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>

scholarly journals Minimizing Energy Consumption by Task Consolidation in Cloud Centers with Optimized Resource Utilization

2016 ◽  
Vol 6 (6) ◽  
pp. 3283 ◽  
Author(s):  
Mahendra Kumar Gourisaria ◽  
S. S. Patra ◽  
P. M. Khilar
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Carbon Dioxide Emission ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Cpu Utilization

<p>Cloud computing is an emerging field of computation. As the data centers consume large amount of power, it increases the system overheads as well as the carbon dioxide emission increases drastically. The main aim is to maximize the resource utilization by minimizing the power consumption. However, the greatest usages of resources does not mean that there has been a right use of energy.  Various resources which are idle, also consumes a significant amount of energy. So we have to keep minimum resources idle. Current studies have shown that the power consumption due to unused computing resources is nearly 1 to 20%. So, the unused resources have been assigned with some of the tasks to utilize the unused period. In the present paper, it has been suggested that the energy saving with task consolidation which has been saved the energy by minimizing the number of idle resources in a cloud computing environment. It has been achieved far-reaching experiments to quantify the performance of the proposed algorithm. The same has also been compared with the FCFSMaxUtil and Energy aware Task Consolidation (ETC) algorithm. The outcomes have shown that the suggested algorithm surpass the FCFSMaxUtil and ETC algorithm in terms of the CPU utilization and energy consumption.</p>

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.

Minimizing Energy Consumption by Task Consolidation in Cloud Centers with Optimized Resource Utilization

2016 ◽  
Vol 6 (6) ◽  
pp. 3283
Author(s):  
Mahendra Kumar Gourisaria ◽  
S. S. Patra ◽  
P. M. Khilar
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Carbon Dioxide Emission ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Cpu Utilization

<p>Cloud computing is an emerging field of computation. As the data centers consume large amount of power, it increases the system overheads as well as the carbon dioxide emission increases drastically. The main aim is to maximize the resource utilization by minimizing the power consumption. However, the greatest usages of resources does not mean that there has been a right use of energy.  Various resources which are idle, also consumes a significant amount of energy. So we have to keep minimum resources idle. Current studies have shown that the power consumption due to unused computing resources is nearly 1 to 20%. So, the unused resources have been assigned with some of the tasks to utilize the unused period. In the present paper, it has been suggested that the energy saving with task consolidation which has been saved the energy by minimizing the number of idle resources in a cloud computing environment. It has been achieved far-reaching experiments to quantify the performance of the proposed algorithm. The same has also been compared with the FCFSMaxUtil and Energy aware Task Consolidation (ETC) algorithm. The outcomes have shown that the suggested algorithm surpass the FCFSMaxUtil and ETC algorithm in terms of the CPU utilization and energy consumption.</p>

Performance Comparison of Energy-Aware Task Scheduling with GA and CRO Algorithms in Cloud Environment

2014 ◽  
Vol 596 ◽  
pp. 204-208 ◽  
Author(s):  
Lin Wu ◽  
Yu Jing Wang ◽  
Chao Kun Yan
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Performance Comparison ◽  
Energy Awareness ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Total Energy Consumption ◽  
Cloud Data

With energy problem of cloud data center is becoming more and more serious, the BoT scheduling algorithm only considering the timespan is not applicable to the cloud computing environment. In order to explore the energy-aware task scheduling algorithm performance, this paper validates simulation experiments with GA algorithms and CRO algorithms, to optimize the makespan as the main objective, to optimize energy consumption indicators for the secondary objective. Experiments show that, GA algorithms and CRO algorithm can be applied to different scenarios, while optimizing makespan, but also to some extent reduce the total energy consumption of the system can be used as task scheduling strategy cloud environments.Keyword: Cloud Computing, Task Scheduling, Energy-awareness, CRO algorithm, GA algorithm

scholarly journals Independent Task Scheduling in Heterogeneous System

2019 ◽  
Vol 8 (4) ◽  
pp. 10093-10099
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Task Scheduling ◽  
Rapid Development ◽  
Network Connectivity ◽  
Variable Number ◽  
Cloud Infrastructure ◽  
Storage Devices ◽  
Quantum Genetic Algorithm ◽  
Test Scenarios

Recently, the rapid development in processing speeds, fast storage devices and better network connectivity, hasaccelerated the popularization of cloud computing. Cloud computing is an on-demand-servicewhich provides users with high end servers,storage and processing capabilities where the user need not be concerned with its infrastructure.Although, there are abundant resources in the cloud infrastructure, for the efficient working and execution of tasks, task scheduling plays a crucial role. Task scheduling results in better performance (throughput) of the system along with better resource utilization which ultimately results inreduced energy consumption. At any given time, a processor should never be in idle state, as it still consumes some amount of energy. In this paper, the use of Quantum Genetic Algorithm has led to the reduction in energy consumption. The objective is to find a scheduling sequencewhich can be implemented ina cloud computing environment. Along with minimizing energy consumption, the algorithm helps reduce makespan time of a processor as well.The results show a decrease in energy consumption by 10-15% under different test scenarios involving a variable number of tasks, processors, and the number of iterations (generations) for which the algorithm was run. The algorithm converges to the desired result within 10-15 iterations, as can be seen from the results published in this paper.

scholarly journals ALGORITHM FOR TASK CONSOLIDATION IN CLOUD COMPUTING: A COMPARATIVE SURVEY

2018 ◽  
Vol 6 (5) ◽  
pp. 340-345
Author(s):  
Rajat Pugaliya ◽  
Madhu B R
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Resource Utilization ◽  
Service Provider ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Service Provider ◽  
Dynamic Task ◽  
Main Challenge ◽  
High Level

Cloud Computing is an emerging field in the IT industry. Cloud computing provides computing services over the Internet. Cloud Computing demand increasing drastically, which has enforced cloud service provider to ensure proper resource utilization with less cost and less energy consumption. In recent time various consolidation problems found in cloud computing like the task, VM, and server consolidation. These consolidation problems become challenging for resource utilization in cloud computing. We found in the literature review that there is a high level of coupling in resource utilization, cost, and energy consumption. The main challenge for cloud service provider is to maximize the resource utilization, reduce the cost and minimize the energy consumption. The dynamic task consolidation of virtual machines can be a way to solve the problem. This paper presents the comparative study of various task consolidation algorithms.

Energy-Efficient System-Based Algorithm for Maximal Resource Utilization in Cloud Computing

2021 ◽  
pp. 307-313
Author(s):  
Arvind Dagur ◽  
Mishra Adityakumar Virendra ◽  
Mayank Pratap Singh ◽  
Deepak Gupta ◽  
Chetanya Sharma ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Energy Efficient ◽  
Efficient System
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