Scalability of efficient and dynamic workload distribution in autonomic cloud computing

The expectations of human desire on the technology are unlimited, so we need much more scalable system that manage themselves and fulfills our desire automatically. The current state of cloud computing infrastructure is not fully industrialized. To provide good quality of service (QOS) through cloud, the autonomic cloud computing, offers self- management properties of Autonomic Computing. With this technique, we scale up workload distribution dynamically and efficiently for cloud environment. With traditional system, centralized approach exists which are inefficient to scale up and proper workload distribution can be complex which required extra cost for large operation. In this paper, we integrate autonomic computing principals for automatic workload distribution through distributed decision in cloud. We will demonstrate the entire scenario based on cloud computing where individual resource allocated to the users (consumers) processes.

[1]  Thomas A. Corbi,et al.  The dawning of the autonomic computing era , 2003, IBM Syst. J..

[2]  Xiaorong Li,et al.  Autonomic Cloud computing: Open challenges and architectural elements , 2012, 2012 Third International Conference on Emerging Applications of Information Technology.

[3]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[4]  Petr Jan Horn,et al.  Autonomic Computing: IBM's Perspective on the State of Information Technology , 2001 .

[5]  Andrzej Kochut,et al.  Dynamic Placement of Virtual Machines for Managing SLA Violations , 2007, 2007 10th IFIP/IEEE International Symposium on Integrated Network Management.

[6]  Deger Cenk Erdil,et al.  Dependable Autonomic Cloud Computing with Information Proxies , 2011, 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum.

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

[8]  Borja Sotomayor,et al.  Enabling Cost-Effective Resource Leases with Virtual Machines , 2007 .

[9]  Hoi Chan,et al.  Dynamic Resource Allocation via Distributed Decisions in Cloud Environment , 2011, 2011 IEEE 8th International Conference on e-Business Engineering.

[11]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[12]  Rao Mikkilineni,et al.  Computing Models for Distributed Autonomic Clouds and Grids in the Context of the DIME Network Architecture , 2012, 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises.

[13]  Bu-Sung Lee,et al.  Optimization of Resource Provisioning Cost in Cloud Computing , 2012, IEEE Transactions on Services Computing.

[14]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[15]  Naveen Sharma,et al.  Towards autonomic workload provisioning for enterprise Grids and clouds , 2009, 2009 10th IEEE/ACM International Conference on Grid Computing.

[16]  Jeffrey O. Kephart,et al.  An artificial intelligence perspective on autonomic computing policies , 2004, Proceedings. Fifth IEEE International Workshop on Policies for Distributed Systems and Networks, 2004. POLICY 2004..

[17]  Norman W. Paton,et al.  Optimizing Utility in Cloud Computing through Autonomic Workload Execution , 2009 .

[18]  UrgaonkarBhuvan,et al.  Resource overbooking and application profiling in shared hosting platforms , 2002 .

[19]  Amin Vahdat,et al.  When Virtual is Harder than Real : Resource Allocation Challenges in Virtual Machine Based IT Environments , 2007 .

[20]  Prashant J. Shenoy,et al.  Resource overbooking and application profiling in shared hosting platforms , 2002, OSDI '02.

[21]  Rajkumar Buyya,et al.  Dynamically scaling applications in the cloud , 2011, CCRV.