Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters

Louai Sheikhani; Weichao Ding; Jonathan Talwana; Chunhua Gu

doi:doi:10.11648/j.iotcc.20190701.14

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Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters

Louai Sheikhani, Weichao Ding, Jonathan Talwana, Chunhua Gu

Published in Internet of Things and Cloud Computing (Volume 7, Issue 1)

Received: 14 March 2019 Published: 15 June 2019

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Abstract

As an increasing number of businesses move toward Cloud based services, issues such as reduce response time, optimize cost, and load balance over data centers are important factor that need to be studied. Selecting the suitable data center to handle the user request is affecting those factors directly. The Broker policy determines which data center should service the request from each user base; so choosing appropriate policy can improve the performance noticeably. One of the benchmarks policies is service proximity-based that routing the request to the data center, which has lowest network latency or minimum transmission delay from a user base. If there are more than one data centers in a region in close proximity, then one of the data centers is selected at random to service the incoming request. However, other factors such as cost, workload, number of virtual machines, processing time etc., are not taken into consideration. Randomly selected data center gives undesirable results in terms of response time, data processing time, cost, and other parameters. this work propose modifying that policy by applying new schedule algorithm that control the load balance. the results showed that the using of this algorithm instead of the random selection would improve the distribution of the workload over the available datacenters noticeably.

Published in	Internet of Things and Cloud Computing (Volume 7, Issue 1)
DOI	10.11648/j.iotcc.20190701.14
Page(s)	25-30
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Cloud Computing, Datacenter Selection, Broker Policy; Min-min Scheduling Algorithm, Load Balance

References

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[2]	S. Ranjan Jena and Z. Ahmad, “Response time minimization of different load balancing algorithms in cloud computing environment”, IJCA, Volume-69, No-17, May 2013 edition.
[3]	K. Nishant, P. Sharma, V. Krishna, C. Gupta, K. Pratap Singh and R. Rastogi, “Load balancing of nodes in cloud using ant colony optimization”, 2012, 14th International conference on modeling and simulation.
[4]	J. James and B. Verma, “Efficient VM load balancing algorithm for a cloud computing environment”, International Journal on Computer Science and Engineering (IJCSE), 2012.
[5]	S. Ray and A. De Sarkar, “Execution analysis of load balancing algorithms in cloud computing environment”, IJCCSA, Vol.2, No.5, October 2012.
[6]	A. Singh. N and M. Hemalatha, “An approach on semi -distributed load balancing algorithm for cloud computing system”, IJCA, Volume 56– No.12, October 2012.
[7]	S. Sethi, A. Sahu and S. Kumar Jena, “Efficient load balancing in cloud computing using Fuzzy logic”, IOSRJEN, ISSN: 2250-3021 Volume 2, Issue 7, 2012
[8]	H. Mehta, P. Kanungo, and M. Chandwani, “Decentralized content aware load balancing algorithm for distributed computing environments”, Proceedings of the International Conference Workshop on Emerging Trends in Technology (ICWET), February 2011, pages 370-375.
[9]	Y. Lua, Q. Xiea, G. Kliotb, A. Gellerb, J. R. Larusb and A. Greenber, “Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services”, An international Journal on Performance evaluation.
[10]	J. Hu, J. Gu, G. Sun, and T. Zhao, A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environ1144 International Journal of Scientific & Engineering Research Volume 5, Issue 3, March-2014 ISSN 2229-5518.
[11]	S. Wang, K. Yan, W. Liao, and S. Wang, “Towards a Load Balancing in a Three-level Cloud Computing Network”, Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), Chengdu, China, September 2010, pages 108-113.
[12]	R. K. Mishra, S. Kumar, B. Sreenu Naik, Priority based Round-Robin service broker algorithm for Cloud-Analyst [C]//Advance Computing Conference (IACC), 2014 IEEE International. IEEE, 2014: 878-881.
[13]	L. Sheikhani, Y. Chang, C. Gu and F. Luo, "Modifying broker policy for better response time in datacenters," 2017 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, 2017, pp. 2459-2464.
[14]	B. Wickremasinghe, R. Buyya, “CloudAnalyst: A CloudSim-based tool for modelling and analysis of large scale cloud computing environments,” MEDC project report, 22 (6), 2009, pp.433-659.

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APA Style

Louai Sheikhani, Weichao Ding, Jonathan Talwana, Chunhua Gu. (2019). Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters. Internet of Things and Cloud Computing, 7(1), 25-30. https://doi.org/10.11648/j.iotcc.20190701.14

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ACS Style

Louai Sheikhani; Weichao Ding; Jonathan Talwana; Chunhua Gu. Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters. Internet Things Cloud Comput. 2019, 7(1), 25-30. doi: 10.11648/j.iotcc.20190701.14

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AMA Style

Louai Sheikhani, Weichao Ding, Jonathan Talwana, Chunhua Gu. Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters. Internet Things Cloud Comput. 2019;7(1):25-30. doi: 10.11648/j.iotcc.20190701.14

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@article{10.11648/j.iotcc.20190701.14,
  author = {Louai Sheikhani and Weichao Ding and Jonathan Talwana and Chunhua Gu},
  title = {Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters},
  journal = {Internet of Things and Cloud Computing},
  volume = {7},
  number = {1},
  pages = {25-30},
  doi = {10.11648/j.iotcc.20190701.14},
  url = {https://doi.org/10.11648/j.iotcc.20190701.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20190701.14},
  abstract = {As an increasing number of businesses move toward Cloud based services, issues such as reduce response time, optimize cost, and load balance over data centers are important factor that need to be studied. Selecting the suitable data center to handle the user request is affecting those factors directly. The Broker policy determines which data center should service the request from each user base; so choosing appropriate policy can improve the performance noticeably. One of the benchmarks policies is service proximity-based that routing the request to the data center, which has lowest network latency or minimum transmission delay from a user base. If there are more than one data centers in a region in close proximity, then one of the data centers is selected at random to service the incoming request. However, other factors such as cost, workload, number of virtual machines, processing time etc., are not taken into consideration. Randomly selected data center gives undesirable results in terms of response time, data processing time, cost, and other parameters. this work propose modifying that policy by applying new schedule algorithm that control the load balance. the results showed that the using of this algorithm instead of the random selection would improve the distribution of the workload over the available datacenters noticeably.},
 year = {2019}
}

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TY  - JOUR
T1  - Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters
AU  - Louai Sheikhani
AU  - Weichao Ding
AU  - Jonathan Talwana
AU  - Chunhua Gu
Y1  - 2019/06/15
PY  - 2019
N1  - https://doi.org/10.11648/j.iotcc.20190701.14
DO  - 10.11648/j.iotcc.20190701.14
T2  - Internet of Things and Cloud Computing
JF  - Internet of Things and Cloud Computing
JO  - Internet of Things and Cloud Computing
SP  - 25
EP  - 30
PB  - Science Publishing Group
SN  - 2376-7731
UR  - https://doi.org/10.11648/j.iotcc.20190701.14
AB  - As an increasing number of businesses move toward Cloud based services, issues such as reduce response time, optimize cost, and load balance over data centers are important factor that need to be studied. Selecting the suitable data center to handle the user request is affecting those factors directly. The Broker policy determines which data center should service the request from each user base; so choosing appropriate policy can improve the performance noticeably. One of the benchmarks policies is service proximity-based that routing the request to the data center, which has lowest network latency or minimum transmission delay from a user base. If there are more than one data centers in a region in close proximity, then one of the data centers is selected at random to service the incoming request. However, other factors such as cost, workload, number of virtual machines, processing time etc., are not taken into consideration. Randomly selected data center gives undesirable results in terms of response time, data processing time, cost, and other parameters. this work propose modifying that policy by applying new schedule algorithm that control the load balance. the results showed that the using of this algorithm instead of the random selection would improve the distribution of the workload over the available datacenters noticeably.
VL  - 7
IS  - 1
ER  -

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Author Information

Louai Sheikhani

School of Information Science and Engineering/East China University of Science and Technology, Shanghai, China
Weichao Ding

School of Information Science and Engineering/East China University of Science and Technology, Shanghai, China
Jonathan Talwana

School of Information Science and Engineering/East China University of Science and Technology, Shanghai, China
Chunhua Gu

School of Information Science and Engineering/East China University of Science and Technology, Shanghai, China

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Plain Text BibTeX RIS

APA Style

Louai Sheikhani, Weichao Ding, Jonathan Talwana, Chunhua Gu. (2019). Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters. Internet of Things and Cloud Computing, 7(1), 25-30. https://doi.org/10.11648/j.iotcc.20190701.14

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ACS Style

Louai Sheikhani; Weichao Ding; Jonathan Talwana; Chunhua Gu. Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters. Internet Things Cloud Comput. 2019, 7(1), 25-30. doi: 10.11648/j.iotcc.20190701.14

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AMA Style

Louai Sheikhani, Weichao Ding, Jonathan Talwana, Chunhua Gu. Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters. Internet Things Cloud Comput. 2019;7(1):25-30. doi: 10.11648/j.iotcc.20190701.14

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@article{10.11648/j.iotcc.20190701.14,
  author = {Louai Sheikhani and Weichao Ding and Jonathan Talwana and Chunhua Gu},
  title = {Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters},
  journal = {Internet of Things and Cloud Computing},
  volume = {7},
  number = {1},
  pages = {25-30},
  doi = {10.11648/j.iotcc.20190701.14},
  url = {https://doi.org/10.11648/j.iotcc.20190701.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20190701.14},
  abstract = {As an increasing number of businesses move toward Cloud based services, issues such as reduce response time, optimize cost, and load balance over data centers are important factor that need to be studied. Selecting the suitable data center to handle the user request is affecting those factors directly. The Broker policy determines which data center should service the request from each user base; so choosing appropriate policy can improve the performance noticeably. One of the benchmarks policies is service proximity-based that routing the request to the data center, which has lowest network latency or minimum transmission delay from a user base. If there are more than one data centers in a region in close proximity, then one of the data centers is selected at random to service the incoming request. However, other factors such as cost, workload, number of virtual machines, processing time etc., are not taken into consideration. Randomly selected data center gives undesirable results in terms of response time, data processing time, cost, and other parameters. this work propose modifying that policy by applying new schedule algorithm that control the load balance. the results showed that the using of this algorithm instead of the random selection would improve the distribution of the workload over the available datacenters noticeably.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters
AU  - Louai Sheikhani
AU  - Weichao Ding
AU  - Jonathan Talwana
AU  - Chunhua Gu
Y1  - 2019/06/15
PY  - 2019
N1  - https://doi.org/10.11648/j.iotcc.20190701.14
DO  - 10.11648/j.iotcc.20190701.14
T2  - Internet of Things and Cloud Computing
JF  - Internet of Things and Cloud Computing
JO  - Internet of Things and Cloud Computing
SP  - 25
EP  - 30
PB  - Science Publishing Group
SN  - 2376-7731
UR  - https://doi.org/10.11648/j.iotcc.20190701.14
AB  - As an increasing number of businesses move toward Cloud based services, issues such as reduce response time, optimize cost, and load balance over data centers are important factor that need to be studied. Selecting the suitable data center to handle the user request is affecting those factors directly. The Broker policy determines which data center should service the request from each user base; so choosing appropriate policy can improve the performance noticeably. One of the benchmarks policies is service proximity-based that routing the request to the data center, which has lowest network latency or minimum transmission delay from a user base. If there are more than one data centers in a region in close proximity, then one of the data centers is selected at random to service the incoming request. However, other factors such as cost, workload, number of virtual machines, processing time etc., are not taken into consideration. Randomly selected data center gives undesirable results in terms of response time, data processing time, cost, and other parameters. this work propose modifying that policy by applying new schedule algorithm that control the load balance. the results showed that the using of this algorithm instead of the random selection would improve the distribution of the workload over the available datacenters noticeably.
VL  - 7
IS  - 1
ER  -

Copy | Download