RETRACTED ARTICLE: Cloud management architecture to improve the resource allocation in cloud IAAS platform

Srinivasan, Jagannathan; Dhas, C. Suresh Gnana

doi:10.1007/s12652-020-02026-7

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…where i r is a load on the current server resource p , As can be seen from the table above, the scatter of values between simple and random algorithms and the algorithm "within the reach" is small and is purely statistical. This is due to the fact that the reallocation of resources does not take into account any parameters of the cloud computing system [8,15].…”

Section: Research Results and Their Discussionmentioning

confidence: 99%

“…Existing approaches to primary allocation and subsequent resource reallocation in cloud computing systems can be divided into the following three types: manual resource assignment, resource scheduling by the cloud computing manager, and resource scheduling in a virtualization environment [5,8].…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

“…The analysis [8,[11][12][13] showed that in cloud computing systems based on the IaaS model, the problems of optimal resource planning arise both at the stage of their reallocation and in the process of use. These problems cannot be solved manually, as in the case of virtualization, for two reasons.…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

“…For example, one of the most popular cloud infrastructure management solutions currently OpenStack supports only 3 methods of resource reallocation: random, random within the availability zone and simple (resource reallocation occurs in turn) [8,14,15]. As for the redistribution of load with its unevennesssuch approaches in cloud computing systems do not yet exist.…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

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Development of the Methods for Resource Reallocation in Cloud Computing Systems

Davydov

Hrebeniuk

2020

Innovative Technologies and Scientific Solutions for Industries

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The subject matter of the article is development of the models and methods of load and resource balancing and reallocation in cloud computing systems based on the infrastructure as a service model. The goal of the work is to increase the efficiency of available resources usage in cloud computing systems (such as RAM, disk space, CPU, network) by developing the model for adaptive management of resource reallocation. This will allow new virtual machines to be launched with minimal performance degradation for already running applications. The following tasks were solved in the article: development of an complex approach to manage resource reallocation in cloud systems, including decomposition of the cloud computing system into zones (based on the defining features of the resources provided in each zone), initial resource allocation (based on the hierarchy analysis method) and resources reallocation within cloud computing system (based on the developed method); development of a method for computing resources reallocation in cloud computing systems; evaluation of the effectiveness of the developed method. To solve the set tasks, approaches and methods of dynamic load balancing were used, as well as methods of theoretical research, which are based on the scientific provisions of the theory of artificial intelligence, static, functional and system analysis. The following results were obtainedon the basis of existing load balancing methods in cloud computing systems analysis, the main features of existing resource allocation methods were identified, their advantages and disadvantages were given. On the basis of the conducted analytical study, the necessity of improving the existing methods of resource reallocation has been proved. A method and an algorithm for computing resources reallocation within cloud computing systems have been developed. This make it possible to reduce the values of the coefficient of computing resources uneven usage while minimizing the cost of moving them. The results obtained have been confirmed by experiments carried out using software for creating private infrastructure cloud services and cloud storages. Conclusions: the improvement of the reallocation and load balancing method in cloud computing systems has increased the ability of these systems to launch new virtual machines with a minimum decrease in the performance of already running applications.

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Section: Research Results and Their Discussionmentioning

confidence: 99%

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

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Development of the Methods for Resource Reallocation in Cloud Computing Systems

Davydov

Hrebeniuk

2020

Innovative Technologies and Scientific Solutions for Industries

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“…In, 13 an Internet of Things‐based edge network ICN caching placement strategy was proposed to improve the in‐network caching efficiency. In, 14 authors developed an efficient memory dynamic allocation method of cloud platform virtual machine. In, 15 authors proposed a dynamic cloud resource allocation model considering user requests and cost of cloud platform.…”

Section: Related Workmentioning

confidence: 99%

Resource allocation of Folk‐Art in Central Plains based on Information‐Centric cloud platform

Wei

2022

Internet Technology Letters

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The folk‐art in Central Plains is rich in connotation and diversified in form, which is an important part of Chinese traditional culture and an art form created by the laborers to meet their life and aesthetic needs. The special symbols in folk‐art reflect the values and aesthetics of the public. It is the crystallization of collective wisdom, the concrete reflection of cultural spirit, and the guarantee of the continuity of local culture, which has unique cultural value. So it is necessary for appreciation of folk‐art in Central Plains. In‐network caching of Information‐Centric Networking (ICN) can cache resources of folk‐art in Central Plains, and then upload to the cloud platform for resource allocation. In this paper, a resource allocation strategy to meet task deadlines in cloud platform is proposed, and a two‐layer resource allocation model is constructed. The primary scheduler allocates meta‐tasks to specific resource districts, and the local scheduler uses an improved banker algorithm to allocate resources to meta‐tasks. Experimental results show that the performance of the proposed algorithm is better than that of traditional resource allocation algorithms in terms of task deadline resource satisfaction, resource utilization rate, and Quality of Experience (QoE).

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