CLBC - Cost effective load balanced resource allocation for partitioned cloud system

Abstract

In cloud computing, remote based massive data storage and dynamic computation services are provided to the users. The cloud enables the user to complete their tasks using pay-as-you-go cost model which typically works on the incurred virtual machine hours, so reducing the execution time will minimize the computational cost. Therefore the scheduler should bring maximum throughput in order to achieve effective resource allocation in cloud. Hence, in this work, DBPS (Deadline Based Pre-emptive Scheduling) and a TLBC (Throttled Load Balancing for Cloud) load balancing model based on cloud partitioning using virtual machine has been proposed. Workload prediction is done using statistics and training set, so that error tolerance can be achieved in TLBC. The preliminary results obtained when measuring performance based on the computational cost of the task set and the number of tasks executed in a particular time shows the proposed TLBC outperforms compared with existing systems. OpenNebula has been used as the cloud management tool for doing real time analysis and improving performance.