Adaptive workflow scheduling in grid computing based on dynamic resource availability (original) (raw)

Adaptive workflow scheduling for dynamic grid and cloud computing environment

Concurrency and Computation: Practice and Experience, 2013

Effective scheduling is a key concern for the execution of performance-driven grid applications such as workflows. In this paper, we first define the workflow scheduling problem and describe the existing heuristicbased and metaheuristic-based workflow scheduling strategies in grids. Then, we propose a dynamic critical-path-based adaptive workflow scheduling algorithm for grids, which determines efficient mapping of workflow tasks to grid resources dynamically by calculating the critical path in the workflow task graph at every step. Using simulation, we compared the performance of the proposed approach with the existing approaches, discussed in this paper for different types and sizes of workflows. The results demonstrate that the heuristic-based scheduling techniques can adapt to the dynamic nature of resource and avoid performance degradation in dynamically changing grid environments. Finally, we outline a hybrid heuristic combining the features of the proposed adaptive scheduling technique with metaheuristics for optimizing execution cost and time as well as meeting the users requirements to efficiently manage the dynamism and heterogeneity of the hybrid cloud environment.

Workflow Scheduling In Grid Environment

Task scheduling in heterogeneous computing environment such as grid computing is a critical and challenging problem. Many parallel applications consist of multiple computational components. While the execution of some of these components or tasks depends on the completion of other tasks, others can be executed at the same time, which increases parallelism of the problem. The task scheduling problem is the problem of assigning the tasks in the system in a manner that will optimize the overall performance of the application, while assuring the correctness of the result. Scientific workflows, usually represented as Directed Acyclic Graphs (DAGs), are an important class of applications that lead to challenging problems in resource management on grid and utility computing systems. In this dissertation, a priority scheduling heuristic is developed which maintains a list of all tasks of a given DAG according to their priorities. It firstly prioritizes all tasks and then selects the best resource for the ready task with highest priority.

An Opportunistic Algorithm for Scheduling Workflows on Grids

Lecture Notes in Computer Science, 2007

The execution of scientific workflows in Grid environments imposes many challenges due to the dynamic nature of such environments and the characteristics of scientific applications. This work presents an algorithm that dynamically schedules tasks of workflows to Grid sites based on the performance of these sites when running previous jobs from the same workflow. The algorithm captures the dynamic characteristics of Grid environments without the need to probe the remote sites. We evaluated the algorithm running a workflow in the Open Science Grid using tweve sites. The results showed improvement up to 120% relative to other four usual scheduling strategies.

SA-Based QoS Aware Workflow Scheduling of Collaborative Tasks in Grid Computing

Computing, 2024

Scheduling workflow tasks in grid computing is a complex process, especially if it is associated with satisfying the user's requirements to complete tasks within a specified time, with lowest possible cost. This paper presents a proposed Simulated Annealing (SA) based Grid Workflow Tasks Scheduling Approach (SA-GWTSA) that takes into account users' QoS (quality of service) constraints in terms of cost and time. For a given set of interdependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time is within the time constraints (deadline) imposed by the user. In SA-GWTSA, the workflow tasks, which are modeled as a DAG, are divided into task divisions, each of which consists of a set of sequential tasks. Then, the optimal sub-schedules of all task divisions are computed applying SA algorithm, and used to obtain the execution schedule of the entire workflow. In the proposed algorithm, the sub-schedule of each branch division is represented by a vector, in which each element holds the ID of the service provider chosen from a list of service providers capable of executing the corresponding task in the branch. The algorithm uses a fitness function that is formulated as a multi-objective function of time and cost, which gives users the ability to determine their requirements of time against cost, by changing the weighting coefficients in the objective function. The paper also exhibits the experimental results of assessing the performance of SA-GWTSA with workflows samples of different sizes, compared to different scheduling algorithms: Greedy-Time, Greedy-Cost, and Modified Greedy-Cost. KEYWORDS grid computing; workflow tasks scheduling; simulated annealing algorithm; quality of service constraints.

AN ADAPTIVE ALGORITHM FOR TASK SCHEDULING FOR COMPUTATIONAL GRID

Grid Computing is a collection of computing and storage resources that are collected from multiple administrative domains. Grid resources can be applied to reach a common goal. Since computational grids enable the sharing and aggregation of a wide variety of geographically distributed computational resources, an effective task scheduling is vital for managing the tasks. Efficient scheduling algorithms are the need of the hour to achieve efficient utilization of the unused CPU cycles distributed geographically in various locations. The existing job scheduling algorithms in grid computing are mainly concentrated on the system's performance rather than the user satisfaction. This research work presents a new algorithm that mainly focuses on better meeting the deadlines of the statically available jobs as expected by the users. This algorithm also concentrates on the better utilization of the available heterogeneous resources.

Dynamic Job Scheduling in Grid Computing

International Journal of Computer and Communication Technology, 2016

Grid computing is growing rapidly in the distributed heterogeneous systems for utilizing and sharing large-scale resources to solve complex scientific problems. Scheduling is the most recent topic used to achieve high performance in grid environments. It aims to find a suitable allocation of resources for each job. A typical problem which arises during this task is the decision of scheduling. It is about an effective utilization of processor to minimize tardiness time of a job, when it is being scheduled. Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources. The efficient scheduling of independent jobs in a heterogeneous computing environment is an important problem in domains such as grid computing. In general, finding optimal schedule for such an environment using the traditional sequential method is an NP-hard problem whereas heuristic approaches will provide near optimal solutions for complex problems. The...

ADAPTIVE JOB SCHEDULING WITH LOAD BALANCING FOR WORKFLOW APPLICATION IN GRID PLATFORM

iaeme

Grid computing servers as the globally connected systems which performs high computing in many practical applications. Scheduling plays a key role in providing performance for grid workflow applications. Various scheduling strategies are proposed, including static scheduling strategies which map jobs to resources before execution time, or dynamic alternatives which schedule individual job only when it is ready to execute. Both of the schedules require significantly high scheduling cost and they may not produce good quality of schedule with low cost. This paper proposes a novel semi dynamic algorithm with load balancing concept, which allows the schedule to adapt and schedule the jobs as per the changes in the dynamic grid environment. The proposed novel algorithm schedules the job statically and continues the schedule with dynamic scheduling due to the dynamic nature of the grid. The makespan and the resource usage are the main to objective of this scheduling algorithm. When the resource and performance fluctuation occur in the grid environment it affects the processing of the jobs which results in the delay in the job completion time. In this algorithm load balancing is incorporated to handle such situation where the jobs are handled after it is dispatched to their respective hosts. When there is resource fluctuation occurs due to the dynamic nature of the grid or over loading of jobs to a processor which delays the makespan, load balancing is done to handle the job execute and to get desired makespan

Performance analysis of dynamic workflow scheduling in multicluster grids

2010

ABSTRACT Scientists increasingly rely on the execution of workflows in grids to obtain results from complex mixtures of applications. However, the inherently dynamic nature of grid workflow scheduling, stemming from the unavailability of scheduling information and from resource contention among the (multiple) workflows and the non-workflow system load, may lead to poor or unpredictable performance.

Workload Modeling and Prediction for Workflow Scheduling in Dynamic Grid Environments

2014

Many scientific applications utilize grid environments for processing their workloads, which consist of workflows. Grid environments are highly dynamic because the grid resources belong to different administrative domains, and have site-specific scheduling and resource management policies. Workflow scheduling in dynamic grid environments involves mapping the tasks of a workflow to the grid resources with an aim of optimizing certain objectives, e.g., the makespan of the workflow, the utilization of the grid resources, etc. Designing and evaluating new workflow scheduling algorithms requires comprehensive workload modeling, which is missing in contemporary research. Moreover, conventional grid workflow scheduling algorithms are based on some unrealistic assumptions, which do not necessarily hold in a dynamic grid environment. E.g., some approaches assume that at a time, only one workflow executes in a grid environment, the computation and the communication time of the workflows are k...

A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids

Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007), 2007

Effective scheduling is a key concern for the execution of performance driven Grid applications. In this paper, we propose a Dynamic Critical Path (DCP) based workflow scheduling algorithm that determines efficient mapping of tasks by calculating the critical path in the workflow task graph at every step. It assigns priority to a task in the critical path which is estimated to complete earlier. Using simulation, we have compared the performance of our proposed approach with other existing heuristic and meta-heuristic based scheduling strategies for different type and size of workflows. Our results demonstrate that DCP based approach can generate better schedule for most of the type of workflows irrespective of their size particularly when resource availability changes frequently.