TY - JOUR
T1 - A predictive workload balancing algorithm in cloud services
AU - Jodayree, Mahdee
AU - Abaza, Mahmoud
AU - Tan, Qing
N1 - Funding Information:
The first author did most of the work when he was at Athabasca University. support of Discovery NSERC Grant of Canada.
Publisher Copyright:
© 2019 The Author(s). Published by Elsevier B.V.
PY - 2019
Y1 - 2019
N2 - Performance of dynamic clouds depends on the efficiency of its load balancing and resource allocation. This paper is an exploratory study on the predictive approach for dynamic resource distribution of cloud services. Efficient cloud resource management can be achieved by simulating cloud services based on the predictions of incoming workloads, which can be more efficient than static allocation methods. This paper introduces a rule-based workload-balancing algorithm based on the predictions of an end-to-end system called Cicada. A simulation of cloud services can be achieved by a cloud service simulator called CloudSim and it will be used to achieve an algorithm with lower computational demand and a faster workload balancing. The final result will demonstrate the effectiveness of a predictive workload balancing approach that can achieve faster workload balancing with a lower computational power usage.
AB - Performance of dynamic clouds depends on the efficiency of its load balancing and resource allocation. This paper is an exploratory study on the predictive approach for dynamic resource distribution of cloud services. Efficient cloud resource management can be achieved by simulating cloud services based on the predictions of incoming workloads, which can be more efficient than static allocation methods. This paper introduces a rule-based workload-balancing algorithm based on the predictions of an end-to-end system called Cicada. A simulation of cloud services can be achieved by a cloud service simulator called CloudSim and it will be used to achieve an algorithm with lower computational demand and a faster workload balancing. The final result will demonstrate the effectiveness of a predictive workload balancing approach that can achieve faster workload balancing with a lower computational power usage.
KW - algorithm
KW - dynamic cloud
KW - load balance
KW - predictive
KW - workload
UR - http://www.scopus.com/inward/record.url?scp=85076259381&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2019.09.250
DO - 10.1016/j.procs.2019.09.250
M3 - Conference article
AN - SCOPUS:85076259381
VL - 159
SP - 902
EP - 912
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2019
Y2 - 4 September 2019 through 6 September 2019
ER -