Cloud resource allocation schemes: review, taxonomy, and opportunities

• Main Authors: Yousafzai, A., Gani, Abdullah, Noor, R.M., Sookhak, M., Talebian, H., Shiraz, M., Khan, M.K.
• Format: Article
• Published: Springer Verlag (Germany) 2017
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.um.edu.my/18892/; http://dx.doi.org/10.1007/s10115-016-0951-y

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.