Optimized Load Balancing based Task Scheduling in Cloud Environment

The fundamental issue of Task scheduling is one important factor to load balance between the virtual machines in a Cloud Computing network. However, the optimal broadcast methods which have been proposed so far focus only on cluster or grid environment. In this paper, task scheduling strategy based on load balancing Quantum Particles Swarm algorithm (BLQPSO) was proposed. The fitness function based minimizing the makespan and data transmission cost. In addition, the salient feature of this algorithm is to optimize node available throughput dynamically using MatLab10A software. Furthermore, the performance of proposed algorithm had been compared with existing PSO and shows their effectiveness in balancing the load

Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing

Cloud Computing systems are widely applied in many fields such as communication data management, web application, network monitoring, financial management and so on. The distributed Cloud Computing technology has been produced as the development of the computer network and distributed computing technology. Researches on data Cloud Computing become the necessary trend in the distributed Cloud Computing system domain since the sources and application of the data are distributed and the scale of the applications enlarges quickly. Load management is the focus of research in both of the area in distributed Cloud Computing systems and centralized Cloud Computing systems. Although researches on the load management in the cloud systems is similar to that of traditional parallel and distributed systems in many aspects, essential differences exist between them. The choice of a scheduling strategy has significant impact on the runtime Central Processing Unit, memory consumption as well as the storage systems. Load balancing optimization techniques such as Ant Colony Optimization (ACO), First Come First Served (FCFS), Round Robin (RR) and Particle Swarm Optimization (PSO) are popular techniques being used for scheduling and load balancing. However, these techniques have its weaknesses in terms of minimizing makespan, computation cost and communication cost. In this study, load balancing technique in Cloud Computing called Quantum Particle Swarm Optimization (QPSO) technique proposed by considering only minimization of makespan, computation cost and communication cost. Performance of the QPSO technique based on many heuristic algorithms it is comprised the following steps. Firstly, tasks are assigned averagely to the machines according to a special initialization policy. Then the optimal criterion for exchanging tasks between two machines is proposed and exploited to speed up the improving process towards load balance. Secondly, this thesis proposes job-combination based static algorithm for load balancing where all jobs should organized into the standard job combinations, each task of which consists of one to four jobs. Then they are assigned to the machines according to the assignment algorithm for job combinations, which is a special integer partition algorithm. Finally, the result of experiment shows that QPSO can achieve at least three times cost saving as compared with ACO, FCFS, RR and PSO

Security based Risk Management based on Multi-Objectives Model using QPSO

Nowadays the security risk management play a crucial role, which is applied to the entire life cycle of information systems and communication technologies but still so many models for security risk management are non-practical, therefore, it should be measured and improved. In this paper, a novel approach, in which Analytic Hierarchy Process (AHP) and Quantum Particles Swarm Optimization (QPSO) can be combined with some changes, is presented. The method consists of; firstly, the analytic hierarchy structure of the risk management is constructed and the method of QPSO comprehensive judgment is improved according to the actual condition of the information security. Secondly, the risk degree put forward is QPSO estimation of the risk probability, the risk impact severity and risk uncontrollability. Finally, it gives examples to prove that this method Multi Objectives Programming Methodology (MOPM) can be well applied to security risk management and provides reasonable data for constituting the risk control strategy of the information systems security. Based on the risk management results, the targeted safety measures are taken, and the risk is transferred and reduced, which is controlled within an acceptable range

Multi-objectives model to process security risk assessment based on AHP-PSO

Nowadays the security risk assessment play a crucial role, which is applied to the entire life cycle of information systems and communication technologies but still so many models for security risk assessment are non practical, therefore, it should be measured and improved. In this paper, a novel approach, in which Analytic Hierarchy Process (AHP) and Particles Swarm Optimization (PSO) can be combined with some changes, is presented. The method consists of; firstly, the analytic hierarchy structure of the risk assessment is constructed and the method of PSO comprehensive judgment is improved according to the actual condition of the information security. Secondly, the risk degree put forward is PSO estimation of the risk probability, the risk impact severity and risk uncontrollability. Finally, it gives examples to prove that this method Multi Objectives Programming Methodology (MOPM) can be well applied to security risk assessment and provides reasonable data for constituting the risk control strategy of the information systems security. Based on the risk assessment results, the targeted safety measures are taken, and the risk is transferred and reduced, which is controlled within an acceptable range