inter-datacenter bandwidth resources. For each arriving request, AGE jointly determines the transfer source selections and bandwidth allocations such that the number of deadline-guaranteed transfers can be maximized. Our simulation experiments show that AGE can accommodate up to 53.3% more transfers whose deadline requirements are met. In addition, AGE can achieve 20% higher network utilization than prior bulk transfer approaches.

Many large-scale compute-intensive and mission-critical online service applications are being deployed on geo-distributed datacenters, which require transfers of bulk business data over Wide Area Networks (WANs). The bulk transfers are often associated with different requirements on deadlines, either a complete transfer before a hard deadline or a best-effort delivery within a soft deadline. In this paper, we study the online bulk transfer problem over inter-datacenter WANs, while taking into consideration the requests with a mixture of hard and soft deadlines. We use Linear Programming (LP) to mathematically formulate the problem with the objective of maximizing a system utility represented by the service provider's revenue, taking into account the revenue earned from deadline-met transfers and the penalty paid for deadline-missed ones. We propose an online framework to efficiently manage mixed bulk transfers and design a competitive algorithm that applies the primal-dual method to make routing and resource allocation based on the LP. We perform theoretical analysis to prove that the proposed approach can achieve a competitive ratio of (e-1)/e with little link capacity augmentation. In addition, we conduct comprehensive simulations to evaluate the performance of our method. Simulation results show that our method irrespective of the revenue model, can accept at least 25% more transfer requests and improve the network utilization by at least 35%, compared to prior solutions.

With the development of cloud computing, virtual machine migration is emerging as a promising technique to save energy, enhance resource utilizations, and guarantee Quality of Service (QoS) in cloud datacenters. Most of existing studies on the virtual machine migration, however are based on a single virtual machine migration. Although there are some researches on multiple virtual machines migration, the author usually does not consider the correlation among these virtual machines. In practice, in order to save energy and maintain system performance, cloud providers usually need to migrate multiple correlated virtual machines or migrate the entire virtual datacenter (VDC) request. In this paper, we focus on the efficient online live migration of multiple correlated VMs in VDC requests, for optimizing the migration performance. To solve this problem, we propose an efficient VDC migration algorithm (VDC-M). We use the US-wide US National Science Foundation (NSF) network as substrate network to conduct extensive simulation experiments. Simulation results show that the performance of the proposed algorithm is promising in terms of the total VDC remapping cost, the blocking ratio, the average migration time and the average downtime.

Network Function Virtualization (NFV) transforms the traditional service provision architecture into a software-based structure. In NFV, Virtual Network Functions Forwarding Graph (VNF FG) [1] is used to describe the logic connections among the VNFs placed on the Physical Network (PN). And end-to-end services are represented as flows traverse the VNF FG. To improve the reliability of the services, redundancy is proposed as an effective method. Existing redundancy method protects the unreliable VNF for end-to-end service independently and embeds the backup without considering the reliability of the PN hardware. Such a scheme ignores the global information of the VNF FG and leads to an inaccurate reliability estimation for the services. These deficiencies result in over backup and the utilization reduction of the underlying resource. To overcome these shortcomings, we propose a novel redundancy scheme. By using the Cost-aware Importance Measure (CIM), the structure of the VNF FG is involved in selecting the qualified backups. Meanwhile, with CIM, the placement decision is made by mapping these backups to the PN nodes with high reliability. In our simulation, our method performs well for cutting down the backup cost for up to 46% with respect to the present algorithms and keeping high cost-efficiency.

Flow completion times (FCTs) are critical for many cloud applications. To minimize the average FCT, recent transport designs, such as pFabric, PASE, and PIAS, approximate the Shortest Remaining Time First (SRTF) scheduling. A common, implicit assumption of these solutions is that the remaining time is only determined by the remaining flow size. However, this assumption does not hold in many real-world scenarios where applications generate data at diverse rates that are smaller than the network capacity. In this paper, we look into this issue from system perspective and find that the operating system (OS) kernel can be exploited to better estimate the remaining time of a flow. In particular, we use the rate of copying data from user space to kernel space to measure the data generation rate. We design RAX, a rate aware flow scheduling method, that calculates the remaining time of a flow more accurately, based on not only the flow size but also the data generation rate. We have implemented a RAX prototype in Linux kernel and evaluated it through testbed experiments and ns-2 simulations. Our testbed results show that RAX reduces FCT by up to 14.9%/41.8% and 7.8%/22.9% over DCTCP and PIAS for all/medium flows respectively.

In big data clusters, task dispatchers assign arriving tasks to one of many workers (servers) for load balancing. Workers schedule task executions for rapidly completing queueing tasks. Both dispatchers and workers are important for optimizing task/job-completion-time (TCT/JCT). Current dispatchers probe loads on workers before assigning every task/job, which incurs expensive message overheads and significant delays. Besides, they use simple First-In-First-Out (FIFO) scheduling on workers, which further harms their TCT/JCT performance due to head-of-line blocking. In our TASCO scheduler, workers report their loads to dispatchers so that dispatchers avoid to probe them, which significantly reduces expensive overheads and delays on current dispatchers. Motivated by recent observations that more than 60% tasks in big data clusters are recurring with predictable task service time, we also use delay- optimal smallest-task-first (STF) scheduling to improve current simple FIFO scheduling on workers. We also derive the average TCT of TASCO based on its equivalence to an M/G/1/STF queue and the insight that workers reporting loads to dispatchers follows a Poisson process in the large- system limit. Our theories and simulation results demonstrate that the average TCT/JCT of TASCO outperforms state-of-art schedulers from 5.3% to 55.9%.

In SDN, forwarding rules are frequently updated to adapt to network dynamics. During the procedure, path consistency needs to be preserved; otherwise, in-flight packets might meet with forwarding errors such as loops and black holes. Despite a large number of suggestions have been proposed, they take either a long duration or have high rule-space overheads, thus fail to be practical for large-scale high dynamic networks. In this paper, we propose FLUS, a Segment Routing (SR) based mechanism, to achieve fast and lightweight path updates. Basically, when a route needs a change, FLUS instantly employs SR to construct its desired new path by concatenating some fragments of the already existing paths. After the actual paths are established, FLUS then shifts incoming packets to them and disables the transitional ones. Such a design helps packets enjoy their new paths immediately without introducing rule-space overheads. This paper presents FLUS's segment allocation, path construction, and the corresponding optimal algorithms in detail. Our evaluation based on real and synthesized networks shows: FLUS can handle up to 92-100% updates using SR in real-time and save 72-88% rule overhead compared to prior methods.

Demand response is widely employed by today's data centers to response to the increasing of electricity cost. To incentivize users of data centers participate in the demand response programs, i.e., breaking the split incentive hurdle, some prior researches proposed market-based mechanisms such as dynamic pricing and static monetary rewards. However, these mechanisms are either intrusive or unfair. In this paper, we use time-varying rewards to incentivize users of data centers grant time-shifting of their requests. With a game-theoretic framework, we model/analyze the game between a single data center and its users. Further, we extend our design via integrating it with another emerging practical demand response strategies: server shutdown or local renewable energy generation. With real-world data traces, we show that a data center with our design can effectively shed its peak electricity load and overall electricity cost without reducing its profit, when compared with the current practice where no incentive mechanism is established.

Previous coflow scheduling proposals improve the coflow completion time (CCT) over per-flow scheduling based on prior information of coflows, which makes them hard to apply in practice. State-of-art information-agnostic coflow scheduling solution Aalo adopts Discretized Coflow-aware Least-Attained-Service (D-CLAS) to gradually demote coflows from the highest priority class into several lower priority classes when their sent-bytes-count exceeds several predefined demotion thresholds. However, current design standards of these demotion thresholds are crude because they do not analyze the impacts of different demotion thresholds on the average coflow delay. In this paper, we model the D-CLAS system by an M/G/1 queue and formulate the average coflow delay as a function of the demotion thresholds. In addition, we prove the valley-like shape of the function and design the Down-hill searching (DHS) algorithm. The DHS algorithm locates a set of optimal demotion thresholds which minimizes the average coflow delay in the system. Real-data-center-trace driven simulations indicate that DHS improves average CCT up to 6.20× over Aalo.

Location-Based Service (LBS) is one of the fundamental and central functionalities of mobile social networks. Since users usually have to report their locations to the LBS providers while using services, the protection of user's location privacy poses a critical challenge. Although many existing approaches can preserve user's location privacy effectively, most of them must include and use the user's real location. In this paper, we first propose an efficient k-anonymity based Dummy Location and divided Circular Area (k-DLCA) approach to protect the user's location privacy. Different from existing studies, the k-DLCA algorithm adopts a greedy strategy to select dummy locations and considers the semantic location information of the location. Moreover, the user's real location may not be contained in the chosen dummy locations. We then show that k-DLCA algorithm can resist the attacks from adversaries, and has a low probability of exposing the user's real location. We conduct extensive simulations to evaluate the efficiency of the proposed scheme. The simulation results demonstrate that our proposed scheme is promising.

This paper presents D2-CAS, a novel decentralized coflow scheduling system, to minimize the rate of deadline missed coflow for datacenter networks. To design D2-CAS, we first formulate the deadline-missed coflow minimization problem and show its equivalence with the well-known problem of minimizing the late jobs in a concurrent open shop, which is NP-hard in ordinary sense. Inspired by Moore-Hodgso's algorithm (MHA), the optimal solution for minimizing late jobs on a single machine, we design an efficient coflow schedule algorithm, CS-MHA, and further propose its decentralized implementation, D2-CAS. Basically, each sender in D2-CAS periodically runs a part of CS-MHA to get a local suggestion for flow priority assignment, and then multiple senders of a coflow negotiate for an orchestrated priority by leveraging their common data receivers. Via delivering each coflow's packets with its negotiated priority, senders finally carry out efficient deadline-aware coflow scheduling in a decentralized fashion. To the best of our knowledge, this is the first paper that theoretically investigates the deadline-aware coflow scheduling problem, while D2-CAS is the first decentralized solution. Real parameter driven simulations imply that, with the simple yet efficient mechanism, D2-CAS greatly outperforms all existing solutions on reducing the deadline-missed coflows (e.g., outperforms Varys more than 2x).

Network Function Virtualization (NFV) is an emerging technology proposed to improve service provisioning in telecommunication industry. The flexibility of NFV enables network operator to dynamically deploy Virtual Network Functions (VNFs) according to real-time requirements. However, a large amount of VNFs with low utilization may be deployed in network due to dynamic arrival and departure of services and excessive deployment of VNFs. In this paper, we analyse this phenomenon and model it as the Network Function Consolidation (NFC) problem, followed by an Integer Linear Programming (ILP) formulation. We also design a greedy based heuristic to solve large scale cases. We conduct simulations on both real-world and random generated network topologies, and the results show that we can reduce 32% number of VNFs in average.

In current data centers, an application (e.g., MapReduce, Dryad, search platform, etc.) usually generates a group of parallel flows to complete a job. These flows compose a coflow and only completing them all is meaningful to the application. Accordingly, minimizing the average Coflow Completion Time (CCT) becomes a critical objective of flow scheduling. However, achieving this goal in today's Data Center Networks (DCNs) is quite challenging, not only because the schedule problem is theoretically NP-hard, but also because it is tough to perform practical flow scheduling in large-scale DCNs. In this paper, we find that minimizing the average CCT of a set of coflows is equivalent to the well-known problem of minimizing the sum of completion times in a concurrent open shop. As there are abundant existing solutions for concurrent open shop, we open up a variety of techniques for coflow scheduling. Inspired by the best known result, we derive a 2-approximation algorithm for coflow scheduling, and further develop a decentralized coflow scheduling system, D-CAS, which avoids the system problems associated with current centralized proposals while addressing the performance challenges of decentralized suggestions. Trace-driven simulations indicate that D-CAS achieves a performance close to Varys, the state-of-the-art centralized method, and outperforms Baraat, the only existing decentralized method, significantly.

Network Function Virtualization (NFV) provides an effective way to reduce the network provider's cost by allowing multiple Virtual Networks (VNs) to share the underlying physical infrastructure. In the NFV environment, especially when supporting multicast service over the VNs, reliability is a critical requirement in the process of VN mapping since the failure of one virtual node can cause the malfunction of all the subsequent nodes that receive multicasting data from it. In this paper, for the first time, we study how to efficiently map VNs for reliable multicast services, while taking into consideration the max-min fairness of the reliability among distinct VNs. We propose a Mixed Integer Linear Programming (MILP) model to determine the upper bound on the max-min fairness reliability. In addition, an efficient heuristic, namely Uniform Reliability Mutation based Genetic (URMG) algorithm, is developed to address reliable multicast VN mapping with a low computational complexity. By encoding multicast tree construction and link mapping into path selection, taking into consideration the max-min reliability fairness goal, and the networking reliability factors during mutation, URMG can globally optimize the reliability and its fairness of all the multicast VN requests. Through extensive simulations, we demonstrate that URMG achieves close to the optimal reliability fairness with a much lower time complexity than the MILP and yields a significant performance improvement in terms of reliability fairness, bandwidth consumption and transmission delay comparing with other heuristic solutions.

In Software Defined Networks (SDN), applications on the controller could enforce fine-grained control on flows by policies employing more packet fields. These policies are converted to flow entries and stored in switch Flow Table. To store these entries, Flow Table requires large storage space because an entry consisted of more packet fields needs more storage space and the number of entries also increases significantly due to fine-granularity definition of flows. However, Flow Table has limited storage space owing to the constraints of Ternary Content Addressable Memory (TCAM). As a result, the switch Flow Table in SDN faces scalability issue. We address this issue by means of adaptive Flow Table management, namely we manage how long the entries occupy the storage space by setting adaptive timeouts to them. Through this means, the storage space could be reused efficiently and more flows could be supported with the same Flow Table (without updating hardware devices). Our proposed method TimeoutX, for the first time, combines traffic characteristics, flow types and Flow Table utilization ratio to decide the timeout of each entry and it outperforms current timeout setting strategies in both metrics of table miss number and blocked packet number, which indicates TimeoutX could make the best of Flow Table and support more flows.

Network Function Virtualization (NFV) allows multiple Virtual Networks (VNs) to share the underlying physical infrastructure via VN mapping, thus improving the utilization of physical resources. In this paper, for the first time, we study the multicast service-oriented VN mapping that can support big data applications over Elastic Optical Networks (EONs). Since the problem of minimizing the spectrum consumption in multicast service-oriented VN mapping is NP-hard, we propose an efficient heuristic algorithm, called Integrated Genetic and Simulated Annealing (IGSA) algorithm to address the problem with low computational complexity. By encoding node mapping, multicast tree construction, link mapping and spectrum requirements in the same gene and auto-adjusted evolution, and utilizing simulated annealing to find the fittest multicast requests mapping order, IGSA can perform joint optimization for all the multicast requests in a global way. Through extensive simulations, we demonstrate that IGSA outperforms the other heuristic solutions in terms of spectrum consumption, blocking probability and normalized throughput, while achieving close to minimum spectrum consumption with a much lower time complexity than MILP.

In current data centers, cloud providers (CPs) need to make decisions on virtual machine (VM) deployment requests that arrive dynamically over time under the constraint of limited physical resources to host VMs. Existing schemes to solve this problem either do not consider the dynamic characteristic of requests when solving the VM provisioning problem or separate admission control and VM provisioning into two independent problems. Both approaches can result in substantially sub-optimal revenue outcomes. In this paper, we propose a joint admission control and VM provisioning scheme based on a Markov Decision Process (MDP) framework to optimize the VM admission and placement decision in an integrated manner to maximize the CP's operating revenue. Because of the large number of physical servers in data centers, the MDP problem is computationally intractable to standard dynamic programming techniques. We propose a simulation-based approximate dynamic programming (ADP) algorithm to effectively solve the problem. Simulation results show that the new joint admission control and VM provisioning algorithm can substantially increase the revenue generated for CPs.

Migration of traffic from one configuration to another is common in SDNs due to node/link failures, network maintenance, policy reconfiguration, intrusion detection, network upgrades, and etc. When the network devices are informed by the controller to execute the traffic migration, it's difficult even impossible to force all network devices to perform the update action in a strict synchronized way. Thus the network is likely to see transient overlapped traffic from both the new configuration and the old one. This kind of overlap may cause overload to those hot spots. This paper reveals the transient congestion problem during traffic migration in an SDN update. According to the observation, it's feasible for the controller to schedule ingress nodes to perform the migration in an order thus the transient congestion is avoided. This scheduling problem is formulated as a Mixed Integer Linear Program (MIP) model. If feasible orders exist for ingress network nodes to perform the migration, the MIP can always find the order that achieves the minimum steps in all possibilities. A heuristic method (named ATOMIP (ATOmic-MIP)) is proposed to speed-up the solving of this MIP. Evaluation based on network topologies observed from real ISPs shows that the lossless migration happens in sub-seconds.

In current data centers, an application (e.g. MapReduce) usually generates a collection of parallel flows sharing a common goal. These flows compose a coflow and only completing them all is meaningful. Accordingly, minimizing the average coflow completion time (CCT) becomes a critical objective for flow scheduling. In this topic, the state-of-the-art centralized method, Varys, achieves a good average CCT; but it has the scalability problem. Alternatively, the only existing decentralized method, Baraat, suffers from the head-of-line blocking problem. To solve these problems, we propose D-CAS, a preemptive, decentralized, coflow-aware scheduling system in this paper. D-CAS pursues coflow-level remaining-time-first (MRTF) principle by leveraging a simple negotiation mechanism between each coflow's data senders and receivers. As the MRTF principle is inherently preemptive and proven to be a near-optimal guideline to minimize average CCT, D-CAS avoids the head-of-line blocking problem and gets good performances. Through extensive simulations, we find that D-CAS achieves a performance close to Varys (gap <; 15%) and outperforms Baraat significantly (about 1.4-4×).

In this paper, we study how to manage the topology reconfiguration in OSA-based datacenter networks (DCNs). Though an OSA-based DCN can change its topology to adapt to the traffic matrix and improve the network scalability, it requires too much time (10ms) to reconfigure the topology, which may not only incur a great amount of traffic loss in high throughput low latency DCNs, but also bring much performance degradation to the delay sensitive flows. Therefore, a progressive topology reconfiguration scheme is required to reduce the traffic loss and guarantee the performance of delay sensitive flows. To this end, we first formulate the problem as a mathematical model, and then analyze its feasibility and complexity. Based on these analyses, topology management algorithm (TMA) is proposed to calculate the topology reconfiguration scheme that can maintain the topology connectivity during reconfiguration. By simulation, we find that TMA can reduce the traffic loss during topology reconfiguration by up to 50% in most of the cases and reconfigure topology without traffic loss in some cases.

Server consolidation schemes whereby each server is replaced with a virtual machine (VM) and multiple such VMs are run on a single physical server can reduce the number of physical servers needed, and in turn, both the cost and energy consumption in datacenters. However, existing schemes have not fully exploited the flexibility in the usage and allocation of virtualization resources, so as to allow one application originally deployed on a single large VM (LVM) to be split and hosted by multiple smaller VMs (SVM). Using multiple SVM instead of a LVM enables resource allocation at a smaller granularity and hence may further increase the utilization and reduce the number of physical servers. However, a major challenge to be overcome when deploying multiple SVMs for one application is to preserve the performance of the application in terms of e.g., response delay. In this paper, we show through experiment based data analysis that in order to preserve the performance of the application, one needs to allocate sufficient resources to each SVM, and the total amount of resources required by all the SVMs will exceed that required by the LVM. Nevertheless, we also show that by using the proposed heuristic algorithm called VM Splitting and Assignment (VMSA), we can substantially improve the utilization and reduce the number of physical servers.

In OpenFlow-based SDN, flow tables are TCAM-hungry and commodity switches suffer from limited concrete flow table size. One method for coping with the limitations is to use aggregation schemes to reduce the number of flow entries required to represent the same forwarding semantics. Unfortunately, the aggregation retards table updates and lengthens the updating time. During which, the data plane is inconsistent with the control plane, forwarding errors such as Reachability Failures, Forwarding Loops, Traffic Isolation and Leakage are prone to occur. Since network updates take place frequently in practice, the aggregation scheme must be efficient enough. In this paper we propose offline FFTA (Fast Flow Table Aggregation) and its online improver iFFTA to shrink the flow table size and to provide practical fast updates. iFFTA is the first online non-prefix aggregation scheme. Extensive experiments demonstrate: (1) FFTA is about 200× faster than the previously published best non-prefix aggregation scheme without loss of compression ratio on offline aggregation; and (2) iFFTA achieves about 3× faster than FFTA on online update incorporations with a loss of an acceptable compression ratio per update. Thus the user could make a combination use of FFTA and iFFTA for table aggregations: call iFFTA usually and recall the efficient FFTA once the switch is running out of concrete flow table space.

Network virtualization facilitates sharing and efficient utilization of computing and bandwidth resources of an underlying substrate network. As network virtualization becomes popular, it is important to efficiently map a virtual infrastructure (VI) onto a substrate network, such that the survivability of the former can be guaranteed against failures in the latter. In this paper, we study a virtualization layer approach to survivability, whereby the virtualization layer customizes a VI request with redundant nodes and links according to its reliability requirements and then passes limited information about the augmented VI to the physical layer, where the mapping of the augmented VI takes place. More specifically, we develop a flexible scheme to enhance the original VI graph with K redundant nodes, in order to fight against an arbitrary substrate node failure. In addition, a scenario-based component group (SBCG) concept is proposed to describe resource sharing of enhanced VI requests at the physical layer. We also develop an efficient heuristic that takes advantage of the limited information on SBCG to reduce costs when mapping the enhanced VI to the substrate network. The efficiency of the proposed solution is compared using extensive simulation under various performance metrics. It is shown that the K-redundant-node scheme with SBCG information is more cost efficient than the existing 1-redundant-node solution.

Cloud based datacenters provide on demand services and resources, both transparently and cost effectively. These services and applications are typically hosted and run on the servers located in interconnected datacenters. The task or application request from users can be abstracted as a virtual network (VN) request. How to efficiently accommodate VN requests by mapping them onto the substrate network is a challenging problem. Current research only considers static VN requests, where the VN request and the demand for resources is fixed and does not change over time. However, most of the application requests submitted to datacenters present dynamic changing characteristics. In this paper, we address the issue of how to optimally reconfigure and map an existing VN while this VN request changes. As the VN provisioning problem is NP-hard, we propose heuristic algorithms for solving it efficiently. We evaluate the performance and effectiveness our algorithms by conducting simulations on realistic network. The simulation results show that our approach solves this problem efficiently and performs well than existing approaches.

In the cloud computing paradigm users access applications/services hosted and run on virtual machines in interconnected datacenters. Applications from the same user may need to interact and change data or information, thus, we may abstract the applications/services request from same user as a virtual network (VN). To improve performance and resource efficiency, it is critical that the VN request be optimally provisioned given the current resource state of the datacenters. However, all of the existing research about optimal VN provisioning conducted for deterministic services. In this work, we formulate the problem of optimal provisioning for elastic service oriented VN request as a mixed integer programming (MIP) model with the objective of maximizing the total revenue of infrastructure provider (InP), and propose a genetic algorithm based heuristic (GAH) algorithm for addressing the optimal provisioning for virtual network request with un-splittable flow (OPVNUF) and optimal provisioning for virtual network request with splittable flow (OPVNSF) problems of elastic services. We demonstrate the effectiveness of our approach for improving the total revenue, by conducting extensive simulations on real substrate network.

With the increasingly popular virtualization of both computing and networking resources in a distributed system (called a physical substrate), multiple virtual infrastructures (VIs) can share the physical resources of the underlying substrate, and accordingly even a single failure in the substrate can affect a large number of VIs and the services they offer. Thus, the problem of efficiently mapping a VI to a substrate while guaranteeing the VI's survivability in the event of failures in the substrate becomes important. In this paper, we study the survivable VI mapping problem to protect against link failures in the substrate. We first propose a solution based on traditional shared protection (survivable virtual infrastructure mapping algorithm, P-SVIMA), and then propose a novel VI node migration protection-based algorithm (MP-SVIMA) to minimize the computing and communication resource costs. The MP-SVIMA scheme takes advantage of the flexibility in where VI nodes are mapped in the substrate by migrating a VI node from the originally mapped physical location to a different location after a physical link fails in order to recover from link failures. We compare the efficiency of our solutions using simulations under various performance metrics.

As network virtualization becomes popular, the problem of efficiently mapping a virtual infrastructure (VI) over a substrate network while guaranteeing its survivability in the event of failures becomes increasingly important. In this paper, we study the survivable VI mapping problem to recover from facility node failures. We develop two solutions namely the 1-redundant scheme and the K-redundant scheme for surviving facility node failures while minimizing network resource costs. We also model the two schemes as a MILP problem and propose efficient heuristics based on the MILP formulations. We compare the efficiency of our solutions using simulation under various performance metrics.

A major disruption may affect many network components and significantly lower the capacity of a network measured in terms of the maximum total flow among a set of source-destination pairs. Since only a subset of the failed components may be repaired at a time due to e.g., limited availability of repair resources, the network capacity can only be progressively increased over time by following a recovery process that involves multiple recovery stages. Different recovery processes will restore the failed components in different orders, and accordingly, result in different amount of network capacity increase after each stage. This paper aims to investigate how to optimally recover the network capacity progressively, or in other words, to determine the optimal recovery process, subject to limited available repair resources. We formulate the optimization problem, analyze its computational complexity, devise solution schemes, and conduct numerical experiments to evaluate the algorithms. The concept of progressive network recovery proposed in this paper represents a paradigm-shift in the field of resilient and survivable networking to handle large-scale failures, and will motivate a rich body of research in network design and other applications.

As virtualization becomes more and more popular, how to guarantee survivability of a virtual infrastructure (VI) over a wide-area optical network is increasingly important. In this paper, we approach the problem of survivable VI mapping (SVIM) from a few unique perspectives. One of the most distinguishing perspectives is that a large-scale regional failure could destroy one or more facility nodes to which some VI nodes are mapped. Accordingly, redundant facility nodes at different geographical locations and redundant optical connections have to be provisioned such that the VI can still be mapped after the failure. Another distinguishing perspective is that with failure-dependent protection, the SVIM problem can be decomposed into several instances of the basic non-survivable VI mapping (NSVIM) problem, whose solution permits effective sharing of the redundant resources among all failures. In this paper, we first formulate the minimum-cost SVIM problem using mixed integer linear programming (MILP). We then propose an efficient heuristic solution to NSVIM, based on which two novel heuristic SVIM algorithms called Separate Optimization with Unconstrained Mapping (SOUM) and Incremental Optimization with Constrained Mapping (IOCM). Simulations are performed to study and compare the performance of the MILP and heuristics.

In this paper, we focus on the problem with the objective to maximize the system performance, while guaranteeing multiple QoS (quality of service) constraints for wireless data networks accommodating multiclass services with different quality requirements. First, we formulate and solve the opportunistic scheduling problem with multiple general long-term QoS constraints. Then, we generalize this problem to include short-term QoS constraints for real-time multimedia users and long-term QoS constraints for non-real-time data users simultaneously in multiclass services networks. Simulation results illustrate that the proposed scheduling schemes guarantee the different QoS constraints, and achieve high system performance.

In this paper, we address the problem of resource allocation with efficiency and quality of service (QoS) support in uplink for a wireless CDMA network supporting real-time (RT) and nonreal-time (NRT) communication services. For RT and NRT users, there are different QoS requirements. We introduce and describe a new scheme, namely the traffic aided uplink opportunistic scheduling (TAUOS). While guaranteeing the different QoS requirements, TAUOS exploits the channel condition to improve the system throughput. In TAUOS, the cross-layer information, file size information, is used to improve the fairness of NRT users. Extensive simulation results show that our scheme can achieve high system throughput in uplink wireless CDMA system, while guaranteeing the QoS requirements.

In this paper we present a novel efficient energy-aware approach for clustering nodes in wireless sensor networks. We use different cluster head (CH) declaration delays for each node to characterize the qualification to be a CH. The approach guarantees the fairly uniform cluster distribution while incurring low overheads. Additionally, we do not make any assumptions about the distribution or node capabilities, e.g., location-awareness. The simulation results show that our clustering approach outperforms LEACH both in cluster characteristics and in the efficiency of prolonging the network lifetime.

The advances in wavelength-division multiplexing (WDM) technology are expected to facilitate bandwidth-intensive multicast applications. A single fiber failure in such a network, however, can disrupt the information dissemination to several destination nodes in a "Iight-tree"-based multicast session. Thus it is imperative to protect the multicast sessions. In this paper, we propose a novel protection scheme, called multicast protection through spanning paths (MPSP), for resource efficient multicast protection with spare capacity sharing. Here, a spanning path is a path from a leaf node to any other leaf node of a multicast tree. The key idea of MPSP is first to identify a backup path for each spanning path and then to appropriately select parts of these backup paths to protect the primary multicast tree, so that the total bandwidth allocated to the primary multicast tree and its protection paths (or trees, etc.) is minimized. While previous studies only consider self-sharing and intra-request sharing, to the best of our knowledge, this is the first time to take inter-request sharing of spare capacity into consideration when protecting dynamic multicast sessions. We use simulations to demonstrate the performance of the MPSP scheme. It is shown that significant performance improvements are achieved in terms of average cost per multicast session and blocking probability. Compared with existing schemes, the average cost is reduced by about 22% and the blocking probability can be reduced by about 27% in average.

Segment protection has been recognized as an efficient way to avoid ldquotrapsrdquo in survivable WDM mesh network. In this paper, we present some insights on this kind of protection. We first prove the correctness of segment protection used for avoiding traps. We then investigate the effect of the number of segments on the blocking performance in several typical real networks (includes middle and large networks). We find that, in most cases, it is sufficient to use two backup segments to protect a given active path. Based on this observation, we then propose a novel and efficient segment protection algorithm, called two-segment (TS), which has a lower computational complexity and comparative performance than existing algorithms.

The advances in wavelength-division multiplexing (WDM) technology are expected to facilitate bandwidth-intensive multicast applications. A single fiber cut on such a network, however, can disrupt the transmission of information to several destination nodes on a "light-tree"- based multicast session. Thus it is imperative to protect multicast sessions. In this article we propose a novel protection scheme called multicast protection through spanning paths (MPSP) for efficient multicast protection. Here, a spanning path is a path from a leaf node to any other leaf node of a multicast tree. The key idea of MPSP is to derive a backup path for each spanning path and then appropriately select part of these backup paths to protect the primary multicast tree such that the total bandwidth allocated to the primary multicast session and its protection paths (or trees, etc.) for all the multicast sessions is minimized. Simulation results are used to demonstrate the good performance of the proposed protection scheme in reducing the spare capacity for protection. Compared with existing schemes, the decrease in average cost can be quite high (e.g., decrease about 20%) and the blocking probability can be reduced approximately 15%.