Performance evaluation of dynamic resource allocation in cloud computing platforms using Stochastic Process Algebra

Cloud computing is a revolutionary concept in which computing services are provided to end users via virtualization. Computing resource are provisioned and released on demand. This elastic nature of resource provisioning allows for the “pay as you go” concept. Hence, it makes the major benefit of adopting cloud-based solutions. Accordingly, there is a need to evaluate the performance of the resource provisioning algorithm adopted by cloud computing platforms. In this paper, an approach for modeling and analyzing the performance of the resource provisioning process in a cloud computing platform is presented. The proposed model utilizes Stochastic Process Algebra (SPA) as an engine to simulate the components involved in the resource provisioning process along with the interactions among them. A case study using the cloud computing platform Aneka is performed. Experimental results show the effect of different parameters on the provisioning process which suggests the suitability of the proposed approach to evaluate design alternatives.

[1]  T. S. Eugene Ng,et al.  The Impact of Virtualization on Network Performance of Amazon EC2 Data Center , 2010, 2010 Proceedings IEEE INFOCOM.

[2]  Rajkumar Buyya,et al.  Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters , 2009, HPDC '09.

[3]  Victor I. Chang,et al.  A Review of Cloud Business Models and Sustainability , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[4]  Ulrich Herzog,et al.  Stochastic process algebras as a tool for performance and dependability modelling , 1995, Proceedings of 1995 IEEE International Computer Performance and Dependability Symposium.

[5]  Vladimir Stantchev,et al.  Performance Evaluation of Cloud Computing Offerings , 2009, 2009 Third International Conference on Advanced Engineering Computing and Applications in Sciences.

[6]  Albert G. Greenberg,et al.  WebProphet: Automating Performance Prediction for Web Services , 2010, NSDI.

[7]  Paolo Ciancarini,et al.  Stochastic Process Algebra: From an Algebraic Formalism to an Architectural Description Language , 2002, Performance.

[8]  G. Harrison,et al.  Process Algebra for Discrete Event SimulationP , 1993 .

[9]  Jorge-Arnulfo Quiané-Ruiz,et al.  Runtime measurements in the cloud , 2010, Proc. VLDB Endow..

[10]  Rajkumar Buyya,et al.  The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds , 2012, Future Gener. Comput. Syst..

[11]  Allan Clark,et al.  Stochastic Process Algebras , 2007, SFM.

[12]  Graham Clark Jane Hillston Towards Automatic Derivation of Performance Measures from PEPA Models , 1996 .

[13]  Jinesh Varia,et al.  Best Practices in Architecting Cloud Applications in the AWS Cloud , 2011 .

[14]  Rajkumar Buyya,et al.  Future Generation Computer Systems Deadline-driven Provisioning of Resources for Scientific Applications in Hybrid Clouds with Aneka , 2022 .

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

[16]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[17]  Jelena V. Misic,et al.  Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems , 2012, IEEE Transactions on Parallel and Distributed Systems.

[18]  Roberto Gorrieri,et al.  MPA: a Stochastic Process Algebra , 1994 .

[19]  Rajkumar Buyya,et al.  SLA-Based Resource Provisioning for Heterogeneous Workloads in a Virtualized Cloud Datacenter , 2011, ICA3PP.

[20]  Miklós Telek,et al.  Fluid Models in Performance Analysis , 2007, SFM.

[21]  Jos C. M. Baeten,et al.  Process Algebra , 2007, Handbook of Dynamic System Modeling.

[22]  Rajkumar Buyya,et al.  Cloud Computing Principles and Paradigms , 2011 .

[23]  Alexandru Iosup,et al.  Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[24]  Rajkumar Buyya,et al.  NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[25]  Rajkumar Buyya,et al.  SLA-oriented resource provisioning for cloud computing: Challenges, architecture, and solutions , 2011, 2011 International Conference on Cloud and Service Computing.

[26]  Mladen A. Vouk,et al.  Cloud Computing – Issues, Research and Implementations , 2008, CIT 2008.

[27]  Rajkumar Buyya,et al.  2011 Fourth IEEE International Conference on Utility and Cloud Computing SMICloud: A Framework for Comparing and Ranking Cloud Services , 2022 .

[28]  Odej Kao,et al.  Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud , 2011, IEEE Transactions on Parallel and Distributed Systems.

[29]  Ulrich Herzog,et al.  Formal Description, Time and Performance Analysis. A Framework , 1990, Entwurf und Betrieb verteilter Systeme.

[30]  Alexandru Iosup,et al.  On the Performance Variability of Production Cloud Services , 2011, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[31]  Xiaowei Yang,et al.  CloudCmp: comparing public cloud providers , 2010, IMC '10.