A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing

Sang, Yongxuan; Cheng, Junqiang; Wang, Bo; Chen, Ming

doi:10.7717/peerj-cs.851

Cited by 15 publications

(7 citation statements)

References 49 publications

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“…In simulated EECC systems, where the simulation parameters are set referring to [18], [21], [23], [15] and reality, there are ten devices, five ES, and ten types of CS. The core number of devices, ES, and CS are set randomly in ranges of [2,8], [4,32], and [1,8], respectively.…”

Section: A Experiments Environmentmentioning

confidence: 99%

“…To improve service quality and resource efficiency in EECC environments, several works focused on addressing the task offloading problem. Sang et al [21] proposed a heuristic offloading algorithm to improve the cooperativeness of EECC resources. They used cloud resources for processing offloaded tasks at first, and rescheduled some tasks from the cloud to ES and devices to improve overall performance.…”

Section: Related Workmentioning

confidence: 99%

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An Improved Genetic Algorithm with Chromosome Replacement and Rescheduling for Task Offloading

Fu,

Li,

Han

et al. 2023

IJACSA

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End-Edge-Cloud Computing (EECC) has been applied in many fields, due to the increased popularity of smart devices. But the cooperation of end devices, edge and cloud resources is still challenge for improving service quality and resource efficiency in EECC. In this paper, we focus on the task offloading to address the challenge. We formulate the offloading problem as mixed integer nonlinear programming, and solve it by Genetic Algorithm (GA). In the GA-based offloading algorithm, each chromosome is the code of a offloading solution, and the evolution is to iteratively search the global best solution. To improve the performance of GA-based task offloading, we integrate two improvement schemes into the algorithm, which are the chromosome replacement and the task rescheduling, respectively. The chromosome replacement is to replace the chromosome of every individual by its better offspring after every crossing, which substitutes the selection operator for population evolution. The task rescheduling is rescheduling each rejected task to available resources, given offloading solution from every chromosome. Extensive experiments are conducted, and results show that our proposed algorithm can improve upto 32% user satisfaction, upto 12% resource efficiency, and upto 35.3% processing efficiency, compared with nine classical and up-to-date algorithms.

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Section: A Experiments Environmentmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Improved Genetic Algorithm with Chromosome Replacement and Rescheduling for Task Offloading

Fu,

Li,

Han

et al. 2023

IJACSA

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“…Therefore, it is highly desirable to lower the latency of execution for user's mobility. A lightweight Heuristic algorithm described in paper 115 that is helpful to get accurate results in short time.…”

Section: Open Issues In Fog Computingmentioning

confidence: 99%

A systematic review of task scheduling approaches in fog computing

Bansal

Aggarwal

2022

Trans Emerging Tel Tech

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Due to increased use of IoT devices and data sensors a huge amount of data is being produced for processing in real-time. Fog computing has evolved as a solution for fast processing of data. To complete the data processing as per requirement of user, it is processed at fog nodes which are near the user. To complete work in a specified time with limited resources, task scheduling is performed. With the increased amount of data to be processed, the completion of task within given time in fog computing is a major challenge. So, scheduling of tasks and resources is a very important issue. A lot of research has been undertaken in recent years. In this survey, authors have reviewed various task scheduling algorithms suggested by researchers to meet the user requirements.The focus of this article is on diverse scheduling techniques being deployed in fog computing. An effort has been made to classify existing approaches, research issues and determine significant issues existing in this field. There are four major scheduling categories that are used by researchers namely, static, dynamic, heuristic, and hybrid. As per this study, 17% researchers deployed static, 23% dynamic, 47% heuristic, and 13% hybrid approaches, respectively.Analysis shows that for QoS (Quality of Service) parameters 19% researchers are focused on response time, 18% on cost and energy consumption, and 16% on makespan. For QoS parameter other factors have much smaller contribution in comparison to the above factors. In the tools used, researchers observed that 40% of the researches have used iFogSim, according to literature. Besides, this research article also discusses open issues and future work in the field of fog computing. This article also underlines various open issues and future directions in the field of fog computing.

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“…Liu et al [23] presented a method with the idea of earliest finish time first, which reduces the amount of the data transfer by task redundant executions, to optimize the finish time of a workflow. Sang et al [24] proposed a heuristic task offloading method to optimize the user satisfaction with deadline constraints. The heuristic methods usually take a little time but have limited performance.…”

Section: Related Workmentioning

confidence: 99%

A Particle Swarm Optimization With Lévy Flight for Service Caching and Task Offloading in Edge-Cloud Computing

Gao

Tang

et al. 2022

IEEE Access

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Edge-cloud computing is an efficient approach to address the high latency issue in mobile cloud computing for service provisioning, by placing several computing resources close to end devices. To improve the user satisfaction and the resource efficiency, this paper focuses on the task offloading and service caching problem for providing services by edge-cloud computing. This paper formulates the problem as a constrained discrete optimization problem, and proposes a hybrid heuristic method based on Particle Swarm Optimization (PSO) to solve the problem in polynomial time. The proposed method, LMPSO, exploit PSO to solve the service caching problem. To avoid PSO trapping into local optimization, LMPSO adds lévy flight movement for particle updating to improve the diversity of particle. Given the service caching solution, LMPSO uses a heuristic method with three stages for task offloading, where the first stage tries to make full use of cloud resources, the second stage uses edge resources for satisfying requirements of latency-sensitive tasks, and the last stage improves the overall performance of task executions by re-offloaded some tasks from the cloud to edges. Simulated experiment results show that LMPSO has upto 156% better user satisfaction, upto 57.9% higher resource efficiency, and upto 155% greater processing efficiency, in overall, compared with other seven heuristic and meta-heuristic methods.

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A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing

Cited by 15 publications

References 49 publications

An Improved Genetic Algorithm with Chromosome Replacement and Rescheduling for Task Offloading

An Improved Genetic Algorithm with Chromosome Replacement and Rescheduling for Task Offloading

A systematic review of task scheduling approaches in fog computing

A Particle Swarm Optimization With Lévy Flight for Service Caching and Task Offloading in Edge-Cloud Computing

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