A flexible deadline-driven resource provisioning and scheduling algorithm for multiple workflows with VM sharing protocol on WaaS-cloud

2022 ◽  
Author(s):  
P. Rajasekar ◽  
Yogesh Palanichamy
Keyword(s):  
Scheduling Algorithm ◽  
Resource Provisioning

scholarly journals An Improved Rasa for Load Balancing in Cloud Computing

2021 ◽  
pp. 52-66
Author(s):  
Oyekanmi Ezekiel Olufunminiyi ◽  
Oladoja Ilobekemen Perpetual ◽  
Omotehinwa Temidayo Oluwatosin
Keyword(s):  
Load Balancing ◽  
Resource Utilization ◽  
Performance Metrics ◽  
Scheduling Algorithm ◽  
Resource Provisioning ◽  
The Common ◽  
Resource Aware ◽  
Cloud Users ◽  
Better Than

Cloud is specifically known to have difficulty in managing resource usage during task scheduling, this is an innate from distributed computing and virtualization. The common issue in cloud is load balancing management. This issue is more prominent in virtualization technology and it affects cloud providers in term of resource utilization and cost and to the users in term of Quality of Service (QoS). Efficient procedures are therefore necessary to achieve maximum resource utilization at a minimized cost. This study implemented a load balancing scheme called Improved Resource Aware Scheduling Algorithm (I-RASA) for resource provisioning to cloud users on a pay-as-you-go basis using CloudSim 3.0.3 package tool. I-RASA was compared with recent load balancing algorithms and the result shown in performance evaluation section of this paper is better than Max-min and RASA load balancing techniques. However, it sometimes outperforms or on equal balance with Improved Max-Min load balancing technique when using makespan, flow time, throughput, and resource utilization as the performance metrics.

Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm

2021 ◽  
pp. 1-13
Author(s):  
Timea Bezdan ◽  
Miodrag Zivkovic ◽  
Nebojsa Bacanin ◽  
Ivana Strumberger ◽  
Eva Tuba ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Search Space ◽  
Resource Provisioning ◽  
New Paradigm ◽  
Multi Objective

Cloud computing represents relatively new paradigm of utilizing remote computing resources and is becoming increasingly important and popular technology, that supports on-demand (as needed) resource provisioning and releasing in almost real-time. Task scheduling has a crucial role in cloud computing and it represents one of the most challenging issues from this domain. Therefore, to establish more efficient resource employment, an effective and robust task allocation (scheduling) method is required. By using an efficient task scheduling algorithm, the overall performance and service quality, as well as end-users experience can be improved. As the number of tasks increases, the problem complexity rises as well, which results in a huge search space. This kind of problem belongs to the class of NP-hard optimization challenges. The objective of this paper is to propose an approach that is able to find approximate (near-optimal) solution for multi-objective task scheduling problem in cloud environment, and at the same time to reduce the search time. In the proposed manuscript, we present a swarm-intelligence based approach, the hybridized bat algorithm, for multi-objective task scheduling. We conducted experiments on the CloudSim toolkit using standard parallel workloads and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. Simulation results prove great potential of our proposed approach in this domain.

scholarly journals Cloud Computing Virtualization of Resources Allocation for Distributed Systems

2020 ◽  
Vol 1 (3) ◽  
pp. 98-105 ◽  
Author(s):  
Hanan Shukur ◽  
Subhi Zeebaree ◽  
Rizgar Zebari ◽  
Diyar Zeebaree ◽  
Omar Ahmed ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Resource Provisioning ◽  
Third Party ◽  
Cloud Provider ◽  
Cloud Data Center ◽  
Resource Allocation Algorithm ◽  
Cloud Data

Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques.  The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

scholarly journals Meta Heuristic Approach for Task Scheduling In cloud datacenter for optimal performance

2019 ◽  
Author(s):  
Girish L
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Optimization Technique ◽  
Resource Provisioning ◽  
Heuristic Approach ◽  
Optimal Performance ◽  
Cloud Environment ◽  
Cloud Datacenter

Task scheduling and resource provisioning is theSoftware-as-a-Service (SaaS)core and challenging issues in cloud environment. Processesrunning in the cloud environment will race for availableresources in order to complete their tasks with the minimumexecution time; it is clear that we need an efficient schedulingtechnique for mapping between processes running andavailable resources. In this research paper, we are presented anon-traditional optimization technique, which mimics theprocess of evolution and based on the mechanics of naturalselection and natural genetics called Genetic algorithm (GA),which minimizes the execution time and in turn reducescomputation cost. We had done comparison with Round Robinalgorithm and used CloudSim toolkit for our tests, resultsshows that Meta heuristic GA gives better performance thanother scheduling algorithm.

An Algorithmic Analysis on Load Balancing and Workflow Scheduling in Cloud

2020 ◽  
Vol 17 (5) ◽  
pp. 2085-2090
Author(s):  
M. Tamil Thendral ◽  
S. Godfrey Winster
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Resource Provisioning ◽  
Network Access ◽  
Configurable Computing ◽  
Research Directions ◽  
Management Effort ◽  
Algorithmic Analysis

Cloud computing is a paradigm for facilitating omnipresent, comfortable, on-demand network access to shared provisions of configurable computing resources like servers, applications, networks, and services that can be quickly provisioned and discharged with minimal management effort or service provider intercommunication. Data centers are the set of hosts that runs Virtual Machines. The broker submits the requests which are provisioned to the data centers that submit the task. The task or an application that runs is known as cloudlet. The term “Cloud computing” is to deliver the hosted services on the internet. Each “cloudlet” requires one or more computing resources from the cloud to complete its job. The executions of these sub-tasks use a scheduling algorithm in a particular fashion. Hence, one of the most important issues is that executing a large number of workflow in cloud environment and consumption of resources. The work proposes literature about the scheduling algorithm that resolves the conflict in the allocation of resources and manages the resource provisioning at dynamic to each task in any workflow. This paper proposes an analysis of proposed schemes with different workflow by executing the scheduling algorithm for the cloud workflow. This paper is presented with their objectives, parameters, and limitations and finally concluded with research directions.

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