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Cluster Computing
Erschienen in: Cluster Computing 1/2019

23.10.2018

Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment

verfasst von: Syed Hamid Hussain Madni, Muhammad Shafie Abd Latiff, Shafi’i Muhammad Abdulhamid, Javed Ali

Erschienen in: Cluster Computing | Ausgabe 1/2019

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Abstract

Resource scheduling is a procedure for the distribution of resources over time to perform a required task and a decision making process in cloud computing. Optimal resource scheduling is a great challenge and considered to be an NP-hard problem due to the fluctuating demand of cloud users and dynamic nature of resources. In this paper, we formulate a new hybrid gradient descent cuckoo search (HGDCS) algorithm based on gradient descent (GD) approach and cuckoo search (CS) algorithm for optimizing and resolving the problems related to resource scheduling in Infrastructure as a Service (IaaS) cloud computing. This work compares the makespan, throughput, load balancing and performance improvement rate of existing meta-heuristic algorithms with proposed HGDCS algorithm applicable for cloud computing. In comparison with existing meta-heuristic algorithms, proposed HGDCS algorithm performs well for almost in both cases (Case-I and Case-II) with all selected datasets and workload archives. HGDCS algorithm is comparatively and statistically more effective than ACO, ABC, GA, LCA, PSO, SA and original CS algorithms in term of problem solving ability in accordance with results obtained from simulation and statistical analysis.
Vorheriger Artikel Efficient searchable public key encryption against keyword guessing attacks for cloud-based EMR systems

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Metadaten
Titel
Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment
verfasst von
Syed Hamid Hussain Madni
Muhammad Shafie Abd Latiff
Shafi’i Muhammad Abdulhamid
Javed Ali
Publikationsdatum
23.10.2018
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 1/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-2856-x

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