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Cloud resource allocation schemes: review, taxonomy, and opportunities

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Abstract

Cloud computing has emerged as a popular computing model to process data and execute computationally intensive applications in a pay-as-you-go manner. Due to the ever-increasing demand for cloud-based applications, it is becoming difficult to efficiently allocate resources according to user requests while satisfying the service-level agreement between service providers and consumers. Furthermore, cloud resource heterogeneity, the unpredictable nature of workload, and the diversified objectives of cloud actors further complicate resource allocation in the cloud computing environment. Consequently, both the industry and academia have commenced substantial research efforts to efficiently handle the aforementioned multifaceted challenges with cloud resource allocation. The lack of a comprehensive review covering the resource allocation aspects of optimization objectives, design approaches, optimization methods, target resources, and instance types has motivated a review of existing cloud resource allocation schemes. In this paper, current state-of-the-art cloud resource allocation schemes are extensively reviewed to highlight their strengths and weaknesses. Moreover, a thematic taxonomy is presented based on resource allocation optimization objectives to classify the existing literature. The cloud resource allocation schemes are analyzed based on the thematic taxonomy to highlight the commonalities and deviations among them. Finally, several opportunities are suggested for the design of optimal resource allocation schemes.

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Acknowledgments

This work is fully funded and partially funded by Bright Spark Program and High Impact Research Grant from the University of Malaya under reference BSP/APP/1635/2013 and UM.C/625/1/HIR/MOE/FCSIT/03, respectively. The authors also extend their sincere appreciations to the Deanship of Scientific Research at King Saud University for its funding this Prolific Research Group (PRG-1436-16). Additionally, the authors are thankful to Saif Ur Rehman Khan for his ample guidance in revising the manuscript. The authors also thank the reviewers for their useful comments, which significantly improved the quality and presentation of this paper.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia

    Abdullah Yousafzai, Abdullah Gani, Rafidah Md Noor & Mehdi Sookhak

  2. Center for Mobile Cloud Computing Research, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia

    Abdullah Yousafzai, Abdullah Gani, Mehdi Sookhak & Hamid Talebian

  3. Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad, Pakistan

    Muhammad Shiraz

  4. Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh, Saudi Arabia

    Muhammad Khurram Khan

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  1. Abdullah Yousafzai
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  2. Abdullah Gani
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Correspondence to Abdullah Yousafzai or Abdullah Gani.

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Yousafzai, A., Gani, A., Noor, R.M. et al. Cloud resource allocation schemes: review, taxonomy, and opportunities. Knowl Inf Syst 50, 347–381 (2017). https://doi.org/10.1007/s10115-016-0951-y

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