SEATS: smart energy-aware task scheduling in real-time cloud computing

2014 ◽  
Vol 71 (1) ◽  
pp. 45-66 ◽  
Author(s):  
Seyedmehdi Hosseinimotlagh ◽  
Farshad Khunjush ◽  
Rashidaldin Samadzadeh
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Task Scheduling ◽  
Energy Aware

Security-Critical Energy-Aware Task Scheduling for Heterogeneous Real-Time MPSoCs in IoT

2020 ◽  
Vol 13 (4) ◽  
pp. 745-758 ◽  
Author(s):  
Junlong Zhou ◽  
Jin Sun ◽  
Peijin Cong ◽  
Zhe Liu ◽  
Xiumin Zhou ◽  
...  
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Critical Energy ◽  
Energy Aware

scholarly journals Energy-Aware Task Scheduling for Real-Time Systems with Discrete Frequencies

2011 ◽  
Vol E94-D (4) ◽  
pp. 822-832
Author(s):  
Dejun QIAN ◽  
Zhe ZHANG ◽  
Chen HU ◽  
Xincun JI
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Real Time Systems ◽  
Energy Aware ◽  
Time Systems

scholarly journals REVIEW OF TASK SCHEDULING METHODS FOR REAL TIME TASKS IN CLOUD ENVIRONMENT

2020 ◽  
Vol 5 (1) ◽  
pp. 85-89
Author(s):  
Dinkan Patel ◽  
Anjuman Ranavadiya
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Dynamic Scheduling ◽  
Scheduling Algorithms ◽  
Minimum Time ◽  
Cloud Environment ◽  
Static Scheduling ◽  
On Demand

Cloud Computing is a type of Internet model that enables convenient, on-demand resources that can be used rapidly and with minimum effort. Cloud Computing can be IaaS, PaaS or SaaS. Scheduling of these tasks is important so that resources can be utilized efficiently with minimum time which in turn gives better performance. Real time tasks require dynamic scheduling as tasks cannot be known in advance as in static scheduling approach. There are different task scheduling algorithms that can be utilized to increase the performance in real time and performing these on virtual machines can prove to be useful. Here a review of various task scheduling algorithms is done which can be used to perform the task and allocate resources so that performance can be increased.

scholarly journals Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Weizhe Zhang ◽  
Hucheng Xie ◽  
Boran Cao ◽  
Albert M. K. Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Real Time ◽  
Task Scheduling ◽  
Particle Swarm ◽  
Energy Aware ◽  
Swarm Optimization ◽  
Heterogeneous Multiprocessors ◽  
Time Task ◽  
Feasible Solutions

Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO) based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

Energy-aware task scheduling in mobile cloud computing

2018 ◽  
Vol 36 (3) ◽  
pp. 529-553 ◽  
Author(s):  
Chaogang Tang ◽  
Mingyang Hao ◽  
Xianglin Wei ◽  
Wei Chen
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Energy Aware
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