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

Peng, Kai; Zhu, Maosheng; Zhang, Yiwen; Liu, Lingxia; Zhang, Jie; Leung, Victor C. M.; Zheng, Lixin

doi:10.1186/s13638-019-1526-x

Cited by 52 publications

(16 citation statements)

References 50 publications

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“…Various algorithms were designed to handle integration of technologies to handle the anomalies and also to enhance the performance. Xu et al [24] combined the technologies of edge computing, wireless technologies and sensors to develop a time-efficient data offloading method to model and preserve the privacy among users of the customers in networking environment. The model prevented leakages in the edge computing data transfers.…”

Section: Anomalies In Integration Of Technologiesmentioning

confidence: 99%

Conceptual Analytics on Integration of Network Technologies with Crowdsourcing Infrastructure

Sivachandiran¹,

Jagan²,

Mohammed³

2021

Atlantis Highlights in Computer Sciences

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Technology seeks a new dimension as collaboration of various methods and technologies in transmission of data. Crowdsourcing enables the availability of huge data over a variety of networks. It is important to understand the needs of the management and monitoring of such data over a long distance and among a huge population especially among a big crowd like that of temples and other public places. Hence integration of network technologies has to be considered for effective attainment of data transfer with high speed and better performance that includes latency. The paper conducts analytics of various collaborated network technologies that was utilised to handle huge data from a crowdsourced network. The network technologies considered for the reviews are Edge computing, Fog computing, IoT based sensors, mobile technologies and cloud-based platforms. After significant analysis of all the research works, it was identified that edge computing has been the chief network technology used to transmit crowdsourced data with speed and efficiency. Cloud platforms assist the transferred data to be stored with privacy and security. The IoT sensors and Fog computing helped in detecting the data and also to increase the bandwidth and improve latency problems. The research gap identified was to enhance the speed and efficiency of transmission in public related problems like surveillance and handling public or social media database which seemed to be more challenging and encouraging future research works.

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Section: Anomalies In Integration Of Technologiesmentioning

confidence: 99%

Conceptual Analytics on Integration of Network Technologies with Crowdsourcing Infrastructure

Sivachandiran¹,

Jagan²,

Mohammed³

2021

Atlantis Highlights in Computer Sciences

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“…In our previous work, we have done some studies on computing offloading in MEC environment [34] and have proposed a multiobjective optimization of energy consumption and cost for Was [48]. Different from the existing research, we use a new WAs' model to evaluate complex tasks, which consist of the time-constrained workflows and the concurrent workflows.…”

Section: Related Workmentioning

confidence: 99%

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

Peng

Zhao

Xue³

et al. 2020

Complexity

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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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“…Usually, these applications are timesensitive, demand intensive computation and highenergy consumption [7]. Due to the physical size constraint, however, mobile devices are in general resourceconstrained, having limited computation resources and limited battery life [8,9]. Therefore, how to solve the contradiction between the need of performing complex tasks and the limited resource in mobile devices becomes an important challenge.…”

Section: Introductionmentioning

confidence: 99%

A novel edge server selection method based on combined genetic algorithm and simulated annealing algorithm

Zhang

Peng

et al. 2020

Automatika

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Mobile edge computing is a new paradigm which provides cloud computing capabilities at the edge of pervasive radio access networks in close proximity to users. The problem of edge server selection in mobile edge environment in terms of user's overhead is investigated in this paper. Due to the limited resources of edge server, we firstly study the task completion probability of edge servers. Secondly, we formally model the problem of edge server selection in terms of time latency and energy consumption. More especially, the computation overhead method for completing the task in cases of both service migration and non-migration is investigated. Then, a new optimized edge server selection algorithm, called combined Genetic algorithm and simulated Annealing algorithm for edge Server Selection (GASS) is designed. Finally, a series of experiments on a real-word data-trace are conducted to evaluate the performance of GASS. The results show that GASS can effectively minimize the overhead of the user and outperform traditional heuristic algorithms.

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

Cited by 52 publications

References 50 publications

Conceptual Analytics on Integration of Network Technologies with Crowdsourcing Infrastructure

Conceptual Analytics on Integration of Network Technologies with Crowdsourcing Infrastructure

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

A novel edge server selection method based on combined genetic algorithm and simulated annealing algorithm

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