Service Performance and Analysis in Cloud Computing

Cloud computing is a new cost-efficient computing paradigm in which information and computer power can be accessed from aWeb browser by customers. Understanding the characteristics of computer service performance has become critical for service applications in cloud computing. For the commercial success of this new computing paradigm, the ability to deliver Quality of Services (QoS) guaranteed services is crucial. In this paper, we present an approach for studying computer service performance in cloud computing. Specifically, in an effort to deliver QoS guaranteed services in such a computing environment, we find the relationship among the maximal number of customers, the minimal service resources and the highest level of services. The obtained results provide the guidelines of computer service performance in cloud computing that would be greatly useful in the design of this new computing paradigm.

[1]  Gunter Bolch,et al.  Queueing Networks and Markov Chains , 2005 .

[2]  Kevin Jeffay,et al.  Variability in TCP round-trip times , 2003, IMC '03.

[3]  R. D. V. D. Meiad,et al.  User Perceived Quality-of-Service for Voice-over-IP in a Heterogeneous Multi-Domain Network Environment , 2005 .

[4]  Harry G. Perros,et al.  Computer Resource Optimization for Differentiated Customer Services , 2006, 14th IEEE International Symposium on Modeling, Analysis, and Simulation.

[5]  J. Sommers,et al.  A Geometric Approach to Improving Active Packet Loss Measurement , 2008, IEEE/ACM Transactions on Networking.

[6]  Louis P. Slothouber,et al.  A Model of Web Server Performance , 1996 .

[7]  Krishna P. Gummadi,et al.  King: estimating latency between arbitrary internet end hosts , 2002, IMW '02.

[8]  Harry G. Perros.  Queueing Networks with Blocking: Exact and Approximate Solutions , 1994 .

[9]  J. Walrand,et al.  Sojourn times and the overtaking condition in Jacksonian networks , 1980, Advances in Applied Probability.

[10]  Paul Barford,et al.  Improving accuracy in end-to-end packet loss measurement , 2005, SIGCOMM '05.

[11]  Won Kim,et al.  Cloud Computing: Today and Tomorrow , 2009, J. Object Technol..

[12]  Jie Wang,et al.  Performance Modeling and Analysis of Web Switch , 2005, Int. CMG Conference.

[13]  Krishna P. Gummadi,et al.  King: estimating latency between arbitrary internet end hosts , 2002, IMW '02.

[14]  Prashant Pandey,et al.  Cloud computing , 2010, ICWET.

[15]  Robert D. van der Mei,et al.  Modelling End-to-end Quality-of-Service for Transaction-Based Services in Multi-Domain Environments , 2006, 2006 IEEE International Conference on Web Services (ICWS'06).

[16]  Brian Hayes,et al.  What Is Cloud Computing? , 2019, Cloud Technologies.

[17]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

[18]  Tayfur Altiok,et al.  Simulation Modeling and Analysis with ARENA , 2007 .

[19]  Harry G. Perros,et al.  SLA-based resource allocation in cluster computing systems , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[20]  Arne A. Nilsson,et al.  On service level agreements for IP networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[21]  Himadeepa Karlapudi.  Web Application Performance Prediction , 2004 .

[22]  Sujata Banerjee,et al.  Per-flow delay performance in traffic aggregates , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[23]  Mark Allman,et al.  Estimating loss rates with TCP , 2003, PERV.

[24]  H. Daduna.  Burke's theorem on passage times in Gordon–Newell networks , 1984, Advances in Applied Probability.