Energy-Efficient Delay-Aware Task Offloading in Fog-Cloud Computing System for IoT Sensor Applications

2021 ◽  
Vol 30 (1) ◽  
Author(s):  
Parvinder Singh ◽  
Rajeshwar Singh
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Computing System ◽  
Sensor Applications ◽  
Cloud Computing System ◽  
Task Offloading

scholarly journals An Energy Efficient Task Scheduling Strategy in a Cloud Computing System and its Performance Evaluation using a Two-Dimensional Continuous Time Markov Chain Model

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 775
Author(s):  
Wenjuan Zhao ◽  
Xiushuang Wang ◽  
Shunfu Jin ◽  
Wuyi Yue ◽  
Yutaka Takahashi
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Performance Measures ◽  
Energy Efficient ◽  
System Optimization ◽  
Computing System ◽  
Continuous Time Markov Chain ◽  
Scheduling Strategy ◽  
Cloud Computing System ◽  
Waking Up

With ongoing energy shortages and rises in greenhouse emissions worldwide, increasing academic attention is being turned towards ways to improve the efficiency and sustainability of cloud computing. In this paper, we present a performance analysis and a system optimization of a cloud computing system with an energy efficient task scheduling strategy directed towards satisfying the service level agreement of cloud users while at the same time improving the energy efficiency in cloud computing system. In this paper, we propose a novel energy-aware task scheduling strategy based on a sleep-delay timer and a waking-up threshold. To capture the stochastic behavior of tasks with the proposed strategy, we establish a synchronous vacation queueing system combining vacation-delay and N-policy. Taking into account the total number of tasks and the state of the physical machine (PM), we construct a two-dimensional continuous-time Markov chain (CTMC), and produce an infinitesimal generator. Moreover, by using the geometric-matrix solution method, we analyze the queueing model in the steady state, and then, we derive the system performance measures in terms of the average sojourn time and the energy conservation level. Furthermore, we conduct system experiments to investigate the proposed strategy and validate the system model according to performance measures. Statistical results show that there is a compromise between the different performance measures when setting strategy parameters. By combining different performance measures, we develop a cost function for the system optimization. Finally, by dynamically adjusting the crossover probability and the mutation probability, and initializing the individuals with chaotic equations, we present an improved genetic algorithm to jointly optimize the sleep parameter, the sleep-delay parameter and the waking-up threshold.

scholarly journals A Task Offloading Scheme in Vehicular Fog and Cloud Computing System

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 1173-1184 ◽  
Author(s):  
Qiong Wu ◽  
Hongmei Ge ◽  
Hanxu Liu ◽  
Qiang Fan ◽  
Zhengquan Li ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Computing System ◽  
Cloud Computing System ◽  
Task Offloading

scholarly journals ERMO2 algorithm: an energy efficient mobility management in mobile cloud computing system for 5G heterogeneous networks

2019 ◽  
Vol 9 (3) ◽  
pp. 1957
Author(s):  
L. Pallavi ◽  
A. Jagan ◽  
B. Thirumala Rao
Keyword(s):  
Cloud Computing ◽  
Heterogeneous Networks ◽  
Mobility Management ◽  
Packet Loss ◽  
Energy Efficient ◽  
Mobile Cloud Computing ◽  
Computing System ◽  
Mobile Users ◽  
Mobile Cloud ◽  
Cloud Computing System

Recently, mobile devices are becoming the primary platforms for every user who always roam around and access the cloud computing applications. Mobile cloud computing (MCC) combines the both mobile and cloud computing, which provides optimal services to the mobile users. In next-generation mobile environments, mainly due to the huge number of mobile users in conjunction with the small cell size and their portable information‟s, the influence of mobility on the network performance is strengthened. In this paper, we propose an energy efficient mobility management in mobile cloud computing (E2M2MC2) system for 5G heterogeneous networks. The proposed E2M2MC2 system use elective repeat multi-objective optimization (ERMO2) algorithm to determine the best clouds based on the selection metrics are delay, jitter, bit error rate (BER), packet loss, communication cost, response time, and network load. ERMO2 algorithm provides energy efficient management of user mobility as well as network resources. The simulation results shows that the proposed E2M2MC2 system helps in minimizing delay, packet loss rate and energy consumption in a heterogeneous network.

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