MULTIOBJECTIVE QoS OPTIMAZATION BASED ON MULTIPLE WORKFLOW SCHEDULING IN CLOUD ENVIRONMENT (original) (raw)
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A Survey on QoS Constraint Based Scheduling Algorithms for cloud Workflows
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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.
A survey on multi-objective task scheduling algorithm in cloud environment
Cloud computing is one of the important subject now-a-days in which services are given to the users by cloud provider. So, according to the use of the services users will pay to the providers. Resource allocation and task scheduling are important to manage the task in cloud environment for load balancing. Task scheduling is an important step to improve the overall performance of the cloud computing. Task scheduling is also essential to reduce power consumption and improve the profit of service providers by reducing processing time. So, for task scheduling, various " quality of service " (QoS) parameters are considered for reducing execution time and maximize throughput. For this purpose, multi-objective optimization of task scheduling is used, which is a sub domain of " multi-criteria decision making " problem. This involves more than one objective function that can be optimized at the same time.
Multiqos-Enhanced Heuristic Model For Scheduling Of Scientific Workflows
2020
Cloud Computing has emerged with the successive advancement of traditional computing like distributed computing and grid computing. Infrastructure as a Service (IaaS) clouds offer computation of workflows at low cost. Objective of this paper is to propose an enhanced heuristic method for scheduling single scientific workflow that reduces time and cost QoS parameters. This paper discusses different workflow scheduling approaches on the basis of data storage system and clustering methods to obtain single QoS objective. Proposed method is based on multiobjective heuristic which is an enhanced version of MaxMin workflow scheduling, called Vertical MaxMin Workflow Scheduling (VMM). The simulated result for comparative study of various heuristics is done on WorkflowSim for single workflow. Comparative results confer that proposed method obtains multi-objective QoS with lower cost in efficient time.
IAEME PUBLICATION, 2020
Cloud computing is an advanced technique involving networks of servers that run on a huge amount of data. These servers and other sources of the cloud can be located in wide remote areas. Different scientific and web applications are representing by using Workflow models in the cloud. Scheduling different workflows in multi-cloud environment is a major issue of concern since they are quite huge and follow specific scientific standards. The need to meet user’s Quality of service (QoS) requirements are the other issues in public cloud computing, such as scalability and reliability and as well maximize the rate of resource utilization to end-users. This paper makes a comparison between three Particle Swarm Optimization (PSO) based algorithms in terms of makespan and cost. These algorithms were tested with the same number of virtual machines (VMs) and workflows. This is intended to help users to decide on which of these three algorithms can provide the required QoS for large scientific workflows in infrastructure as a service (IaaS) cloud platform as well as help them map tasks to resources. These algorithms are simulated on different simulation packages and tested with different scientific workflow datasets such as LIGO, Montage, CyberShake and Epigenome. The algorithms considered in this article can effectively distribute tasks to available resources for efficient optimization of makespan and cost. Simulation experiments reveal that ACO-PSO outperforms the basic PSO, C-PSO and PSO-DS in the same working environment.
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 Evolutionary Study of Multi-Objective Workflow Scheduling in Cloud Computing
International Journal of Computer Applications, 2016
Cloud computing become more popular in every field of life nowadays. This happened only due to its amazing services that provide to clients in the form of everything-as-a-service(XaaS). Where at one side cloud computing is gaining popularity and another side its faces some issues i.e. security issue, total cost issue, energy consumption issue, performance issue, QoS issue, etc. In above all challenges the quality of services is the most noticeable challenge and affects the cloud computing services. Quality of services can be improved by considering the several factors, scheduling of workload for suitable cloud computing resources one of them. If the cloud computing resources are scheduled accurately, it affects the response time of services, total cost of cloud resources, reduce the energy consumption, reduce the CO2 emission and enhance the performance of whole cloud system. In this paper, we characterize a comparative review of multi-objective workflow scheduling algorithms that are listed below.
Cost-based multi-QoS job scheduling algorithm using genetic approach in cloud computing environment
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One of the best methods for cloud scheduling is the genetic algorithm (GA). The simple and parallel features of this algorithm make it applicable to several optimization problems. A GA searches the problem space globally and is unable to search locally. In the proposed model, the task scheduler calls the GA scheduling function every task scheduling [4] cycle. This function creates a set of task schedules and evaluates the quality of each task schedule with user satisfaction and virtual machine [6] availability. The function iterates genetic operations to make an optimal task schedule. In the presented work, task scheduling is done to reduce the total cost of task processing (on the processing units of the cloud) for the cloud provider by reducing the execution time and hence the delay cost or penalty cost.
Efficient Task Scheduling Strategy Towards Qos Aware Optimal Resource Utilization in Cloud Computing
2015
QoS (Quality of Service) aware task scheduling in cloud computing is a continuous practice due to the divergent scope of user needs. Henceforth the current research is moving in a direction to find optimal solutions for efficient task scheduling towards QoS aware resource utilization in cloud workflow management. Much of the existing solutions are specific to one or two QoS factors mainly task completion and bandwidth. According to the real-time practices, the QoS assessment by one or two factors is impractical. Moreover much of the existing approaches are delivering the computational complexity as O(n 2 ), which is due to the magnification of the increment in number of tasks due to overwhelmed users and their requirements. In this context here we devised an explorative statistical approach, which is based on metrics called resource optimal value ( ropt ) and coupling between tasks ( cbt ), which enables to assess the optimal order of tasks to utilize desired cloud resource. The oth...
Symmetry, 2020
Cloud computing is an innovative technology that deploys networks of servers, located in wide remote areas, for performing operations on a large amount of data. In cloud computing, a workflow model is used to represent different scientific and web applications. One of the main issues in this context is scheduling large workflows of tasks with scientific standards on the heterogeneous cloud environment. Other issues are particular to public cloud computing. These include the need for the user to be satisfied with the quality of service (QoS) parameters, such as scalability and reliability, as well as maximize the end-users resource utilization rate. This paper surveys scheduling algorithms based on particle swarm optimization (PSO). This is aimed at assisting users to decide on the most suitable QoS consideration for large workflows in infrastructure as a service (IaaS) cloud applications and mapping tasks to resources. Besides, the scheduling schemes are categorized according to the variant of the PSO algorithm implemented. Their objectives, characteristics, limitations and testing tools have also been highlighted. Finally, further directions for future research are identified.