Identifying Overloaded Servers and Managing Dynamic Placement of Virtual machines in Cloud

Cloud computing is becoming one of the most popular commercial infrastructure due to its little maintenance expense and on demand resource utilization. Cloud computing possesses many kinds of technical challenges such as fault tolerance, reliability, availability, integrity etc. due to its complex and distributed nature. But the main problem related to all those is overload incurred by Virtual Machines (VM). So, load balancing is one of the most significant issues that can help to gain rapid performance of cloud infrastructure. This research proposes algorithms for detecting failed servers due to overloaded VMs. The failure detection algorithm checks server status after a predefined time interval. This algorithm gives proactive technique to deal with overloaded VMs. When any failure in the server is found, the resource balancing algorithm migrates its VMs to an adequate healthy Physical Machine (PM). To distribute workload evenly, the resource utilization skew is measured. This VM to PM mapping is done in a way that every PM will do almost equal amount of work. General Terms Cloud computing

[1]  Aameek Singh,et al.  Server-storage virtualization: Integration and load balancing in data centers , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[2]  Asif Imran,et al.  An empirical investigation of cost-resource optimization for running real-life applications in open source cloud , 2012, 2012 International Conference on High Performance Computing & Simulation (HPCS).

[3]  Jun Wei,et al.  Fault detection for cloud computing systems with correlation analysis , 2015, 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM).

[4]  Rajkumar Buyya,et al.  Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints , 2013, IEEE Transactions on Parallel and Distributed Systems.

[5]  B. S. Shylaja.  DYNAMIC ALLOCATION METHOD FOR EFFICIENT LOAD BALANCING IN VIRTUAL MACHINES FOR CLOUD COMPUTING ENVIRONMENT , 2012 .

[6]  Diane Barrett.  Security Architecture and Forensic Awareness in Virtualized Environments , 2013 .

[7]  LiuAnna,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012 .

[8]  Asif Imran,et al.  Time-Waved Monitoring and Emergent Self Adaption of Software Components in Open Source Cloud , 2015 .

[9]  Asif Imran,et al.  A peer to peer resource provisioning scheme for cloud computing environment using multi attribute utility theory , 2013, Third International Conference on Innovative Computing Technology (INTECH 2013).

[10]  V. Suma,et al.  Enhanced Load Balancing Approach to Avoid Deadlocks in Cloud , 2012, ArXiv.

[11]  Jorge G. Barbosa,et al.  Dynamic Power- and Failure-Aware Cloud Resources Allocation for Sets of Independent Tasks , 2013, 2013 IEEE International Conference on Cloud Engineering (IC2E).

[12]  Zhen Xiao,et al.  Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[13]  Qian Zhu,et al.  Dynamic Resource Provisioning for Data Streaming Applications in a Cloud Environment , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[14]  Kevin Lee,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012, Future Gener. Comput. Syst..

[15]  Sanggil Kang,et al.  A Novel Adaptive Virtual Machine Deployment Algorithm for Cloud Computing , 2012 .

[16]  Jorge Munoz-Gama,et al.  A Framework for Recommending Resource Allocation Based on Process Mining , 2015, Business Process Management Workshops.

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

[18]  Junwei Cao,et al.  Dynamic Controlling of Data Streaming Applications for Cloud Computing , 2011 .

[19]  A. Khiyaita,et al.  Load balancing cloud computing: State of art , 2012, 2012 National Days of Network Security and Systems.

[20]  Xiaosong Ma,et al.  SigLM: Signature-driven load management for cloud computing infrastructures , 2009, 2009 17th International Workshop on Quality of Service.

[21]  Kuo-Qin Yan,et al.  Towards a Load Balancing in a three-level cloud computing network , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[22]  Asif Imran,et al.  Cloud-Niagara: A high availability and low overhead fault tolerance middleware for the cloud , 2014, 16th Int'l Conf. Computer and Information Technology.