In scenarios such as disaster response, remote area surveillance, and military reconnaissance, where stable communication infrastructure may be unavailable, unmanned aerial vehicles (UAVs) are often deployed to physically carry data. Ensuring data reliability in such scenarios, especially in adversarial environments, requires data backup mechanisms for UAV swarms. This paper addresses the critical yet often overlooked challenge of data recovery difficulty in UAV swarm networks. We introduce Data Recovery Entropy (DRE) to quantify the spatial dispersal of backed-up data across the swarm. Based on this metric, we propose two data backup strategies: one enables adjustable level of DRE through a tunable parameter, and the other prioritizes minimizing DRE. The proposed data backup strategies are tailored to the unique characteristics of UAV swarms, focusing particularly on their dynamic communication topologies. Finally, we vary system parameters, including node deployment parameters, communication parameters, and mobility parameters, to validate the proposed approach.

In recent years, the frequency and severity of network attacks have increased significantly, posing serious threats to network security. Network topology information is often exploited by attackers to identify critical bottlenecks, which are prime targets for attacks. Typically, attackers probe the network topology using network tomography or traceroute. Network tomography, compared to traditional methods like traceroute, offers greater flexibility and is more challenging to detect. These characteristics make it a preferred technique for attackers. In response, network operators seek to implement topology obfuscation strategies that expose a deliberately designed fake topology to mislead attackers. However, existing obfuscation techniques against network tomography have primarily focused on protecting tree-like topologies and have been insufficient in concealing bottleneck nodes and links. To this end, we propose TopoFaker, a novel topology obfuscation system designed to protect general network topologies while effectively concealing bottleneck nodes and links. TopoFaker consists of three main components: a topology generator that creates secure fake topologies, a policy deployer that ensures attackers perceive only the obfuscated topology, and obfuscation nodes that implement proactive delay policies in the data plane. Experimental evaluations on real-world network topologies demonstrate that TopoFaker effectively enhances network security by obscuring bottleneck nodes and links, achieving a 73.99% reduction in maximum degree centrality and a 79.48% reduction in maximum edge connectivity. Furthermore, TopoFaker reduces the average proactive delay time by 50.82%, minimizing the negative impact on normal packets misclassified due to the classifier’s false alarms. TopoFaker outperforms existing mechanisms by achieving a runtime of under one minute and reducing memory allocation by four orders of magnitude on large-scale problems.

As resource-intensive and latency-sensitive applications continue to expand, the integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) has emerged as a viable solution, offering flexible, on-demand services for mobile users (MUs) without reliance on terrestrial infrastructure. The adoption of non-orthogonal multiple access (NOMA) further reduces latency by allowing MUs to offload tasks simultaneously over a single subchannel. However, many existing offloading methods do not explicitly incorporate a priority-based task scheduling mechanism and instead optimize task execution based on system constraints such as latency or energy consumption. To bridge this gap, we propose a QoS-oriented task offloading scheme that systematically optimizes task scheduling. We formulate an average system utility maximization problem that jointly optimizes UAVs’ 3D trajectories, MU association, task offloading ratios, and resource allocation. The optimization problem is inherently complex due to its non-convex nature and multiple constraints. To address this, we first employ Lagrange duality to decouple constraints, reducing computational complexity. Subsequently, we propose a novel improved soft actor-critic (ISAC) algorithm, which incorporates a perturbation term into the loss function to guide the training process away from local minima and toward globally optimal solutions. Through extensive simulation, we demonstrate that the ISAC algorithm guarantees convergence and significantly outperforms benchmark methods on offloading transmission rates, task completion rates, and overall system utility.

The prominent paradigm of federated learning (FL) is increasingly being applied to emerging and cross-silo applications, particularly with edge computing systems serving as pivotal agents. However, this shift also renders FL training more energy and carbon intensive. To this end, we propose GreenFL, a carbon-aware FL training framework designed to systematically navigate the trade-offs between carbon emission, training accuracy, and training efficiency. GreenFL employs a hybrid training strategy that combines inter-group asynchronous training and intra-group synchronous training to mitigate the straggler effect caused by inefficient participants. In the overall design of the framework, we promote the participation of edge computing nodes with abundant renewable energy sources and implement strategic participant selection to balance carbon emissions and training accuracy. We prove the solvability of optimizing the selection strategy and provide an online greed-based solution based on penalty values and bipartite greedy algorithms. Through extensive data-driven experiments, we demonstrate that GreenFL can significantly improve the carbon efficiency of the entire FL procedure, while maintaining or exceeding state-of-the-art levels of training accuracy and efficiency.

Efficient set representation and membership testing are important in high-speed network measurement. Fast insertions, space efficiency, fast query, and low false positive rate are the core requirements of traffic measurement, but existing solutions, such as hash tables and Bloom filters(BFs), cannot satisfy these requirements simultaneously. The state-of-the-art cuckoo filter and its variants(CFs) rely on the random eviction relocation strategy to resolve hash collisions, improving space utilization while reducing false positives and maintaining high query efficiency. However, CFs suffer a critical challenge in practical applications: insertion could trigger multiple evictions when all candidate buckets for an element are saturated, leading to insertion performance degradation, especially when space utilization exceeds 0.8. To solve the above problem, we propose a novel relocation strategy based on a random graph model, called the local minimum strategy. Our core idea is to use the implicit meaning of the number of evictions in each bucket as an indication to minimize the relocation of elements. We theoretically and experimentally prove that the eviction threshold for each bucket is$O(\log m)$, where$m$is the number of buckets. The threshold establishes the bounds for the probability of a successful insertion. The experimental results show that, the local minimum strategy significantly reduces the number of relocations by 67 %, as well as increasing the insertion throughput by more than 10 %.

The rapid development of unmanned aerial vehicle (UAV) technology and mobile edge computing (MEC) has created new opportunities for efficient data processing and transmission. UAV-assisted MEC enables data transmission from UAVs to a base station (BS) equipped with MEC capabilities. However, ensuring the security of these transmissions is a concern, especially when UAVs operate in open airspace. In this paper, we introduce a coordinated multi-point (CoMP) offloading model aimed at enhancing the secure transmission performance of the network. The airspace is divided into several equal-sized hexagonal cells, with multiple UAVs collaborating to offload data to a BS with MEC. During this process, the locations of potential eavesdroppers are randomized as they attempt to intercept the data transmitted by the UAVs. Based on this model, we first derive the success communication probability (SCP) for a typical BS and an eavesdropper using stochastic geometry. We then introduce the concept of secure transmission rate, precisely the average secrecy rate (ASR). Further, we characterize the ASR in the presence of random eavesdroppers. Finally, we analyze the effects of various parameters on transmission performance. The results of our simulations closely align with our numerical findings, confirming the accuracy of our analysis. Notably, the ASR of the proposed system is nearly four times higher than that of an offloading model without cooperation. Moreover, compared to a user-centric offloading model with CoMP, the ASR increases by 5.37 %, enhancing system performance and reducing search overheads.

As mobile device usage and data traffic increase, the demand for faster data processing becomes crucial. Mobile edge computing (MEC) meets this need by placing servers at the network’s edge for real-time computing. However, fixed terrestrial MEC servers struggle with scalability, limiting their effectiveness. Integrating unmanned aerial vehicles (UAV) with MEC technology offers a promising solution, enhancing communication efficiency and service quality. This paper proposes a joint communication and computation offloading model for coordinated multi-point (CoMP) UAV-assisted MEC networks utilizing hexagonal cell partitioning. Within each cell, a cluster of UAVs, each equipped with its own MEC server and connected to a central server via a reliable backhaul, collaborates to serve terrestrial user equipment. To analyze this system, we develop a unified analytical framework integrating stochastic geometry and queuing theory. Furthermore, we define the success probability of edge computing (SPEC) metric to quantitatively evaluate communication reliability and computational efficiency. Finally, we explore the effects of critical parameters on network performance. Simulation results closely match the theoretical predictions, confirming our proposed model’s validity and our analysis’s accuracy. Notably, our proposed model demonstrates an improvement in SPEC of approximately 57.24% over non-CoMP model and 24.97% over the user-centric CoMP model.

Edge computing is an emerging paradigm poised to process a substantial portion of latency-sensitive and computation-intensive tasks through edge service providers (ESPs). However, these ESPs typically operate independently and locally to serve their registered users. When processing burst tasks, the ESPs have to either scale up their respective capacities by introducing additional hardware or compromise user experience by rejecting some user requests, leading to high commercial investment or service degradation. Inspired by the promise of the win-win situation for ESPs and users, we envision a novel task offloading strategy that realizes the following rationales simultaneously: 1) collaborative service, 2) rapid response, and 3) sustainable profitability, while the existing methods fail to achieve them at one shot. To this end, we report CoEdge, a collaborative architecture for efficient task offloading among multiple ESPs in the edge network, aiming at simultaneously minimizing service delay for users and enhancing service profit for ESPs. To achieve this, CoEdge employs a central optimizer to implement a two-stage strategy that determines the task scheduling and service pricing hierarchically. We then formulate these problems and prove their NP-hardness. Additionally, we also propose efficient approximate algorithms to accommodate large-scale computing scenarios with low complexity. Experimental results using real-world datasets demonstrate that our CoEdge can significantly reduce service delay by 2.87x to 4.15x for users and considerably increase service profit by 32% for ESPs.

Currently, deduplication techniques are utilized to minimize the space overhead by deleting redundant data blocks across large-scale servers in data centers. However, such a process exacerbates the fragmentation of data blocks, causing more cross-server file retrievals with plummeting retrieval throughput. Some attempts prefer better file retrieval performance by confining all blocks of a file to one single server, resulting in non-trivial space consumption for more replicated blocks across servers. An ideal network storage system, in effect, should take both the deduplication and retrieval performance into account by implementing reasonable assignment of the detected unique blocks. Such a fine-grained assignment requires an accurate and comprehensive abstraction of the files, blocks, and the file-block affiliation relationships. To achieve this, we innovatively design the weighted hypergraph to profile the multivariate data correlations. With this delicate abstraction in place, we propose HyperPart, which elegantly transforms this complex block allocation problem into a hypergraph partition problem. For more general scenarios with dynamic file updates, we further propose a two-phase incremental hypergraph repartition scheme, which mitigates the performance degradation with minimal migration volume. We implement a prototype system of HyperPart, and the experiment results validate that it saves around 50% of the storage space and improves the retrieval throughput by approximately 30% of state-of-the-art methods under the balance constraints.

As an ambitious training paradigm, federated learning has garnered increasing attention in recent years, which enables collaborative training of a global model without accessing users’ private data. However, due to the simultaneous and constant model updates gathering from massive distributed clients, the central server generally becomes a performance bottleneck. Additionally, the stateful aggregation (retaining all the updates from each client) conducted by the central server further poses potential threats to privacy, since it may recover the raw data based on such model updates inversely. The state-of-the-art methodologies, however, fail to address these two problems concurrently and efficiently. To this end, we propose GAIN, a secure aggregation acceleration service for federated learning. At its core, GAIN leverages programmable switches deployed at the edge network to aggregate model updates in a stateless manner before transmitting them to the central server. Consequently, GAIN can accelerate the transmission and aggregation of model updates while eliminating the chance of recovering private data. We evaluate the performance of GAIN through FPGA-based experiments and large-scale simulations. The results show that GAIN can effectively reduce bandwidth overhead and achieve up to 4.11× training throughput acceleration while prioritizing privacy protection.

Many network attacks, like link flooding attacks (LFAs), heavily rely on network topology information. Therefore, network topology obfuscation has been applied to counteract network topology inference and prevent topology information leakage. One effective way is to scheme a fake topology intentionally for attackers to map out. Focusing on reducing the similarity between the real and fake topologies, however, existing methods cannot promise that critical information of the network, such as critical nodes and links, is well hidden. To this end, we propose a new topology obfuscation mechanism, namely ChameleonNet, to protect the critical topology information of a given network. Specifically, ChameleonNet achieves topology obfuscation through a two-stage operation: 1) generating fake topology and 2) deploying fake topology. Our experiments on three real-world and two large-scale generated network topologies demonstrate that ChameleonNet can effectively reduce similarity between inferred and real topologies by 31%-37% and reliably hide critical topology information in terms of multiple statistical metrics.

Edge storage systems have drawn many efforts to extend the storage and service capabilities of cloud data centers. A pivotal aspect lies in the data-sharing mechanism, which integrates geographically dispersed weak edge servers into an efficient storage system. It enables users to launch data operations at any server and retrieve the desired data across the distributed system. However, it remains open to meeting the increasing demand for similarity retrieval across edge servers. The intrinsic reason is that the existing solutions can only return an exact data match for a query while more general edge applications require the data similar to a query input from any server. To fill this gap, this paper pioneers the similarity edge data sharing mechanism, a new paradigm to support high-dimensional similarity search at network edges. First, through deeply thinking about the nature of similarity data sharing, we propose the problem of Optimal Indexing and formulate it as the optimal transport problem from the data space to the network space. On this basis, we propose Prophet, the first known architecture for similarity data indexing at the edge. We first divide the feature space of data into plenty of subareas, then project both subareas and edge servers into a virtual space where the distance between any two points can reflect not only data similarity but also network latency. When any edge server submits a request for data insert, delete, or query, it computes the data feature and the virtual coordinate; and then iteratively forwards the request via greedy routing based on the forwarding tables and the virtual coordinates. By Prophet, similar high-dimensional features would be stored by a common server or several nearby servers. Compared with distributed hash tables in P2P networks, Prophet requires to visit logarithmic servers for a data request and reduces the network latency from the logarithmic to the constant level of the server number. Evaluation results indicate that Prophet achieves the comparable retrieval accuracy and significantly shortens the query latency compared with centralized schemes, while the load balancing performance is nearly optimal.

The performance of distributed applications has long been hindered by network communication, which has emerged as a significant bottleneck. At the core of this issue, the many-to-one incast transfer stands out as one of the primary culprits. Existing works typically decompose the transmission into multiple concurrent sub-processes and utilize servers to aggregate relevant traffic, thus avoiding the incast transfer. However, limited by their theoretical bounds, these methods can only obtain limited performance improvement. In this paper, we discover that leveraging network devices for aggregating incast traffic proves highly effective in surpassing such limitations, while the advent of programmable switches further makes this envision practical. Based on this, we propose GISA as a solution for providing network acceleration across diverse distributed applications. GISA offers generic and uniform interfaces to various applications along with a switch resource sharing mechanism and policy for concurrent tasks. It also ensures correct and reliable transport while minimizing overhead through a low-overhead routing mechanism. Our FPGA-based prototype demonstrates that GISA can achieve line-rate processing when performing data aggregation with minor traffic overhead. Additionally, it supports a wide range of concurrent applications with little development effort.

Graphlets are small, connected, and non-isomorphic induced subgraphs that describe the topological structure of a graph. Counting graphlets is a fundamental task in graph mining and social network analysis. It has numerous applications in many fields, including dense subgraph discovery, anomaly detection, etc. Most existing work assumes a static graph. However, graphs are dynamic in the real world, which can be described as graph streams. Counting graphlets in graph streams is a challenge due to the streaming nature of the input. While there have been several studies on counting graphlets in graph streams, these works are limited to simple graphlets like triangles and butterflies. In this paper, we propose SGES algorithm to estimate more complex graphlets in graph streams. In SGES, we first propose an unbiased sampling strategy to maintain fixed-size sampled edges, which in turn allows us to unbiasedly estimate the number of subgraphs and then count graphlets based on the combinational relationship between the number of subgraphs and the number of graphlets. Extensive experiments over large real-world graph streams prove that our algorithm can obtain accurate estimation values of graphlet counts with high throughput.

Due to the prosperity of Decentralized Finance (DeFi) ecosystems and the rise of Decentralized Autonomous Organization (DAO) groups, blockchain as the underlying revolutionary theory has been attracted a lot of attention. How to achieve cryptographically unpredictable randomness in the publicly verifiable blockchain network, one of the typical collaborative systems, is a critical issue. Since Ethereum finished merging its mainnet with beacon chain, the research on randomness beacon in the blockchain field has become a hotspot. Most of the current distributed randomness beacon schemes are interactive protocols. They are constructed with Public Verifiable Secret Sharing (PVSS), leading high communication complexity O(n2). In contrast, randomness beacons constructed based on Verifiable Delay Functions (VDFs) rely on the sequentiality and uniqueness of VDFs could solve this problem. This paper proposes a blockchain non-interactive randomness beacon protocol: InfinityRand (IR), which decoupled from the underlying message distribution mechanism. It could generate publicly verifiable, strongly bias-resistant, and fair random numbers. In designing InfinityRand, we also design a new trapdoor VDF scheme, which is constructed using negative wrapped convolution (NWC) based number theoretic transform (NTT) on polynomial ring. We conduct security analysis and evaluation experiments. Experiments show that InfinityRand could provide well unpredictability, leader election fairness and scalability guarantees.

Blockchain technology, due to its decentralized, traceable, and tamper-resistant characteristics, has been applied in a wide range of collaborative computing scenarios, including smart grid, industrial production, and smart cities. Consensus algorithm, as the key blockchain technology, plays a decisive role in terms of performance, security, and scalability, ensuring that system nodes reach consensus on transaction data. However, with the increase of user nodes and the diversity of application scenarios, existing consensus algorithms face some performance and security challenges. To overcome these challenges, this article proposes a novel blockchain consensus mechanism, namely the Reputation-Aware Randomization Consensus Algorithm (RRCA). The proposed mechanism first constructs a dynamic reputation evaluation model to divide nodes into ordinary ones and candidate ones. Secondly, a selection model is constructed for candidate nodes to select consensus nodes, which run the consensus algorithm. Finally, a random selection mechanism for the leader node of the proposed consensus algorithm is constructed to ensure the unpredictability and reduce the probability of the leader node being subjected to malicious attacks. Theoretical analysis and experimental results indicate that the RRCA can significantly reduce consensus latency and increase the unpredictability of the leader node, which improves the performance and security of blockchain systems.

In edge computing, the disparity between static available resources and dynamic computational workloads can cause edge servers to become either overburdened or under-utilized. This imbalance, characterized by either insufficient or idle resources, leads to reduced quality of service and decreased profitability, especially when handling a large number of latency-sensitive tasks. To address this issue, collaborative edge-cloud computing, which allows edge servers to execute all complex tasks with the help of the cloud, has been proposed as a potential solution. However, few studies have focused on optimizing profit through efficient resource management. To this end, we investigate how to achieve higher profit in the collaborative edge-cloud computing system by efficiently sharing, expanding and purchasing resources. In this paper, we model this resource management problem as a profit optimization problem, aiming to determine the optimal strategies for resource expanding, sharing, and real-time purchasing to maximize total profits. To solve this optimization problem effectively, we first analyze the distribution of computational workloads and the relationships among shared resources, expanded resources, and the minimum expected purchasing costs. Based on this analysis, we then design a profit-driven resource management scheme (PDRM). It first maximizes expected profit through an efficient resource expanding and sharing approach, then minimizes purchasing costs by employing a real-time resource purchasing strategy, ultimately optimizing overall profit. Finally, through designed experiments, we demonstrate that our proposed profit-driven resource management scheme can enhance total profits while satisfying the low-latency requirements of computation tasks.

In federated learning, sending the trained models (instead of raw data) from clients to the central server can surely decrease the volume of exchanged data and preserve data privacy to some extent. However, the central server can still be a system bottleneck due to the simultaneous and constant model gathering from massive distributed clients. Besides, the central server conducts stateful aggregation (retaining all the updates from each client), making it a potential threat to privacy, since it may recover the raw data based on such model updates inversely. The state-of-the-art methodologies, however, fail to address these two problems concurrently. To this end, we propose GAIN, a secure aggregation acceleration service for federated learning. At its core, GAIN aggregates the model updates at the programmable ingress switches in a stateless manner (storing the aggregated model parameters from the clients temporarily rather than permanently) before proceeding to the central server. Consequently, GAIN can accelerate the transmission and aggregation of model parameters while eliminating the chance of data recovery. We implemented a prototype of GAIN on an FPGA-based testbed to validate its performance. The results demonstrate that GAIN can achieve up to 4.11x speedup in training throughput and reduce up to 86.5% of traffic overhead. Furthermore, through theoretical analysis, we illustrate that GAIN can achieve even more substantial performance gains with a larger number of clients while guaranteeing privacy protection.

The rapid expansion of Internet of Things (IoT) de-vices poses substantial challenges to the Quality of Service (QoS) provided by edge servers, which are often constrained by limited resources, particularly when handling numerous time-sensitive tasks. To address these challenges cost-effectively, collaborative edge computing has been introduced. This approach enhances system performance by enabling edge servers to offload tasks to neighboring edge servers or remote cloud servers, thereby alleviating the computational burden. However, existing research often falls short in providing robust long-term solutions for real-time online scenarios where tasks arrive unpredictably, lacking prior information. To this end, this paper explores the problem of online workload management in collaborative edge computing systems, aiming to improve the long-term utility of edge servers. We formulate the problem as a non-linear optimization problem. To solve this problem efficiently within polynomial time, we intro-duce an online two-phase workload management scheme called OTWMS. This scheme breaks down the workload management problem into several distributed and parallelizable local resource allocation problems and a centralized task migration matching problem. Through evaluation experiments, we demonstrate that the proposed scheme surpasses several benchmarks in terms of long-term utility and service performance optimization.

The novel IoT-based data sensing and service mode promotes the booming development of crowdsensing-based mobile communication services (MCSs). MCS facilitates people’s daily lives by providing appropriate services according to the user’s mobile travels. These traveling trajectories, combined with open-source network information, reveal multimodal semantic information implicit in user mobility. Mining these mobile semantics contributes to understanding user mobility more sufficiently. It covers a wide spectrum of applications in mobile scenarios. For service providers, it improves the quality of their services. For mobile users, it helps to design a more rigorous privacy-preserving mechanism. For third-party platforms, such mobility analysis enhances their data management, analysis, and reusage. It has always been an open research issue in mobile computing. We are motivated to conduct a complete and comprehensive survey on semantic mining within the scope of MCS, forming a complete overview of mobile semantic perception. Specifically, we first review existing research works on feature selection. We classify them into five categories, depending on their representation forms. Then, we summarize the research on mobile semantic perception and cluster them to be three groups according to the digging depth of the represented semantics. To complete the overview, we also review the applications of learning algorithms and discuss the open opportunities and challenges for future works.

Service Function Chaining (SFC) dynamically links multiple Virtual Network Functions (VNFs) to provide flexible and scalable network services for network entities and users. Implementing SFCs at the network edge provides instant VNF service yet is confined by the limited edge resources. Existing strategies suggest either to deploy new VNFs for diverse service provision or to deploy more installed VNFs for reliable service provision. However, these one-sided optimizations fail to realize comprehensive improvements in the network service quality. To this end, the motivation of this paper is to consider a more comprehensive SFC deployment plan to provide more efficient network services. In this paper, we propose DeepSFC, an online SFC deployment scheme at network edge. Our DeepSFC considers the impact of resource allocations and deployment locations on the average latency of overall service requests. It realizes an elegant trade-off between the diversity and the availability of SFCs by adopting the Deep Reinforcement Learning (DRL) method. To be specific, we first determine the type and number of VNFs that need to be deployed. Thereafter, we optimize the deployment locations of these chosen VNFs in the service chain, considering the impact of dynamic bandwidth in the real network. For more general scenarios wherein users’ service requirements change or the deployed server crashes, we further relocate the VNF deployment with the joint consideration of performance degradation and migration cost. Evaluation results show that DeepSFC outperforms its competitors in various experimental settings and responds the requests with lower average latency.

Blockchain, as an emerging technology, has gained widespread research in academia and industry due to its decentralization and traceability. As an important form of blockchain, consortium chains are often applied in the Internet of Things (IoT) to ensure the authenticity and reliability of data. Within consortium chains, the practical Byzantine fault tolerance (PBFT) method is a key technology for ensuring the data consistency. It plays a central role in enhancing the system performance, security, and scalability. However, with the increase in the number of user nodes and the diversification of application scenarios, PBFT faces significant challenges in maintaining performance and security, particularly due to the increased communication overhead, longer consensus latency (CL), and risks of malicious attacks on the leader node. To overcome these challenges, this article proposes a new blockchain consensus mechanism, namely the reputation awareness randomization consensus mechanism in the blockchain systems (RARCs). This mechanism first builds an evaluation model for the nodes, dividing them into ordinary nodes and candidate nodes through the reputation assessment. Second, it constructs a consensus node selection strategy to select the high-quality consensus nodes from the candidate nodes. Finally, RARC establishes a leader node randomization selection mechanism, increasing the unpredictability of the leader node and reducing the probability of the malicious attacks. Through the theoretical analysis and simulation experiments, we demonstrate that the RARC can significantly reduce the CL, enhance the throughput, and increase the unpredictability of the leader node, thereby improving the performance and security of the blockchain systems.

Geo-distributed edge data centers (EDCs) are expected to handle a large portion of tasks offloaded from cloud data centers for various emerging edge services. However, the high energy consumption and cost add a huge burden to edge service providers (ESPs). This presents a unique challenge as traditional energy-saving strategies applicable in cloud data centers fail to apply to EDCs, given the latency-sensitive nature of edge services. In response, we put forward an innovative electric vehicle (EV)-assisted edge computing architecture that leverages idle computing resources and stored energy of EVs. Our design aims to decrease energy expenditures for ESPs by choosing EVs with more economical service costs to handle a portion of the edge services during critical periods. We construct an energy cost-aware workload offloading model and discretize the original model into multiple small-scale solvable forms in both temporal and spatial dimensions. Furthermore, we reconfigure the Kuhn-Munkres algorithm to produce an online joint matching solution to counter QoS decline, generating a mutually advantageous situation for ESPs and EV participants. Upon experimentation with real-world traces, our design demonstrates a significant reduction in total energy cost (up to 31%) and offers considerable incentives for EV participants.

Each flow usually needs to traverse a specific service function chain (SFC), which is composed of multiple network functions implemented through virtualization technology or hardware, before reaching their destinations. All network functions are deployed across commodity nodes inside a network environment. Each flow needs to change its default routing path to visit the corresponding SFC correctly. These changed routing paths will cause network load imbalance. Therefore, an intelligent routing planning method is needed to balance the traffic load while satisfying various SFC requirements of different flows. In this paper, we propose to leverage SRv6, a new routing technology, to centrally plan the routing path for each flow with any SFC request. We then present a general model of the SFC planning problem (SFCP), planning flows’ routing paths to minimize the maximum link utilization of the network, and prove that the problem is NP-hard. For this reason, we transform the SFCP problem into a graph theory optimization problem and propose SFCPlanner, an online SFC planning method based on deep reinforcement learning. Moreover, we design the node mask and incremental training mechanisms to make SFCPlanner achieve better performance. The experiment results show that our SFCPlanner can solve the SFCP problem in large-scale networks more precisely. It can reduce the maximum link utilization by 32% compared with the benchmark algorithm while ensuring each flow traverses the correct SFC.

Edge storage promises to be crucial for edge computing infrastructure, which enables users to access data within a low delay from widespread storage nodes at the network edge. The key challenge is how to integrate massive geographically distributed weak edge nodes to form an efficient storage system, enabling users to launch data operations from any node or retrieve the desired data across the entire distributed system. To address this data-sharing problem, researchers from both the traditional peer-to-peer (P2P) overlay networking and emerging edge computing fields have proposed some decentralized indexing mechanisms. However, existing studies lack insightful descriptions and analyses about the nature of the data-sharing problem at the network edge. It motivates us to rethink the edge data-sharing framework and provide the problem reformulation for analyzing the limitations of existing schemes. We reveal that the existing data-sharing schemes fail in complex network topologies which can be regarded as high-dimensional network spaces beyond the representation of low-dimensional Euclidean spaces or other existing hash spaces. A better space abstraction is an urgent need to alleviate the performance degradation due to the dimensional mismatch between network spaces and virtual spaces. To fill this gap, this paper proposes the Kautz metric space, a novel space abstraction extended from Kautz graphs, where the coordinates and the metric are defined as Kautz strings and Kautz distances (i.e., the shortest distances in undirected Kautz graphs), respectively. We design a dynamic programming algorithm to directly compute the Kautz distances. Then, we propose KMSharing, an efficient edge data-sharing scheme: both nodes and data are represented in a Kautz metric space, where the Kautz distance of any two Kautz strings reflects the network delay of the corresponding nodes. The workflow of KMSharing consists of three core components: the virtual address allocation represents edge nodes in the Kautz metric space; the data-to-node mapping ensures the uniqueness of target nodes; and forwarding table construction ensures the data delivery. Theoretical analyses confirm that KMSharing ideally achieves$\mathcal {O}\left ({{ \tau }}\right)$network delays,$\mathcal {O}\left ({{ \log N }}\right)$overlay hops, and$\mathcal {O}\left ({{ 1 }}\right)$forwarding entries in an N-node edge system with the network radius$\tau $, while the successive ensuring data delivery. Its worst-case network delay$\mathcal {O}\left ({{ \tau \log N }}\right)$is also much better than${\mathcal {O}\left ({{ \tau N^{\alpha } }}\right)},\alpha \mathrm {\in }(0,1)$, the worst case of the baselines using Euclidean spaces. Evaluation on various network topologies also shows that our KMSharing effectively reduces network delays and indexing costs than existing data-sharing schemes.

To repair a failed block in the erasure-coded storage system, multiple related blocks have to be retrieved from other storage nodes across the network. Such a process can lead to significant incast-type repair traffics and delays. The existing efforts mainly try to schedule the transmission of the requested blocks across different storage nodes to avoid network congestion. At their cores, they utilize part of the involved hosts to rely on or aggregate the file blocks from others. While we notice that, the programmability and capability of today’s network devices (i.e., routers and switches) bring a great opportunity to further speed up the repair progress by aggregating the file blocks with such devices. By mitigating the aggregation operations from the network edges to network cores, it is possible to save more time and bandwidth. With this intuition, we propose Paint, a parallelized in-network aggregation framework for failure repair. Paint utilizes programmable switches to aggregate relevant data and improves the repair performance by implementing multiple parallelized repair pipelines. We propose a series of novel and time-friendly algorithms to construct the routing paths for Paint and design the Aggregation Control Protocol to implement Paint in production clusters. For all we know, this is the first work to explore and implement parallelized in-network repair with programmable switches. The extensive experiments on the prototype system and real-world datasets indicate that Paint can significantly improve repair performance while effectively reducing bandwidth overhead.

Air-to-ground (A2G) networks, using unmanned aerial vehicles (UAVs) as base stations to serve terrestrial user equipments (UEs), are promising for extending the spatial coverage capability in future communication systems. Coordinated transmission among multiple UAVs significantly improves network coverage and throughput compared to a single UAV transmission. However, implementing coordinated multi-point (CoMP) transmission for UAV mobility requires complex cooperation procedures, regardless of the handoff mechanism involved. This paper designs a novel CoMP transmission strategy that enables terrestrial UEs to achieve reliable and seamless connections with mobile UAVs. Specifically, a computationally efficient CoMP transmission method based on the theory of Poisson-Delaunay triangulation is developed, where an efficient subdivision search strategy for a CoMP UAV set is designed to minimize search overhead by a divide-and-conquer approach. For concrete performance evaluation, the cooperative handoff probability of the typical UE is analyzed, and the coverage probability with handoffs is derived. Simulation results demonstrate that the proposed scheme outperforms the conventional Voronoi scheme with the nearest serving UAV regarding coverage probabilities with handoffs. Moreover, each UE has a fixed and unique serving UAV set to avoid real-time dynamic UAV searching and achieve effective load balancing, significantly reducing system resource costs and enhancing network coverage performance.

A frequency bursting pattern (FBP) in temporal graphs represents some interaction behavior that accumulates its frequency at the fastest rate. Mining FBPs is essential to early warning of emergencies. However, existing studies on frequency-based pattern mining in graphs do not consider the temporal information and bursting features of a subgraph pattern. As a result, they may not provide effective and efficient mining algorithms for FBP discovery. In this paper, we study the problem of discovering top-k FBPs in temporal graphs. We present a novel model, referred to as maximal (m, θ)-bursting pattern, to describe FBPs in a temporal graph, which is a subgraph with a size larger than m that accumulates its frequency at the fastest rate during a time interval of length no less than θ. A naive solution for top-k FBPs discovery is to use the best-first search algorithm, where the burstiness threshold changes as more patterns are mined. However, this method will result in huge search space since we need to check every possible time interval for a candidate pattern in the temporal graph. To tackle this problem, we devise an online top-k framework in which k candidate results are maintained from the initial timestamp to the end in the temporal graph. Under the new framework, we further conceive two optimization strategies by exploiting incremental subgraph matching and Evolutionary Game Theory to boost the performance. Extensive experiment results on five real temporal graphs show that our algorithm has higher efficiency, effectiveness and scalability.

Storing files at the network edge has become a new paradigm of storage systems, which is promising to mitigate network congestion and reduce file retrieval latency. However, the traditional file storage scheme cannot effectively meet the requirements of rapid indexing and load balance when applied directly to the edge. Moreover, due to the dynamic nature of the edge environment where edge servers can join or leave at will, it is necessary for the storage scheme to adjust with minimal disruption. In this paper, we propose EdgeAnchor, a novel edge storage strategy that is composed of the two-layer hash mappings. The first layer, file-to-bucket mapping, adopts the pseudo-deletion algorithm to deal with the variations in file size, while the second layer utilizes the multiple bucket-to-server mapping to adapt to the heterogeneity in the servers’ storage capacities. Furthermore, EdgeAnchor constructs a list of deleted or added working sets for each bucket and creates a dictionary for the mappings between buckets and edge servers. In the manner, EdgeAnchor ensures a rapid file index and balances server load at the dynamic network edge. We also attach the mathematical analyses to EdgeAnchor, which theoretically proves its logarithmic complexity of hash operations and memory accesses. The experiments conducted on real-world datasets demonstrate that EdgeAnchor achieves the file index throughput twice as high as that of Consistent Hashing, under the constraints of load balance. Additionally, it ensures a low and stable data migration volume, when adding or removing edge servers consecutively.

E-Voting systems play a vital role in various communities, aiming to enhance the efficiency, transparency, and citizen engagement in elections. However, as technology advances, ensuring the security and reliability of e-voting systems becomes profound. While researchers have explored the integration of blockchain technology to improve e-voting systems, there still are technical challenges, particularly regarding voters’ privacy, ballots’ confidentiality, and system performance. To address these challenges, we propose SvaVoting, an innovative blockchain-based e-voting scheme. SvaVoting utilizes the Borda counting method and the identity-based ring signature based on symmetric primitives to anonymously verify the validity of the voter’s identity and ballot format. In addition, we introduce secret sharing and a Cloud Service Provider (CSP) to conduct the final counting while preserving the privacy of the ballots. All users in the system can verify the results of CSP calculations, thereby meeting security goals such as correctness, unforgeability, anonymity and verifiability. The identity-based ring signature in our scheme provides 128-bit security, and even it can work in a quantum computing environment. Experimental results demonstrate that our solution significantly enhances the security and performance of e-voting systems, offering a practical path forward for future e-voting systems.

Mobile edge computing emerges as a new paradigm to provide low-latency network services in the close proximity to users. Based on the network function virtualization (NFV) technology, network services can be flexibly provisioned as service function chain (SFC) deployed at edge servers. In some scenarios, such as the vehicular or UAV-assisted edge computing, the network topology varies rapidly due to the mobile edge servers, which changes the routing path between adjacent VNFs in an SFC. Migrating SFC to adapt to the frequent topology change can reduce the SFC latency, and improve the quality of users’ experience. However, frequent SFC migration will unavoidably increase the operation cost. In this paper, to optimize the system performance in a cost-efficient manner, we study the SFC migration problem in dynamic networks with a long-term cost budget constraint. We then propose the Topology-aware Min-latency SFC Migration (TMSM) method to strike a desirable balance between the SFC latency and the migration cost. Specifically, we first apply the Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization sub-problems. Since the decomposed problem is still NP-hard, a Markov approximation based heuristic is proposed to seek a near-optimal solution for each sub-problem. Compared with the rerouting-only strategy, which does not migrate any VNF, our TMSM reduces the latency by at least 21% on average in each time slot. Extensive evaluations show that the proposed algorithm achieves a better tradeoff between the SFC latency and migration cost than the baselines.

For the efficient analysis of geo-distributed datasets, cloud providers implement data-parallel jobs across geo-distributed sites (e.g., datacenters and edge clusters), which are generally interconnected by wide-area network links. However, current state-of-the-art geo-distributed data analytic methods fail to make full use of the available network and computing resources. The main reason is that such geo-distributed methods must wait for bottleneck sites to complete the corresponding transmission and computation in each phase. Furthermore, such geo-distributed methods may be impractical to the network bandwidth dynamicity and diverse job parallelism. To this end, we propose a Simultaneous Data Transfer and Processing (SDTP) mechanism to accelerate wide-area data analytics, with the joint consideration of network bandwidth dynamics and job parallelism. In the SDTP, a site can execute the computation, provided that it obtains the required input data. As a result, the input data loading, map, shuffle, and reduce phases at each site need not wait for the completion of the previous phases of other sites. We further improve the SDTP method by offering more accurate time estimation and generalizing the mechanism to dynamic situations. The trace-driven results demonstrate that SDTP can improve the wide-area analytic job response time by 19% to 72% compared to other methods.

With the widespread use of Internet of Things (IoT) devices and the arrival of the 5G era, edge computing has become an attractive paradigm to serve end-users and provide better QoS. Many efforts have been paid to provision some merging public network services at the network edge. We reveal that it is very common that specific users call for private and isolated edge services to preserve data privacy and enable other security intentions. However, it still remains open to fulfill such kind of mixed requests in edge computing. In this article, we propose a cooperative edge computing framework, i.e., HyEdge, to offer both public and private edge services systematically. To fully exploit the benefits of this novel framework, we define the problem of optimal request scheduling over a given placement solution of hybrid edge servers to minimize the response delay. This problem is further modeled as a mixed integer non-linear programming problem (MINLP), which is typically NP-hard. Accordingly, we propose the partition-based optimization method, which can efficiently solve this NP-hard problem via the problem decomposition and the branch and bound strategies. We finally conduct extensive evaluations with a real-world dataset to measure the performance of our method. The results indicate that the proposed method achieves elegant performance with low computation complexity.

The Device-as-a-service (DaaS) Internet of Things (IoT) business model enables distributed IoT devices to sell collected data to other devices, paving the way for machine-to-machine (M2M) economy applications. Cryptocurrencies are widely used by various IoT devices to undertake the main settlement and payment task in the M2M economy. However, the cryptocurrency market, which lacks effective supervision, has fluctuated wildly in the past few years. These fluctuations are breeding grounds for arbitrage in IoT data trading. Therefore, a practical cryptocurrency market supervision framework is very imperative in the process of IoT data trading to ensure that the trading is completed safely and fairly. The difficulty stems from how to combine these unlabeled daily trading data with supervision strategies to punish abnormal users, who disrupt the data trading market in IoT. In this article, we propose a closed-loop hybrid supervision framework based on the unsupervised anomaly detection to solve this problem. The core is to design the multi-modal unsupervised anomaly detection methods on trading prices to identify malicious users. We then design a dedicated control strategy with three levels to defend against various abnormal behaviors, according to the detection results. Furthermore, to guarantee the reliability of this framework, we evaluate the detection rate, accuracy, precision, and time consumption of single-modal and multi-modal detection methods and the contrast algorithm Adaptive KDE [ 19 ]. Finally, an effective prototype framework for supervising is established. The extensive evaluations prove that our supervision framework greatly reduces IoT data trading risks and losses.

Set query is a fundamental problem in computer systems. Plenty of applications rely on the query results of membership, association, and multiplicity. A traditional method that addresses such a fundamental problem is derived from Bloom filter. However, such methods may fail to support element deletion, require additional filters or apriori knowledge, making them unamenable to a high-performance implementation for dynamic set representation and query. In this paper, we envision a novel sketch framework that is multi-functional, non-parametric, space efficient, and deletable. As far as we know, none of the existing designs can guarantee such features simultaneously. To this end, we present a general shifting framework to represent auxiliary information (such as multiplicity, association) with the offset. Thereafter, we specify such design philosophy for a hash table horizontally at the slot level, as well as vertically at the bucket level. Theoretical and experimental results jointly demonstrate that our design works exceptionally well with three types of set queries under small memory.

Sketches are widely deployed to represent network flows to support complex flow analysis. Typical sketches usually employ hash functions to map elements into a hash table or bit array. Such sketches still suffer from potential weaknesses upon throughput, flexibility, and functionality. To this end, we propose Ark filter, a novel sketch that stores the element information with either of two candidate buckets indexed by the quotient or remainder between the fingerprint and filter length. In this way, no further hash calculations are required for future queries or reallocations. We further extend the Ark filter to enable capacity elasticity and more functionalities (such as frequency estimation and top-$k$query). Comprehensive experiments demonstrate that, compared with Cuckoo filter, Ark filter has$2.08\times$,$1.34\times$, and$1.68\times$throughput of deletion, insertion, and hybrid query, respectively; compared with Quotient filter, Ark filter has$4.55\times$,$1.74\times$, and$22.12\times$throughput of deletion, insertion, and hybrid query, respectively; compared with Bloom filter, Ark filter has$2.55\times$and$2.11\times$throughput of insertion and hybrid query, respectively.

The traditional migration methods are confronted with formidable challenges when data deduplication technologies are incorporated. First, the deduplication creates data-sharing dependencies in the stored files; breaking such dependencies in migration may attach extra space overhead. Second, the redundancy elimination makes the storage system reserves only one copy for each storage file, and heightens the risk of data unavailability. The existing methods fail to tackle them in one shot. To this end, we propose Jingwei, an efficient and adaptive data migration strategy for deduplicated storage systems. To be specific, Jingwei tries to minimize the extra space cost in migration for space efficiency. Meanwhile, Jingwei realizes the service adaptability by encouraging replicas of hot files to spread out their data access requirements. We first model such a problem as an integer linear programming (ILP) and solve it with a commercial solver when only one empty migration target server is allowed. We then extend this problem to a scenario wherein multiple non-empty target servers are available for migration. We solve it by effective heuristic algorithms based on the Bloom Filter-based data sketches. The Jingwei strategy can suffer from performance degradation when the heat degree varies significantly. Therefore, we further present incremental adjustment strategies for the two scenarios, which adjust the number of block replicas and their locations in an incremental manner. The mathematical analyses and trace-driven experiments show the effectiveness of our Jingwei strategy. To be specific, Jingwei fortifies the file replicas by 25% with only 5.7% of the extra storage space, compared with the latest “Goseed” method. With the small extra space cost, the file retrieval throughput of Jingwei can reach up to 333.5 Mbps, which is 12.3% higher than that of the Random method.

Placing popular data at the network edge helps reduce the retrieval latency, but it also brings challenges to the limited edge storage space. Currently, using available yet not necessarily reliable edge resources is common sense for edge space expansion, while deploying deduplication storage strategies is a general method for better space utilization. However, a contradiction arises when jointly implementing data deduplication with unreliable edge resources. On the one hand, the deduplication policy stipulates that any data chunk can be stored exactly once; on the other hand, the use of unreliable resources imposes that data should be backed up for the seek of file availability. To resolve such contradiction, we propose MEAN, a deduplication-enabled storage system using unreliable resources at the network edge. The core idea of MEAN is to place similar files together for better deduplication and maintain replicas of popular files for higher reliability. We first formulate this problem and prove its NP-hardness, then provide efficient heuristics based on similarity-aware hierarchical clustering. Three different reliability scenarios are comprehensively considered to develop our algorithms. We also implement a prototype system and evaluate the performance of MEAN with a real-world dataset. The results show that MEAN can fortify the file hit ratio under unreliable environments by 77% while reducing the file retrieval delay up to 71%, compared with the state-of-the-art approach.

RDF stream processing (RSP) has become a vibrant area of research in the Semantic Web community, which guarantees interoperability and opens up important applications. There have been efforts to extend RDF data and SPARQL query for representing streaming information and continuous querying functionalities. However, existing solutions will incur significant low throughput due to the recomputation of the results from scratch as the window slides. In this paper, we propose a novel graph-based framework, referred as IncTreeRDF, towards continuous SPARQL query evaluation over RDF data streams. Under the framework, the RDF data streams are modeled as streaming graphs; the SPARQL queries are translated into graph patterns and evaluated via continuous sub-graph pattern-matching over streaming RDF graphs. IncTreeRDF employs a query-centric auxiliary data structure called TStore to store some intermediate results, which supports fast incremental maintenance. Based on TStore, we can not only avoid re-computing matches of the query but also prune invalid updates. Besides, we define matching-free update, in which subgraph matching calculation can be avoided under this scenario. Extensive experimental results show that IncTreeRDF significantly outperforms existing competitors.

The traditional migration methods are confronted with formidable challenges when data deduplication technologies are incorporated. Firstly, the deduplication creates data-sharing dependencies in the stored files; breaking such dependencies in migration would attach extra space overhead. Secondly, the redundancy elimination heightens the risk of data unavailability during server crashes. The existing methods fail to tackle them at one shot. To this end, we propose Jingwei, an efficient and adaptable data migration strategy for deduplicated storage systems. To be specific, Jingwei tries to minimize the extra space cost in migration for space efficiency. Meanwhile, Jingwei realizes the service adaptability by encouraging replicas of hot data to spread out their data access requirements. We first model such a problem as an integer linear programming (ILP) and solve it with a commercial solver when only one empty migration target server is allowed. We then extend this problem to a scenario wherein multiple non-empty target servers are available for migration. We solve it by effective heuristic algorithms based on the Bloom Filter-based data sketches. Trace-driven experiments show that Jingwei fortifies the file replicas by 25%, while only 5.7% of the extra storage space is occupied compared with the latest "Goseed" method.

Data centers usually employ scale-out network topologies to provide sufficient network bandwidth for applications. The traditional equal-cost multi-path (ECMP) routing method is proposed to tackle the serious load imbalance problem across all links. However, it does not achieve the desired performance and still incurs low network throughput. Consequently, researchers recently redesigned some load balancing mechanisms for data center networks (DCNs) from different design dimensions. However, it remains open to systematically measure and evaluate their performance in various settings. It is impractical for evaluators to implement or simulate involved load balancing mechanisms. In this paper, we propose a unified framework, UFLB, which can well model and emulate representative load balancing mechanisms for data center networks in a lightweight way. This framework has overcome three significant challenges: model traffic distribution in the symmetry as well as asymmetry data center networks, characterize mainstream load balancing methods, and systematically combine them with high accuracy. We evaluate the effectiveness of our model under not only general settings of data center networks but also some special settings, such as various link failures and asymmetric topologies. The results indicate that the deviation rate of UFLB is within 15% against the implementation of load balancing mechanisms, such as ECMP, CONGA, DRILL, HERMES, PRESTO, in NS2, while it can be several orders of magnitude faster.

Edge computing emerges as a new paradigm to provide low-latency network services in close proximity to end users. Based on the network function virtualization (NFV) technology, network services can be flexibly and scalably provisioned as virtual network function (VNF) chains deployed at edge servers. With such advantages, both the industry and research communities have done extensive studies on deploying VNF chains at network edge. The existing works mainly take an ideal assumption that the network is totally safe and there are no malicious users. Therefore, they leverage all available resources to serve their users. However, such an assumption is impractical in real networks. Security services, such as firewall, deep packet detection, intrusion detection, are always required for production networks. The existing service deployment methods fail to consider the co-existence of security services and functionality services. In this paper, we present the topic of joint deployment of both security and functionality services, wherein the security services are responsible to check the data flows before being processed by the functionality services. To solve this problem, we propose the Secure Deployment Pattern, which aims to simultaneously satisfy the security protection and QoS requirements at network edge. It divides the services into two kinds, i.e., the user-oriented functionality services, and the service provider-oriented security services. In this case, it is very challenging to jointly deploy the security services and functional services with respect to the resource and latency constraints. We formulate this problem as an integer programming model, and propose the heuristic algorithms to solve it. As far as we know, this paper is the first step, which targets at a proper orchestration of security and functionality services in edge computing. Extensive evaluations show that the proposed algorithms are effective and efficient, in terms of the execution time and the average number of served requests.

Owing to the advancement of the Internet of Things (IoT) and 5G mobile technologies, various IoT devices produce massive data, which is usually transferred to nearby sites, such as edge nodes or datacenters. Many large-scale IoT applications need to analyze the data distributed across multiple sites to obtain final results. A dominant challenge of this type of data analytics is the heterogeneities of resource capacities across geo-distributed sites. In this article, we find that the resource capacity as well as the resource price differ among sites, and the price heterogeneity has a significant impact on geo-distributed IoT data analytics. Thus, each geo-distributed IoT data analytics job prefers to minimize the job execution cost while guaranteeing its deadline requirement under the resource constraints of involved sites. Specifically, we propose to jointly consider the resource heterogeneities of both capacity and price, and minimize the cost of each job before its deadline. We characterize this optimization problem as a quadratically constrained quadratic programming problem. To tackle such an NP-hard problem, we propose the minimize the job completion cost before a given deadline (MCGL) method, which calculates a task placement solution by the gradient adjustment strategy according to the remarkable negative correlation relationship between job completion time and job completion cost of geo-distributed IoT data analytics job. The task placement strategy can optimize resource cost with respect to the deadline requirement of any geo-distributed data analytics job. The trace-driven evaluations indicate that MCGL significantly reduces the total cost compared with existing methods; moreover, they satisfy the deadline constraints simultaneously.

The user demands to delay-sensitive applications have put forward new requirements for the mobile networks. Edge caching as a promising way is proposed to enhance the Quality of Service (QoS) for end users at the network edge. Given the widely distributed edge nodes, the content providers (CPs) usually prefer to integrate them with cooperative caching services, by forming a cache coalition. Although such a coalition could be beneficial as a whole, it neglects the profits of individual members, which is one major concern in forming the coalition itself. Besides, due to the poor scalability, the conventional cooperation scheme, which only considers fixed edge nodes, cannot adapt to the spatial and temporal imbalance of user requests. In this article, we tackle the problems of coalition establishment and profit allocation among the coalition members. Particularly, by adopting both fixed edge nodes and mobile vehicles as caching nodes, we propose a hybrid service provisioning framework and cooperative service caching and workload scheduling methods. To maximize the profits in managing the caching resources in the established coalition, we devise an optimization model with a mixed-integer programming (MIP), in which the QoS requirements of end users and caching capacities of each coalition member are also considered as constraints. In addition, based on the Hedonic game theory, we propose a dynamic coalition algorithm to guide each member to join or leave the coalition at each time slot out of its own profits. The experimental results demonstrate that compared to the cases only considering fixed caching nodes, our hybrid caching scheme can improve: 1) the overall profit of the coalition by 53% and 2) the average profit of individual participants by 42%, respectively.

In online learning, the dynamic regret metric chooses the reference oracle that may change over time, while the typical (static) regret metric assumes the reference solution to be constant over the whole time horizon. The dynamic regret metric is particularly interesting for applications, such as online recommendation (since the customers' preference always evolves over time). While the online gradient (OG) method has been shown to be optimal for the static regret metric, the optimal algorithm for the dynamic regret remains unknown. In this article, we show that proximal OG (a general version of OG) is optimum to the dynamic regret by showing that the proved lower bound matches the upper bound. It is highlighted that we provide a new and general lower bound of dynamic regret. It provides new understanding about the difficulty to follow the dynamics in the online setting.

Edge computing responds to users’ requests with low latency by storing the relevant files at the network edge. Various data deduplication technologies are currently employed at edge to eliminate redundant data chunks for space saving. However, the lookup for the global huge-volume fingerprint indexes imposed by detecting redundancies can significantly degrade the data processing performance. Besides, we envision a novel file storage strategy that realizes the following rationales simultaneously: 1) space efficiency, 2) access efficiency, and 3) load balance, while the existing methods fail to achieve them at one shot. To this end, we report LOFS, a Lightweight Online File Storage strategy, which aims at eliminating redundancies through maximizing the probability of successful data deduplication, while realizing the three design rationales simultaneously. LOFS leverages a lightweight three-layer hash mapping scheme to solve this problem with constant-time complexity. To be specific, LOFS employs the Bloom filter to generate a sketch for each file, and thereafter feeds the sketches to the Locality Sensitivity hash (LSH) such that similar files are likely to be projected nearby in LSH tablespace. At last, LOFS assigns the files to real-world edge servers with the joint consideration of the LSH load distribution and the edge server capacity. Trace-driven experiments show that LOFS closely tracks the global deduplication ratio and generates a relatively low load std compared with the comparison methods.

Many hardware-based security middleboxes have been deployed in the networks to defend against different threats. However, these hardware middleboxes are hard to upgrade or migrate. The emergence of network functions virtualization (NFV), which realizes various security functions in the form of virtual network functions (VNFs), brings many benefits to network security. To improve the security level further, several VNFs are coordinated in a pre-defined order to form service function chains (SFCs). It is expected that the SFCs are embedded properly with low cost, including the VNF setup cost and the flow routing cost. In this paper, we find that when an SFC is required by multiple flows for the identical network security threats, the total cost could be reduced by embedding an aggregated service function tree (ASFT) instead of multiple independent SFCs. We formally characterize the integer programming model of this problem and prove that it is NP-hard. Then we propose a performance-guaranteed approximation algorithm and prove that the algorithm could find the optimal solution in a special case. Extensive experiments indicate that our method can reduce the total cost by$22.0\%$and$24.1\%$against two compared algorithms, respectively.

Cuckoo filter (CF) and its variants are emerging as replacements of Bloom filters in various networking and distributed systems to support efficient set representation and membership testing. Cuckoo filters store item fingerprints directly with two candidate buckets and a reallocation scheme is implemented to mitigate the bucket overflow problem for higher space utilization. Such a reallocation scheme, once triggered, however, can be time-consuming. This shortcoming makes the existing CFs not applicable for insertion-intensive scenarios such as online applications wherein the items join and leave frequently. To this end, in this paper, we propose the Vertical Cuckoo filter (VCF) which extends the standard Cuckoo filter by providing more candidate buckets to each item. Another challenging issue with such a design is how to ensure that the candidate buckets can be indexed by each other such that no additional hash computation and item access are necessary during fingerprint reallocation. Therefore, we present the vertical hashing, which indexes the candidate buckets with the fingerprint and given bitmasks. We further generalize and improve the VCF by realizing$k$(≥ 4) candidate buckets and avoiding unnecessary computation. The comprehensive experiments indicate that VCF outperforms its same kinds in terms of space utilization and insertion throughput, with a slight compromise of lookup speed.

Existing Internet routing protocols much focus on providing interconnection service for independent autonomous systems (ASes) rather than end-to-end low latency transmission. Nowadays, a growing number of applications and platforms have high requirements for low latency. However, developing new routing protocols in the wide area network that provides low latency routing service is very challenging, and remains an open problem due to the obstacles of compatibility, feasibility, scalability and efficiency. On the other hand, the ignorance of latency performance results in triangle inequality violations (TIV). In this paper, we leverage TIV and a new routing technology, SRv6, to build a new distributed routing protocol, SRv6 underlay federation (SRUF), which aims to provide low-latency routing services in network core. We design a novel method to find alternative paths with lower latency between any pair of ASes in SRUF. This method can achieve high scalability as it incurs only$O(n)$bandwidth overhead in each member of SRUF. SRv6 is then employed to steer the flows along the selected indirect low-latency paths, while keeping compatibility to legacy routing systems. The experimental results with realworld datasets demonstrate that SRUF can effectively reduce the average end-to-end delay by 5.4% ~ 58.9%.

Cuckoo filter (CF), Bloom filter (BF) and their variants are space-efficient probabilistic data structures for approximate set membership queries. However, their data synopsis would inevitably become unusable when there are a number of member updates on the set; while updates are not uncommon for the real-world data streaming applications such as duplicate item detection, malicious URL checking, and caching applications. It has been shown that some variants of BF can be adaptive to stream applications. However, current extensions of BF structures generally incur unstable performance or intolerant membership testing errors. In this paper, we aim to design a data synopsis for membership testing on data streams with stable performance and tolerant query errors. To this end, we propose Stable Cuckoo Filters (SCF), which take a fine-grained manner to evict the stale elements and store those more recent ones. SCF absorbs the design philosophy from several unsuccessful designs. Specifically, SCFs take elegant update operations to embed time information with insertion operation and carefully evict the stale elements. We show that a tight upper bound of the expected false positive rate (FPR) remains asymptotically constant over the insertion of new members. The query error for recent elements of SCF (FNR) is related to the characteristics of the input data stream and query workloads. Extensive experiments on the real-world and synthetic datasets show that our designs are more stable than the existing variants of BF and realize 7 x smaller false errors and up to 3 x throughput.

The emergence of edge computing can effectively tackle the problem of large transmission delays caused by the long-distance between user devices and remote cloud servers. Users can offload tasks to the nearby edge servers to perform computations, so as to minimize the average task response time through effective task dispatching and scheduling methods. However: 1) in the task dispatching phase, the dynamic features of network conditions and server loads make it difficult for the offloaded tasks to select the optimal edge server and 2) in the task scheduling phase, each edge server may face a large number of offloading tasks to schedule, resulting in long average task response time, or even severe task starvation. In this article, we propose an online task dispatching and fair scheduling method OTDS to tackle the above two challenges, which combines online learning (OL) and deep reinforcement learning (DRL) techniques. Specifically, using an OL approach, OTDS performs real-time estimating of network conditions and server loads, and then dynamically assigns tasks to the optimal edge servers accordingly. Meanwhile, at each edge server, by combing the round-robin mechanism with DRL, OTDS is able to allocate appropriate resources to each task according to its time sensitivity and achieve high efficiency and fairness in task scheduling. Evaluation results show that our online method can dynamically allocate network resources and computing resources to those offloaded tasks according to their time-sensitive requirements. Thus, OTDS outperforms the existing methods in terms of the efficiency and fairness on task dispatching and scheduling by significantly reducing the average task response time.

Recently, convex-concave bilinear Saddle Point Problems (SPP) is widely used in lasso problems, Support Vector Machines, game theory, and so on. Previous researches have proposed many methods to solve SPP, and present their convergence rate theoretically. To achieve linear convergence, analysis in those previouse studies requires strong convexity of φ( z ). But, we find the linear convergence can also be achieved even for a general convex but not strongly convex φ( z ). In the article, by exploiting the strong duality of SPP, we propose a new method to solve SPP, and achieve the linear convergence. We present a new general sufficient condition to achieve linear convergence, but do not require the strong convexity of φ( z ). Furthermore, a more efficient method is also proposed, and its convergence rate is analyzed in theoretical. Our analysis shows that the well conditioned φ( z ) is necessary to improve the efficiency of our method. Finally, we conduct extensive empirical studies to evaluate the convergence performance of our methods.

The emergence of large-scale dynamic sets in networked and distributed applications attaches stringent requirements to approximate set representation. The existing data structures (including Bloom filter, Cuckoo filter, and their variants) preserve a tight dependency between the cells or buckets for an element and the lengths of the filters. This dependency, however, degrades the capacity elasticity, space efficiency and design flexibility of these data structures when representing dynamic sets. In this paper, we first propose the Index-Independent Cuckoo filter (I2CF), a probabilistic data structure that decouples the dependency between the length of the filter and the indices of buckets which store the information of elements. At its core, an I2CF maintains a consistent hash ring to assign buckets to the elements and generalizes the Cuckoo filter by providing optional${k}$candidate buckets to each element. By adding and removing buckets adaptively, I2CF supports the bucket-level capacity alteration for dynamic set representation. Moreover, in case of a sudden increase or decrease of set cardinality, we further organize multiple I2CFs as a Consistent Cuckoo filter (CCF) to provide the filter-level capacity elasticity. By adding untapped I2CFs or merging under-utilized I2CFs, CCF is capable of resizing its capacity instantly. The trace-driven experiments indicate that CCF outperforms its alternatives and realizes our design rationales for dynamic set representation simultaneously, at the cost of a little higher complexity.

Edge computing (EC) is a promising paradigm for providing ultra-low latency experience for IoT applications at the network edge, through pre-caching required services in fixed edge nodes. However, the supply-demand mismatch can arise while meeting the peak period of some specific service requests. The mismatch between capacity provision and user demands can be fatal to the delay-sensitive user requests of emerging IoT applications and will be further exacerbated due to the long service provisioning cycle. To tackle this problem, we propose the mobile-assisted edge computing framework to improve the QoS of fixed edge nodes by exploiting mobile edge nodes. Furthermore, we devise a CRI (Credible, Reciprocal, and Incentive) auction mechanism to stimulate mobile edge nodes to participate in the services for user requests. The advantages of our mobile-assisted edge computing framework include higher task completion rate, profit maximization, and computational efficiency. Meanwhile, the theoretical analysis and experimental results guarantee the desirable economic properties of our CRI auction mechanism.

Multi-party set reconciliation is a key component in distributed and networking systems. It naturally contains two dimensions, i.e., set representation and reconciliation protocol. However, existing sketch data structures are insufficient to satisfy the new needs brought by the multi-party scenario simultaneously, including space-efficiency, mergeability, and completeness. The current reconciliation protocols, on the other hand, fail to achieve the global optimization of communication cost. To this end, in this article, we propose the marked cuckoo filter (MCF), a data structure for representing set members. Grounded on MCF, we implement the MCFsyn protocol to reconcile multiple sets. MCFsyn aggregates and distributes sets information represented by MCFs along with an underlying minimum spanning tree among the participants. The participants then identify the different elements by traversing the overall MCF which contains the information of all elements in the union set. For the identified missing elements, MCFsyn helps the participants to choose the optimal senders to fetch with the minimum communication cost. Comprehensive evaluations indicate that MCFsyn significantly outperforms existing alternatives in terms of both reconciliation accuracy and communication cost.

Set reconciliation is a common and fundamental task in distributed systems. In many cases, given set A on $Host_A$ and set B on $Host_B$, applications need to identify those elements that appear in set A but not in set B, and vice versa. However, existing methods incur unsatisfactory space utilization and non-trivial false positives and false negatives. In this paper, we present a novel reconciliation method based on Cuckoo filter (CF). After exchanging the CFs each of which represents a set of elements, we query the local elements against the received CF to determine the elements that only belong to the local host and should be transmitted to the other host. The evaluation results indicate that the CF-based reconciliation method outperforms existing methods significantly.

The mission of set reconciliation (also called set synchronization) is to identify those elements which appear only in exactly one of two given sets. In this paper, we extend the set reconciliation problem into three design rationales: (i) multiset support; (ii) near 100% reconciliation accuracy; (iii) communication-friendly and time-saving. Prior reconciliation methods fail to realize the three rationales simultaneously. To this end, we redesign Trie and Fenwick Tree (FT), to near-accurately represent and reconcile two types of multisets that we refer to as unsorted and sorted multisets, respectively. Comprehensive evaluations are conducted to quantify the performance of our proposals. The trace-driven evaluations demonstrate that Trie and FT achieve near-accurate multiset reconciliation, with 4.31 and 2.96 times faster than the CBF-based method, respectively.

The emergence of large-scale dynamic sets in networking applications attaches stringent requirements to approximate set representation. The existing data structures (including Bloom filter, Cuckoo filter, and their variants) preserve a tight dependency between the cells or buckets for an element and the lengths of the filters. This dependency, however, degrades the capacity elasticity, space efficiency and design flexibility of these data structures when representing dynamic sets. In this paper, we first propose the Index-Independent Cuckoo filter (I2CF), a probabilistic data structure that decouples the dependency between the length of the filter and the indices of buckets which store the information of elements. At its core, an I2CF maintains a consistent hash ring to assign buckets to the elements and generalizes the Cuckoo filter by providing optional k candidate buckets to each element. By adding and removing buckets adaptively, I2CF supports the bucket-level capacity alteration for dynamic set representation. Moreover, in case of a sudden increase or decrease of set cardinality, we further organize multiple I2CFs as a Consistent Cuckoo filter (CCF) to provide the filter-level capacity elasticity. By adding untapped I2CFs or merging under-utilized I2CFs, CCF is capable of resizing its capacity instantly. The trace-driven experiments indicate that CCF outperforms its alternatives and realizes our design rationales for dynamic set representation simultaneously, at the cost of a little higher complexity.

The mission of set reconciliation (also called set synchronization) is to identify those elements which appear only in exactly one of two given sets. In this paper, we extend the set reconciliation problem into three design rationales: (i) multiset support; (ii) near 100 percent reconciliation accuracy; and (iii) communication-friendly and time-saving. These three rationales, if realized, will lead to unprecedented benefits for the set reconciliation paradigm. Generally, prior reconciliation methods are mainly designed for simple sets and thus remain inapplicable for multisets. Methods based on probabilistic data structures, e.g., the Counting Bloom Filter (CBF), support efficient representation, and multiplicity queries. Based on these probabilistic data structures, approximate multiset reconciliation can be enabled. However, they often cannot achieve a statisfying accuracy, due to potential hash collisions. The reconciliations enabled by logs or lists incur high time-complexity and communication overhead. Therefore, existing reconciliation methods, fail to realize the three rationales simultaneously. To this end, we redesign Trie and Fenwick Tree (FT), to near-accurately represent and reconcile two types of multisets that we refer to as unsorted and sorted multisets, respectively. Moreover, to further reduce the communication overhead during the reconciliation process, we design a partial transmission strategy when exchanging two Tries or FTs. Comprehensive evaluations are conducted to quantify the performance of our proposals. The trace-driven evaluations demonstrate that Trie and FT achieve near-accurate multiset reconciliation, with 4.31 and 2.96 times faster than the CBF-based method, respectively. The simulations based on synthetic datasets further indicate that our proposals outperform the CBF-based method in terms of accuracy and communication overhead at most time.

The topology of a network may change inevitably, due to dynamic behaviors of nodes and links, and failures of hardware and software. Many protocols and applications must be aware of the up-to-date topology of the underlying network. This triggers the topology calibration problem, which means to deduce those different nodes and links between two topologies. The Bloom filter and its variants are efficient to represent and calibrate two general sets. They, however, fail to represent all links and nodes in a topology simultaneously, and thus remain inapplicable to the topology calibration problem. In this paper, we design the graph filter, a novel space-efficient data structure to record not only the node set but also the link set of any given topology. Accordingly, given two topologies we aim to represent them via two respective graph filters, and thereafter deduce those different links in an invertible manner. To this end, we design three essential operations for graph filter, i.e., encoding, subtracting and decoding. Although such operations are sufficient to solve the topology calibration problem, two challenging issues still remain open. First, the XOR traps which occur with low probability at the encoding stage may result in a few miscalculations at the decoding stage. Thus, we propose another augmented decoding algorithm to lessen the impact of XOR traps via terminating illegal decodings. Second, several different links may form cycles in the worst case; hence, we further design a cycle destruction algorithm to make such different links decodable. We implement the graph filter and the associated topology calibration method. Comprehensive evaluations indicate that our method finishes the topology calibration task efficiently with high probability, incurs the least space overhead, and supports invertible decoding reasonably.

The cloud datacenter network is consistently undergoing changing, due to a variety of topology and traffic updates, such as the VM migrations. Given an update event, prior methods focus on finding a sequence of lossless transitions from an initial network state to an end network state. They, however, suffer frequent and global search of the feasible end network states. This incurs non-trivial computation overhead and decision-making delay, especially in large-scale networks. Moreover, in each round of transition, prior methods usually cause the cascaded migrations of existing flows; hence, significantly disrupt production services in IaaS data centers. To tackle such severe issues, we present a simple update mechanism to minimize the amount of flow migrations during the congestion-free network update. The basic idea is to replace performing the sequence of global transitions of network states with local reschedule of involved flows, caused by an update event. We first model all involved flows due to an update event as a set of new flows, and then propose a heuristic method Lupdate. It motivates to locally schedule each new flow into the shortest path, at the cost of causing the extra migration of at most one existing flow if needed. To minimize the amount of migrated traffic, the migrated flow should be as small as possible. To further improve the success rate, we propose an enhanced method Lupdate-S. It shares the similar design of Lupdate, but permits to migrate multiple necessary flows on the shortest path allocated to each new flow. We conduct large-scale trace-driven evaluations under widely used Fat-Tree and ER data centers. The experimental results indicate that our methods can realize congestion-free network with as less amount of traffic migration as possible even when the link utilization of a majority of links is very high. The amount of traffic migration caused by our Ludpate method is 1.2 times and 1.12 times of the optimal result in the Fat-Tree and ER random networks, respectively.

In data centers, files are stored with the method of erasure code or replication to guarantee data reliability. However, both of the methods are not communication-friendly. An erasure coding system spends vast time to extract k data blocks across the racks during the decoding process. The transmission time contributes up to 94% of the total decoding time. In a multi-replica system, the frequent data writing and updating also lead to non-trivial bandwidth overhead. In this paper, we consider server-centric data centers (such as BCube), in which any pair of nodes are interconnected with multiple parallel paths. In such data centers, the transmissions for file storage can be significantly speed up by utilizing the parallel paths concurrently. With this insight, we first define the disjoint node in BCube. Based on this definition, we design the node-disjoint storage strategy (NDSS)and the nested node-disjoint storage strategy (N-NDSS)to improve the transmission between distributed storage nodes. Comprehensive simulations show the performance of our strategies in both erasure coding system and multi-replica system.

The Visible Light Communication (VLC) has the potential to provide dense and fast connectivity at low cost. In this paper, we propose WSWDC, a novel VLC enabled wireless small-world data center. It employs VLC links to achieve a fully wireless data center network (DCN) across racks for the first time. The using of VLC links eliminates hierarchical switches and inter-rack cables, and thus reducing hardware investment, as well as maintenance cost. More precisely, to simplify the configuration and control operations, we propose three DCN design rationales: (1) fully-wireless, all inter-rack links are wireless; (2) easy-deployable, it is not necessary to change the existing infrastructure inside data center; (3) plug-and-play, no extra centralized control operations are required. Previous proposals, however, cannot achieve the three rationales simultaneously. To this end, we first use regular VLC links to interconnect racks as a regular grid DCN. To further exploiting the benefits of VLC links, a few random VLC links are carefully introduced to update the wireless grid DCN as a wireless small-world DCN. To avoid the potential interference among VLC links, we deploy VLC transceivers at different height on the top of each rack. In this way, VLC links would not interfere with others at each height level. Moreover, we design a greedy but efficient routing method for any pair of racks using their identifiers as inputs. Comprehensive evaluation results indicate that our WSWDC exhibits good network performance.

The topology of data centers changes dynamically due to link malpositions, hardware failures or software crushes. However, many topology enabled protocols or applications must know the current topology of data center precisely, which triggers the topology calibration problem. Topology calibration needs to deduce the different nodes and links between two given topologies effectively. Based on the existing method, deriving the different nodes is relatively simple, since they can be uniquely identified by their IP or MAC addresses. On the contrary, picking the different links from the massive links can be costly. Therefore, we envision a method to locate the different links with respect to the following rationales: 1) efficient, the caused storage cost or communication overhead should be low; 2) without priori knowledge, there is no support information, thus the different links should be decoded inversely. However, the existing strategies based on Bloom filter, Hash table, or Search trees fail to achieve the two rationales simultaneously. Thus, we propose graph filter, a space-efficient data structure to represent and deduce the different links in an invertible manner. To this end, the associated encoding, subtracting and decoding algorithms are proposed. The simulations highlight the strength of graph filter reasonably.

Set synchronization is an essential job for distributed applications. In many cases, given two sets A and B, applications need to identify those elements that appear in set A but not in set B, and vice versa. Bloom filter, a spaceefficient data structure for representing a set and supporting membership queries, has been employed as a lightweight method to realize set synchronization with a low false positive probability. Unfortunately, bloom filters and their variants can only be applied to simple sets rather than more general multisets, which allow elements to appear multiple times. In this paper, we first examine the potential of addressing the multiset synchronization problem based on two existing variants of the bloom filters: the IBF and the counting bloom filter (CBF). We then design a novel data structure, invertible CBF (ICBF), which represents a multiset using a vector of cells. Each cell contains two fields, id and count, which record the identifiers and number of elements mapped into them, respectively. Given two multisets, based on the encoding results, the ICBF can execute the dedicated subtracting and decoding operations to recognize the different elements and differences in the multiplicities of elements between the two multisets. We conduct comprehensive experiments to evaluate and compare the three dedicated multiset synchronization approaches proposed in this paper. The evaluation results indicate that the ICBF-based approach outperforms the other two approaches in terms of synchronization accuracy, timeconsumption, and communication overhead.

Recent results have made a promising case for offering oversubscribed wired data center networks (DCN) with extreme costs. Inter-rack wireless networks are drawing intensive attention to augment such wired DCNs with a few wireless links. Inspired by the promise of easy deployment and plug-and-play, we present VLCcube, a novel inter-rack wireless solution that extends the design of wireless DCN into three further dimensions: (1) all inter-rack links are wireless; (2) there is no imposition of any infrastructure-level alteration on wired production data centers; and (3) it should be plug-and-play, without any need of additional mechanical or electronic control operations. This vision, if realized, will lead to increased flexibility, reduced reconstructing cost, simplified configuration and usage, and outstanding compatibility with existing wired DCNs. Previous proposals, however, are opposed to the last two design rationales. To achieve this vision, the proposed VLCcube augments Fat-Tree, a representative DCN in production data centers, by organizing all racks into a wireless Torus structure via the emerging visible light links. We further present the topology design, hybrid routing, and flow scheduling schemes for VLCcube. Extensive evaluations indicate that VLCcube outperforms Fat-Tree significantly under the existing ECMP flow scheduling scheme, irrespective of the undergoing traffic pattern. Moreover, the performance of VLCcube can be significantly promoted by our congestion-aware flow scheduling scheme. More precisely, compared to ECMP, our flow scheduling scheme makes VLCcube achieve$\times 1.50$throughput under batched flows,$\mathrm{\times}2.21$and$\times 2.59$throughput under two different kinds of online flows.