Green Cloud Computing for Multi Cell Networks

Masoudi, Meysam; Khamidehi, Behzad; Çavdar, Çiçek

doi:10.1109/wcnc.2017.7925672

Cited by 22 publications

(14 citation statements)

References 18 publications

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“…An application-aware wireless transmission scheduling algorithm was presented to minimize the energy consumption, subject to the application deadline. Masoudi et al [15] considered three practical constraints, i.e. the backhaul capacity, the maximum tolerable delay, and the interference level.…”

Section: Related Workmentioning

confidence: 99%

“…With the obtained completion times of all applications on all SMDs, the average completion time (ACT) of applications in the MEC system can be calculated using Eq. (15).…”

Section: F Problem Formulationmentioning

confidence: 99%

“…Given a solution x, its objective function values, F(x) = (fACT(x), fAEC(x)), can be calculated using Eqs. (15) and (16) in Section III-F.…”

Section: A Solution Representation and Evaluationmentioning

confidence: 99%

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A Multiobjective Computation Offloading Algorithm for Mobile-Edge Computing

Song

Xing

Luo

et al. 2020

IEEE Internet Things J.

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In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This paper studies the trade-off between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e. ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed. 1) The problem-specific population initialization scheme uses a latency-based execution location initialization method to initialize the execution location (i.e. either local SMD or MEC server) for each task. 2) The dynamic voltage and frequency scaling based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and meta-heuristics in terms of the convergence and diversity of the obtained nondominated solutions.

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Section: Related Workmentioning

confidence: 99%

“…With the obtained completion times of all applications on all SMDs, the average completion time (ACT) of applications in the MEC system can be calculated using Eq. (15).…”

Section: F Problem Formulationmentioning

confidence: 99%

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A Multiobjective Computation Offloading Algorithm for Mobile-Edge Computing

Song

Xing

Luo

et al. 2020

IEEE Internet Things J.

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“…The problem was formulated as a computation and transmits power minimization subject to latency and reliability constraints. In [11], the power minimization for the mobile devices by data offloading was investigated. Centralized and distributed algorithms for joint power allocation and channel assignment together with decision making were proposed.…”

Section: Related Workmentioning

confidence: 99%

QoE Aware and Cell Capacity Enhanced Computation Offloading for Multi-Server Mobile Edge Computing Systems with Energy Harvesting Devices

Zhao¹,

Du²,

Liu³

et al. 2019

EasyChair Preprints

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The increasing complexity of intelligent services requires new paradigm to overcome the problems caused by resource-limited mobile devices. Mobile edge computing systems with energy harvesting devices is such a promising technology. By offloading the computation tasks from the mobile devices to the MEC servers, users could experience services with low latency. In addition, energy harvesting technology releases the tension between high energy consumption of intelligent services and capacity-constrained mobile device batteries. However, in multi-user and multi-server scenarios where mobile devices can move arbitrarily, computation offloading strategies are faced with new challenges because of resource competition and server selection. In this paper, we will develop an intelligent computation offloading strategy. The quality of user experience cost and the cell capacity in terms of the ratio of computation tasks offloaded will be adopted as the performance metrics. An online algorithm, namely, the LODCO-Based Genetic Algorithm with Greedy Policy, will be proposed. Specifically, the algorithm is based on Lyapunov Optimization and the LODCO Algorithm. By choosing the execution mode among local execution, offloading execution and task dropping for each mobile device, our algorithm can asymptotically obtain the optimal results for the whole system. The algorithm proposed is low-complexity and could work without too much priori knowledge. Moreover, the algorithm not only inherits every advantage from the LODCO Algorithm but also adapts perfectly to the more complex environment. Simulation results illustrate that the algorithms could improve the ratio of offloading computation tasks by more than 10% while the QoE is guaranteed.

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“…Meysam Masoudi, et.al, (2017), have investigated [7] the problem of power minimization for the user terminals by application offloading in multi-cell multi-user OFDMA mobile cloud computing networks where some practical constraints such as backhaul capacity limitation, interference level on each channel and maximum tolerable delay as user's quality of service is taken into account. By the use of D.C. approximation the mixed integer nonlinear problem is furthermore converted into a convex form.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Technique for Load Balancing in Cloud Computing

Sharma¹,

Awasthi²

2018

IJCA

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The cloud is the architecture in which virtual machines, data centers, hosts and brokers are involved in the communication. The broker search most reliable virtual machine for the cloudlet execution. In the network uncertainty may happen due to which system get overloaded. In this research, work technique is proposed to increase fault tolerance of the system. The proposed improvement is based on the ACO algorithm which can select the best virtual machine on which cloudlet will be migrated. The performance of the proposed algorithm is testing on cloudsim in terms of execution time, energy consumption. The simulation results demonstrated that execution time and energy consumption of ACO is least as compared to TESA Algorithm. The proposed algorithm can be used for the load balancing in cloud computing.

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Green Cloud Computing for Multi Cell Networks

Cited by 22 publications

References 18 publications

A Multiobjective Computation Offloading Algorithm for Mobile-Edge Computing

A Multiobjective Computation Offloading Algorithm for Mobile-Edge Computing

QoE Aware and Cell Capacity Enhanced Computation Offloading for Multi-Server Mobile Edge Computing Systems with Energy Harvesting Devices

Novel Technique for Load Balancing in Cloud Computing

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