Workflow tasks scheduling optimization based on genetic algorithm in clouds

Cui, Yang; Zhang, Xiaoqing

doi:10.1109/icccbda.2018.8386458

Cited by 33 publications

(26 citation statements)

References 9 publications

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“…d) For each task determine the difference between its minimum and second minimum completion time over all the machines. e) For each task determine the difference between its minimum and second minimum completion time over all the machines 14) Genetic Algorithm [9]: The Genetic Algorithm mainly works in eight phases: a) Genetic Encoding: Two-dimension coding is the coding of the population individual. b) Genetic Decoding: The decoding scheme of the encoded chromosomes is, the first char in genetic encode is decoded directed as the host resource.…”

Section: ) Shortest Job First (Sjf)mentioning

confidence: 99%

Task Scheduling in Cloud Based on Three Stage Method using Historical Data

Venu¹

2021

IJRASET

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The on-demand availability of computer system resources such as data storage and computing power is cloud computing. Scheduling is the method of allocating jobs onto resources in time. Scheduling increases the efficiency and performance of cloud environment by maximizing the resource utilization. This scheduling process has to respect constraints given by the jobs and the cloud providers. Ordering the tasks by scheduler along with maintaining the balance between Quality of Service (QoS), fairness and efficiency of jobs is difficult. Scheduling algorithms are designed and implemented considering some parameters like latency, cost, priority, etc. This paper proposes a method for task scheduling in cloud using a three-stage method. The first stage makes use of historical scheduling data to classify tasks. Based on this VMs are created. In the second stage newly arrived tasks are considered. Based on Bayes classifier principle a matching degree is calculated to mark a task with a VM Type. In the third stage, tasks are matched with corresponding VMs. Then the tasks are scheduled using a scheduling algorithm and are executed in the host. Keywords: Task scheduling, Cloud computing. I. INTRODUCTION Cloud is a trendy term for a network or remote servers that can be accessed via an Internet connection store and manage information. Two types of models present such as deployment models and service models. The deployment model includes private, public and hybrid cloud. Private Clouds are Data center architecture owned by a single company. E.g., IBM's BlueCloud, SunCloud, WindowAzure. Public Clouds are basically the Internet. To make resource available to general people, service provider use internet. E.g., Gmail, Office 365, Dropbox. In case of hybrid clouds, for instance during peak periods individual applications or portion of applications can be migrate to the public cloud. The service model includes Software as a Service (SaaS) such as Dropbox, Google Apps, Platform as a Service (PaaS) such as Google Compute Engine, Apache Stratos and Infrastructure as a Service (IaaS) such as Virtual Machine, Storage, Servers. Cloud provides on demand computational resources in the form of virtual machines. These Virtual Machines are deployed in a cloud provider's data center. The computational resources are shared among different cloud consumers who pay for the service accessed as per the usage. Allocation of resources and proper scheduling has a considerable impact on the performance and efficiency of the system. The main goal of cloud computing is to provide efficient access to remote and geographically distributed resources. An efficient scheduling is a key to manage the access to different resources, load balancing as well as resource allocation. Different types of resource scheduling algorithms are available in cloud computing based on certain parameters like time, cost, and performance, utilization of resources, throughput, bandwidth, resource availability, physical distances and priority. A. Scheduling In Cloud Computing Scheduli...

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Section: ) Shortest Job First (Sjf)mentioning

confidence: 99%

Task Scheduling in Cloud Based on Three Stage Method using Historical Data

Venu¹

2021

IJRASET

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“…They show improvement of previous work on makespan minimizing, but lack of other metrics. On Reference , the authors present a priority organization of tasks, to reduce the price of scheduling. Their solution demands more of human intervention, as the user must set the constraints for the executions.…”

Section: Related Workmentioning

confidence: 99%

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

et al. 2020

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Science Gateways provide portals for experiments execution, regardless of the users' computational background. Nowadays its construction and performance need enhancement in terms of resource provision and task scheduling. We present the Modular Distributed Architecture to support the Protein Structure Prediction (MDAPSP), a Service-Oriented Architecture for management and construction of Science Gateways, with resource provisioning on a heterogeneous environment. The Decision Maker, central module of MDAPSP, defines the best computational environment according to experiment parameters. The proof of concept for MDAPSP is presented in WorkflowSim, with two novel schedulers. Our results demonstrate good Quality of Service (QoS), capable of correctly distributing the workload, fair response times, providing load balance, and overall system improvement. The study case relies on PSP algorithms and the Galaxy framework, with monitoring experiments to show the bottlenecks and critical aspects. K E Y W O R D Soptimization, protein structure prediction, science gateways, scientific workflow, service-oriented architecture, WorkflowSim 1 Softw: Pract Exper. 2020;50:899-924. wileyonlinelibrary.com/journal/spe © 2020 John Wiley & Sons, Ltd. 899 900 MARTINS DE OLIVEIRA et al.Another limitation of scientific gateways is the resource provision for its machines, that affect usability and performance. The problem with resource provisioning is a quest for the best configuration, requiring the smallest investment and minimal overheads. In general, inexperienced users cannot make good decisions on what machines to contract. Limited economic resources complicate the scenario.Cloud computing paradigm is among the principal resources available, as it has many advantages: scalability, pay-as-you-go features, on-demand provisioning, and management by third parties. Multiple providers infrastructure and decentralization of resources are expanding and are expected to create impact on diverse areas. 4 Resource providing and management are one of the challenging aspects of this expansion. 5 Despite its success, cloud is not the only option for provisioning. 6 Computer clusters are reliable infrastructures for conducting experiments, mainly High-Performance Computing (HPC) and concurrent algorithms. The advantages of cluster machines are local management, freedom of configuration, reliable storage, and fast network transfers. 3 For those reasons, we investigate the scientific gateway Galaxy, because it is an adaptable system, capable to operate over different infrastructures, including local workstations.Galaxy is a well-known framework for scientific experiments, with workflow management and reproducibility support. Its tools offer practical solutions and addition of new algorithms according to the evolution of the system. The focus is Biomedical science, protein prediction, gene sequencing, and other bioinformatics-related areas. Its advantages are simple interface, reliability, powerful tools, and an active community. 7 However, as any other...

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“…The experimental result shows that the proposed algorithm outperforms the existing Max-min algorithm. Yang Cui, Zhang Xiaoqing proposed a workflow Scheduling optimization algorithm using Genetic Algorithm [8]. The proposed algorithm reduces the execution cost of the workflow under the deadline and budget constraint.…”

Section: Related Workmentioning

confidence: 99%

“…The main objective of using genetic algorithm for workflow scheduling is to minimize the makespan of workflow. The fitness function f(n) is as given in equation 8.…”

Section: F Fitness Functionmentioning

confidence: 99%

Design of Energy Aware Scheduling Algorithm for Executing Scientific Workflows in Cloud

Balamurugan¹,

Saraswathi²

2019

IJEAT

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The usage of cloud computing and its resources for the execution of scientific workflow is a rapidly increasing demand. The Scientific applications are generally large in scale; even a single scientific workflow includes more number of complex tasks. Execution of these tasks can be made successful only by deploying it in the cloud virtual machines, because only cloud environment can only provide very large number of computing assets. In cloud, every processing resource is given as Virtual Machine. Any scientific workflow deployed in the cloud needs large number of virtual machines so; huge amount of computational energy is spent by the virtual machines to execute multifaceted scientific workflows. Hence there arises the need to utilize the cloud resources in an energy efficient way. Also, if the virtual machines are planned to schedule in an energy efficient manner there is an increase of makepsan of the workflow which is going to be an important parameter for completing the workflow within the deadline. So, the need for executing scientific workflows in energy efficient way with reduced makespan becomes a major issue among the researchers. It also becomes very challenging task to executing a scientific workflow in within the given deadline of a task in the given workflow. To address these issues, a new Energy Aware workflow scheduling algorithm is proposed and designed with improved makespan for the execution of different scientific applications in cloud environment.

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Workflow tasks scheduling optimization based on genetic algorithm in clouds

Cited by 33 publications

References 9 publications

Task Scheduling in Cloud Based on Three Stage Method using Historical Data

Task Scheduling in Cloud Based on Three Stage Method using Historical Data

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

Design of Energy Aware Scheduling Algorithm for Executing Scientific Workflows in Cloud

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