An efficient framework for resource allocation in cloud computing

Kumar, Aman; Pilli, Emmanuel S.; Joshi, R. C.

doi:10.1109/icccnt.2013.6726596

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…There are few techniques which consider the SLA parameters for the allocation of the resources. A. Kumar, E. S. Pilli, and R. C. Joshi [40] proposed a technique named "EARA". It is an efficient agent-based resource allocation technique that considers various SLA parameters, such as memory, bandwidth, and execution time in which few agents gather accessible resource data to allocate it according to the consumer's demands depending on the SLA arrangement.…”

Section: Quality Of Servicementioning

confidence: 99%

Resource Allocation Techniques in Cloud Computing: A Review and Future Directions

Manzoor

Abid

Farooq

et al. 2020

ELEKTRON ELEKTROTECH

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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.

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Section: Quality Of Servicementioning

confidence: 99%

Resource Allocation Techniques in Cloud Computing: A Review and Future Directions

Manzoor

Abid

Farooq

et al. 2020

ELEKTRON ELEKTROTECH

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“…Existing methods for implementing organizational polices [34,30,18,35,29,31], address only some of the issues in isolation and hence cannot meet all the requirements of dynamic adaptation. This proposal proposed a single comprehensive organizational policy that can operate under time constraints and varying computational loads.…”

Section: Summary and Future Scopementioning

confidence: 99%

A Robust Organizational Policy for Task and Resource Allocation: A Novel Framework

Khan¹,

Sharma²

2019

IJCET

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In a Multi-Agent System existing formalisms for implementing organizational policies assign specific roles to each agent. Examples are hierarchical organization, contract net protocol, social reasoning mechanism, and the use of matchmaker agents. These policies allow the problem solving roles of the agents to change dynamically but do not adapt to variations in computational load on the multi-agent system. They are designed to operate for a predefined maximum problem-solving load and fail to respond, when the number of task requests, exceed this limit. This paper proposes an adaptive organizational policy, called E-RTA (Efficient Resource and Task Allocation), for Multi-Agent-Systems that operate under time constraints and varying computational loads. We consider MAS (Multi-Agent-Systems) as consisting for several problem-solving organizations where each organization is comprises multiple agents that may be grouped into teams for specific problem solving.

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“…This technique also boosts up the objectives of cloud computing i.e. pay as per use because the client need not pay for the resources that he has not used [5]. Dynamic resource allocation shoots up the work flow implementation and allows the users to differentiate among different policies available.…”

Section: Resource Allocationmentioning

confidence: 99%