Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

Alfakih, Taha; Hassan, Mohammad Mehedi; Gumaei, Abdu; Savaglio, Claudio; Fortino, Giancarlo

doi:10.1109/access.2020.2981434

Cited by 237 publications

(95 citation statements)

References 25 publications

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“…Most of them take delay as an optimization goal, but it is easy to result in the problem of load imbalance among computing nodes. Intelligent heuristic task scheduling algorithms mainly include Genetic Algorithm (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO), Simulated Annealing (SA), Bat algorithm, artificial immune algorithm, and Tabu Search (TS) [ 34 , 35 ]. These algorithms are based on heuristic rules to quickly get the solution of a problem, but they cannot guarantee the optimality of their solutions [ 36 ].…”

Section: Computing Task Scheduling Schemementioning

confidence: 99%

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Chen

Zhou

et al. 2021

Sensors

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In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge server, it can be overloaded, thereby resulting in the high processing delay of many computing tasks and unexpectedly high energy consumption. On the other hand, the resources in idle edge devices may be wasted and resource-rich cloud centers may be underutilized. Therefore, it is essential to explore a computing task collaborative scheduling mechanism with an edge server, a cloud center and edge devices according to task characteristics, optimization objectives and system status. It can help one realize efficient collaborative scheduling and precise execution of all computing tasks. This work analyzes and summarizes the edge computing scenarios in an edge computing paradigm. It then classifies the computing tasks in edge computing scenarios. Next, it formulates the optimization problem of computation offloading for an edge computing system. According to the problem formulation, the collaborative scheduling methods of computing tasks are then reviewed. Finally, future research issues for advanced collaborative scheduling in the context of edge computing are indicated.

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Section: Computing Task Scheduling Schemementioning

confidence: 99%

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Chen

Zhou

et al. 2021

Sensors

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“…ere have lots of proposals on task offloading in MEC. In [11], there were three offloading options, i.e., nearest edge server, adjacent edge server, and remote cloud. It proposed a Reinforcement Learning (RL)based algorithm to make the optimal offloading decision for minimizing system cost, including energy consumption and computing time delay.…”

Section: Task Offloading In Mecmentioning

confidence: 99%

Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking

Pan

Wang

Jing-sheng

2021

Mobile Information Systems

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The body health plays an important metric in people’s everyday life, and it directly determines whether people have the ability to preferably contribute to the society. In fact, the physical training is a universal sport to enhance the body health. Therefore, the evaluation and analysis of physical training become particularly significant. With the rapid development and emerging of new techniques and networking paradigms, the traditional offline physical training evaluation and analysis cannot be performed well. Instead, this paper uses Mobile Edge Computing (MEC) and Software-Defined Networking (SDN) to implement the evaluation and analysis of physical training, shortened for MSPT, where MEC is the new computing technique and SDN is the new networking paradigm. The proposed MSPT includes two parts. At first, the physical training data from different mobile devices are migrated into the edge server for computing according to the current condition, in which the game theory is used to complete the task scheduling. Then, SDN is responsible for the global scheduling in the centralized control manner, in which the multigranularity scheduling strategy is used to handle the traffic between the SDN controller and edge computing server. The experiments are driven by OMNet, including three aspects of evaluation, i.e., task offloading of MEC, traffic scheduling of SDN, and performance analysis of physical training, and the results show that the proposed MSPT has better performance than the corresponding baselines.

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“…Data-driven proactive management will be essentially supported by ML/AI techniques. These techniques, once deployed in future networked systems, should offer a new range of networking-based services such as smart routing in networks with a cross-layer design [59], task offloading and resource allocation [60], optimized operation of next-generation mobile networks [61], distributed storage and computation at the network edge [62], and accurate localization estimation of mobile robots [63]. In parallel with this expected network evolution, novel challenges such as privacy, e.g.…”

Section: Data-triggered Management Mechanismmentioning

confidence: 99%

Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV

Moura

Hutchison

2020

SN Appl. Sci.

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Cyber-Physical Systems (CPSs) are increasingly important in everyday applications including the latest mobile devices, power grids and intelligent buildings. CPS functionality has intrinsic characteristics including considerable heterogeneity, variable dynamics, and complexity of operation. These systems also typically have insufficient resources to satisfy their full demand for specialized services such as data edge storage, data fusion, and reasoning. These novel CPS characteristics require new management strategies to support the resilient global operation of CPSs. To reach this goal, we propose a Software Defined Networking based solution scaled out by Network Function Virtualization modules implemented as distributed management agents. Considering the obvious need for orchestrating the distributed agents towards the satisfaction of a common set of global CPS functional goals, we analyze distinct incentive strategies to enact a cooperative behavior among the agents. The repeated operation of each agent's local algorithm allows that agent to learn how to adjust its behavior following both its own experience and observed behavior in neighboring agents. Therefore, global CPS management can evolve iteratively to ensure a state of predictable and resilient operation.

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Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

Cited by 237 publications

References 25 publications

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking

Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV

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