Score Based Budget Constraint Workflow Scheduling Algorithm for Cloud System

Cloud Computing is the technology that provides on demand services and resources like storage space, networks, programming language execution environment on the top of Internet using pay as you go model. The concept of Cloud Computing emerging as a latest model of service provisioning in distributed system encourage researchers to investigate its advantages and drawbacks in executing scientific applications involving workflows. Workflow scheduling is one of the major issues in Cloud environment that maps and allows the execution of inter-dependent tasks on different resources. It allocates desire resources to workflow tasks so that the execution can be completed to satisfy the QoS constraint defined by the users. At present, the workflow scheduling algorithms only focus on certain QoS parameters which are mainly execution cost and execution time during the allocation of virtual machines to workflow applications. Sometimes resources (virtual machines) are unreliable at data centers. These resources frequently results into failure when workflow applications are scheduled on these resources. The user workflow application may contain sensitive data that cannot tolerate failure of resources on which it is scheduled. In this paper the problem of workflow scheduling that is based on the concept of score is presented. A score based budget constraint workflow scheduling algorithms has been design and simulated. These algorithms reduce the execution time and failure rate of workflow applications within user specified budget

Score Based Budget Constraint Workflow Scheduling Algorithm for Cloud System

Cloud Computing is the technology that provides on demand services and resources like storage space, networks, programming language execution environment on the top of Internet using pay as you go model. The concept of Cloud Computing emerging as a latest model of service provisioning in distributed system encourage researchers to investigate its advantages and drawbacks in executing scientific applications involving workflows. Workflow scheduling is one of the major issues in Cloud environment that maps and allows the execution of inter-dependent tasks on different resources. It allocates desire resources to workflow tasks so that the execution can be completed to satisfy the QoS constraint defined by the users. At present, the workflow scheduling algorithms only focus on certain QoS parameters which are mainly execution cost and execution time during the allocation of virtual machines to workflow applications. Sometimes resources (virtual machines) are unreliable at data centers. These resources frequently results into failure when workflow applications are scheduled on these resources. The user workflow application may contain sensitive data that cannot tolerate failure of resources on which it is scheduled. In this paper the problem of workflow scheduling that is based on the concept of score is presented. A score based budget constraint workflow scheduling algorithms has been design and simulated. These algorithms reduce the execution time and failure rate of workflow applications within user specified budget

Energy Efficient Policies in Cloud Computing

Energy efficient processing is known as Green Cloud Computing. It is getting popular with the demand of time and become important to reduce the energy consumption of the Cloud datacenters in order to create a more nature friendly Cloud environment. In this paper, two policies are proposed i.e. minimum CPU utilization policy and low utilization host selection policy. These two policies are deciding which VM will be picked for migration and which host will be selected to reallocate that VM. The Min-Min scheduling algorithm is used to schedule the tasks at Cloud datacenter. The load balancing concept is proposed that further helps in reducing the energy consumption. The results of proposed energy efficient Cloud scenario is compared with power aware and non-power aware Clouds

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers

QoS-aware predictive workflow scheduling

This research places the basis of QoS-aware predictive workflow scheduling. This research novel contributions will open up prospects for future research in handling complex big workflow applications with high uncertainty and dynamism. The results from the proposed workflow scheduling algorithm shows significant improvement in terms of the performance and reliability of the workflow applications