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How can Periodic Workload Cloud Pattern benefit from Periodically Peaking Utilization?

by Ravi (Ravinder) Prakash G., Kiran M.
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 5
Year of Publication: 2016
Authors: Ravi (Ravinder) Prakash G., Kiran M.
10.5120/ijais2016451505

Ravi (Ravinder) Prakash G., Kiran M. . How can Periodic Workload Cloud Pattern benefit from Periodically Peaking Utilization?. International Journal of Applied Information Systems. 10, 5 ( February 2016), 27-36. DOI=10.5120/ijais2016451505

@article{ 10.5120/ijais2016451505,
author = { Ravi (Ravinder) Prakash G., Kiran M. },
title = { How can Periodic Workload Cloud Pattern benefit from Periodically Peaking Utilization? },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2016 },
volume = { 10 },
number = { 5 },
month = { February },
year = { 2016 },
issn = { 2249-0868 },
pages = { 27-36 },
numpages = {9},
url = { https://www.ijais.org/archives/volume10/number5/863-2016451505/ },
doi = { 10.5120/ijais2016451505 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:02:36.305529+05:30
%A Ravi (Ravinder) Prakash G.
%A Kiran M.
%T How can Periodic Workload Cloud Pattern benefit from Periodically Peaking Utilization?
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 10
%N 5
%P 27-36
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Measurability is a concept in periodically peaking that is based on two assumptions: (1) every cloud service provider is cautious, i.e., does not exclude any cloud consumer’s Periodic Workload resource pooling pattern choice from consideration, and (2) every cloud service provider respects the cloud consumer’s Periodic Workload resource pooling pattern preferences, i.e., deems one cloud consumer’s Periodic Workload resource pooling pattern choice to be infinitely more likely than another whenever it premises the cloud consumer to prefer the one to the other. In this paper we provide a new approach for measurability, by assuming that cloud service providers have asymmetric Periodic Workload resource pooling pattern about the cloud consumer’s Periodic Workload utilities. We show that, if the uncertainty of each cloud service provider about the cloud consumer’s Periodic Workload utilities vanishes gradually in some regular manner, then the Periodic Workload resource pooling pattern choices it can measurably make under common conjecture in measurability are all actually measureable in the original periodically peaking with no uncertainty about the cloud consumer’s utilities.

References
  1. Kiran M., Saikat Mukherjee, Ravi Prakash G., Characterization of Randomized Shuffle and Sort Quantifiability in MapReduce Model, International Journal of Computer Applications, 51-58, Volume 79, No. 5, October 2013.
  2. Amresh Kumar, Kiran M., Saikat Mukherjee, Ravi Prakash G., Verification and Validation of MapReduce Program model for Parallel K-Means algorithm on Hadoop Cluster, International Journal of Computer Applications, 48-55, Volume 72, No. 8, June 2013.
  3. Barroso, L.A., Ho¨lzle, U.: The datacenter as a computer: an introduction to the design of warehouse-scale machines. Synth. Lect. Comput. Architect. 4, 1–45 (2009).
  4. Kiran M., Amresh Kumar, Saikat Mukherjee, Ravi Prakash G., Verification and Validation of MapReduce Program Model for Parallel Support Vector Machine Algorithm on Hadoop Cluster, International Journal of Computer Science Issues, 317-325, Vol. 10, Issue 3, No. 1, May 2013.
  5. Ravi Prakash G, Kiran M. Saikat Mukherjee, On Randomized Preference Limitation Protocol for Quantifiable Shuffle and Sort Behavioral Implications in MapReduce Programming Model, Parallel & Cloud Computing, Vol. 3, Issue 1, 1-14, January 2014.
  6. Fehling, C., Leymann, F., Mietzner, R., Schupeck, W.: A collection of patterns for cloud types, cloud service models, and cloud-based application architectures. Technical report, University of Stuttgart (2011)
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  8. Ravi (Ravinder) Prakash G, Kiran M., On Randomized Minimal MapReduce Sets for Filtering Expressions, International Journal of Computer Applications, Volume 98, No. 3, Pages 1-8, July 2014.
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  12. Ravi (Ravinder) Prakash G, Kiran M., Shuffling Expressions with MapReduce Arrangements and the Role of Binary Path Symmetry. International Journal of Computer Applications 102(16): 19-24, September 2014.
  13. Dimitri P. Bertsekas and John N. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, Athena Scientific, Hardcover Edition (appeared in 2015), ISBN: 1-886529-15-9 Publication: 2015, 735 pages.
  14. Ravi (Ravinder) Prakash G, Kiran M; How Replicated Join Expressions Equal Map Phase or Reduce Phase in a MapReduce Structure? International Journal of Computer Applications, Volume 107 (12): 43-50, December 2014.
  15. Fehling, C., Ewald, T., Leymann, F., Pauly, M., Ru¨tschlin, J., Schumm, D.: Capturing cloud computing knowledge and experience in patterns. In: Proceedings of the 5th IEEE International Conference on Cloud Computing (CLOUD), Honolulu, (2012).
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  17. Ravi (Ravinder) Prakash G, Kiran M., On Composite Join Expressions of Map-side with many Reduce Phase. International Journal of Computer Applications Volume 110(9): 37-44, January 2015.
  18. Dimitri P. Bertsekas, Convex Optimization Algorithms, Athena Scientific, Hardcover Edition ISBN: 1-886529-28-0, 978-1-886529-28-1, Publication: February, 2015, 576 pages.
  19. Ravi (Ravinder) Prakash G, Kiran M; How Reduce Side Join Part File Expressions Equal MapReduce Structure into Task Consequences, Performance? International Journal of Computer Applications, Volume 105(2):8-15, November 2014
  20. Ravi (Ravinder) Prakash G, Kiran M. "On the MapReduce Arrangements of Cartesian product Specific Expressions". International Journal of Computer Applications 112(9):34-41, February 2015.
  21. Ravi (Ravinder) Prakash G, Kiran M., On Job Chaining MapReduce Meta Expressions of Mapping and Reducing Entropy Densities. International Journal of Computer Applications 113(15): 20-27, March 2015.
  22. Ravi (Ravinder) Prakash G, Kiran M. "On Chain Folding Problems of Chain Mapper and Chain Reducer Meta Expressions". International Journal of Computer Applications 116(16): 35-42, April 2015.
  23. Ravi (Ravinder) Prakash G, Kiran M."On Job Merging MapReduce Meta Expressions for Multiple Decomposition Mapping and Reducing". International Journal of Computer Applications 118 (13):14-21, May 2015.
  24. Ravi (Ravinder) Prakash G, Kiran M." Characterization of Randomized External Source Output Map Reduce Expressions". International Journal of Computer Applications 123(14):9-16, August 2015.
  25. Ravi (Ravinder) Prakash G, Kiran M., Does there Exist Pruning Decomposition for MapReduce Expressions Arrangements?. International Journal of Computer Applications 125(12): 41-48, September 2015.
  26. Ravi (Ravinder) Prakash G, Kiran M: Can one find External Source Input Expressions for which there exist Map Reduce Configurations? International Journal of Computer Applications 128(12): 14-21, October 2015.
  27. Ravi (Ravinder) Prakash G. and Kiran M., Is It True for Static Scaling Cloud Model there Exists a Centrally Asymmetric Static Workload Pattern? Communications on Applied Electronics 3(4): 39-48, November 2015.
  28. Ravi (Ravinder) Prakash. G and Kiran M., Given a Static Workload Cloud Computing Patterns does it have an Elastic Scaling? Communications on Applied Electronics 4(2): 17-26, January 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud service provider cloud consumer Periodic Workload asymmetric resource pooling pattern utilities periodically peaking behavioral measurably

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