Automatic Scaling In Cloud Environment
Cloud Computing, Automatic Scaling, Demand Satisfaction Ratio, Energy Conservation, Load balancing
An energy-efficient load balancing technique can be used to improve the performance of Cloud Computing by balancing the workload across all the nodes in the cloud with maximum resource utilization and reducing energy consumption. This paper presents a system that provides automatic scaling for Internet applications in the cloud environment. A two level hierarchical load balancing model for cloud is proposed with a Global Centralized Scheduling Center (GCSC) at higher level and the Local Centralized Scheduling Center (LCSC) at lower level. The Global Centralized Scheduling Center uses the agent based adaptive balancing. The revenue of cloud provider can be increased by allocating the task to the appropriate server which executes the task with minimum cost and without violating the QOS constraints. It distributes the system workload based on the processing elements capacity which leads to minimize the overall job mean response time and maximize the system utilization and throughput. The Local Centralized Scheduling Center is modelled as the Class Constrained Bin Packing (CCBP) problem where each server is a bin and each class represents an application. It achieves good demand satisfaction ratio and saves energy by reducing the number of servers used when the load is low.
Volume 3 Issue 2
Pages. 761-765
