Computation offloading for multimedia workflows with deadline constraints in cloudlet-based mobile cloud

Huang, Tao; Ruan, Feng; Xue, Shengjun; Qi, Lianyong; Duan, Yucong

doi:10.1007/s11276-019-02053-z

Cited by 8 publications

(5 citation statements)

References 41 publications

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“…A ready partitioning stage, which partitions each ready job taking into account the effective state of the network. The Cloudlet environment 39 and the applications available in References 40,41 have been used for simulation. By evaluating the method, the amount of energy consumption is reduced.…”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Golmohammadi

Tabbakh

Ghaemi

2023

Trans Emerging Tel Tech

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The advent of distributed computing and mobile clouds made it possible to transfer and distribute the heavy processes of complex workflows to the cloud. Managing and with the help of the mobile cloud, virtualization and shared resources managing the executing large‐scale workflows is possible. Since virtual resources are used, scheduling and resource allocation are important and key research topics in public cloud environment and mobile clouds. The cloud model of mobile devices with public or virtual cloud, which consists of temporary mobile devices, is an interesting topic to be investigated. Workflow scheduling and availability of resources in mobile clouds by high mobility and Energy efficient, it is one of the challenges and problems that must be investigated one of the main challenges in this research area. The purpose of scheduling algorithms is to improve service quality criteria by observing the constraints. In this article, the main goal is to extensively review the scheduling algorithms for the complex scientific workflow in public and mobile clouds. Furthermore, a review of the use of heuristic and meta‐heuristic techniques in dynamic and static states, with various constraints for task scheduling in cloud and mobile computing has been performed. This article accomplishes a review of using existing techniques for scheduling tasks in mobile clouds. We also present a comprehensive analysis and systematic comparison between these scheduling algorithms.

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Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Golmohammadi

Tabbakh

Ghaemi

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Zhu et al [24] focused on how to build an efficient offloading mechanism and proposed a deep Q-learning based offloading algorithm, which greatly shortened the energy consumption and the execution time of service workflow. Huang et al [39] designed an offloading method based on nondominated sorting differential evolution.…”

Section: Related Workmentioning

confidence: 99%

“…However, the capacity of the cloudlet is generally limited [35][36][37]. If multiple tasks request for computing resources, and the number of the tasks exceeds the capacity of a cloudlet, the queue latency will be generated [38][39][40]. Furthermore, as the number of tasks increases, serious network congestion will occur between MDs and the cloudlet [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

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Energy- and Resource-Aware Computation Offloading for Complex Tasks in Edge Environment

Peng

Zhao

Xue³

et al. 2020

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Mobile users typically have a series of complex tasks consisting of time-constrained workflows and concurrent workflows that need to be processed. However, these tasks cannot be performed directly locally due to resource limitations of the mobile terminal, especially for battery life. Fortunately, mobile edge computing (MEC) has been recognized as a promising technology which brings abundant resource at the edge of mobile network enabling mobile devices to overcome the resource and capacity constraints. However, edge servers, such as cloudlets, are heterogeneous and have limited resources. Thus, it is important to make an appropriate offloading strategy to maximize the utility of each cloudlet. In view of this, the time consumption and energy consumption of mobile devices and resource utilization of cloudlets have been taken into consideration in this study. Firstly, a multiconstraint workflow mode has been established, and then a multiobjective optimization mode is formulated. Technically, an improved optimization algorithm is proposed to address this mode based on Nondominated Sorting Genetic Algorithm II. Both extensive experimental evaluations and detailed theoretical analysis are conducted to show that the proposed method is effective and efficiency.

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“…However, their approach focuses on the single-user and single-objective optimization scenario. Huang et al [48] proposed a computation offloading method for multimedia workflows with deadline constraints in cloudlet-based mobile cloud. The objective of them is to minimize the energy consumption with the constraints of meeting the deadline of each multimedia workflow.…”

Section: Related Workmentioning

confidence: 99%

An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing

Peng

Zhu

Zhang

et al. 2019

J Wireless Com Network

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Mobile edge computing is becoming a promising computing architecture to overcome the resource limitation of mobile devices and bandwidth bottleneck of the core networks in mobile cloud computing. Although offloading applications to the cloud can extend the performance for the mobile devices, it may also lead to greater processing latency. Usually, the mobile users have to pay for the cloudlet resource or cloud resource they used. In this paper, we bring a thorough study on the energy consumption, time consumption, and cost of using the resource of cloudlet and cloud for workflow applications in mobile edge computing. Based on theoretical analysis, a multi-objective optimization model is established. In addition, a corresponding multi-objective computation offloading method based on non-dominated sorting genetic algorithm II is proposed to find the optimal offloading strategy for all the workflow applications. Finally, extensive experimental evaluations are performed to show that our proposed method is effective and energy-and cost-aware for workflow applications in MEC.

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Computation offloading for multimedia workflows with deadline constraints in cloudlet-based mobile cloud

Cited by 8 publications

References 41 publications

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Energy- and Resource-Aware Computation Offloading for Complex Tasks in Edge Environment

An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing

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