Review on Mapping of Tasks to Resources in Cloud Computing

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0

Resource allocation and scheduling algorithms are the two essential factors that determine the satisfaction of cloud users. The major cloud resources involved here are servers, storage, network, databases, software and so on based on requirements of customers. In the competitive scenario, each service provider tries to use factors like optimal configuration of resources, pricing, Quality of Service (QoS) parameters and Service Level Agreement (SLA) in order to benefit cloud users and service providers. Since, many researchers have proposed different scheduling algorithms and resource allocation strategies, it becomes a cumbersome task to conclude which ones really benefit customers and service providers. Hence, this paper analyses and presents the most relevant considerations that would help the cloud researchers in achieving their goals in terms of mapping of tasks to cloud resources.

Author(s):  
Amandeep Kaur Sandhu ◽  
Jyoteesh Malhotra

This article describes how a rapid increase in usage of internet has emerged from last few years. This high usage of internet has occurred due to increase in popularity of multimedia applications. However, there is no guarantee of Quality of Service to the users. To fulfill the desired requirements, Internet Service Providers (ISPs) establish a service level agreement (SLA) with clients including specific parameters like bandwidth, reliability, cost, power consumption, etc. ISPs make maximum SLAs and decrease energy consumption to raise their profit. As a result, users do not get the desired services for which they pay. Virtual Software Defined Networks are flexible and manageable networks which can be used to achieve these goals. This article presents shortest path algorithm which improves the matrices like energy consumption, bandwidth usage, successful allocation of nodes in the network using VSDN approach. The results show a 40% increase in the performance of proposed algorithms with a respect to existing algorithms.


2020 ◽  
Vol 39 (6) ◽  
pp. 8345-8356
Author(s):  
M. Alamelu ◽  
T.S. Pradeep Kumar ◽  
V. Vijayakumar

Service Level Agreement (SLA) is an agreement between the service provider and consumer to provide the verifiable quality of services. Using the valuable metrics in SLA, a service consumer could easily evaluate the service provider. Though there are different types of SLA models are available between the consumer and provider, the proposed approach describes the Fuzzy rule base SLA agreement generation among multiple service providers. A negotiation system is designed in this work to collect the different sets of provider services. With their desired quality metrics, a common Fuzzy based SLA report is generated and compared against the existing consumer requirements. From the analysis of the common agreement report, consumers can easily evaluate the best service with the desired Impact service, cost and Quality. The main advantage of this approach is that it reduces the time consumption of a consumer. Moreover, the best service provider can be selected among multiple providers with the desired QoS parameters. At the same time, the bilateral negotiation is enhanced with the approach of multilateral negotiation to improve the searching time of consumers.


2017 ◽  
Vol 17 (2) ◽  
pp. 83-96
Author(s):  
G. Arun Kumar ◽  
Aravind Sundaresan ◽  
Snehanshu Saha ◽  
Bidisha Goswami ◽  
Shakti Mishra

Abstract Cloud computing offers scalable services to the user where computing resources are owned by a cloud provider. The resources are offered to clients on pay-per-use basis. However, since multiple clients share the cloud’s resources, they could potentially interfere with each others’ task during peak load instances. The environment changes every instant of time with a new set of job requests demanding resource while another set of jobs relieving another set of resources. A major challenge among the service providers is to maintain a balance without compromising Service Level Agreement (SLA). In case of peak load, when each client strives for a particular resource in minimal time, the resource allocation problem becomes more challenging. The important issue is to fulfil the SLA criterion without delaying the resource allocation. The paper proposes a n-player game-based Machine learning strategy that would forecast outcome using a priori information available and measure/estimate existing parameters such as utilization and delay in an optimal load-balanced paradigm. The simulation validates the conclusion of the theorem by showing that average delay is low and stays in that range as the number of job requests increase. In future, we shall extend this work to multi-resource, multi-user environment.


2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Harvinder Singh ◽  
Anshu Bhasin ◽  
Parag Ravikant Kaveri

AbstractCloud resource allocation, a real-time problem can be dealt with efficaciously to reduce execution cost and improve resource utilization. Resource usability can fulfill customers’ expectations if the allocation has performed according to demand constraint. Task Scheduling is NP-hard problem where unsuitable matching leads to performance degradation and violation of service level agreement (SLA). In this research paper, the workflow scheduling problem has been conducted with objective of higher exploitation of resources. To overcome scheduling optimization problem, the proposed QoS based resource allocation and scheduling has used swarm-based ant colony optimization provide more predictable results. The experimentation of proposed algorithms has been done in a simulated cloud environment. Further, the results of the proposed algorithm have been compared with other policies, it performed better in terms of Quality of Service parameters.


2021 ◽  
Vol 11 (11) ◽  
pp. 4942
Author(s):  
Jorge E. Preciado-Velasco ◽  
Joan D. Gonzalez-Franco ◽  
Caridad E. Anias-Calderon ◽  
Juan I. Nieto-Hipolito ◽  
Raul Rivera-Rodriguez

The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of Experience (QoE) to users. Service classification allows 5G service providers to accurately select the network slices for each service, thereby improving the QoS of the network and the QoE perceived by users, and ensuring compliance with the Service Level Agreement (SLA). Some projects have developed systems for classifying these services based on the Key Performance Indicators (KPIs) that characterize the different services. However, Key Quality Indicators (KQIs) are also significant in 5G networks, although these are generally not considered. We propose a service classifier that uses a Machine Learning (ML) approach based on Supervised Learning (SL) to improve classification and to support a better distribution of resources and traffic over 5G/B5G based networks. We carry out simulations of our proposed scheme using different SL algorithms, first with KPIs alone and then incorporating KQIs and show that the latter achieves better prediction, with an accuracy of 97% and a Matthews correlation coefficient of 96.6% with a Random Forest classifier.


2020 ◽  
Vol 26 (6) ◽  
pp. 40-51
Author(s):  
Muhammad Faraz Manzoor ◽  
Adnan Abid ◽  
Muhammad Shoaib Farooq ◽  
Naeem A. Azam ◽  
Uzma Farooq

Cloud computing has become a very important computing model to process data and execute computationally concentrated applications in pay-per-use method. Resource allocation is a process in which the resources are allocated to consumers by cloud providers based on their flexible requirements. As the data is expanding every day, allocating resources efficiently according to the consumer demand has also become very important, keeping Service Level Agreement (SLA) between service providers and consumers in prospect. This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. In the light of the uniqueness of the models and techniques, the main aim of the resource allocation is to limit the overhead/expenses associated with it. This research aims to present a comprehensive, structured literature review on different aspects of resource allocation in cloud computing, including strategic, target resources, optimization, scheduling and power. More than 50 articles, between year 2007 and 2019, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and they are reviewed under clearly defined objectives. It presents a topical taxonomy of resource allocation dimensions, and articles under each category are discussed and analysed. Lastly, salient future directions in this area are discussed.


2018 ◽  
Vol 7 (3) ◽  
pp. 1677 ◽  
Author(s):  
K R RemeshBabu ◽  
Philip Samuel

Cloud computing provides on demand access to a large pool of heterogeneous computational and storage resources to users over the internet. Optimal scheduling mechanisms are needed for the efficient management of these heterogeneous resources. The optimal scheduler can improve the Quality of Services (QoS) as well as maintaining efficiency and fairness among these tasks. In large scale distributed systems, the performance of these scheduling algorithms is crucial for better efficiency. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Comparing these scheduling algorithms from different perspectives is needed for further improvement. This paper provides a comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing. These algorithms have been evaluated in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served. This study identifies current and future research directions in this area for QoS enabled cloud scheduling.  


2021 ◽  
Vol 13 (4) ◽  
pp. 65-74
Author(s):  
Ramesh C. ◽  
Santhiya K. ◽  
Rakesh Kumar S. ◽  
Rizwan Patan

Cloud computing is a booming technology in the area of digital markets. Tackling the nonfunctional characteristics is a big challenge between service consumers (SC) and service providers (SP). Without proper negotiation between the participants specifying their quality of service (QoS) requirements, service level agreement (SLA) cannot be achieved. Two strategies that are commonly prevalent in the negotiation process are concession model and trade off model. The concession model assures the service consumer (SC) receiving the services on time without any deferment. But service consumer has only limited utility. To balance the utility and achievement rates, the authors propose a mixed negotiation approach for cloud service negotiation, which is based on “Game of Chicken.” Extensive results show that a mixed negotiation approach brings equal amount of satisfaction to both service consumer and service provider in terms of achieving higher utility and outperforms the concession approach, while taking fewer time delays than that of a tradeoff approach.


Cloud computing shows a vibrant role in existing scenario and the enactment of infrastructure as a service is perilous because of its discrepancy in the area. The cloud users have increased hastily, and the accessibility of resources for the users are less. Infrastructure as a service (IaaS) mentions the particulars of infrastructure like physical computing resources such as stowage, compute, and networking services. IaaS- cloud providers underwrite these resources based on their necessity from their vast content of resources presents any where all over the universe. Observing of these resources continually is precarious. For monitoring the availability of resources and notifying to the users about the resources is one of the challenges taken in Iaas layer is Service Level Agreement (SLA) and provided with a solution. The foremost objective of the scheduling algorithms in a cloud environment is to exploit the resources proficiently while balancing the load between resources, to get the least possible execution time. Hence, rank based task scheduling algorithm is proposed to utilize the resources efficiently and perform high performance. A simulation result gives the Quality of Service (QoS) Parameters such as length (size), CPU, throughput, and bandwidth.


Author(s):  
Durga S. ◽  
Mohan S. ◽  
Dinesh Peter J. ◽  
Martina Rebecca Nittala

Cloud users expect high Quality of service (QoS) levels within their budget and the cloud service providers (CSPs) to maximize their profits, always strive for the cost and energy minimization and better resource utilization. Any error in the management of resources causes Service Level Agreement (SLA) violations, high penalties, low customer satisfaction, and long-term losses. The objective of this article is to present a literature review on various resource provisioning strategies and also to present a novel cluster-based resource provisioning (CB-RP) technique that satisfies the needs of both cloud users and CSP. CB-RP employs a heart algorithm to cluster the arriving requests based on its characteristics. The CB-RP technique aims to analyze the requests and provision the resources according to the request category. Simulation results show that our technique produces significant improvements in terms of cost savings, resource utilization and turnaround time compared with state of art technique.


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