scholarly journals A power efficient genetic algorithm for resource allocation in cloud computing data centers

2014 ◽  
Author(s):  
Giuseppe Portaluri ◽  
Stefano Giordano ◽  
Dzmitry Kliazovich ◽  
Bernabe Dorronsoro
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Cloud Computing ◽  
Data Centers ◽  
Power Efficient

Improving Cost for Data Migration in Cloud Computing Using Genetic Algorithm

2020 ◽  
Vol 8 (3) ◽  
pp. 69-81
Author(s):  
Nitin Chawla ◽  
Deepak Kumar ◽  
Dinesh Kumar Sharma
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Software As A Service ◽  
Data Migration ◽  
Platform As A Service ◽  
Challenging Tasks ◽  
Enterprise Applications ◽  
Client Data

Cloud computing is gradually increasing its popularity in enterprise-wide organizations. Information technology organizations e.g., IBM, Microsoft, and Amazon have already shifted towards Cloud computing. Cloud-based offerings such as Software as a Service, Platform as a Service and Infrastructure as a Service (IAAS) are the most famous offerings. Most of the existing enterprise applications are deployed using an on-premise model. Organizations are looking for Cloud based offerings to deploy or upgrade their existing applications. SAP, Microsoft Dynamics, and Oracle are the most famous ERP or CRM application OEMs. These enterprise applications generate lots of data are hosted in an organization or on client data centers. Moving data from one data center to the Cloud is always a challenging tasks which cost a lot and takes much effort. This study proposes an efficient approach to optimize cost for data migration in cloud computing. This study also proposes the approach to optimize cost for data collection from multiple locations which can be processed centrally and then migrate to Cloud Computing.

Heuristic Task Consolidation Techniques for Energy Efficient Cloud Computing

2015 ◽  
pp. 238-260 ◽  
Author(s):  
Dilip Kumar ◽  
Bibhudatta Sahoo ◽  
Tarni Mandal
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Data Centers ◽  
Optimal Solution ◽  
Solution Space ◽  
Allocation Problem ◽  
Resource Allocation Problem ◽  
Cloud Infrastructure ◽  
Two Phase ◽  
Phase Selection

The energy consumption in the cloud is proportional to the resource utilization and data centers are almost the world's highest consumers of electricity. The complexity of the resource allocation problem increases with the size of cloud infrastructure and becomes difficult to solve effectively. The exponential solution space for the resource allocation problem can be searched using heuristic techniques to obtain a sub-optimal solution at the acceptable time. This chapter presents the resource allocation problem in cloud computing as a linear programming problem, with the objective to minimize energy consumed in computation. This resource allocation problem has been treated using heuristic approaches. In particular, we have used two phase selection algorithm ‘FcfsRand', ‘FcfsRr', ‘FcfsMin', ‘FcfsMax', ‘MinMin', ‘MedianMin', ‘MaxMin', ‘MinMax', ‘MedianMax', and ‘MaxMax'. The simulation results indicate in the favor of MaxMax.

Resource Allocation based on Genetic Algorithm for Cloud Computing

2021 ◽  
Author(s):  
Yi-Liang Chen ◽  
Shih-Yun Huang ◽  
Yao-Chung Chang ◽  
Han-Chieh Chao
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Cloud Computing

Smart Configuration and Auto Allocation of Resource in Cloud Data Centers

2018 ◽  
Vol 5 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Merzoug Soltane ◽  
Kazar Okba ◽  
Derdour Makhlouf ◽  
Sean B. Eom
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Data Centers ◽  
Multi Agent Systems ◽  
Energy Aware ◽  
Green Cloud Computing ◽  
Cloud Data ◽  
Operational Costs ◽  
Green Cloud ◽  
Save Energy

Cloud computing is one of emerging computing models that has many advantages. The IT industry is keenly aware of the need for Green Cloud computing solutions that save energy for the environment as well as reduce operational costs. This article presents a new green Cloud Computing framework based on multi agent systems for optimizing resource allocation in data centers (DCs). Our framework based on a new cloud computing architecture that benefits from the combination of the Cloud and agent technologies. DCs hosting Cloud applications need energy-aware resource allocation mechanisms that minimize energy costs and other operational costs. This article offers a logical solution to manage physical and virtual resources in smarter data center.

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