Cooperative PSO-ACO Approach for Job Scheduling and Load Balancing in Public Cloud Network Indra Nath Sahu Ph.D. Scholar Dr. Jitendra Sheetalani Associate Professor Sri Satya Sai University & Medical Sciences, Sehore, India rc.insahu@gmail.com Sri Satya Sai University & Medical Sciences, Sehore, India dr.jsheetlani@gmail.com Abstract Resource allocation and management of job is major issue in public cloud networks. For the allocation of resource and management of job used scheduling techniques. The conventional and dynamic job scheduling techniques increases the time span and failure of jobs are occurred. The failure of job and resource raised the problem of load balancing. For the management of load balancing used various dynamic methods based on probability theory and heuristic function. In this paper proposed the hybrid methods of load balancing for public cloud networks. The hybrid methods are combination of two well know heuristic function particle swarm optimization and ant colony optimization. The particle swarm optimization maps the job according to their resource and the ant colony optimization works as scheduler of jobs. The proposed algorithm simulated in cloudSim simulator and measure two parameter one is data processing time and other is response time. Keywords: - Cloud Computing, Load Balancing, ACO, PSO, CloudSim. I. INTRODUCTION Cloud computing play a vital role in current decade IT based services. The IT based services used infrastructure, platform and software. The all buckets of services provided by the cloud network with minimum cost. The reduction of cost of IT based infrastructure is big story of the success of IT based industry[1-3]. The efficiency and utility of cloud computing network based on the management of resource and jobs scheduling. For the managements of job and scheduling of resource used various algorithms and methods based on the concept of conventional and dynamic load balancing. Resource provisioning is the task of mapping of the resources to different entities of cloud on demand basis. Resources must be allocated in such a manner that no node in the cloud is overloaded and all the available resources in the cloud do not undergo any kind of wastage [17-20]. Virtual machines reside on the host. More than one instance of VM can be mapped onto a single host subject to its availability and capabilities. Host is responsible for assigning processing cores to VM. Provisioning policy define the basis of allocating processing cores to VM on demand. Allocation policy or algorithm must ensure that critical characteristics of Host and VM do not mismatch [30]. Applications or tasks are actually executed on VM. Each application requires certain amount of processing power for their completion. VM must provide required processing power to the tasks mapped onto it and these tasks must be mapped onto appropriate VM based upon its configuration and availability [22]. The scalability of network is generally marked by efficient usage of the resources available [27]. This could only be achieved Load balancing in cloud computing provides an efficient solution to various issues residing in cloud computing environment set-up and usage. Load balancing must take into account two major tasks, one is the resource provisioning or resource allocation and other is task scheduling in distributed environment [28]. Efficient provisioning of resources and for measuring the efficiency and effectiveness of Load Balancing algorithms simulation environment are required [29]. Cloud Sim is the most efficient tool that can be used for modelling of Cloud. During the lifecycle of a Cloud, Cloud Sim allows VMs to be managed by hosts which in turn are managed by datacenters. Cloud sim provides architecture with four basic entities. These entities allow user to set-up a basic cloud computing environment and measure the effectiveness of Load Balancing algorithms [21]. Now a day’s various authors used swarm based searching technique for the scheduling of task for the proper execution of task. The family of swarm intelligence gives verity of algorithm such as ant colony optimization, particle of swarm optimization and glowworm swarm optimization the policy of cloud partition based on the consideration of master and slave concept. The all load initially distributed in terms of this manner. The master partition of load shares the basic information in terms of server and the slave node share the information according to the client node in network[5-7]. The slave node of partition flows the instruction of master node. The particle swarm optimization algorithm based on the concept of bird fork in the sky. The bird fork sky in the fly and maintains the constant velocity and gives the concept of optimization behavior of swarm. The family of swarm intelligence derives in terms of artificial intelligence and used various domains for the process of optimization. Now a day’s particle swarm optimization used load balancing for the optimization of load during scheduling of process. The proposed methods are combination Indra nath Sahu et al , International Journal of Computer Technology & Applications,Vol 8(4),544-550 IJCTA | July-August 2017 Available online@www.ijcta.com 544 ISSN:2229-6093