Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.

AN ADAPTIVE AQM TO PROVIDE DELAY GUARANTEES FOR DIFFERENTIATED SERVICES

RIO(RED with IN and OUT) is the primary queue management mechanism proposed for assured forwarding in the DiffServ (Differentiated Service) framework. Although RIO can generally provide bandwidth guarantees, its queuing delay is sensitive to the traffic load. This paper presents a qualitative explanation for its origin. As a solution, an Adaptive RIO for Delay (ARIO-D) is proposed to provide guaranteed delay for multimedia traffic. Simulation results show that by trading loss for delay, ARIO-D can effectively improve the robustness of RIO under different and dynamic traffic, and provide stable and differentiated performance of queuing delay without any degradation in performance of throughput.

Providing Guaranteed Network Performance across Tenants:Advances,Challenges and Opportunities

The varied network performance in the cloud hurts application performance.This increases the tenant’s cost and becomes the key hindrance to cloud adoption.It is because virtual machines(VMs)belonging to one tenant can reside in multiple physical servers and communication interference across tenants occasionally occurs when encountering network congestion.In order to prevent such unpredictability,it is critical for cloud providers to offer the guaranteed network performance at tenant level.Such a critical issue has drawn increasing attention in both academia and industry.Many elaborate mechanisms are proposed to provide guaranteed network performance,such as guaranteed bandwidth or bounded message delay across tenants.However,due to the intrinsic complexities and limited capabilities of commodity hardware,the deployment of these mechanisms still faces great challenges in current cloud datacenters.Moreover,with the rapid development of new technologies,there are new opportunities to improve the performance of existing works,but these possibilities are not under full discussion yet.Therefore,in this paper,we survey the latest development of the network performance guarantee approaches and summarize them based on their features.Then,we explore and discuss the possibilities of using emerging technologies as knobs to upgrade the performance or overcome the inherent shortcomings of existing advances.We hope this article will help readers quickly Received:Apr.07,2020 Revised:Oct.23,2020 Editor:Haifeng Zheng understand the causes of the problems and serve as a guide to motivate researchers to develop innovative algorithms and frameworks.

Multi-agent reinforcement learning for edge information sharing in vehicular networks

To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.