MLMGAD Configuration to Expand Load Management for Cloud
Akhilesh Kumar Bhardwaj1, Rajiv Mahajan2, Surender Kumar3
1Akhilesh Kumar Bhardwaj, Research Scholar, Department of Computer Science and Engineering, IKG PTU, Punjab, India.
2Rajiv Mahajan, Professor, Department of Computer Science and Engineering, GCET, Gurdaspur, Punjab, India.
3Surender Kumar, Assistant Professor, Department of Computer Science, Guru Teg Bahadur College, Sangrur, Punjab, India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 548-553 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6697068819/19©BEIESP
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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: Load management is a principle challenge in distributed cloud communication to circulate the load over various nodes to guarantee that no individual node is sitting idle, overburdened or under-burdened. In the cloud, load management is a key apprehension. The number of cloud clients is likewise expanded from 2.4 billion in 2013 to 3.6 billion in 2018. It implies 33% ascent in the number of clients in the last half decade. So, better load management is constantly required for a cloud client fulfillment to benefit the related services without any delay or information loss. Clearly, cloud organizations require better and reshaped workload engineering with the progression of time. This work of interest has persuaded us to pick this task and to create a modified load management genetic algorithm design (MLMGAD) is projected to compute and compare different performance measurements. The central goal of this work is clustered into five segments. (1) To test whether the implemented procedure is improving throughput and limiting packet delay. (2) To examine and reduce packet loss. (3) To examine and improve the response time by reason. (4) To examine and improve overall response time, overall data center processing time and average, minimum and maximum response time. (5) To examine and enhance execution time.
Keyword: Cloud, Load management, MLMGAD, Performance measurements.
Scope of the Article: Simulation Optimization and Risk Management.