Dynamic Provisioning and Resource Management for Multi-Tier Cloud Based Applications

Abstract Dynamic capacity provisioning is a useful technique for handling the workload variations seen in cloud environment. In this paper, we propose a dynamic provisioning technique for multi-tier applications to allocate resources efficiently using queueing model. It dynamically increases the mean service rate of the virtual machines to avoid congestion in the multi-tier environments. An optimization model to minimize the total number of virtual machines for computing resources in each tier has been presented. Using the supplementary variable and the recursive techniques, we obtain the system-length distributions at pre-arrival and arbitrary epochs. Some important performance indicators such as blocking probability, request waiting time and number of tasks in the system and in the queue have also been investigated. Finally, computational results showing the effect of model parameters on key performance indicators are presented.

[1]  Anirban Kundu,et al.  Introducing New Services in Cloud Computing Environment , 2010, J. Digit. Content Technol. its Appl..

[2]  Danilo Ardagna,et al.  SLA Based Profit Optimization in Multi-tier Systems , 2005, Fourth IEEE International Symposium on Network Computing and Applications.

[3]  Yinong Chen,et al.  Virtualization-based autonomic resource management for multi-tier Web applications in shared data center , 2008, J. Syst. Softw..

[4]  Calton Pu,et al.  Generating Adaptation Policies for Multi-tier Applications in Consolidated Server Environments , 2008, 2008 International Conference on Autonomic Computing.

[5]  Erich M. Nahum,et al.  Yaksha: a self-tuning controller for managing the performance of 3-tiered Web sites , 2004, Twelfth IEEE International Workshop on Quality of Service, 2004. IWQOS 2004..

[6]  Prashant J. Shenoy,et al.  Agile dynamic provisioning of multi-tier Internet applications , 2008, TAAS.

[7]  Asser N. Tantawi,et al.  An analytical model for multi-tier internet services and its applications , 2005, SIGMETRICS '05.

[8]  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 .

[9]  P. Sai Kiran,et al.  Issues in Cloud Computing , 2011 .

[10]  Jeffrey O. Kephart,et al.  An architectural approach to autonomic computing , 2004, International Conference on Autonomic Computing, 2004. Proceedings..

[11]  Zhiliang Zhu,et al.  Dynamic Provisioning Modeling for Virtualized Multi-tier Applications in Cloud Data Center , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[12]  Dejan S. Milojicic,et al.  SLA Decomposition: Translating Service Level Objectives to System Level Thresholds , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[13]  Dirk Beyer,et al.  Optimal Server Resource Allocation Using an Open Queueing Network Model of Response Time , 2002 .