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Cloud Computing Resource Allocation Methods

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Cloud Computing Resource Allocation Methods

Cloud Computing Resource Allocation Methods

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Cloud Computing Resource Allocation Method In Multi User Mec System

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Prasanta Kumar Bal Prasanta Kumar Bal Scilit Google Scholar View Publications 1 , Sudhir Kumar Mohapatra Sudhir Kumar Mohapatra Scilit Google Scholar View Publications 2 , Tapan Kumar Das Tapan Kumar Das Scilit Google Scholar , Kathir van 3 Srinivasan Kathiravan Srinivasan Scilit Google Scholar View Publications 4 and Yuh-Chun Hu Yuh-Chun Hu Scilit Google Scholar View Publications 5, *

Submitted: 8 December 2021 / Revised: 30 December 2021 / Accepted: 3 February 2022 / Published: 6 February 2022

Pdf] Energy Aware Resource Allocation Heuristics For Efficient Management Of Data Centers For Cloud Computing

The rapid growth of the cloud computing environment and the number of customers ranging from individual users to large enterprises or business houses has become a challenge for organizations handling large amounts of data and various resources in the cloud. Poor resource management can degrade cloud computing performance. Therefore, resources should be allocated to various stakeholders without compromising the organization’s revenue and user satisfaction. The client request cannot be stopped because the main resources on the board are busy. This paper proposes to integrate efficient task scheduling and resource allocation in cloud computing using machine learning (RATS-HM) to solve these problems. The design methods of RATS-HM are presented as follows: First, optimization of the algorithm-based modeling algorithm Short-Term Scheduling Platform (ICS-TS) reduces the execution time of scheduled tasks and increases productivity. Second, a group based on deep neural networks (GO-DNN) for efficient resource allocation using various design constraints, including bandwidth and load resources. Thirdly, it is a pure authentication software ie NSUPREME designed to protect data to ensure security in data storage. Finally, the proposed RATS-HM method is tested with another experimental set and the results are compared with state-of-the-art methods to confirm its effectiveness. Resource usage, energy consumption, response time, etc. The results show that the proposed method is superior instead.

Cloud computing is the amazing innovation that PCs have done in the advanced era of farm workers and helps to stop virtualization operations [1]. Appropriate management is represented as an “organization” that integrates software, platform as a service, and platform as a service (PaaS) [2, 3]. Everyone has a unique idea about business. The purpose of the calculation is to create a dedicated asset of PCs, employees and experts to develop that application to serve customers, taking into account the sales model [4]. Additionally, Internet connections and infrastructure are important because the cloud is built on two key foundation stones, such as cloud computing and networking. For many cloud applications, the network can be used for cloud computing and ancillary applications [5]. Network-wide QoS in the cloud is integrated with its infrastructure and capabilities. As a result, many Service Providers (ASPs) [6] know the difference between using on-demand infrastructure and using business owners’ leased assets. For example, Force Square uses Amazon EC2 Analytics for more than 5 million days, saving 53% of its cost to meet the needs of computing [7], making it the first source of cloud resources.

Specifically, based on the design of demand models, ASP periodically reviews the list service, makes appropriate decisions on goals and resource allocation, and does not spend money on comparison, storage or transmission of information [8, 9] . In addition, resource allocation should be compatible with integration, as a company can provide unique types of services and/or combine these services based on resources, which complicates the problem rather than requiring complex bidding [10]. The needed resources can be obtained from many sources and many users can compete for the same resources [11, 12], with sellers who need buyers, sales people and delivery customers. Many issues in cloud computing are related to data security, power, security, service availability, memory management, and performance scheduling. However, project planning is often the main topic of cloud computing research. Many tasks in cloud computing require high performance, optimal completion, low response time, and free space to use useful resources. Because of the different objectives of this distribution plan, the tasks must be well organized.

Cloud Computing Resource Allocation Methods

It is possible to provide services to customers using various resources on the Internet [13]. Since Amazon introduced the concept of cloud computing, it has developed many cloud computing systems such as Amazon EC2, Google’s Engine, Apache Hardtop, and Microsoft Azure. Amazon EC2 is a virtualized resource pool. Web services are provided by the Linux computing resource, Amazon Data Center [14]. Incidents can be divided into three categories by size: small, large and very large [15]. Cloud computing has a particular impact on the IT industry [16] and there is close competition between companies regarding the quality of their services [17].

Pdf] Optimal Resource Allocation Of Cluster Using Hybrid Grey Wolf And Cuckoo Search Algorithm In Cloud Computing

Companies are also trying to improve or enhance their services through various resources for customers to enroll in the cloud [18]. Therefore, one of the most important factors affecting the quality of service is the allocation of resources and SLA [19], which indicates the level of satisfaction of users. However, scales and bounds need to be verified and upper bounds are difficult to achieve [20].

The remainder of the paper is organized as follows: Section 2 discusses previous work; Section 3 presents the problem and the communication model; the research design is presented in Section 4, Section 5 presents the model, and the paper is concluded in Section 6.

Wei et al. [21] proposed an asset allocation model based on specific assets to evaluate multiple SPs and different asset allocations simultaneously, which improves profitability. Interesting results show that the CSAMIISG research price is close to the real exchange rate, while the exchange rate is not the real selling price. The method is comparable for SP and IN. They will update the application process for future actions and change the terms to make it more efficient.

Tan et al. [22] proposed the YARN initiative to address these issues. Active asset spreads are considered at one level. Another basis for allocating assets for single asset allocation is called long-term asset adequacy (LTRF). They offer a wide range of long-term assets (H-LTRF) with the ability to expand the LTRF to include advanced resources such as LTRF and H-LTRF. LTYARN provides subjects with LTRF and H-LTRF, and their tests show that it induces positive regulation of these assets.

Software Defined Networks For Resource Allocation In Cloud Computing: A Survey

Zhang et al. [23] proposed a distributed computing system that offers asset tagging and valuation, and offers evaluation and quality-based performance evaluation. Depending on the payment model, customers can submit multiple requests at the same time. However, they can face various penalties, one of which is called vagueness. They show that retailers can have social benefits and tangible benefits for relationships. They offer a way to manage asset classification to make orders faster and improve the social quality of companies selling cloud assets. The financing method takes into account the retail needs of all customers. They break the schedule on a first-come, first-served basis based on social support, delivery time, asset utilization and customer base.

Jiang et al. [24] proposed to combine the use of VM DCNS components, the classification of assets used to achieve energy efficiency and reduce server farm management knowledge by taking into account recovery size and transport duration. This method reduces energy consumption, the number of operations and the duration of return to unique cloud management.

Gong et al. [25] presented a task management system based on load and demand factors. This allows different sources to respond to different post-disaster failures and provide additional joint assistance.

Cloud Computing Resource Allocation Methods

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