The large-scale utilization of cloud computing services for hosting industrial/enterprise applications has led to the emergence of cloud service reliability as an important issue for both cloud service providers and users. To enhance cloud service reliability, two types of fault tolerance schemes, reactive and proactive, have been proposed. Existing schemes rarely consider the problem of coordination among multiple virtual machines (VMs) that jointly complete a parallel application. Without VM coordination, the parallel application execution results will be incorrect. To overcome this problem, we first propose an initial virtual cluster allocation algorithm according to the VM characteristics to reduce the total network resource consumption and total energy consumption in the data center. Then, we model CPU temperature to anticipate a deteriorating physical machine (PM). We migrate VMs from a detected deteriorating PM to some optimal PMs. Finally, the selection of the optimal target PMs is modeled as an optimization problem that is solved using an improved particle swarm optimization algorithm. We evaluate our approach against five related approaches in terms of the overall transmission overhead, overall network resource consumption, and total execution time while executing a set of parallel applications. Experimental results demonstrate the efficiency and effectiveness of our approach.

Internet of Things (IoT) devices have become an integral part of our lives and are increasingly used in almost every field. Subsequently, there are a large number of latency-sensitive IoT applications (e.g., face recognition and autonomous driving) targeted for mobile edge computing environments. These IoT applications are often split into multiple collaborative tasks and offloaded onto containers or virtual machines (VMs) with certain failure rates and recovery rates. If these containers or VMs are not deployed in the same edge servers, the bandwidth resources of edge clouds must be consumed to transfer data. These factors increase the completion time of IoT applications to different degrees, and then affect their reliability level. Therefore, there exists equilibrium between the reliability level and bandwidth consumption. In this article, we investigate the equilibrium of minimizing the bandwidth consumption of IoT applications while maximizing the reliability level of these IoT applications during task offloading. We propose a multiobjective optimization problem, and transform it to a single-objective optimization problem. Furthermore, we introduce two efficient approaches to acquire two near-optimal solutions. The results of simulation experiments demonstrate that our proposed approaches can observably enhance the reliability level and reduce the bandwidth consumption of IoT applications compared with other related approaches. Meanwhile, we also make a comparative analysis of our proposed approaches.

With the rapid upgrading and explosive growth of Internet of Things (IoT) devices in mobile-edge computing, more and more IoT applications with high resource requirements are developed and utilized. Meanwhile, there are large quantities of edge nodes (e.g., switches and edge servers) with limited resources, higher operating costs, and certain failure probabilities in the mobile-edge computing environment. Therefore, when an IoT application is split into multiple collaborative tasks and offloaded into multiple edge clouds, there is an urgent need to increase the availability level of the task allocation scheme and the resource utilization of edge servers under the condition of certain communication delay. In this article, we first present a joint optimization objective to evaluate the unavailability level, communication delay, and resource wastage while allocating the same batch of IoT applications to multiple edge clouds. We then propose an approach to minimize the joint optimization objective under the condition of certain communication delay. Finally, we performed a comprehensive simulation experiment analysis to demonstrate that our proposed approach is superior to other related approaches.

Vehicle edge computing (VEC) offers users low-latency and high-reliability services by using computational resources at the network's edge. Nevertheless, because of inadequate infrastructure and limited resources, computation-intensive and delay-sensitive vehicle applications cannot be performed efficiently at the edge. Therefore, several studies have used the idle resources of parked vehicles to assist in computation offloading. In this paper, we propose a parked vehicle-assisted vehicle edge computing architecture considering multi-agent collaboration, including intelligent vehicles and edge servers. Additionally, we propose a framework for a parallel Internet of Vehicles (IoV) utilizing computational experiment. The service provider is assigned the role of owning VEC resources and recruiting parking vehicle resources. The model was constructed by using the resource consumption-service relationship of both offloading parties to ensure service quality. First, a Stackelberg game model was constructed based on the interaction between requesting vehicles and a service provider. The latter was the leader, and the requesting vehicles were the followers. The Nash equilibrium for optimal pricing and offloading allocations was attained and verified, and a distributed gradient-based equilibrium algorithm was designed to solve the Stackelberg game model and obtain the final decision through mutual communication. The method also protects the privacy of participants and respects the willingness of requesting vehicles to offload. Finally, the simulation experiments confirmed that the proposed algorithm can achieve game equilibrium. Furthermore, it outperformed state-of-the-art algorithms in improving the service provider's utility.

Unmanned aerial vehicles (UAVs) will be a vital part of the massive Industrial Internet of Things (IIoT) in the 5G and 6G paradigms. The UAVs are required to collaborate with each other to deal with some computation-intensive IIoT applications in an autonomous UAV system. However, due to the limited processing capacity of UAVs, they are occasionally unable to handle certain tasks adequately (e.g., crowdsensing). Therefore, it is an important issue to realize efficient offloading of these computation-intensive IIoT applications. In this article, we first partition the computation-intensive IIoT application into a directed acyclic graph with multiple collaborative tasks. Then, we establish a joint optimization problem based on the models of the processor resources and energy consumption for the task offloading scheme. Third, we propose a cooperative resource allocation approach to optimize the joint optimization problem under the constraints of resource and communication latency, and then can migrate more computation-intensive tasks to the edge clouds. Finally, we build an aerial computing simulation system and make a comparative evaluation and analysis of our proposed cooperative resource allocation approach in terms of effectiveness and performance. The experimental results show that our proposed approach performs better than other related approaches.

The COVID-19 pandemic has elicited interest in the development of novel, low-cost solutions for tele-healthcare that patients can use to consult with doctors using consumer electronics (CEs). Android CEs typically have multiple network interfaces. Simultaneous access to multiple interfaces can greatly enhance users’ quality of experience (QoE). To enhance data transmission throughput and robustness in tele-healthcare systems, we propose a QoE-driven data communication framework which can cater to all types of healthcare apps. The framework includes a QoE-driven congestion controller (QCC) and a QoE-driven MPTCP (multipath transmission control protocol) scheduler (QMS). Different app types correspond to specific QoE parameters that influence the QCC and QMS for meeting diverse healthcare app requirements. No server-side modifications are needed for this framework; only changes on the Android client side are necessary. We implement and experiment with the framework on an Android 13 smartphone in a real environment. Experimental results confirm the framework’s effectiveness and availability, and show that the QCC and QMS reduce completion time for transferring small files by at least 4.71% and for transferring big file by at least 13.94% compared with existing solutions.