Abstract Background/Objectives: In cloud computing, the existing job scheduling algorithms were focused either on efficient job scheduling or optimal load balancing among the Virtual Machines. Methods/Statistical Analysis: This paper introduces a novel approach called Dynamic Load Balancing with effective Bin Packing and VM Reconfiguration (DLBPR) in the cloud. In the proposed work, the jobs are initially classified using the deadline based job scheduler and stored in a different job queue based on the expected processing speed of the job. After classification, the jobs in the various queues are prioritized using their attributes. Findings: The proposed approach dynamically splits and coalesces the VMs based on the required processing speed of the job. The VMs in the data center are dynamically clustered based on their processing speed with the support of VM live migration and the jobs are processed using the VMs in the cluster. Applications/Improvements: The proposed work is experimented in a cloudsim that minimizes the physical machine nearly 22% compared to other existing algorithms. A Novel Approach for Dynamic Load Balancing with Effective Bin Packing and VM Reconfiguration in Cloud Dinesh Komarasamy* and Vijayalakshmi Muthuswamy Department of Information Science and Technology, Anna University, Chennai - 600025, Tamil Nadu, India; dinesh@auist.net, vijim@annauniv.edu Keywords: Bin Packing, Cloud Computing, Load Balancing, VM Reconfiguration 1. Introduction A large number of naïve users are moving towards cloud computing for processing their job in a cost-effective manner. In the cloud, the user submitted jobs are catego- rized into two types, namely dependent and independent jobs 1,2 . Among these, the independent jobs do not require the output of other jobs to perform their execution. But, the dependent jobs need the results of the some other job to complete their execution. In cloud computing, the job response time has become the most important factor while running the jobs. Hence, scheduling plays an essen- tial role to minimize the response time of the job and to complete the jobs within their deadline. Along with job scheduling, load balancing plays a vital role to improve the resource utilization and to minimize the network congestion. Load balancing can be done either statically or dynamically 3 . In static load balancing, the jobs distrib- ute among the VMs based on the predefined set of rules that is not suitable for the cloud system. So, dynamic load balancing is preferable for cloud computing due to the dynamic nature of cloud computing. Several job scheduling algorithms were proposed for scheduling and processing the jobs. Still, job scheduling is an NP problem due to the dynamic behavior of the cloud system 4 . From the state of the art, First Come First Serve (FCFS) is one of the most famous existing scheduling algorithms to process the jobs based on the arrival time 5 . Shortest Job First (SJF) and Round Robin algorithms were introduced to process the jobs using the job length and time slice respectively 6 . e above algorithms cannot optimally process the deadline based jobs, so Earliest Deadline First (EDF) algorithm was introduced to schedule the dead- line based jobs which minimize the waiting time of the job by changing the order of job execution 7,8 . Moreover, some of the jobs were processed using the arrival time of the jobs. In the above scenario, FCFS algorithm cannot optimally schedule the jobs and so EASY backfilling has *Author for correspondence Indian Journal of Science and Technology, Vol 9(11), DOI: 10.17485/ijst/2016/v9i11/89290, March 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645