A Novel Approach to Cost-Efficient Scheduling of Multi-workflows in the Edge Computing Environment with the Proximity Constraint

Ma, Yuyin; Zhang, Junyang; Wang, Shu; Xia, Yunni; Chen, Peng; Wu, Lei; Zheng, Wanbo

doi:10.1007/978-3-030-38991-8_43

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…The main idea and the research road-map of Edge Intelligence were provided. VOLUME 8, 2020 In 2020, a scheduling method has been proposed [63] to address the multi-workflow scheduling problem with proximity constraint in the Edge Computing environment. The main aim is to minimise costs.…”

Section: B Related Workmentioning

confidence: 99%

Edge on Wheels With OMNIBUS Networking for 6G Technology

Ergen

Inan²,

Ergen

et al. 2020

IEEE Access

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In recent years, both the scientific community and the industry have focused on moving computational resources with remote data centres from the centralized cloud to decentralised computing, making them closer to the source or the so called ''edge'' of the network. This is due to the fact that the cloud system alone cannot sufficiently support the huge demands of future networks with the massive growth of new, time-critical applications such as self-driving vehicles, Augmented Reality/Virtual Reality techniques, advanced robotics and critical remote control of smart Internet-of-Things applications. While decentralised edge computing will form the backbone of future heterogeneous networks, it still remains at its infancy stage. Currently, there is no comprehensive platform. In this article, we propose a novel decentralised edge architecture, a solution called OMNIBUS, which enables a continuous distribution of computational capacity for end-devices in different localities by exploiting moving vehicles as storage and computation resources. Scalability and adaptability are the main features that differentiate the proposed solution from existing edge computing models. The proposed solution has the potential to scale infinitely, which will lead to a significant increase in network speed. The OMNIBUS solution rests on developing two predictive models: (i) to learn timing and direction of vehicular movements to ascertain computational capacity for a given locale, and (ii) to introduce a theoretical framework for sequential to parallel conversion in learning, optimisation and caching under contingent circumstances due to vehicles in motion.INDEX TERMS Edge computing, 5G, 6G, V2X, ubiquitous AI, distributed AI, multi-access edge computing (MEC).

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Section: B Related Workmentioning

confidence: 99%

Edge on Wheels With OMNIBUS Networking for 6G Technology

Ergen

Inan²,

Ergen

et al. 2020

IEEE Access

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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

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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

Liu

Chen

et al. 2020

Lecture Notes in Computer Science

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A Novel Approach to Cost-Efficient Scheduling of Multi-workflows in the Edge Computing Environment with the Proximity Constraint

Cited by 6 publications

References 24 publications

Edge on Wheels With OMNIBUS Networking for 6G Technology

Edge on Wheels With OMNIBUS Networking for 6G Technology

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

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

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