Time efficient task allocation in cloud computing environment (original) (raw)
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A Review on Engery Efficient Strategy for Task Allocation in Cloud Environment
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
Although cloud computing is now becoming more advanced and matured as many companies have released their own computing platforms to provide services to public, but the research on cloud computing is still in its infancy. Apart from many other challenges of cloud computing, efficient management of energy is one of the most challenging research issues. In this paper we review the existing algorithm of dynamic resource provisioning and allocation algorithms and holistically work to boost data center energy efficiency and performance. This particular paper purposes a) heterogeneous workload and its implication on data centers energy efficiency b) solving the problem of VM resource scheduling to cloud applications
An Efficient Max-Min Resource Allocator and Task Scheduling Algorithm in Cloud Computing Environment
International Journal of Computer Applications, 2016
Cloud computing is a new archetype that provides dynamic computing services to cloud users through the support of datacenters that employs the services of datacenter brokers which discover resources and assign them Virtually. The focus of this research is to efficiently optimize resource allocation in the cloud by exploiting the Max-Min scheduling algorithm and enhancing it to increase efficiency in terms of completion time (makespan). This is key to enhancing the performance of cloud scheduling and narrowing the performance gap between cloud service providers and cloud resources consumers/users. The current Max-Min algorithm selects tasks with maximum execution time on a faster available machine or resource that is capable of giving minimum completion time. The concern of this algorithm is to give priority to tasks with maximum execution time first before assigning those with the minimum execution time for the purpose of minimizing makespan. The drawback of this algorithm is that, the execution of tasks with maximum execution time first may increase the makespan, and leads to a delay in executing tasks with minimum execution time if the number of tasks with maximum execution time exceeds that of tasks with minimum execution time, hence the need to improve it to mitigate the delay in executing tasks with minimum execution time. CloudSim is used to compare the effectiveness of the improved Max-Min algorithm with the traditional one. The experimented results show that the improved algorithm is efficient and can produce better makespan than Max-Min and DataAware.
Optimization of Multi-Dimensional Metrics through Task Scheduling in Cloud Computing Systems
2018
Cloud-based data centers consume a considerable amount of energy, which is an expensive system. The virtualization technique helps to overcome various issues including the energy issue. Because of the dynamic nature of workload, task consolidation is an effective technique to decrease the total number of servers and unnecessary migrations and consequently optimize energy. Effective task allocation techniques act as a key issue to optimize several performance parameters in the cloud system. This paper presents a novel task consolidation technique to achieve energy-makespan-throughput optimally balanced in the cloud data center. We evaluate the performance of our proposed algorithm using simulation analysis in Java-based CloudSim simulator environments. Results of performance evaluation certify that our proposed algorithm has reduced the energy consumption as compared to existing standard algorithms, and optimized the makespan and throughput of the cloud data center. Keywords—Cloud co...
Workload prioritization and optimal task scheduling in cloud
Wireless Networks, 2024
Cloud computing represents an evolved form of cluster, client server, and grid computing, enabling users to seamlessly access resources over the internet. The quality and reliability of the cloud computing services are depends on the specific tasks undertaken by the users. Task Scheduling emerges as a pivotal factor in enhancing the efficiency and reliability of a cloud environment, aiming to optimize resource utilization. Furthermore, efficient task scheduling holds a prime importance in achieving superior performance, minimizing response time, reducing energy consumption and maximizing throughput. Assigning work to essential resources is a challenging process to achieve better performance. However, this paper plans to propose a novel workload prioritization and optimal task scheduling in the cloud with two steps. At first, the ranks are allotted to the tasks with Analytical Hierarchy Process based ranking process that uses a k-means clustering strategy to group the workloads. Then, the tasks are scheduled under the consideration of constraints like makespan, utilization cost, and migration cost and risk probability; based on priority. Accordingly, the task scheduling is done optimally by the proposed hybrid optimization Blue Updated Jellyfish Search Optimization that combines algorithms like Blue Monkey Optimization and Jelly fish Search Optimization algorithms. The performance of the proposed scheduling process is validated and proved over the conventional methods.
Journal of Computer and Knowledge Engineering, 2022
In cloud computing, task scheduling is one of the most important issues that need to be considered for enhancing system performance and user satisfaction. Although there are many task scheduling strategies, available algorithms mainly focus on reducing the execution time while ignoring the profits of service providers. In order to improve provider profitability as well as meet the user requirements, tasks should be executed with minimal cost and without violating Quality of Service (QoS) restrictions. This study presents a Cost and Energy-aware Task Scheduling Algorithm (CETSA) intending to reduce makespan, energy consumption, and cost. The proposed algorithm considers the trade-off between cost, energy consumption, and makespan while considering the load on each virtual machine to prevent virtual machines from overloading. Experimental results with CloudSim show that the CETSA algorithm has better results in terms of energy consumption, waiting time, success rate, cost, improvement ratio, and degree of imbalance compared with MSDE, CPSO, CJS, and FUGE.
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]
International Journal on Cloud Computing: Services and Architecture, 2014
Modern day continued demand for resource hungry services and applications in IT sector has led to development of Cloud computing. Cloud computing environment involves high cost infrastructure on one hand and need high scale computational resources on the other hand. These resources need to be provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is minimized. The tasks are scheduled on machines in either space shared or time shared manner. We evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our approach to the statistics obtained when provisioning of resources was done in First-Cum-First-Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs minimizes significantly in different scenarios.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/review-paper-on-optimized-utilization-of-resources-using-various-task-scheduling-algorithms-in-cloud-computing https://www.ijert.org/research/review-paper-on-optimized-utilization-of-resources-using-various-task-scheduling-algorithms-in-cloud-computing-IJERTV3IS041256.pdf Cloud computing is a new topic in the field of information technology, which is developing drastically. Cloud computing delivers an elastic execution environment of resources over the internet. Task scheduling is a challenging issue to gain maximum profit and to efficiently increase working of cloud computing. In this paper we are studying task scheduling algorithms and various issues related to them i.e. how to allocate resources and maximize the profit while guaranteeing quality of service (QoS). This paper surveys Particle Swarm Optimization (PSO), Particle Swarm-simulated Annealing (P-S) algorithm and improved PSO. To get maximum benefit from resources, optimized utilization of resources is important and for this scheduling plays an important role.
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.
An Approach to Optimized Resource Scheduling using Task Grouping in Cloud
Cloud computing refers to Internet based development and utilization of computer technology, and hence, cloud computing can be described as a model of Internet-based computing. Scheduling is a critical problem in Cloud computing, because a cloud provider has to serve many users in Cloud computing system. So scheduling is the major issue in establishing Cloud computing systems. The main goal of scheduling is to maximize the resource utilization and minimize processing time of the tasks. In this thesis, an efficient task-grouping based approach has been proposed for task scheduling in computational cloud. Proposed work is grouping the tasks before resource allocation according to resource capacity to reduce the communication overhead. Cloud Resources are heterogeneous in nature, owned and managed by different organizations with different allocation policies. In our scheduling algorithm tasks are scheduled based on resources computational and communication capabilities. Here tasks are ...