Towards energy-aware task scheduling (EATS) framework for divisible-load applications in cloud computing infrastructure

2017 ◽  
Author(s):  
Leila Ismail ◽  
Abbas A. Fardoun
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Energy Aware ◽  
Divisible Load ◽  
Computing Infrastructure

scholarly journals Enhancing the Distribution of Idle Cost for Scheduling Tasks without Setup Cost in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Redwan A. Al-dilami ◽  
Ammar T. Zahary ◽  
Adnan Z. Al-Saqqaf
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Setup Time ◽  
Setup Cost ◽  
Idle State ◽  
The Cost ◽  
Computing Infrastructure ◽  
Better Than

Issues of task scheduling in the centre of cloud computing are becoming more important, and the cost is one of the most important parameters used for scheduling tasks. This study aims to investigate the problem of online task scheduling of the identified job of MapReduce on cloud computing infrastructure. It was proposed that the virtualized cloud computing setup comprised machines that host multiple identical virtual machines (VMs) that need to be activated earlier and run continuously, and booting a VM requires a constant setup time. A VM that remains running even though it is no longer used is considered an idle VM. Furthermore, this study aims to distribute the idle cost of the VMs rather than the cost of setting up them among tasks in a fair manner. This study also is an extension of previous studies which solved the problems that occurred when distributing the idle cost and setting up the cost of VMs among tasks. It classifies the tasks into three groups (long, mid, and short) and distributes the idle cost among the groups then among the tasks of the groups. The main contribution of this paper is the developing of a clairvoyant algorithm that addressed important factors such as the delay and the cost that occurred by waiting to setup VM (active VM). Also, when the VMs are run continually and some VMs become in idle state, the idle cost will be distributed among the current tasks in a fair manner. The results of this study, in comparison with previous studies, showed that the idle cost and the setup cost that was distributed among tasks were better than the idle cost and the setup cost distributed in those studies.

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

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

scholarly journals Optimization Algorithms based Task Scheduling Method for Cloud Computing Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 3608-3613
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Large Scale ◽  
Data Centers ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Scheduling Method ◽  
Consistent Performance ◽  
Computing Structure

There are various enhancements in the world of technology. Among that Cloud computing delivers numerous amenities over the Internet. It employs data centers which comprise hardware and software provision for loading, servers, and systems. The primary reason for the popularity of Cloud computing is consistent performance, economical operation, prompt accessibility, rapid scaling and much more. The chief cause for concern in cloud computing are the errors that happen either in the software or the hardware and energy consumption on a large scale. The clients pay only for resources utilized by them and assets which are accessible during the computing in a cloud setting. In the environment of cloud computing, Task scheduling is significant concepts which can be used to minimize the energy and time spent. The algorithms in Task scheduling might employ various measures toward dispense preference to subtasks that may generate many schedules to the divergent computing structure. Moreover, consumption of energy could be dissimilar for every source which is allocated to a job. This present research explores that the PSO-CA based energy aware task scheduling method can predict with the aim to enhance the resource distribution.

An Energy-Aware Task Scheduling in the Cloud Computing Using a Hybrid Cultural and Ant Colony Optimization Algorithm

2017 ◽  
Vol 7 (4) ◽  
pp. 20-40 ◽  
Author(s):  
Poopak Azad ◽  
Nima Jafari Navimipour
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Heterogeneous Computing ◽  
Heuristic Algorithms ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Energy Aware

In a cloud environment, computing resources are available to users, and they pay only for the used resources. Task scheduling is considered as the most important issue in cloud computing which affects time and energy consumption. Task scheduling algorithms may use different procedures to distribute precedence to subtasks which produce different makespan in a heterogeneous computing system. Also, energy consumption can be different for each resource that is assigned to a task. Many heuristic algorithms have been proposed to solve task scheduling as an NP-hard problem. Most of these studies have been used to minimize the makespan. Both makespan and energy consumption are considered in this paper and a task scheduling method using a combination of cultural and ant colony optimization algorithm is presented in order to optimize these purposes. The basic idea of the proposed method is to use the advantages of both algorithms while avoiding the disadvantages. The experimental results using C# language in cloud azure environment show that the proposed algorithm outperforms previous algorithms in terms of energy consumption and makespan.

scholarly journals An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

2018 ◽  
Vol 8 (2) ◽  
pp. 1018 ◽  
Author(s):  
Kavita A. Sultanpure ◽  
L. S. S. Reddy
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Energy Efficient ◽  
Energy Aware ◽  
Comprehensive Overview ◽  
Environmentally Sustainable ◽  
Cloud Network ◽  
Control Traffic

<p>Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads.</p>

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