The integration of vehicle edge computing (VEC) and microservice architectures improves real-time data processing and computational optimization in the Internet of Vehicles. Specifically, in high-traffic areas, the dynamic deployment and request routing of microservices with complex data dependencies within vehicle clusters can effectively reduce the computational load on edge devices. However, existing research has primarily focused on efficiently utilizing vehicular resources, overlooking the dynamic nature of vehicular cluster networks and the additional communication costs arising from data dependencies between microservices. Therefore, we propose a joint service deployment and request routing problem for vehicle collaboration. We first design a vehicle-road collaborative service framework assisted by temporary vehicle workers, expanding available resources and coverage by deploying microservice instances on selected temporary vehicle nodes. Second, recognizing the dependency between service deployment and request routing, we propose a dual-timescale service-deployment and request-routing policy. On a long timescale, a microservice-aware deployment method optimizes request selection and response time. On a short timescale, we propose a decentralized, swarm intelligence-based collaborative request routing method that constructs a response threshold model through agent interaction, thereby enhancing the collaborative optimization capability of the system. Finally, experimental results using real datasets show that our method outperforms other approaches in reducing request response time when communication costs are taken into account.

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.

Mobile Edge Computing (MEC) integrates computing, storage and other resources on the edge of the network and constructs a unified user service platform. Then, according to the principle of nearest service, MEC responds to the task requests of the edge nodes in time and effectively processes them. In MEC, edge servers are virtualized into several slots so that resources can be shared among different mobile users. However, there are many unpredictable risks in MEC, these risks can cause edge servers to fail, the virtual machine deployed in the server slot fails and the task cannot be executed normally. The introduction of primary-backup virtual machines solves this problem well. However, when the primary virtual machine is working normally, its backup virtual machine is idle, this will result in a waste of resources. In order to improve the resource utilization of the system, this paper firstly overbooks the backup virtual machine reasonably, and then formulates the virtual machine deployment problem as a combinatorial optimization problem. Finally, Virtual Machine Deployment Algorithm (VMDA) is proposed based on genetic algorithm. With the increase of the number of algorithm iterations and the population size of the virtual machine deployment scheme, there may be more optimal virtual machine deployment scheme individuals in the population. Therefore, the algorithm can obtain the approximate optimal value of resource utilization within the risk range allowed by the system, and the algorithm is compared with two other typical bin packing algorithms. The results confirm that VMDA outperforms the other two algorithms.

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.

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.

The abnormal detection of the workload sequence is designed to achieve intelligent operation and management of the cloud platform to improve operational efficiency. Due to the diversity of workload sequence variation patterns in the large-scale cloud, it is difficult for the traditional abnormal detection methods to extract features effectively, which leads to detecting anomalies inaccurately. In this paper, a deep unsupervised anomaly sequence detection model with fusion of spatial and temporal features of workload sequence (TS-DeepSVDD) is proposed. To extract the spatial and temporal features of the workload sequence, the model introduces convolutional recurrent neural network (CRNN) to improve network architecture with deep support vector data description (DeepSVDD). First, a convolutional neural network (CNN) module extracts the spatial features of the workload sequence. Second, for the acquired spatial features vectors, the bidirectional long short-term memory (BiLSTM) module extracts temporal features. Finally, the features vectors training support vector data description (SVDD) classifier fuses with the deep features of the workload sequence spatial and temporal features. TS-DeepSVDD extracts the deep features of the workload sequence from the spatial and temporal dimensions. It achieves a comprehensive description of the inherent law of the workload sequence, increasing the differentiation between normal and abnormal sequences. The simulation dataset and Google trace dataset are used respectively for verification. The results show TS-DeepSVDD can detect different abnormal sequences more accurately than the traditional unsupervised anomaly detection methods.

As greater numbers of data-intensive applications are required to process big data in bandwidth-constrained datacenters with heterogeneous physical machines (PMs) and virtual machines (VMs), network core traffic is experiencing rapid growth. The VMs of a virtual cluster (VC) must be allocated as compactly as possible to avoid bandwidth-related bottlenecks. Since each PM/switch has a certain failure probability, a VC may not be executed when it meets with any PM/switch fault. Although the VMs of a VC can be spread out across different fault domains to minimize the risk of violating the availability requirement of the VC, this increases the network core traffic. Therefore, avoiding the decrease in availability caused by the heterogeneous PM/switch failure probabilities and bandwidth-related bottlenecks has been a constant challenge. In this paper, we first introduce a joint optimization function to measure the overall risk cost and overall bandwidth usage in the network core to allocate the same set of data-intensive applications. We then introduce an approach to maximize the value of the joint optimization function. Finally, we performed a side-by-side comparison with prior algorithms, and the experimental results show that our approach outperforms the other existing algorithms.

Cloud data center traffic is experiencing a rapid growth as more and more data-intensive applications are required to process big data in a cloud data center. Although fat-tree networks own rich path multiplicity, and have been widely adopted as network topologies in cloud data center networks to transmit vast bisection bandwidth, they lead to bandwidth-related bottlenecks. To address this issue, in this paper, we propose a traffic-aware virtual cluster reallocation approach via biogeography-based optimization to allocate some reallocated virtual machines (VMs) as compact as possible with those VMs in the same virtual clusters. In order to validate our approach, we build a system model to perform a thorough evaluation of its performance. Experimental results show that our proposed approach outperforms six existed approaches in term of total transmission cost, total processing time, and total network resource consumption.

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.

Exiting schemes rarely take into account coordinated problem among multiple virtual machines (VMs) which collectively complete a task, since if these VMs are uncoordinated, the execution results of a task are not correct. To solve the problem, we first exploit a proactive prediction scheme to predict the VM's status. Then, if VM's status is deteriorating, coordinated checkpoint is adopted to suspend the current task to search an optimal target host. Finally, we introduce an efficient heuristic algorithm to solve the optimal target host selection problem. The experimental results demonstrate the efficiency and effectiveness of our proposed approach.