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Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems

Published: 01 May 2012 Publication History

Abstract

Successful development of cloud computing paradigm necessitates accurate performance evaluation of cloud data centers. As exact modeling of cloud centers is not feasible due to the nature of cloud centers and diversity of user requests, we describe a novel approximate analytical model for performance evaluation of cloud server farms and solve it to obtain accurate estimation of the complete probability distribution of the request response time and other important performance indicators. The model allows cloud operators to determine the relationship between the number of servers and input buffer size, on one side, and the performance indicators such as mean number of tasks in the system, blocking probability, and probability that a task will obtain immediate service, on the other.

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  • (2024)Performance analysis of parallel composite service-based applications in cloudsFuture Generation Computer Systems10.1016/j.future.2023.11.021153:C(27-40)Online publication date: 16-May-2024
  • (2024)A performance evaluation model for users’ satisfaction in federated cloudsCluster Computing10.1007/s10586-023-04231-327:4(4983-5004)Online publication date: 10-Jan-2024
  • (2024)A Hierarchical Modeling Approach for Assessing the Reliability and Performability of Burst BuffersArchitecture of Computing Systems10.1007/978-3-031-66146-4_18(266-281)Online publication date: 13-May-2024
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Information & Contributors

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Published In

cover image IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems  Volume 23, Issue 5
May 2012
191 pages

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IEEE Press

Publication History

Published: 01 May 2012

Author Tags

  1. Cloud computing
  2. embedded Markov chain.
  3. performance analysis
  4. queuing theory
  5. response time
  6. semi-Markov process

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Cited By

View all
  • (2024)Performance analysis of parallel composite service-based applications in cloudsFuture Generation Computer Systems10.1016/j.future.2023.11.021153:C(27-40)Online publication date: 16-May-2024
  • (2024)A performance evaluation model for users’ satisfaction in federated cloudsCluster Computing10.1007/s10586-023-04231-327:4(4983-5004)Online publication date: 10-Jan-2024
  • (2024)A Hierarchical Modeling Approach for Assessing the Reliability and Performability of Burst BuffersArchitecture of Computing Systems10.1007/978-3-031-66146-4_18(266-281)Online publication date: 13-May-2024
  • (2023)Reasoning About Expected Job Completion Time in Dynamic Vehicular CloudsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.327129124:8(8279-8289)Online publication date: 1-Aug-2023
  • (2023)Performance Evaluation of IoST–Mist–Fog–Cloud Framework for Geospatial Crime Data Visualization: A State Dependent Queueing ApproachSN Computer Science10.1007/s42979-023-02400-05:1Online publication date: 8-Dec-2023
  • (2023)Efficient Proactive Resource Allocation for Multi-stage Cloud-Native MicroservicesAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0801-7_24(411-432)Online publication date: 20-Oct-2023
  • (2022)A Bi-Objective Learn-and-Deploy Scheduling Method for Bursty and Stochastic Requests on Heterogeneous Cloud ServersIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.319647533:12(4547-4562)Online publication date: 1-Dec-2022
  • (2021)Performance Analysis of the IOTA DAG-Based Distributed LedgerACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/34851886:3(1-20)Online publication date: 2-Dec-2021
  • (2021)Optimizing the Performance of Containerized Cloud Software Systems Using Adaptive PID ControllersACM Transactions on Autonomous and Adaptive Systems10.1145/346563015:3(1-27)Online publication date: 18-Aug-2021
  • (2021)The hidden cost of the edgeProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476142(1-12)Online publication date: 14-Nov-2021
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