With the continuous expansion of the power Internet of Things and the number of Smart Devices (SDs), the data generated by SDs has exponentially increased. The traditional cloud-based smart grid cannot meet the low latency and high reliability requirements of emerging applications. By moving computing, data, and services from the centralized cloud to Edge Servers (ESs), edge computing exhibits excellent performance in communication delay and traffic reduction. Simultaneously, service caching also shows attractive advantages in handling the surge in data traffic. This paper considers the joint optimization of computing offloading and service caching in edge computing-based smart grid, and formulates the problem as a Mixed-Integer Non-Linear Program, aiming to minimize the task cost of the system. The original problem is decomposed into an equivalent master problem and sub-problem, and a Collaborative Computing Offloading and Resource Allocation Method (CCORAM) is proposed to solve the problem, which includes two low-complexity algorithms. Specifically, a gradient descent allocation algorithm is first proposed to determine the computing resource allocation strategy, and then a game theory-based algorithm is proposed to determine the computing strategy. Simulation results show that CCORAM with low-complexity is very close to the optimal method, and performs much better than other benchmark methods.

Loosely coupled and highly cohesived microservices running in containers are becoming the new paradigm for application development. Compared with monolithic applications, applications built on microservices architecture can be deployed and scaled independently, which promises to simplify software development and operation. However, the dramatic increase in the scale of microservices and east-west network traffic in the data center have made the cluster management more complex. Not only does the scale of microservices cause a great deal of pressure on cluster management, but also cascading QoS violations present a substantial risk for SLOs (Service Level Objectives). In this paper, we propose a Microservice-Oriented Topology-Aware Scheduling Framework (MOTAS), which effectively utilizes the topologies of microservices and clusters to optimize the network overhead of microservice applications through a heuristic graph mapping algorithm. The proposed framework can also guarantee the cluster resource utilization. To deal with the dynamic environment of microservice, we propose a mechanism based on distributed trace analysis to detect and handle QoS violations in microservice applications. Through real-world experiments, the framework has been proved to be effective in ensuring cluster resource utilization, reducing application end-to-end latency, improving throughput, and handling QoS violations.

Mobile edge computing (MEC) reduces data service latency by pushing data to the network edge. However, due to the dynamic and diverse requests of mobile users, the problem of mobile edge caching is more complex than cloud caching. Therefore, the existing model-based caching strategies cannot be directly used in the mobile edge caching environment. Besides, when taking the cooperative storage relationship between neighbor edge servers into consideration, the caching problem becomes more difficult. To this end, we formulate an mobile edge caching problem to minimize the total latency in mobile edge computing. Firstly, a heuristic caching strategy is proposed to solve the mobile edge caching problem in the single-time-slot scenario. Then, with the consideration of users’ mobility and the correlation of files, we propose a caching strategy for the multiple-time-slot scenario based on multi-agent deep reinforcement learning. To address the cold start problem in deep reinforcement learning, we adopt the proposed heuristic caching strategy used in the single-time-slot scenario to further optimize the training results. Extensive experiments on generated data and real-world datasets are conducted to verify that the proposed edge caching strategies can achieve the minimum latency compared with the state-of-the-art strategies.

We consider the design and analysis of Data Center Network (DCN) architectures for interconnecting dual-port servers. Unlike existing works, we propose the concept of Normalized Switch Delay (NSD) to distinguish a server-to-server-direct hop and a server-to-server-via-switch hop, to unify the design of DCN architectures. We then consider a fundamental problem: maximizing the number of dual-port servers, given network diameter and switch port number; and give an upper bound on this maximum number. Two novel architectures are proposed: SWCube and SWKautz, based on the generalized hypercube and Kautz graph, respectively, which in most cases accommodate more servers than BCN [1], which was claimed to be the largest known architecture. Compared with three existing architectures, SWCube and SWKautz demonstrate various advantages. Analysis and simulations also show that SWCube and SWKautz have nice properties for DCNs, such as low diameter, good fault-tolerance, and capability of efficiently handling network congestion.

With the increase of Internet of vehicles (IoVs) traffic, the contradiction between a large number of computing tasks and limited computing resources has become increasingly prominent. Although many existing studies have been proposed to solve this problem, their main consideration is to achieve different optimization goals in the case of edge offloading in static scenarios. Since realistic scenarios are complicated and generally time‐varying, these studies in static scenes are imperfect. In this paper, we consider a collaborative computation offloading in a time‐varying edge‐cloud network, and we formulate an optimization problem with considering both delay constraints and resource constraints, aiming to minimize the long‐term system cost. Since the set of feasible solutions to the problem is nonconvex, and the complexity of the problem is very large, we propose a Q‐learning‐based approach to solve the optimization problem. In addition, due to the dimensional catastrophes, we further propose a DQN‐based approach to solve the optimization problem. Finally, by comparing our two proposed algorithms with typical algorithms, the simulation results show that our proposed approaches achieve better performance. Under the same conditions, by comparing our two proposed algorithms with typical algorithms, the simulation results show that our proposed Q‐learning‐based method reduces the system cost by about 49% and 42% compared to typical algorithms. And in the same case, compared with the classical two schemes, our proposed DQN‐based scheme reduces the system cost by 62% and 57%.