Developing Energy-Aware Task Allocation Schemes in Cloud-Assisted Mobile Workflows

Gao, Bo; He, Ligang; Lu, Xin; Chang, Cheng; Li, Kenli

doi:10.1109/cit/iucc/dasc/picom.2015.188

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…e algorithm can perform the unloading well even when there are a large number of users. Gao et al [63] built a quadratic binary program, which is able to assign tasks in mobile cloud computing environment. Two algorithms are presented to obtain the optimal solution.…”

Section: Mecmentioning

confidence: 99%

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Wang

Sun

et al. 2020

Complexity

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With an increase of service users’ demands on high quality of services (QoS), more and more efficient service computing models are proposed. The development of cloud computing, fog computing, and edge computing brings a number of challenges, e.g., QoS optimization and energy saving. We do a comprehensive survey on QoS optimization and energy saving in cloud computing, fog computing, edge computing, and IoT environments. We summarize the main challenges and analyze corresponding solutions proposed by existing works. This survey aims to help readers have a deeper understanding on the concepts of different computing models and study the techniques of QoS optimization and energy saving in these models.

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Section: Mecmentioning

confidence: 99%

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Wang

Sun

et al. 2020

Complexity

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“…Hence, we must make some constraints in partial execution time, that is, tasks should be completed in a required time, denoted as (9); Constraint 3 (Fixed Tasks): in fact, there are some mobile cloud tasks only can be executed on the cloud or on the device, which have no choices to choose the other way. We defined these special tasks as fixed tasks and divide them into two categories: set M denotes the tasks only can be executed on the mobile device and set C denotes the tasks only can be executed on the cloud serve, illustrated as (10); Therefore, the optimization resource allocation problem can be further formulated as a minimization problem of the UtilityCost with constraints (7)- (12). The objective is to find the optimal offloading decision X that can minimize the UtilityCost in (7).…”

Section: Eresource Allocation Optimization Modelmentioning

confidence: 99%

“…Recently, a lot of attention has been paid to design energy efficient resource allocation strategies for mobile cloud workflow based on those frameworks mentioned above [26,27]. These strategies can be categorized into three types based on their objectives: enhancing QoS, saving energy or the hybrid [28][29][30][31][32][33]. The work in [39] considers the allocation problem in an edge cloud computing system as an M/M/c queue and solves the problem form two angles: the flat deployment and the hierarchical deployment to minimize the overall average response time of the system applications, which doesn't consider the system energy consumption.…”

Section: Virelated Workmentioning

confidence: 99%

“…The work in [39] considers the allocation problem in an edge cloud computing system as an M/M/c queue and solves the problem form two angles: the flat deployment and the hierarchical deployment to minimize the overall average response time of the system applications, which doesn't consider the system energy consumption. The work in [28] systematically analyzes the characteristics of cloud-assisted mobile application workflows and indicates that the task allocation problem becomes a critical step in deciding the energy-footprint of the workflow. The authors construct a quadratic binary program to model the task allocation problem in MCC and then propose an implementation of the simulated annealing algorithm and the greedy autonomous offload algorithm to find the approximate optimal solution with minimum energy, which ignores the execution time of the workflow.…”

Section: Virelated Workmentioning

confidence: 99%

“…A mobile cloud workflow in this paper is an application that can be recognized as a set of tasks in a partial order to achieve a specific business goal (e.g. an order processing workflow in an online sale system, and a travel approval workflow in a mobile office system) using a group of mobile devices and cloud servers [10]. Generally speaking, all the tasks can be executed either on the mobile locally or on the cloud servers remotely in a specific order determined by the workflow logic.…”

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confidence: 99%

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EERA: An Energy-Efficient Resource Allocation Strategy for Mobile Cloud Workflows

2020

IEEE Access

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Mobile cloud computing (MCC) which can invoke cloud services and offload tasks from the mobile device to the cloud has become an appropriate computing paradigm to provide many useful and complex workflow applications. However, offloading tasks needs extra communication time and energy, which leads to a conflict between saving the energy and improving the QoS. Thus, how to allocate these different resources (mobile or cloud) efficiently during the workflow execution process to optimize the system energy consumption under the deadline constraint is still a huge challenge in MCC. To address such an issue, this paper proposes an energy-efficient resource allocation strategy for workflow applications in MCC. Firstly, we formulate the resource allocation problem into an optimization problem with the goal of minimizing the UtilityCost which represents the trade-off between energy consumption and the total execution time. Then, we use an energy-efficient resource allocation algorithm shortened as EERA to solve the problem. The algorithm firstly finds all the Partial critical paths of the workflow graph to segment the whole optimization problem into several local problems, and then uses the discrete particle swarm optimization algorithm to find the local optimal solution for every local problem, finally the local optimal solution is integrated to obtain the global one. In addition, we have adapted several representative algorithms for evaluation and comparison purpose. Comprehensive experimental results show that our EERA based strategy achieves the best overall performance on three key measurements including the energy consumption of the device, the execution time of the workflow and the UtilityCost. INDEX TERMS Workflow, resource allocation, energy saving, task offloading, mobile cloud computing I.INTRODUCTION

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A Survey of QoS Optimization and Energy Saving in Cloud, Edge and IoT

Qu¹,

Wang²,

Sun³

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Developing Energy-Aware Task Allocation Schemes in Cloud-Assisted Mobile Workflows

Cited by 8 publications

References 25 publications

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

EERA: An Energy-Efficient Resource Allocation Strategy for Mobile Cloud Workflows

A Survey of QoS Optimization and Energy Saving in Cloud, Edge and IoT

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