Resource-constrained IoT devices in Industrial Internet environments are highly vulnerable to DDoS attacks due to infrequent security updates and insufficient built-in protection mechanisms. Existing defense solutions primarily rely on external filtering servers or programmable switches, but these approaches fail to simultaneously meet the stringent real-time performance and high accuracy requirements of industrial applications. To address these limitations, we propose a novel cross-plane defense framework that exploits the temporal invariance characteristics of attack traffic patterns. In the data plane, an adaptive variance threshold mechanism immediately mitigates high-volume, low-variance traffic flows, while a bidirectional dual-hash table captures low-collision flow features for efficient export to the control plane. The control plane constructs temporally-enhanced flow sequences that enable deep learning models to perform accurate attack detection, subsequently directing the data plane to block identified malicious sources. We implemented and evaluated a prototype of this framework on a software switch platform using both real-world attack datasets and custom-generated traffic patterns. Experimental results demonstrate that our framework successfully mitigates 86% of attack traffic within milliseconds and achieves complete source blocking within 52 seconds. Compared to baseline methods, our framework can effectively counter both DoS and DDoS attacks without generating false positives on benign traffic.

Federated Learning (FL) enables decentralized model training on distributed data sources, aggregating information to enhance generalization while preserving privacy. With the growing demand for personalized AI models, Personalized Federated Learning (PFL) has become essential for adapting models to individual user preferences. However, achieving strong global generalization while maintaining effective local personalization presents a significant challenge, especially under non-i.i.d. data distributions. We propose APO-PFL (Aligned Proximal Policy Optimization Personalized Federated Learning), a novel PFL framework that leverages Proximal Policy Optimization (PPO) to align aggregation weights dynamically. APO-PFL optimizes the fusion of local models using reinforcement learning to enhance global model performance while striving to maintain effective personalized inferences. Experimental results on CNN and GPT-2 models demonstrate that APO-PFL achieves superior global generalization and outperforms baseline methods while delivering consistent personalized predictions across heterogeneous devices.

Wireless rechargeable sensor networks (WRSNs) have emerged as a promising solution to overcome the energy bottleneck in traditional battery-powered sensor networks. However, the uncertain energy demands and dense deployment of sensor nodes pose significant challenges to efficient charging scheduling in WRSNs. To address these challenges, this article proposes a novel multi-AAV assisted on-demand partial charging scheduling (MOPCS) algorithm. MOPCS integrates the advantages of one-to-many charging, partial charging, and dynamic multi-AAV coordination to maximize the network lifetime and energy utilization. The key contributions of this work include a real-time adaptive charging scheduling trigger mechanism, an energy-efficient charging cluster division method, a spatiotemporally balanced task allocation among multiple autonomous aerial vehicles (AAVs), and a hybrid priority-based charging path planning algorithm. Extensive simulations demonstrate that MOPCS significantly outperforms state-of-the-art algorithms in terms of charging request response timeliness, node survival rate, and AAV energy efficiency, especially in dense network deployments. This work provides valuable insights and practical solutions for the design and optimization of AAV-assisted charging scheduling in WRSNs, paving the way for more sustainable and scalable wireless sensor networks in various application scenarios.

LoRa, as a typical representative of Low Power Wide Area Networks (LPWAN), has been widely used to connect massive IoT devices. However, in mobile applications, there is significant packet loss in LoRa transmission due to link performance degradation. Existing studies take little account of end-devices' movement, particularly when the movement pattern is unknown. We propose LMLoRa to enhance theLink Performance forMobile LoRa networks in general scenarios for both single-gateway and multi-gateway applications. The key observation is that, due to LoRa's unique feature, repeating the original packet content enables the use of smaller, more energy-saving transmission parameters, which not only enhances link performance but also reduces energy consumption. Technically, we propose a link performance estimation model based on packet content repetition for both single-gateway and multi-gateway mobile networks. Then, we propose the corresponding channel frequency selection model to avoid transmission collisions. Finally, we design low-overhead communication mechanisms to operate the system. To evaluate the performance of LMLoRa in various scenarios, we design and implement real-world testbeds and a simulation platform for both single-gateway and multi-gateway scenarios. Extensive results show that LMLoRa improves packet delivery ratio by an average of 33.4% to 69.2% compared with the state-of-the-art.

Unmanned aerial vehicles (UAVs) are being widely employed in wireless communication applications, e.g., collecting data from ground nodes (GNs). Minimizing UAV energy in these applications is crucial due to the limited energy supply onboard. Unlike previous studies that assume UAVs fly at a fixed altitude and simplify the energy consumption model of UAVs, we consider the impact of varying UAV altitudes on the ground-to-air communication and utilize a general communication model for GN. Furthermore, we conduct real-world flight tests and introduce a practical speed-related flight energy consumption model of UAVs. This paper focuses on the UAV altitude-speed scheduling and GN transmission switching (UASS-GTS) problem, specifically in scenarios where the UAV flies straight for monitoring applications such as power transmission lines, roads, and water/oil/gas pipes. However, minimizing energy consumption presents challenges due to the tight coupling of altitude scheduling and speed scheduling. To tackle this, first, we develop the looking before crossing algorithm for speed scheduling. We then extend this algorithm by integrating altitude scheduling to propose the Altitude-Speed Scheduling of UAV for Minimizing Energy (ASSUME) algorithm, using a dynamic programming method. The ASSUME algorithm is theoretically proven to be optimal. Additionally, based on ASSUME, we propose an offline-inspired online heuristic algorithm to handle agnostic situations where GN information is not available unless flies close. Simulations indicate that the ASSUME algorithm saves an average of 26.1%–62.7% energy compared to the baseline methods, and the performance gap between the online algorithm and the offline optimal algorithm ASSUME is 22.8%.

Low-altitude airspace in major cities across the world is increasingly congested with unmanned aerial vehicles (UAVs) and other aircraft. Emerging technologies, innovative business models, and supportive government policies are driving the growth of the low-altitude economy, where UAVs play a crucial role. Given the limited on-board energy of UAVs, this paper investigates the Joint UAV Speed and Altitude Scheduling (JUSAS) problem for data collection from sensors deployed along power transmission lines, bridges, highways, railways, water/gas/oil pipelines, or rivers/coasts. Distinct from existing work, the paper focuses on jointly optimizing UAV speed and altitude scheduling while determining the wireless sensor collection order. It accounts for the altitude-specific sensor transmission range model and the complexities of overlapping range relationships. We first propose theSlowest Segment First(SSF) policy to obtain an optimal UAV speed scheduling for fixed-altitude scenarios. Building upon this, we then reformulate JUSAS as a shortest-path-type problem using our novel flight scheduling graph, solved efficiently through theSSF-based Ant Colony Optimization(SSF-ACO) algorithm. To handle practical scenarios without prior sensor information along the path, we develop SSF-ACO-Online for real-time scheduling. Extensive simulations demonstrate that SSF-ACO significantly outperforms four other algorithms (i.e., SSF-Only, SSF-GA, SSF-PSO, and SSF-SA) in energy efficiency, and reduces 13.11% energy consumption on average. SSF-ACO-Online achieves comparable performance with energy consumption 1.24% higher than offline counterpart in average.

Supply Chain Networks (SCNs) play a vital role in achieving strategic decision-making for production and distribution facilities, aiming to meet market demands and gain competitive advantages. With the application of new-generation information technology in the supply chain, enterprises within SCNs generate a substantial volume of relevant business data. Sharing this data among SCN enterprises can effectively reduce operating costs, optimize business processes, and enhance the overall efficiency of the supply chain. However, effective data sharing among SCN participants faces challenges, such as data leakage, data quality assurance, and fair data value allocation. To address these challenges, this paper proposes a secure cross-domain data sharing model in SCNs (named SCN-CDSM) based on consortium blockchain technology. The model introduces trust, enables cross-domain data exchange, and promotes cooperation among supply chain enterprises. To ensure privacy, group signatures and access control smart contracts are designed, along with an approach to reduce blockchain throughput limitations. Furthermore, a sharing incentive mechanism utilizing the Stackelberg game model based on data value is designed to foster fairness and collaboration. Extensive numerical simulations are conducted to demonstrate the effectiveness of the proposed schemes, achieving both security and efficiency in data sharing within SCNs.

LoRa, as a Low-Power Wide Area Networks (LP-WAN) technology, is extensively employed for connecting Internet of Things (IoT) applications. LoRa time-slotted networks have gained popularity due to their high channel utilization and robust anti-interference capability. However, the queueing latency of end-devices (EDs) in these networks is often overlooked in the time-slot-scheduled LoRa network, leading to data obsolescence and insufficient notification time. Existing research mainly focuses on reducing transmission delay and avoiding collisions in LoRa networks, while neglecting the importance of ensuring low queueing latency for EDs. In this paper, we propose a semidefinite relaxation (SDR)-based channel scheduler called Q-MAC to achieve low queueing latency in time-slotted LoRa networks. The core idea is to allocate time slots and channels effectively for packets while avoiding collisions. To accomplish this, we formulate an optimization model to minimize latency and packet collisions. This model is a multivariable-coupled non-convex integer problem, we transform the model into a Quadratically Constrained Quadratic Programming (QCQP) problem. Subsequently, we employ the SDR and heuristic algorithms to obtain feasible solutions. Simulation results demonstrate that Q-MAC can significantly reduce queueing latency, achieving an average improvement of 8.57 × compared to existing approaches.

LoRa, as a typical representative of Low Power Wide Area Networks (LPWAN), has been widely used to connect massive IoT devices. However, in mobile applications, there is massive packet loss in LoRa transmission due to link performance degradation, especially when LoRa end-devices move far from the gateway or into obstructed areas. Existing studies take little account of end-device movement, particularly when the movement pattern is unknown. We propose LMLoRa to enhance the Link Performance for Mobile LoRa networks in general scenarios. The key observation is that repeating the original packet content enhances link performance and allows smaller and more energy-efficient transmission parameter selections. Technically, LMLoRa proposes a link performance estimation model for mobile LoRa networks based on packet content repetition. Second, we exploit key hardware features of LoRa to obtain much continuous RSSI information for link quality prediction. Additionally, LMLoRa develops a channel frequency allocation policy to mitigate transmission collisions. Finally, LMLoRa designs a communication mechanism to assist the estimation model and work the whole system with low communication overhead. We design and implement LMLoRa in complex realworld environments, results show that LMLoRa enhances packet reception rate by 33.4% and energy efficiency by 14.4% on average compared with the state-of-the-art.

LoRa Localization, a fundamental service in LoRa networks, has garnered significant attention due to its long-range capabilities and low power consumption. However, existing approaches for LoRa localization are either incompatible with commercial devices or highly susceptible to environmental factors. To tackle this challenge, we propose SyncLoc, a TDoA-based LoRa localization framework that integrates a dedicated node for multi-dimensional time-drift correction. Our proposal is built on two key observations: firstly, the nanosecond-level measurement of time differences between gateways, and secondly, the substantial impact of SNR on gateway time drift. To accomplish our objective, we present three progressively enhanced versions of SyncLoc, each intended to comprehensively analyze the factors influencing LoRa time synchronization accuracy across different deployment scenarios involving nodes, carrier frequencies, and spreading factors. In addition to improving accuracy, we identify inefficiencies in LoRa’s multi-node concurrent localization, and introduce SyncLoc-4, a multi-node localization scheduling mechanism that optimizes efficiency with a 2-approximation ratio. Extensive experiments utilizing commercial LoRa devices in real-world demonstrates a 2.44× improvement in accuracy. Furthermore, simulations of large-scale networks exhibit a 2.47× boost in localization scalability (i.e., the number of concurrently located nodes) when employing SyncLoc instead of LoRaWAN.

As an emerging Low-Power Wide Area Networks (LPWAN) technology, LoRa is dedicated to providing long-range connections for pervasive Internet-of-Things devices. As LoRa operates in the unlicensed spectrum with an ALOHA-based MAC-layer protocol stack, transmissions from multiple LoRa end-devices inevitably collide with each other, leading to packet losses and increased transmission delay. Targeting at collisions caused by interferences under thesamespreading factor (SF) settings, researchers introduce multiple lines of techniques. Despite their efforts, these techniques commonly neglect the potential collisions caused by interferences underdifferentSF settings, resulting in imperfect orthogonality. Given the disparate transmission power configurations and diverse deployed locations, the collisions under different SFs commonly exist in practical networks and significantly limit the LoRa reliability. This paper presents X-MAC, the first scheduler aware of imperfect orthogonality. Technically, X-MAC detects the collisions under different SFs via tracking historical transmissions, and performs dynamic channel scheduling to avoid collisions caused by interferences under the same and different SFs. Extensive evaluations on testbed devices show that, compared with the state-of-the-art methods, X-MAC boosts the network scalability (number of concurrent end-devices) by 1.26× to 2.41× with packet reception rate requirement of > 95%.

Industrial Internet of Things (IIoT) is considered one of the most revolutionary technologies that can significantly improve manufacturing efficiency and realize intelligent production. With the increasing complexity of industrial manufacturing, the manufacturing process of a product often involves several different IoT domains (e.g., factories). To achieve a common production goal, devices from various domains share their data for cooperative work, which raises privacy and security concerns for cross-domain communication. Most existing data-sharing schemes rely on a trusted third party. Hence, the privacy and security issues in cross-domain data sharing are still challenging research directions. This paper proposes CBDS, a consortiumblockchain-based cross-domain IIoT data-sharing mechanism. Specifically, we introduce consortium blockchain to construct trust among different domains in IIoT. A group signature is presented to ensure each device’s privacy to achieve anonymous authentication. In addition, the collected data should be stored in the ciphertext. The smart contract and proxy re-encryption are utilized in the CBDS to realize secure cross-domain data sharing. The experimental results demonstrate the effectiveness and efficiency of the proposed CBDS.

The mental health of older adults is a vital issue in the era of global aging. Empathy towards older adults constitutes a crucial component of social interaction, as it engenders awareness of the physical and mental obstacles encountered by this population. Empathy promotes individuals to be more friendly, considerate, and prosocial when interacting with older adults on various occasions, such as volunteering and professional caring. Various approaches have been used to promote people's empathy, but disadvantages like low participation enthusiasm, high cost, and time-consuming cannot be ignored and are not easy to deal with. Aimed at facilitating people's empathy towards older adults, we developed EmpathiaVR, a virtual reality older adults experience simulation system. It provides a multi-sensory mixed reality experience, including vision, hearing, and kinesthesis, from an older person's perspective, which is beneficial for provoking users' empathy towards older adults. To investigate the effectiveness of the system, an empirical study was conducted with 24 participants. The experiment employed a between-subjects design with two groups. The experiment results from both the subjective reports and behavioral variables indicated that the system enhanced people's empathy.

Summary With their high flexibility and versatility, unmanned aerial vehicles (UAVs) have maneuvered their way into many applications. Thanks to their ability to plan and coordinate, multiple UAVs complete tasks more effectively, which boosts their popularity in battlefield surveys, formation performances, and targeted searches. However, the risk of security threats also rises alongside their popularity. The UAV ad hoc network (UANET) has endeavored to contend with such risks through the optimized link state routing (OLSR) protocol. To test the security and strength of this effort, we present a sky black hole attack (SBHA) algorithm for OLSR, which is undetectable, based on the UANET's multi‐hop routing and the OLSR's known topology. This algorithm obtains the network's maximum profits by approaching and then replacing the calculated topology center and traffic center in UANET. Because of the ever‐changing topology, SBHA aims at UANET's single central node that cannot be detected in advance. This attack is difficult to detect by UANET and therefore difficult to defend. The simulation results show that SBHA can cause greater damage to UANET compared to a traditional black hole attack, and ordinary defense algorithms cannot reduce the negative impact of SBHA on UANET. In addition, SBHA also gains UANET control, and leads to drastic changes in UAVs' movement trajectory, which has more intuitive effects.

Protein structure prediction (PSP) is predicting the three-dimensional of protein from its amino acid sequence only based on the information hidden in the protein sequence. One of the efficient tools to describe this information is protein energy functions. Despite the advancements in biology and computer science, PSP is still a challenging problem due to its large protein conformation space and inaccurate energy functions. In this study, PSP is treated as a many-objective optimization problem and four conflicting energy functions are used as different objectives to be optimized. A novel Pareto-dominance-archive and Coordinated-selection-strategy-based Many-objective-optimizer (PCM) is proposed to perform the conformation search. In it, convergence and diversity-based selection metrics are used to enable PCM to find near-native proteins with well-distributed energy values, while a Pareto-dominance-based archive is proposed to save more potential conformations that can guide the search to more promising conformation areas. The experimental results on thirty-four benchmark proteins demonstrate the significant superiority of PCM in comparison with other single, multiple, and many-objective evolutionary algorithms. Additionally, the inherent characteristics of iterative search of PCM can also give more insights into the dynamic progress of protein folding besides the final predicted static tertiary structure. All these confirm that PCM is a fast, easy-to-use, and fruitful solution generation method for PSP.

Wireless sensing has emerged as a powerful environmental sensing technology that is vulnerable to the impact of all kinds of ambient noises. LoRa is a novel interference-resilient technology of low-power wide-area networks (LPWAN), which has attracted wide attention from scientific and industrial communities. However, LoRa transmission suffers from serious latency in those complex wireless sensing environments requiring transmission reliability. In this article, we present CH-MAC, the first MAC-layer protocol based on the local corruption nature of packets and the time-varying nature of channels to reduce end-to-end transmission latency in LPWAN with reliable communication requirements. Specifically, CH-MAC employs Luby Transform code to divide and encode the payload into several blocks such that the receiver can retain part of the coded information in the corrupted packets. In addition, CH-MAC utilizes hopping to transmit different blocks of a packet with various channels to avoid sudden noise collision. Moreover, CH-MAC adopts a dynamic packet length adjustment mechanism to mitigate network congestion. Extensive evaluations on a real-world hardware testbed and a simulation platform show that CH-MAC can reduce end-to-end transmission latency by 2.63× with a communication success rate requirement of >95% compared with state-of-the-art methods.

Unmanned aerial vehicles (UAVs) are being widely exploited for various applications,e.g., traversing to collect data from ground sensors, patrolling to monitor key facilities, moving to aid mobile edge computing. We summarize these UAV applications and formulate a problem, namely thegeneral waypoint-based PoI-visiting problem. Since energy is critical due to the limited onboard storage capacity, we aim at minimizing flight energy consumption. In our problem, we pay special attention to the energy consumption for turning and switching operations on flight planning, which are usually ignored in the literature but play an important role in practical UAV flights according to our real-world measurement experiments. We propose specially designed graph parts to model the turning and switching cost and thus transfer the problem into a classic graph problem,i.e., general traveling salesman problem, which can be efficiently solved. Theoretical analysis shows that such problem transformation has the graph redefinition approximation ratio upper bound,$max\lbrace \Theta /\delta ,2\rbrace$, where$\Theta$is related to the designed graph parts and$\delta$is a constant. Finally, we evaluate our proposed algorithm by simulations. The results show that it costs less than 107% of the optimal minimum energy consumption for small scale problems and costs only 50% as much energy as a naive algorithm for large scale problems.

UAV Ad-hoc Network (UANET) has been widely used in many fields. However, the collaborative communication and data sharing among multiple UAVs in UANET are often attacked and threatened, due to the limited software and hardware capability of UAVs and the openness of wireless network environment. In this work, we introduce blockchain technology into UANET to enhance its security. Instead of directly adopting traditional blockchain which require huge storage, computation and communication resources, we propose a lightweight reputation-based blockchain scheme for resource-constrained UANET, named AirBC. Firstly, we present a lightweight storage strategy by elimination and compression to reduce storage overhead for UAV nodes. Secondly, we propose an improved reputation-enhanced Practical Byzantine Fault Tolerance (PBFT) consensus, as well as a reputation evaluation scheme based on reliable recording of UAV behaviors. In our scheme, UAVs with high reputation are selected into a miner committee to perform the consensus, thus improving efficiency. Meanwhile, the committee is updated at regular intervals to ensure scalability of UANET. Thirdly, we adopt a weighted proposal voting scheme to enhance the ability of group decision-making for UANET. Finally, to evaluate our approach, simulations are conducted and their results demonstrate that AirBC can reduce 63% storage overhead and 69% consensus latency on average for different scale UANET.

As an emerging Low-Power Wide Area Networks (LPWAN) technology, LoRa is dedicated to providing long-range connections for pervasive Internet-of-Things devices. As LoRa operates in the unlicensed spectrum, transmissions from multiple LoRa end-devices inevitably collide into each other, leading to packet losses and increased transmission delay. Targeting at collisions caused by interferences under the same spreading factor (SF) settings, researchers introduce multiple lines of techniques. Despite their efforts, these techniques commonly neglect the potential collisions caused by interferences under different SF settings, which are resulted by the imperfect orthogonality. Given the disparate transmission power configurations and diverse deployed locations, the collisions under different SFs commonly exist in practical networks, and significantly limit the LoRa reliability. In this paper, we present X-MAC, the first scheduler that is aware of imperfect orthogonality. Technically, X-MAC detects the collisions under different SFs via tracking historical transmissions, and further performs dynamic channel scheduling to avoid collisions caused by interferences both under the same and different SFs. Extensive evaluations on testbed devices show that, compared with the state-of-the-art methods, X-MAC boosts the network scalability (number of concurrent end-devices) by 2.41× with packet reception rate (PRR) requirement of > 95%.

Low-Power Wide Area Networks (LPWANs) are widely used to connect large-scale Internet of Things (IoT) applications. Long Range (LoRa) is a promising LPWAN technology sensitive to energy consumption, since LoRa nodes are generally battery-powered, and the battery life will influence the lifetime of the LoRa network. In practice, the battery life of LoRa nodes is short in many scenarios, due to the long transmission distance form the gateway leading to high energy consumption. Existing techniques for energy-efficient data transmission mainly focus on static gateways, and will consume huge energy of remote nodes. In this paper, we propose to integrate LoRa with mobility to minimize the energy consumption of nodes by effectively shortening the transmission distances, and design the first mobile LoRa data transmission system called LoRaDrone by leveraging the unmanned aerial vehicle (UAV) gateway flying close to nodes. Specifically, we present a low-power communication mechanism and a dynamic channel allocation policy to minimize the energy consumed in sensing and communicating with the UAV gateway, while considering the distinctive LoRa parallel reception and complex transmission collisions. Then, an optimal speed scheduling strategy is designed to ensure the reliability of data transmission, and minimize the energy consumption of the UAV. Evaluations on various scales verify the effectiveness of LoRaDrone under different nodes' distributions and UAV paths. Compared with the baselines, the energy consumption of nodes using LoRaDrone is at most reduced by$\mathbf{70.37}\times$at 5000 nodes.

Unmanned aerial vehicles (UAVs) are being widely exploited for various applications, e.g., traverse to collect data from ground sensors, patrol to monitor key facilities, move to aid mobile edge computing. We summarize these UAV applications and formulate an abstract problem, namely the general waypoint-based PoI-visiting problem, aiming at minimizing flight energy consumption, which is critical due to its limited onboard storage capacity. In our problem, we pay special attention to the energy consumption for turning and switching operations on flight planning, which is usually ignored in the literature but plays an important role in practical UAV flights. We propose a novel method that uses specially designed graph parts to model the turning and switching cost and thus transfer the problem into a classic graph problem, i.e., traveling salesman problem, which can be efficiently solved. Finally, we evaluate our proposed algorithm by simulations. The results show it costs less than 107% of the optimal minimum energy consumption for small scale problem and costs only half as much energy as a naive algorithm for large scale problem.

Unmanned aerial vehicles (UAVs) are being widely used in wireless communication, e.g., collecting data from ground nodes (GNs), where energy is critical. Existing works combine speed scheduling, i.e., the controlling of speed, with trajectory design for UAVs, making it complicated to solve while loses focus on the fundamental nature of speed scheduling. We focus on speed scheduling by considering straight line flights, with applications in monitoring power transmission lines, roads, water/oil/gas pipes and rivers/coasts. By real-world flight tests, we disclose a speed-related flight energy consumption model, distinct from typical distance-related or duration-related models. Based on such a practical energy model, we develop the looking before crossing (virtual rooms) algorithm, where virtual rooms on the time-distance diagram represent the spatio-temporal constraint of GNs in wireless transmission. This algorithm is proved to be optimal in solving the offline problem, where all information is known before scheduling. For the online problem, i.e., GN information is not unavailable unless flies close, we propose an offline-inspired online heuristic. Simulation shows its performance is near the offline optimal. Our study on the practical flight energy model and speed scheduling sheds light on a new research direction on UAV-aided wireless communication.

Packet loss is a common problem in data center networks. The factors causing packet loss are various. Among them, micro-burst is the most important reason. Some previous works have studied the causes and influence o f micro-burst in single queue data center. However, through simulations and experiments, we find that micro-burst could bring m ore serious performance degradation in multi-queue data centers. The micro-burst traffic could cause E CN marking ratio rising from 4% to 22%, and cause throughput loss by up to 40%. Through observing queue length, we find that the standard E CN, which adopts immutable threshold, is not suitable for micro-burst traffic because micro-burst could trigger spurious congestion signals frequently, especially in DCTCP. In this paper, we not only show how much influence the micro-burst brings, but also propose Queue-length Aware ECN (QA-ECN) scheme to mitigate micro-burst. Finally, the simulations and experiments show that QAECN could reduce ECN marking ratio to 2.5%. In addition, the throughput and flow completion time could be improved by up to 22.9% and 34.1%, respectively.

Summary Sharing the network infrastructure, the performance of emerging distributed applications and services in data centers is directly impacted by the network. As these applications are becoming more and more demanding, it is challenging to satisfy their requirements of low latency, high throughput, and low packet loss rate simultaneously. Prior approaches typically resort to flow control or scheduling mechanisms, prioritizing flows according to their demands. However, none of the methods can solely satisfy the various demands of data center applications. Addressing this challenge, we propose tasch, a preference aware flow scheduling mechanism equipped in the software network edge (ie, end‐host networking). This mechanism utilizes multiple separate queues for flows with different preferences, which guarantees low packet delay for latency‐sensitive flows and provides bandwidth guarantees for throughput‐sensitive flows. A coordinating algorithm is presented to share the network resource among multiple queues with pareto‐optimality. tasch is implemented as a thin and plugable kernel module in Linux based hypervisors, which lies between the complicated physical network and tenants VMs. Subsequently, based on the flow traces of real‐world applications, extensive experiments were conducted to verify the effectiveness of network management mechanism.

Edge Computing is a new computing paradigm that performs data processing at the edge of the network (i.e., edge servers) to lower data processing latency. Existing research works have paid lots of attention to how to offload computation tasks from terminals to edge servers, but most of them ignored how to store tasks' necessary data like pretrained models or databases on edge servers. Recently, the data-intensive tasks like deep learning and augmented reality are becoming common, which need large data storages and powerful computation resources. This leads to a cumbersome challenge, since many lightweight edge servers have limited resources. If an edge server does not have a task's necessary data, it needs to offload the task to cloud data centers or download the necessary data from the cloud. Both cases could increase the data processing latency. To address this problem, this paper proposes an edge-side collaborative storage framework (ECS). In ECS, the edge servers collaboratively store and process data-intensive tasks' necessary data. Particularly, if an edge server does not have the necessary data, it will forward the task to the nearest servers that contain the data. An effective iterative data placement algorithm is also proposed to improve ECS's performance. The experimental results show that ECS is 2× better than the traditional non-shared storage framework in terms of the cache hit rate.

Summary Enterprises increasingly use the Hadoop Distributed File System (HDFS) to manage and store big data for many applications. However, HDFS uses triple replication, leading to staggering data center storage costs. As big data increases in volume and its heat levels becomes more sensitive, there comes a point where storing so much cold data actually makes it less accessible and more expensive. Meanwhile, as data centers expand, the heterogeneity of nodes also becomes an issue. Rack‐aware data placement adopted by HDFS results in an unbalanced load and uneven resource allocation because it ignores the data nodes' heterogeneity. Here, we attempt to resolve these problems by proposing a hotness‐aware data placement strategy (named HaDaap). In HaDaap, the first step is to use a hotness‐aware data clustering algorithm to set the data's degree of heat. Then, cold data (with a redundancy of erasure code) are placed through a Double Sort Exchange algorithm to reduce storage costs and increase data availability. Finally, hot data are placed via a dynamic replication placement mechanism that comprehensively factors availability, load, and storage costs. Experimental results show that with these enhancements, HaDaap uses resources rationally and substantially reduces storage costs by considering the difference of data hotness in heterogeneous Hadoop clusters.

Diagnosing faults in virtual networks is always a popular research area. Existing researches primarily focus on diagnosing faults in physical networks, while they could not identify the faults introduced by virtual networks. Besides, the high complexity of algorithms and the requirement for modifying hardware may limit their scope of use. To address these drawbacks, in this paper, we propose a novel approach to diagnose faults in virtual networks. The rational of our approach is that the faults can be identified when located in the packet traces, with the knowledge that the possible known faults that can happen in that location. To achieve this goal, we apply packet marking, fault injection and machine learning techniques to provide precise fault diagnosis. Experimental results show that our approach can efficiently identify 73% of the faults while for virtual network-specific faults, our approach can diagnose 86% of them. Our system can also support real-time or near real-time fault analysis.

Large scale data processing is increasingly common in cloud computing systems like MapReduce, Hadoop, and Dryad in recent years. In these systems, files are split into many small blocks and all blocks are replicated over several servers. To process files efficiently, each job is divided into many tasks and each task is allocated to a server to deals with a file block. Because network bandwidth is a scarce resource in these systems, enhancing task data locality(placing tasks on servers that contain their input blocks) is crucial for the job completion time. Although there have been many approaches on improving data locality, most of them either are greedy and ignore global optimization, or suffer from high computation complexity. To address these problems, we propose a heuristic task scheduling algorithm called Balance-Reduce(BAR), in which an initial task allocation will be produced at first, then the job completion time can be reduced gradually by tuning the initial task allocation. By taking a global view, BAR can adjust data locality dynamically according to network state and cluster workload. The simulation results show that BAR is able to deal with large problem instances in a few seconds and outperforms previous related algorithms in term of the job completion time.

Data replication is an important technique to reduce access latency and bandwidth consumption in Grid environment. As one of the major functions of data replication, replica selection determines the best replica according to some specific criteria in Data Grid environment, where the data resources are limited and Grid users compete for these resources. In this paper, we focus mainly on a novel QoS preference-aware replica selection strategy which will meet individual QoS sensitivity (IQS) constraints for different users/applications. We first present a framework that characterize QoS properties of replica services and establish its mathematical model by introducing quantification methods. In order to deal with the IQS constraints and to perceive Grid users' QoS preferences accurately, we propose a QoS preference acquisition algorithm based on Analytic Hierarchy Process (AHP). We then design and implement a novel effective and efficient parallel genetic algorithm (PGA) based on Map Reduce paradigm for optimizing the objective function which corresponds to the optimal replica. Simulation results show that our strategy has a better performance in validity as well as scalability, and the optimal replica can always be obtained for Grid users with different IQS constraints under Data Grid environments that vary in system loads, scheduling strategies and user types.