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

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

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

Hybrid scheduling algorithms in cloud computing: a review

Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms