An improved task Scheduling Algorithm for Segregating User Requests to different Virtual Machines (original) (raw)
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Task Scheduling and VM Allocation in Cloud Computing: A Survey
Publishing India Group, 2019
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A Survey on Different VM Scheduling Approach in Cloud Computing Environment
International Journal of Computer Applications, 2016
Cloud is the one of the fastest emerging technology in the IT world. Its supply on-demand IT resources to the client on the rent basis. In a very short time demand for the computing resources is increasing dramatically. To accomplish this demand virtualization approach is used which allow the sharing of physical resources. PM is virtualized by using the hypervisor that creates the VM according to the user need and assign to the users. Each user has its own VM and number of VM is running in single physical machine. As the size of the datacenter is increases, it can serve the more and more users but it will also introduce some issues that have to resolve for the proper utilization of the cloud services. Resource scheduling or the proper distribution of the physical resources is one of the critical issues in the cloud. Proper resource distribution can not only maximize the throughput but also increase the resource utilization of the physical machine. Resource scheduling is a challenging task in the cloud because user requirement for the resource can change dynamically. Various techniques have been proposed during the last decade in the field of resource scheduling. This paper discussed some of the existing resource scheduling algorithm with their anomalies..
International Journal of Computer Applications, 2015
The Cloud Computing is a most recent computing paradigm where IT services are provided and delivered over the Internet on demand and pay as you go. On the other hands, the task scheduling problem is considered one of the main challenges in the Cloud Computing environment, where a good mapping between the available resources and the users's tasks is needed to reduce the execution time of the users' tasks (i.e., reduce make-span), in the same time, increase the degree of capitalization from resources (i.e., increase resource utilization). In this paper, a new task scheduling algorithm has been proposed and implemented to reduce the make-span, as well as, increase the resources utilization by considering independent tasks. The proposed algorithm is based on calculating the total processing power of the available resources (i.e., VMs) and the total requested processing power by the users' tasks, then allocating a group of users' tasks to each VM according to the ratio of its needed power corresponding to the total processing power of all VMs. To evaluate the performance of the proposed algorithm, a comparative study has been done among the proposed algorithm, and the existed GA, and PSO algorithms. The experimental results show that the proposed algorithm outperforms other algorithms by reducing make-span and increasing the resources utilization.
SLA-based Virtual Machine Management for Mixed Workloads of Interactive Jobs in a Cloud Datacenter
International Journal of Computer Applications
Efficient provisioning of resources is a challenging problem in cloud computing environments due to its dynamic nature and the need for supporting heterogeneous applications. Even though VM (Virtual Machine) technology allows several workloads to run concurrently and to use a shared infrastructure, still it does not guarantee application performance. Thus, currently cloud datacenter providers either do not offer any performance guarantee or prefer static VM allocation over dynamic, which leads to inefficient utilization of resources. Also, the workload may have different QoS (Quality Of Service) requirements due to the execution of various types of applications such as HPC and web, which makes resource provisioning much harder. Earlier work either concentrate on single type of SLAs (Service Level Agreements) or resource usage patterns of applications, such as web applications, leading to inefficient utilization of datacenter resources. In this paper, we tackle the resource allocatio...
Scheduling Virtual Machines for Load balancing in Cloud Computing Platform
2013
Cloud computing enables developers to automatically deploy applications during task allocation and storage distribution by using distributed computing technologies in numerous servers. To gain the maximum benefit from cloud computing, developers must design mechanisms that optimize the use of architectural and deployment paradigms. The role of Virtual Machine’s (VMs) has emerged as an important issue because, through virtualization technology, it makes cloud computing infrastructures to be scalable. Therefore developing on optimal scheduling of virtual machines is an important issue. In this paper a analysis of different existing Virtual Machine’s (VM’s) scheduling algorithms are done and proposed a weighted Round Robin algorithm over Round Robin algorithm in Virtual Machine environment of cloud computing in order to achieve better overall response time and processing time. The simulation results show the weighted round robin algorithm shows better improvements over Round-Robin algo...
Implementation of various task scheduling algorithm on cloud environment
2022
The provider of the service today is expected to serve many users. The increasing number of requests for services from the users to the providers has caused the providers of the service to have to offer scalable solutions. In the cloud computing environment, various scheduling algorithms have been proposed, such as the (SJF) and (FCFS) algorithms. Using cloud computing, the paper seeks to improve the shortest job scheduling algorithm. In tasks scheduling (TS), makepan and response time are the most important parameters. In order to reduce the length of time to complete the last task (Makespan), decrease the average response time, and maximize resource utilization, we present a Shortest Job First algorithm. This method consists of two functions, one is the calculation of the average task length, and the other is load balancing between virtual machines. Sending the longest tasks to the fastest machine is one of the benefits of SJF.
Efficient Resource Utilization in Virtual Cloud Computing Environment
International Journal of Computer Applications
Cloud computing is a platform that provides user to implement revolutionary technologies. The main phenomena of cloud computing is based on accessing the resources using remote computation. Task scheduling is one of the major area that should be focused on. In cloud environment there may be a condition where the resources are limited that may affect resource availability. This paper presents an Enhanced version of MaxMin task scheduling algorithm that improves the turnaround time. The tasks are divided in two groups and the larger task is assigned to the resource (virtual machine) with high mips rate and other task is assigned to resource with low mips rate. To perform the experiment CloudSim toolkit is used. Our result shows that the Enhanced MaxMin algorithm gives the better result.
Optimized Task Scheduling in Cloud Computing: A Survey
International Journal for Research in Applied Science and Engineering Technology -IJRASET, 2020
The increase of cloud computing is so exponential that it offers facts connection between special structures and devices. Due to this boom in connectivity and rapid utilization cloud network desires a statistics grid or computing grid comprising of different type of processing gadgets to perform the query this is despatched to the cloud network. This work provides a review on optimized undertaking scheduling in cloud computing environment. The main element of cloud computing is offering desirable response time for end users, that affords a primary impediment in achievement of cloud computing. All components should coordinate to deal with this mission. This can be handled through a suitable Task scheduling algorithm. So, there's a need of efficient mission scheduling method in implementation of cloud computing surroundings. Due to boom in era and increase in range of statistics facilities the venture dealing with ability of each information centres is foremost concern. Keywords: Cloud computing, task scheduling, Make span, Minimum/Maximum Execution Time, Minimum/Maximum Completion Time, and Load balancing. I. INTRODUCTION Today's age is the age of technology. Technology is growing at a totally speedy charge, every and the whole lot is getting connected. Cloud computing has attracted a whole lot interest these days from each enterprise and academia. However, the size and surprisingly dynamic nature of cloud utility imposes enormous new demanding situations to useful resource management. Thus, efficient aid scheduling schemes is still a task. As a new computing version, cloud computing has converted the IT industry with its developing utility and popularization. Though cloud computing gives considerable opportunities, those are many undertaking faces in its improvement process. This research, introduces Task Scheduling strategies and Load Balancing techniques to improve the cloud assets. With the immense growing business areas, distributed computing has all the earmarks of being the main alternative to meet their extending needs. A cloud supplier initially builds up a processing framework called cloud, where a couple of virtual machines are interconnected through this; the provider shapes the undertaking of the customers. Distributed computing is certifiably not a respectful model to offer the customer to a typical pool of configurable processing assets that can be promptly given and discharged low care effort or administration will consider the particular errand planning [7] of better execution registering approaches. Cloud load adjusting server allocates the heap at the period of growing the couple of CPUs or memories for their assets to scale up with the extended solicitations. This administration is in a general sense associated for business undertaking demands. In cloud, the heap balancer is a host to screen the heap and circulate the heap to VMs by using booking draws near. The heap balancer is used for two significant techniques, one is generally to help the availability of cloud assets and the other is alternatively to propel execution. Asset provisioning framework is used to give best bring about burden adjusting and unwavering quality on distributed computing. It is planned for rendering steady administrations among the distinctive VMs. There are a few sorts of calculations that show up in the writing. Fig 1: Scheduling Model in Cloud Computing [1]
A Survey on Task Scheduling For Parallel Workloads in the Cloud Computing System
International Journal of Innovative Research in Science, Engineering and Technology, 2014
Cloud computing is a computing paradigm where applications, data, memory, bandwidth and IT services are provided over the Internet. Cloud computing is based on pay per usage model. Cloud service providers provide virtual resources to the cloud users. The ultimate goal of cloud service providers is to gain maximum profit and use resources efficiently. Scheduling refers to a set of policies to control the order of work to be performed by a system. Task scheduling plays vital role in cloud computing system to manage heavy load or traffic. Efficient task scheduling improves resource utilization, response time and also meets user requirements. In this paper, Survey on various task scheduling methods for parallel workloads is made.
Balancing Load of Cloud Data Center using Efficient Task Scheduling Algorithm
International Journal of Computer Applications, 2017
Cloud computing is one of the most popular terms of today's computer world. The pay-as-you-use model of cloud permits users to pay only according to their requirement. The enormous increase in popularity of cloud is due to its ubiquitous use through common hardware only. So it must provide high performance gain to the user and at the same time must be beneficial for the Cloud Service Provider (CSP). To achieve this goal many challenges have to be faced. Load balancing is one of them. To distribute the load evenly in cloud the resources and workloads must be scheduled efficiently. A variety of scheduling algorithms are used by load balancers to determine which backend server to send a request to. The selected server allocates resources and schedules the job dynamically on some virtual machine (VM) located on the same physical machine. In this paper, we have proposed a task scheduling algorithm which will distribute the task among all the available virtual machines in a way such that none of them become overloaded. Further we have simulated our algorithm in CloudAnalyst and compared it with the existing load balancing algorithms. Results show that the proposed method not only balances the load more efficiently but also improves the response time.