Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid

The concept of Grid computing is becoming the most important research area in the high performance computing. Under this concept, the jobs scheduling in Grid computing has more complicated problems to discover a diversity of available resources, select the appropriate applications and map to suitable resources. However, the major problem is the optimal job scheduling, which Grid nodes need to allocate the appropriate resources for each job. In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. Our model presents the method of the jobs classifications based mainly on Fuzzy C-Mean algorithm and mapping the jobs to the appropriate resources based mainly on Genetic algorithm. In the experiments, we used the workload historical information and put it into our simulator. We get the better result when compared to the traditional algorithms for scheduling policies. Finally, the paper also discusses approach of the jobs classifications and the optimization engine in Grid scheduling

SGA Model for Prediction in Cloud Environment

With virtual information, cloud computing has made applications available to users everywhere. Efficient asset workload forecasting could help the cloud achieve maximum resource utilisation. The effective utilization of resources and the reduction of datacentres power both depend heavily on load forecasting. The allocation of resources and task scheduling issues in clouds and virtualized systems are significantly impacted by CPU utilisation forecast. A resource manager uses utilisation projection to distribute workload between physical nodes, improving resource consumption effectiveness. When performing a virtual machine distribution job, a good estimation of CPU utilization enables the migration of one or more virtual servers, preventing the overflow of the real machineries. In a cloud system, scalability and flexibility are crucial characteristics. Predicting workload and demands would aid in optimal resource utilisation in a cloud setting. To improve allocation of resources and the effectiveness of the cloud service, workload assessment and future workload forecasting could be performed. The creation of an appropriate statistical method has begun. In this study, a simulation approach and a genetic algorithm were used to forecast workloads. In comparison to the earlier techniques, it is anticipated to produce results that are superior by having a lower error rate and higher forecasting reliability. The suggested method is examined utilizing statistics from the Bit brains datacentres. The study then analyses, summarises, and suggests future study paths in cloud environments

Design of testbed and emulation tools

The research summarized was concerned with the design of testbed and emulation tools suitable to assist in projecting, with reasonable accuracy, the expected performance of highly concurrent computing systems on large, complete applications. Such testbed and emulation tools are intended for the eventual use of those exploring new concurrent system architectures and organizations, either as users or as designers of such systems. While a range of alternatives was considered, a software based set of hierarchical tools was chosen to provide maximum flexibility, to ease in moving to new computers as technology improves and to take advantage of the inherent reliability and availability of commercially available computing systems

Allocation of Virtual Machines in Cloud Data Centers - A Survey of Problem Models and Optimization Algorithms

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and also environmental impact. Therefore, cloud providers must optimize the usage of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This paper surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, also pointing out the areas that need further research in the future

Design and Development of an Energy Efficient Multimedia Cloud Data Center with Minimal SLA Violation

Multimedia computing (MC) is rising as a nascent computing paradigm to process multimedia applications and provide efficient multimedia cloud services with optimal Quality of Service (QoS) to the multimedia cloud users. But, the growing popularity of MC is affecting the climate. Because multimedia cloud data centers consume an enormous amount of energy to provide services, it harms the environment due to carbon dioxide emissions. Virtual machine (VM) migration can effectively address this issue; it reduces the energy consumption of multimedia cloud data centers. Due to the reduction of Energy Consumption (EC), the Service Level Agreement violation (SLAV) may increase. An efficient VM selection plays a crucial role in maintaining the stability between EC and SLAV. This work highlights a novel VM selection policy based on identifying the Maximum value among the differences of the Sum of Squares Utilization Rate (MdSSUR) parameter to reduce the EC of multimedia cloud data centers with minimal SLAV. The proposed MdSSUR VM selection policy has been evaluated using real workload traces in CloudSim. The simulation result of the proposed MdSSUR VM selection policy demonstrates the rate of improvements of the EC, the number of VM migrations, and the SLAV by 28.37%, 89.47%, and 79.14%, respectively

Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures

Model-based techniques are a powerful approach to engineering autonomic and self-adaptive systems. This thesis presents a model-based approach for proactive and autonomic performance-aware resource management in dynamic IT infrastructures. Core of the approach is an architecture-level modeling language to describe performance and resource management related aspects in such environments. With this approach, it is possible to autonomically find suitable system configurations at the model level

A Fuzzy Predictable Load Balancing Approach in Cloud Computing

Cloud computing is a new paradigm for hosting and delivering services on demand over the internet where users access services. It is an example of an ultimately virtualized system, and a natural evolution for data centers that employ automated systems management, workload balancing, and virtualization technologies. Live virtual machine (VM) migration is a technique to achieve load balancing in cloud environment by transferring an active overload VM from one physical host to another one without disrupting the VM. In this study, to eliminate whole VM migration in load balancing process, we propose a Fuzzy Predictable Load Balancing (FPLB) approach which confronts with the problem of overload VM, by assigning the extra tasks from overloaded VM to another similar VM instead of whole VM migration. In addition, we propose a Fuzzy Prediction Method (FPM) to predict VMs migration time. This approach also contains a multi-objective optimization model to migrate these tasks to a new VM host. In proposed FPLB approach there is no need to pause VM during migration time. Furthermore, considering this fact that VM live migration contrast to tasks migration takes longer to complete and needs more idle capacity in host physical machine (PM), the proposed approach will significantly reduce time, idle memory and cost consumption