PHD Research Proposal for Resource Allocation in Cloud Computing

Cloud computing [1] is the shared pool of resources available in the remote area. This computing offers both the hardware and software resources such as operating system, applications, storage, etc… over the internet. It uses the above mentioned services from anywhere in the world by renting instead of buying these resources. The Cloud computation works on the pay-per-use basis that is it charges the users based on the usage of their resources. It has several advantages such as cost, speed, global scale productivity, performance, security. The Cloud computing maximizes the profit to both the service provider and user by allocating resources optimally in the Cloud. In Cloud computing, resources allocation [2] plays a vital role, since the improper resource allocation leads to the wastage and the starvation of the resources. The resource allocation helps in the optimal provisioning of the quality resources within the minimum cost. It also maximizes the profit of the service providers by increasing the efficiency of the resource utilization in order to improve the satisfaction level of users. There are certain resource allocation strategy for the effective utilization of the resources which are described as follows:
A dynamic resource allocation method (DRAM) [3] is proposed for the dynamic allocation of resources in the Fog and Cloud computing environment achieved through the static resource allocation and the dynamic service migration. The DRAM approach effectively performs the migration technique in order to achieve the load-balance. However, it increases the latency and the cost while allocating the resources, since it performs the migration for load-balancing. Some existing system [4] optimizes the resource allocation using the two stage optimization strategy known as the cloudlet selection model and resource allocation model based on mixed integer linear programming (MILP). The cloudlet selection model obtains the cloudlet and resource allocation model allocates the resource in the obtained cloudlet in order to optimize the latency, mean resource usage. For achieving the high throughput and less time consumption few system [5] proposes the effective resource allocation. The resource allocation is performed using the Social-Group-Optimization (SGO) and the task scheduling is performed using the Shortest-Job-First Scheduler (SJF). Some of the system uses the Ant Colony Optimization (ACO) for the resource allocation in order to minimize cost, response time. They aims to maximize the resource utilization by monitoring the Virtual Machine in order to migrate the VM when physical machine is over-utilized.
Currently, there are certain algorithm used for the resource allocation still faces challenges such as resource contention, resources scarcity, resource fragmentation, over and under provisioning of resources. The existing system fails to optimize the cost and the latency while performing the resource allocation. Similarly, Some approach promotes the rate of migration in order to perform the load-balancing operation. In consequence, it automatically increases the power consumption and the makespan. Although, most of the existing system effectively optimizes only two metric from the energy consumption, latency, and cost. Hence, it is necessary to optimize the all these metric together along with the optimization of fore mentioned challenges.


  • [1] Moghaddam, Faraz Fatemi, Mohammad Ahmadi, Samira Sarvari, Mohammad Eslami, and Ali Golkar. “Cloud computing challenges and opportunities: A survey,” 1st IEEE International Conference on Telematics and Future Generation Networks (TAFGEN), pp.34-38, 2015.

  • [2] Hameed, Abdul, Alireza Khoshkbarforoushha, Rajiv Ranjan, Prem Prakash Jayaraman, Joanna Kolodziej, Pavan Balaji, Sherali Zeadally, “A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems,” Computing, Vol.98, No.7, pp.751-774, 2016.

  • [3] Xu, Xiaolong, Shucun Fu, Qing Cai, Wei Tian, Wenjie Liu, Wanchun Dou, Xingming Sun, and Alex X. Liu, “Dynamic Resource Allocation for Load Balancing in Fog Environment,” Wireless Communications and Mobile Computing, 2018.

  • [4] Liu, Li, and Qi Fan, “Resource Allocation Optimization Based on Mixed Integer Linear Programming in the Multi-Cloudlet Environment,” IEEE Access, Vol.6, pp.24533-24542, 2018.

  • [5] Praveen, S. Phani, K. Thirupathi Rao, and B. Janakiramaiah, “Effective allocation of resources and task scheduling in cloud environment using social group optimization,” Arabian Journal for Science and Engineering, Vol.43, No.8 pp.4265-4272, 2018.

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