A heuristic-based task scheduling algorithm for scientific workflows in heterogeneous cloud computing platforms (original) (raw)
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International Journal of Web Services Research, 2019
The cloud computing paradigm provides an ideal platform for supporting large-scale scientific-workflow-based applications over the internet. However, the scheduling and execution of scientific workflows still face various challenges such as cost and response time management, which aim at handling acquisition delays of physical servers and minimizing the overall completion time of workflows. A careful investigation into existing methods shows that most existing approaches consider static performance of physical machines (PMs) and ignore the impact of resource acquisition delays in their scheduling models. In this article, the authors present a meta-heuristic-based method to scheduling scientific workflows aiming at reducing workflow completion time through appropriately managing acquisition and transmission delays required for inter-PM communications. The authors carry out extensive case studies as well based on real-world commercial cloud sand multiple workflow templates. Experiment...
SCIENTIFIC WORKFLOW SCHEDULING IN CLOUD COMPUTING ENVIRONMENT: A SURVEY
IAEME PUBLICATION, 2018
Workflow scheduling is one of the most challenging issues in cloud computing. Workflow is widely used paradigm in collaborative research and managing complex large scale distributed application. Various distributed environment such as cluster, grid and cloud use workflow to process complex and discrete tasks. Each task may include entering data, processing, accessing software, or storage functions. The task resource mapping, QoS requirement, on-demand resource provisioning, performance fluctuation and failure management in workflow scheduling is considered to be an NP-hard problem. An efficient scheduling algorithms are required to select the best suitable resources for workflow execution. In this paper, a comprehensive survey of workflow scheduling strategies that have been proposed for cloud computing platforms to help researchers systematically and objectively gather and aggregate research evidences.
HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing
International Journal of Advanced Computer Science and Applications, 2020
Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user's tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user's tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data-and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).
A Unified Mechanism for Cloud Scheduling of Scientific Workflows
IEEE Access, 2022
Scheduling plays a vital role in the efficient utilization of the available resources in clouds. This paper investigates the capabilities of the current scheduling algorithms of WorkFlowSim framework for processing scientific workflows. These investigations used four different sizes of workloads each, for, five well-known workflows. It was revealed that none of the existing algorithms is capable of efficiently executing all the four sizes of workload for the complete set of workflows. Different algorithms performed better, when they were applied to various workloads of a particular workflow. This fact was used in developing an improved unified mechanism, which is capable of using an existing algorithm that performed well in the past, against the given workload. Evaluation results showed that the proposed mechanism improved over the existing algorithms for 4 out of 5 workflows (Epigenomics, Inspiral, Cyber Shake, and Montage), when tested against an aggregated load of all sizes, in terms of simulation time. For the workflow named SIPHT, however, it responded exactly the same as Max-Min algorithm. The minimum and maximum improvements, against the existing best and worst algorithms, in percentage, for Epigenomics, Inspiral, SIPHT, Cyber Shake and Montage were 16-63, 30-68, 0-69, 30-68, and 9-71 in corresponding order. This work has an additional overhead in terms of a dedicated module to find and store algorithmic performance. It is, however, required once and, thus, the increase in execution time might be marginal. The future work intends to check the impact of compute time towards optimization parameters such as makespan, pricing and deadlines.
Comparative analysis of Scientific Workflow Scheduling in Cloud Environment
International Conference on Innovations in Power and Advanced Computing Technologies [i-PACT2017], 2017
Cloud platform is the model of parallel and distributed computing. It offers the facilities by pay-as per-usage policy. Also provides the platform for high performance application like scientific applications. Virtualization, elasticity and pay by use are the important features of cloud computing. It allows the user to submit their tasks by providing varieties of resources through internet. Since cloud provides the facility of running multiple tasks simultaneously, an eminent scheduling algorithm is needed for the better performance. Numerous task based schedulers are available in the cloud environment. In this paper we have done the analysis of various traditional scheduling algorithms based on the parameters like average makespan and cost.