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A Systemmatic way to balance the load on cloud using Hybrid (OLB+LBMM) Technique

File Size:
1.48 MB
Volume:
Volume 2, Issue 8 (August, 2016)
Publication No:
IJTC201608007
Author:
Navpreet kaur aulakh, Rishideep Singh
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8 x

Abstract:
Cloud computing is an emerging computing paradigm with a large collection of heterogeneous autonomous systems with flexible computational architecture. Load Balancing is an important step to improve the overall performance of the cloud computing. Load Balancing is also essential to reduce power consumption and improve the profit of service providers by reducing processing time. In cloud computing environment, user services always demand heterogeneous resources (e,g CPU, I/O, Memory etc.). Load balancing in large distributed server systems is a complex optimization problem of critical importance in cloud systems and data centers. Load balancing algorithms are classified as static and dynamic algorithms. Static algorithms are mostly suitable for homogeneous and stable environments and can produce very good results in these environments. However, they are usually not flexible and cannot match the dynamic changes to the attributes during the execution time. Dynamic algorithms are more flexible and take into consideration different types of attributes in the system both prior to and during run-time. Load balancing aims to optimize resource use, maximize throughput, minimize response time, and avoid overload of any single resource. In the paper, we have studied and implemented three algorithm using Java language and simulate using CloudSim. CloudSim also offers novel support for modeling and simulation of virtualized Cloud based data center environments such as dedicated management interfaces for VMs, memory, storage, and bandwidth. CloudSim layer manages the instantiation and execution of core entities (VMs, hosts, data centers, application) during the simulation period. Hence it is proven that proposed algorithm OLB+LBMM gives the better results as compared to Honeybee Foraging Behavior and Active Clustering. We also analyzed the research results on the basis of distinct performance parameters such as Response time, Total Execution time, Throughput, Processing Time and Energy consumption.

Indexed Terms:
Load balancing, OLB+LBMM, Active Clustering, Honeybee, Virtual Machines, API.