Energy-Efficient Heterogeneous Multi-Processor Environment in Cloud using Modern Artificial BEE Colony
Sakshi Kapoor1, Surya Narayan Panda2
1Sakshi Kapoor, Chitkara University Institute of Engineering and Technology Chitkara University, Punjab, India.
2Surya Narayan Panda, Chitkara University Institute of Engineering and Technology Chitkara University, Punjab, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on December 08, 2019. | Manuscript published on December 30, 2019. | PP: 976-981 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2835129219/2020©BEIESP | DOI: 10.35940/ijeat.B2835.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Cloud Computing, Parallelization, Multiprocessor, Energy Consumption, Scheduling, Heterogeneous Environment.