Scheduling Multi-workflows over Edge Computing Resources with Time-Varying Performance, A Novel Probability-Mass Function and DQN-Based Approach

Liu, Huan; Ma, Yuyin; Chen, Peng; Xia, Yunni; Ma, Yong; Zheng, Wanbo; Li, Xiaobo

doi:10.1007/978-3-030-59618-7_13

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…In addition, this paper does not pay attention to the security problem of workflow scheduling in MEC. Liu [28] proposes a novel maximum probability function and deep Q network-based multiworkflow scheduling scheme to solve the scheduling problem in multiuser edge computing environment, which can find a high-quality workflow scheme in a dynamic environment. However, this paper does not pay attention to the security problem of workflow scheduling in dynamic MEC.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

Huang

Xiang

et al. 2021

Security and Communication Networks

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Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

Huang

Xiang

et al. 2021

Security and Communication Networks

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“…By deploying computational access points (CAPs) at the network edge, users can offload their computational tasks to the nearby CAPs through reasonable task partition and offloading, in order to achieve a low latency and energy consumption [9][10][11]. In this direction, the authors in [12][13][14][15][16] investigated a multiuser multi-CAP MEC network, and proposed a deep Q-network (DQN) based offloading strategy for the task offloading. Moreover, the system cost was studied in [17][18][19][20] in terms of a combination of latency and energy consumption, where a joint optimization framework of offloading decision and resource allocation was proposed to enhance the network performance.…”

Section: Introductionmentioning

confidence: 99%

DQN Based Mobile Edge Computing for Smart Internet of Vehicle

Zhang

Zhou

Xia

et al. 2022

Preprint

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In this paper, we investigate a multiuser mobile edge computing (MEC)-aided smart internet of vehicle (IoV) network, where one edge server can help accomplish the intensive computational tasks from the vehicular users. For the MEC networks, most of existing works mainly focus on minimizing the system latency to guarantee the user's quality of service (QoS) through designing some offloading strategies, which however fail to consider the pricing from the server and hence fail to take into account the the budget constraint from the users. To address this issue, we jointly incorporate the budget constraint into the system design of the MEC based IoV networks, and then propose a joint deep reinforcement learning (DRL) approach combined with the convex optimization algorithm. Specifically, a deep Q-network (DQN) is firstly used to make the offloading decision, and then the Lagrange multiplier method is employed to allocate the computational capability of the server to multiple users. Simulations are finally presented to demonstrate that the proposed schemes outperforms the conventional ones. In particular, the proposed scheme can effective reduce the system latency up to 56% compared to the conventional schemes.

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“…Recently, the edge computing paradigm has evolved as an increasingly popular force for supporting and enabling business process and scientific workflow execution [3][4][5]. A workflow is a set of dependent or independent tasks illustrated as a directed acyclic graph (DAG) [6][7][8], in which the nodes indicate the tasks and a directed arch represents the interdependency among the corresponding tasks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Coevolutionary Approach to Reliability Guaranteed Multi-Workflow Scheduling upon Edge Computing Infrastructures

Wang

Zheng

Chen

et al. 2020

Security and Communication Networks

Self Cite

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Recently, mobile edge computing (MEC) is widely believed to be a promising and powerful paradigm for bringing enterprise applications closer to data sources such as IoT devices or local edge servers. It is capable of energizing novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. Nevertheless, various challenges, especially the reliability-guaranteed scheduling of multitask business processes in terms of, e.g., workflows, upon distributed edge resources and servers, are yet to be carefully addressed. In this paper, we propose a novel edge-environment-based multi-workflow scheduling method, which incorporates a reliability estimation model for edge-workflows and a coevolutionary algorithm for yielding scheduling decisions. The proposed approach aims at maximizing the reliability, in terms of success rates, of services deployed upon edge infrastructures while minimizing service invocation cost for users. We conduct simulative experimental case studies based on multiple well-known scientific workflow templates and a well-known dataset of edge resource locations as well. Simulative results clearly suggest that our proposed approach outperforms traditional ones in terms of workflow success rate and monetary cost.

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Scheduling Multi-workflows over Edge Computing Resources with Time-Varying Performance, A Novel Probability-Mass Function and DQN-Based Approach

Cited by 6 publications

References 33 publications

Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

DQN Based Mobile Edge Computing for Smart Internet of Vehicle

A Novel Coevolutionary Approach to Reliability Guaranteed Multi-Workflow Scheduling upon Edge Computing Infrastructures

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