Resource Allocation in Cooperative Cloud Environments

In cloud computing environment, cloud application services and resources belong to different virtual organizations with different objectives. Each component of cloud environment is self-governing and self-interested. They share their resources and services to achieve their objectives. The cloud computing environment provides infinite number of computing resources such as CPU, memory and storage to the users in such a way that they can dynamically increase or decrease their resources and its use according to their demands. In resource allocation model having two basic objectives as cloud provider wants to maximize their revenue by achieving high resource utilization while cloud users want to minimize their expenses while meeting their requirements. However, it is essential to allocate resources in an optimized way between two parties. In some situations, single cloud may not satisfy all the requirements of the users. To achieve this objective, two or more cloud providers cooperatively work together to satisfy the user’s requirements. These cooperative cloud providers should share and optimize the computational resources in a reasonable technique to make sure that no users get much resource than any other users and also improve the resource utilization.

[1]  M. Shamim Hossain,et al.  Cooperative game-based distributed resource allocation in horizontal dynamic cloud federation platform , 2012, Information Systems Frontiers.

[2]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[3]  Himansu Das,et al.  The Complex Network Analysis of Power Grid: A Case Study of the West Bengal Power Network , 2013, ICACNI.

[4]  Naixue Xiong,et al.  A game-theoretic method of fair resource allocation for cloud computing services , 2010, The Journal of Supercomputing.

[5]  Jyh-Horng Chou,et al.  Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm , 2013, Comput. Oper. Res..

[6]  Chenn-Jung Huang,et al.  An adaptive resource management scheme in cloud computing , 2013, Eng. Appl. Artif. Intell..

[7]  Himansu Das,et al.  Grid Computing-Based Performance Analysis of Power System: A Graph Theoretic Approach , 2015 .

[8]  Judith Kelner,et al.  Resource allocation for distributed cloud: concepts and research challenges , 2011, IEEE Network.

[9]  Himansu Das,et al.  Big Data and Cyber Foraging: Future Scope and Challenges , 2016 .

[10]  Diptendu Sinha Roy,et al.  The Topological Structure of the Odisha Power Grid: A Complex Network Analysis , 2013 .

[11]  Himansu Das,et al.  Energy aware scheduling using genetic algorithm in cloud data centers , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[12]  Himansu Das,et al.  Task-Scheduling Algorithms in Cloud Environment , 2017 .

[13]  Diptendu Sinha Roy,et al.  A Grid Computing Service for Power System Monitoring , 2013 .

[14]  K. Hemant Kumar Reddy,et al.  A Data Aware Scheme for Scheduling Big Data Applications with SAVANNA Hadoop , 2017 .

[15]  Huiqun Yu,et al.  A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing , 2014 .

[16]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[17]  Shrisha Rao,et al.  Resource Allocation in Cloud Computing Using the Uncertainty Principle of Game Theory , 2016, IEEE Systems Journal.