ABSTRACT Objective Collaborative edge computing (CEC) addresses the service quality issues that arise from the limited resources of a single node in traditional edge computing architectures by integrating resources from multiple edge nodes. However, ensuring reliable task offloading in this collaborative environment remains a significant challenge. Existing solutions often struggle to balance the intelligence and trustworthiness of offloading decisions effectively. This imbalance can lead to poor performance and reduced task success rates, especially if tasks are offloaded to malicious nodes. Methods To tackle these challenges, this paper proposes a trust‐enabled decentralized task offloading scheme that combines blockchain technology and deep reinforcement learning (DRL). First, we introduce a blockchain‐based reputation mechanism within the CEC architecture to facilitate trusted collaboration among nodes, utilizing smart contracts for reputation management. Next, we propose a beta distribution‐based three‐factor reputation update (BTRU) algorithm to enhance the accuracy of reputation evaluation. Finally, we present a decentralized and trust‐enabled task offloading (DTTO) algorithm based on DRL, which uses on‐chain reputation data to guide agents in learning trustworthy task offloading policies, thereby maximizing offloading trustworthiness and task success rates. Result To thoroughly assess the effectiveness and practicality of our proposed scheme, we develop a testbed for CEC task offloading based on Kubernetes and Ethereum. Experimental results demonstrate that the BTRU algorithm effectively distinguishes malicious nodes, reducing their average reputation by 97.54%, with an improvement of 9.94% compared to competitive algorithms. Meanwhile, the DTTO algorithm significantly enhances the efficiency and reliability of task offloading, raising the task success rate by at least 3.04%, especially when the proportion of malicious nodes reaches 40%, its task success rate is at least 5.41% higher than that of competitive algorithms. Conclusion The proposed trust‐enabled decentralized task offloading scheme successfully combines blockchain‐based reputation management with DRL to achieve both intelligent and trustworthy task offloading in the CEC environments. The experimental validation confirms the scheme's effectiveness in identifying malicious nodes and improving task success rates under various system conditions.

This paper presents a novel Convolutional Based Temporal Attention (CBTA) module that improves the performance of temporal convolutional networks (TCN) in lipreading tasks without requiring any additional data.Our CBTA method enhances recognition accuracy by focusing attention on relevant frames in the time sequence of a video.The study demonstrates how our strategy achieves groundbreaking sucacess on the Lip Reading in the Wild (LRW) dataset, achieving an accuracy of 92.61%-surpassing contemporary methods by approximately 0.5%.The experiment also indicates the broad utility and effectiveness of the adaptable CBTA.The proposed module substantially boosts Top-1 Accuracy for challenging words, offering a promising direction for overall performance improvement in lip-reading tasks.

Security and trust have become the key issues in the Internet of Things (IoT) environment. Characterized by the centralized control and high-energy consumption, the traditional trust management schemes are not suitable for the IoT systems, in which most of the interactions are short-duration, random and maybe one-time, and the terminal devices always have resource constraints. Therefore, this article proposes a distributed and two-layered trust management framework based on blockchain architecture for IoT. The hierarchical architecture of the cloud, the edge, the IoT subgroups, and devices solves the resource limitation problem and improves the privacy protection of the IoT applications. And a novel lightweight$Q$-learning improved DPoS consensus algorithm named QV-DPoS is proposed to solve the problems of large energy consumption and high complexity of consensus mechanism of blockchain. Ethereum is used to build a blockchain-based IoT prototype system, and some experiments were designed to verify whether the proposed platform can successfully conduct trust management and achieve identity and behavior authentication between the IoT entities. Moreover, the simulation experiments based on NetLogo is also designed to test the performance of the trust and consensus mechanisms. The results of the experiments show that the proposed mechanisms can effectively enhance the credibility of the interactions in the IoT environments, improve the transaction success rate, and reduce energy consumption at least 10% compared with the traditional algorithms.

Summary When large‐scale content‐based publish/subscribe systems face dynamic workloads, it is challenging to stabilize event delivery latency. In this article, we propose a latency‐aware parallelism framework (Lap) to address this challenge. Specifically, we propose a lightweight parallelism method called PhSIH for event matching algorithms. In addition, we design a reactive parallelism degree adjustment (RPDA) mechanism in the backpressure way to determine the parallelism degree. We implement Lap in Apache Kafka and evaluate the parallelism effect of PhSIH and the adaptability of RPDA on trace data. The experiment results demonstrate that PhSIH achieves linear speedup on three existing algorithms and RPDA possesses a desirable adaptability to the dynamic workloads.

The cloud-fog-edge hybrid system is the evolution of the traditional centralized cloud computing model. Through the combination of different levels of resources, it is able to handle service requests from terminal users with a lower latency. However, it is accompanied by greater uncertainty, unreliability, and instability due to the decentralization and regionalization of service processing, as well as the unreasonable and unfairness in resource allocation, task scheduling, and coordination, caused by the autonomy of node distribution. Therefore, this paper introduces blockchain technology to construct a trust-enabled interaction framework in a cloud-fog-edge environment, and through a double-chain structure, it improves the reliability and verifiability of task processing without a big management overhead. Furthermore, in order to fully consider the reasonability and load balance in service coordination and task scheduling, Berger’s model and the conception of service justice are introduced to perform reasonable matching of tasks and resources. We have developed a trust-based cloud-fog-edge service simulation system based on iFogsim, and through a large number of experiments, the performance of the proposed model is verified in terms of makespan, scheduling success rate, latency, and user satisfaction with some classical scheduling models.

With the rapid development of cloud computing, various types of cloud services are available in the marketplace. However, it remains a significant challenge for cloud users to find suitable services for two major reasons: (1) Providers are unable to offer services in complete accordance with their declared Service Level Agreements, and (2) it is difficult for customers to describe their requirements accurately. To help users select cloud services efficiently, this article presents a Trust enabled Self-Learning Agent Model for service Matching (TSLAM). TSLAM is a multi-agent-based three-layered cloud service market model, in which different categories of agents represent the corresponding cloud entities to perform market behaviors. The unique feature of brokers is that they are not only the service recommenders but also the participants of market competition. We equip brokers with a learning module enabling them to capture implicit service demands and find user preferences. Moreover, a distributed and lightweight trust model is designed to help cloud entities make service decisions. Extensive experiments prove that TSLAM is able to optimize the cloud service matching process and compared to the state-of-the-art studies, TSLAM improves user satisfaction and the transaction success rate by at least 10%.

A distributed cluster provides a common computation platform to efficiently run different types of jobs, such as real-time and batch jobs that have different QoS requirements. One challenge of a distributed cluster is to find a scheduling strategy that can reduce the overtime ratio of real-time jobs as much as possible while also improving the completion time of batch jobs, aiming to promote QoS even though the resources in the cluster are stretched. To address this issue, many efficient scheduling methods have been proposed. However, most existing approaches are naive and coarse-grained in means of either killing batch jobs to preempt resources for real-time jobs or reserving some resources in advance for real-time jobs to minimize the overtime ratio of real-time jobs, which prolongs the completion time of batch jobs and reduces the resource utilization of the cluster. In this paper, we propose UpPreempt, a fine-grained preemptive scheduling strategy for container-based clusters. When scheduling real-time jobs, UpPreempt considers the deadline of jobs and the resource usage of existing batch jobs. Performing resource preemption from multiple batch jobs is the basic idea of UpPreempt. The set of batch jobs to be preempted and the amount of resources taken from each job are finely determined. In this way, UpPreempt greatly alleviates the effect of preemption on the batch jobs in terms of completion time and avoids reserving resources for real-time jobs. We implement UpPreempt in YARN to evaluate its performance. The evaluation with various workloads shows that our proposed scheduling strategy can achieve a good trade-off between the overtime ratio of real-time jobs and the completion time of batch jobs without reducing the resource utilization of the cluster.

To reduce the computation complexity of the optimization algorithm used in energy management of a multi-microgrid system, an energy optimization management method based on model predictive control is presented. The idea of decomposition and coordination is adopted to achieve the balance between power supply and user demand, and the power supply cost is minimized by coordinating surplus energy in the multi-microgrid system. The energy management model and energy optimization problem are established according to the power flow characteristics of microgrids. A dual decomposition approach is imposed to decompose the optimization problem into two parts, and a distributed predictive control algorithm based on global optimization is introduced to achieve the optimal solution by iteration and coordination. The proposed method has been verified by simulation, and simulation results show that the proposed method provides the demanded energy to consumers in real time, and improves renewable energy efficiency. In addition, the proposed algorithm has been compared with the particle swarm optimization (PSO) algorithm. The results show that compared with PSO, the proposed method has better performance, faster convergence, and significantly higher efficiency.

With the maturity of Internet and rapid development of the Mobile Internet, the network protocols drafted by IETF, 3GPP, 3GPP2 and other standard organizations grow massively, and at the same time attacks against the network protocols are also increasing rapidly. The Robustness test for the network protocols is becoming increasingly important, since the protocols and applications will be absolutely safe if the protocols have been tested according to the requirements of the Robustness Test. But Robustness Test is different from Conformance Test, Interoperability Test and Performance Test in its requirement of a huge number of test cases, and the number of test cases is more than 2^320 for a packet with 40-Byte protocol header, thus, it's too difficult to generate all the test cases for the serial algorithm. In this paper a parallel algorithm based on MapReduce is proposed, where an original input packet is divided into different fields and the Map function processes on each single field and the Reduce function reassembles the processed fields to a new completed packet. Finally, experiment results show the parallel algorithm is far better than the serial algorithm when generating large data set. In addition, the scalability of the cluster is also verified.

In the existing large-scale performance test of IPsec tunnel, it often needs special software and hardware. To solve the problem, this article proposed a new method, in which packet was encapsulated in user space, and a multi-tunnel controller was designed and implemented with the method of FSM(finite state machine), which controlled the negotiation and establishment of multiple tunnel, including L2tp, IKEv1, IKEv2, IKEv2+EAP and L2tp Over IPsec. Libpcap was used as the bottom layer driver of package, and the application of zero copy technique had reduced system cost immensely. At last, the result of the experiment verified the performance of the IKEv1 tunnel on Tunnel-mode and Transport-mode.