Open AccessEnergy-Efficient Heterogeneous Multi-Processor Environment in Cloud using Modern Artificial BEE Colony
Author(s) -
Sakshi Kapoor,
S. N. Panda,
Sougata Panda
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b2835.129219
Subject(s) - computer science , cloud computing , distributed computing , scheduling (production processes) , ant colony optimization algorithms , job scheduler , load balancing (electrical power) , energy consumption , job shop scheduling , grid , artificial intelligence , operating system , mathematical optimization , engineering , schedule , geometry , mathematics , electrical engineering
Cloud Computing is an expansion in distributed, parallel as well as grid computing. The purpose behind cloud computing is the provision of dynamic hiring of server proficiencies as a virtualized and accessible service for customers and end-users. A key issue found in the cloud is the management of resources. Load balancing is a key problem in the management of resources. The job scheduling issue has charmed abundant courtesy in the field of operation research. There are various algorithms like Ant optimization, genetic algorithms, artificial bee colony which can be used to solve the problem of scheduling. No doubt, Parallelization is proved to be the best method that can be utilized for improving the concert of the above algorithms. In this article, a modified artificial bee colony is utilized in order to crack the problem of scheduling in a heterogeneous multi-processor environment. In this, ABC has various colonies located on dissimilar network hosts as well as the algorithm is accepted in several colonies in parallel fashion. The colonies communicate with each other, which is approved through exchanging immigrants. In order to determine the communication of colonies with neighbors, a dynamic strategy is followed up. The algorithm is useful in making the parallel environment more efficient by reducing energy consumption. The energy consumption is reduced for each job in the DAG. Scheduling with MABC in the heterogeneous environment becomes easy as well as effective