Multiqos-Enhanced Heuristic Model For Scheduling Of Scientific Workflows (original) (raw)
Related papers
IEEE Access
The provision of resources and services for scientific workflow applications using a multi-cloud architecture and a pay-per-use rule has recently gained popularity within the cloud computing research domain. This is because workflow applications are computation intensive. Most of the existing studies on workflow scheduling in the cloud mainly focus on finding an ideal makespan or cost. Nevertheless, there are other important quality of service metrics that are of critical concern in workflow scheduling such as reliability and resource utilization. In this respect, this paper proposes a new multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS) for scheduling scientific workflow based on particle swarm optimization (PSO) method. The algorithm minimizes cost and makespan while considering reliability constraint. The coding scheme jointly considers task execution location and data transportation order. Simulation experiments reveal that FR-MOS outperforms the basic MOS over the PSO algorithm.
An Exploration of Multi-Objective Scientific Workflow Scheduling in Cloud
2018
Today's scientific community highly relies upon scientific workflow applications for various operations related to their field. Distributed environment like cloud perfectly matches for executing large-scale scientific applications by providing enormous on demand resources. Hence scientific workflows scheduling in cloud environment gain importance. The cloud resources renting follows pay per use model and thus the cloud users are constrained to work under multiple objectives like time and cost. This paper explorers the work of research community in the area of Multi-Objective scheduling of scientific workflows in the cloud environment.
Multi-Objective Optimization for scientific workflow task scheduling in IaaS Cloud
International Journal of Engineering & Technology, 2018
The use of scientific applications on cloud networks increases day by day generating volumes of data and consuming large computational power. These scientific applications find its importance in the field of astronomy, geology, genetics and bio-technology etc. Complex and mission critical scientific applications can be modeled as scientific workflows and can be executed in cloud. The tasks of the scientific applications are generally data intensive and compute intensive. Traditional computer networks are not suitable for handling scientific applications and hence ubiquitous distributed networks like cloud are prominent in hosting scientific applications. The cloud hosted scientific applications and the cloud network need to satisfy many objectives to the interest of its users. This paper explores the multi-objective optimization applications in scientific workflow task scheduling in IaaS cloud and the related algorithms employed.
Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds
IEEE Transactions on Cloud Computing, 2014
Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user's quality of service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. We present an algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. Our heuristic is evaluated using CloudSim and various well-known scientific workflows of different sizes. The results show that our approach performs better than the current state-of-the-art algorithms.
Multi-objective workflow optimization strategy (MOWOS) for cloud computing
Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work "Cost Optimised Heuristic Algorithm (COHA)" and presented a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We have carefully tested the performance of MOWOS with a list of workflow inputs. The simulation results have demonstrated that MOWOS can effectively perform VM allocation and deployment, and well handle incoming streaming tasks with a random arriving rate. The performance of the proposed algorithm increases significantly in large and extra-large workflow tasks than in small and medium workflow tasks when compared to the state-of-art work. It can greatly reduce cost by 8%, minimize makespan by 10% and improve resource utilization by 53%, while also allowing all tasks to meet their deadlines.
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.
Multi-Objective Scientific-Workflow Scheduling with Data Movement Awareness in Cloud
IEEE Access
Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan are probably the most two primitive objectives. Another critical factor in a large-scale scientific workflow is tremendous amount of data during execution. Therefore, this work also includes Data Movement as an additional objective as it has a major impact on network utilization and energy consumption in network equipment in cloud data center. In considering these three objectives, this work proposes a framework for scheduling solutions which combines a new nodes clustering technique in Directed Acyclic Graph (DAG) model known as Multilevel Dependent Node Clustering (MDNC) and the multiobjective optimization, Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). E-NSGA-III is the recent extension of Nondominated Sorting Genetic Algorithm (NSGA-III). Five well-known scientific workflows, CyberShake, Epigenomics, LIGO, Montage, and SIPHT are selected as testbeds, while the commonly known Hypervolume is chosen as the performance metric. In this work, MDNC is also experimented with both NSGA-III. Comparison among three approaches, E-NAGA-III alone, E-NAGA-III with Peer-to-Peer clustering and E-NAGA-III with MDNC are carried out. The superiority of the proposed framework among them and its limitation are discussed.
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).
2015
Cloud computing is that the latest distributed computing model and it offers big opportunities to resolve large-scale scientific issues. However, it presents varied challenges that require to be addressed so as to be with efficiency utilized for progress applications. Although the advancement programing downside has been wide studied, there area unit only a few initiatives tailored for cloud environments. Furthermore, the present works fail to either meet the user's quality of service (QOS) needs or to include some basic principles of cloud computing like the physical property and no uniformity of the computing resources. This paper proposes a resource provisioning and programing strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. we tend to gift associate algorithm supported the meta-heuristic improvement technique, particle swarm improvement (PSO), that aims to reduce the general workflow execution value whereas meeting point in time constraints. Our heuristic is evaluated victimization CloudSim and numerous well-known scientific workflows of various sizes. The results show that our approach performs higher than the present progressive algorithms.
Game multi objective scheduling algorithm for scientific workflows in cloud computing
2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015], 2015
Cloud computing is the latest utility computing that enables the dynamic provisioning for applications over the Internet. Cloud computing provide large number of opportunities to solve large scale scientific problems. Scheduling the tasks in the workflows is an NPhard problem and in this work we focus on optimizing the scheduling process of the workflow. Scheduling of the workflow applications that satisfy the given constraints for the scientific tasks is an essential requirement for the workflow scheduling .Mapping of each task to suitable resource and allowing the task to satisfy performance constraints is the main aim of this paper. In the present work we propose game theoretic algorithm for multiobjective scheduling of scientific workflows in cloud computing environment. The scheduling problem is formulated as a new sequential cooperative game based on two user objectives that are execution time and economic cost while fulfilling two constraints network bandwidth and storage requirements. We call it as Game Multi Objective (GMO) algorithm. In this paper we apply Game Multi Objective algorithm for minimizing the execution time and cost of an workflow application.