Resource allocation for distributed cloud: concepts and research challenges

Endo, Patrícia Takako; Palhares, André Vitor de Almeida; Pereira, Natalia; Gonçalves, Glauco Estácio; Sadok, Djamel; Kelner, Judith; Melander, Bob; Mångs, Jan-Erik

doi:10.1109/mnet.2011.5958007

Cited by 181 publications

(80 citation statements)

References 11 publications

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“…From the cloud user's angle, the application requirement and Service Level Agreement (SLA) are major inputs to RAS. The offerings, resource status and available resources are the inputs required from the other side to manage and allocate resources to host applications [25] by RAS. The outcome of any optimal RAS must satisfy the parameters such as throughput, latency and response time.…”

Section: A Significance Of Resource Allocationmentioning

confidence: 99%

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A Survey on Resource Allocation Strategies in Cloud Computing

Vinothina¹,

Ganapathi²

2012

IJACSA

108

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Abstract-Cloud computing has become a new age technology that has got huge potentials in enterprises and markets. Clouds can make it possible to access applications and associated data from anywhere. Companies are able to rent resources from cloud for storage and other computational purposes so that their infrastructure cost can be reduced significantly. Further they can make use of company-wide access to applications, based on payas-you-go model. Hence there is no need for getting licenses for individual products. However one of the major pitfalls in cloud computing is related to optimizing the resources being allocated. Because of the uniqueness of the model, resource allocation is performed with the objective of minimizing the costs associated with it. The other challenges of resource allocation are meeting customer demands and application requirements. In this paper, various resource allocation strategies and their challenges are discussed in detail. It is believed that this paper would benefit both cloud users and researchers in overcoming the challenges faced.

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Section: A Significance Of Resource Allocationmentioning

confidence: 99%

“…Patricia et al [25], investigates the uncertainties that increase difficulty in scheduling and matchmaking by considering some examples of recent research.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Resource Allocation Strategies in Cloud Computing

Vinothina¹,

Ganapathi²

2012

IJACSA

108

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“…Such form of pay-for-use introduces new business opportunities among cloud providers. Nevertheless, the inter-cloud raises many challenges concerning cloud federation, security, interoperability, QoS, monitoring and billing (Majumdar and Garimella, 2012;Endo et al, 2011).…”

Section: Figure 11 Cloud Computing Layersmentioning

confidence: 99%

A GGQS-based hybrid algorithm for inter-cloud time-critical event dissemination

Bae

2012

Journal of the Korean Data and Information Science Society

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Cloud computing has rapidly become a new infrastructure for organizations to reduce their capital cost in IT investment and to develop planetary-scale distributed applications. One of the fundamental challenges in geographically distributed clouds is to provide efficient algorithms for supporting inter-cloud data management and dissemination. In this paper, we propose a geographic group quorum system (GGQS)-based hybrid algorithm for improving the interoperability of inter-cloud in time-critical event dissemination service, such as computing policy updating, message sharing, event notification and so forth. The proposed algorithm first organizes these distributed clouds into a geographic group quorum overlay to support a constant event dissemination latency. Then it uses a hybrid protocol that combines geographic group-based broadcast with quorum-based multicast. Our numerical results show that the GGQS-based hybrid algorithm improves the efficiency as compared with Chord-based, Plume and GQS-based algorithms.

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“…Resource allocation in distributed Grid/Clouds has become an important problem involving resource modeling, resource selection and optimization, resource offering and treatment, resource discovery and monitoring [1]. For the resource selection and optimization challenges, the provider needs to fulfill all requirements and optimize the usage of the infrastructure when given the information regarding Grid/Cloud resource availability at hand.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Tabu search based heuristic for optimized joint resource allocation and task scheduling in Grid/Clouds

Peng

Ding

Ramamurthy

2013

2013 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

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Abstract-Nowadays the development of Grid/Cloud networks has accelerated to meet the increasing requirements for largescale computing, storage and network capabilities by consumers. Therefore how to improve the resource utilization in the Grid/Cloud to satisfy more task requests from users is becoming important. The objective of our investigation in this paper is to minimize the expense the consumers incur while obtaining the resources they request from Grid/Cloud networks. We propose a Tabu search based heuristic to solve joint resource allocation and task scheduling problem in Grid/Cloud networks, and examine the performance of the proposed method. The experimental results are analyzed and compared with the Best-Fit method we proposed in our earlier work. The results show that the Tabu search based heuristic method will equal or outperform the BestFit heuristic, and both can achieve approximate optimal solutions to the corresponding MILP (Mixed Integer Linear Programming) solutions. In addition, compared to the Best-Fit method, the Tabu search based heuristic will reduce the traffic blocking rate by 4%∼30% generally under different job scheduling policies.

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Resource allocation for distributed cloud: concepts and research challenges

Cited by 181 publications

References 11 publications

A Survey on Resource Allocation Strategies in Cloud Computing

A Survey on Resource Allocation Strategies in Cloud Computing

A GGQS-based hybrid algorithm for inter-cloud time-critical event dissemination

A Tabu search based heuristic for optimized joint resource allocation and task scheduling in Grid/Clouds

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