PHD Research Proposal for Energy Efficient Methods in Cloud Computing

Cloud computing [1] is one of the hottest exploring technology in the field of computation. This environment comprises several heterogeneous resources that have the capability to perform the computation for the various applications. It achieves re-usability, reduced cost, flexibility, cost-efficient; this results in the increased rate of the service providers. However, the cloud providers struggle to provide certain services within the minimized energy consumption, since several algorithms focus on achieving the minimum makespan. Even though the minimized makespan can reduce the SLA violation, it fails to minimize energy efficiency. To reduce the energy consumption along with the makespan, there is a specific energy-aware scheduling technique that minimizes the under-loaded resources [2] are described as follows:
In some of the existing system, the efficient resource utilization and the energy-aware scheduling takes place successfully using the Energy-aware Load Balancing (ELB) technique in [3] balances the load through the effective VM migration. The ELB uses the Energy-aware Resource Utilization (ERU) for maximizing the resource usage, and Energy-aware Virtual Machine Migration (FFO-EVMM) for performing the migration without degrading the performance of the resources. The ERU is based on the Artificial Bee Colony optimization algorithm, and FFO-EVMM is based on the Firefly optimization algorithm. Some of the existing systems recommend a cost-effective VM selection algorithm for increasing the energy efficiency in the data center, and for reducing the SLA violation. This VM selection algorithm uses the three policies, namely, Median Migration Time (MedMT) Policy, Maximum Utilization (MaxUT) Policy, and Hotspot Migration (HP) Policy. Few of the researchers suggest an energy-efficient dynamic offloading and resource scheduling (eDors) policy in [4] achieves the minimized energy consumption and the reduced task completion time. This eDors algorithm performs the scheduling based on the completion time, clock frequency control, and transmission power of the task and its immediate predecessor in order to achieve effective aware scheduling. Numerous researchers suggest an efficient VM migration decision-making algorithm for achieving the aware energy scheduling with the minimization of the migration. This technique improves resource utilization; and solves the problems such as energy consumption, load balancing, system maintenance, and SLA violation. Only a few existing systems use the adaptive three-threshold energy-aware algorithm (ATEA) in [5] for reducing the energy consumption and SLA violation through the segregation of resources in the data center into the four types. They are little-loaded resources, lightly loaded resources, moderately loaded resources, and heavily loaded resources. For balancing the resources, the ATEA algorithm migrates VM from the heavily loaded resources and little-loaded resources to the lightly loaded resource. This algorithm replaces the dynamic threshold instead of the static threshold; thus achieves the minimized energy consumption.
Most of the research papers based on the energy-aware scheduling fail to support the parameters such as availability, and accuracy on the real-time applications. The existing research papers struggle in case of optimizing a trade-off between SLA violation and energy efficiency. The few existing algorithms employ the dynamic threshold for monitoring the load in the resource, in order to minimize energy consumption. However, they reduce the workload in the resource using the VM migration; this tends to degrade the performance and to increase the operational cost. The emerging techniques have the responsibility to develop an algorithm for the service providers with the aim of improving the coordination among the distributed data, resource provisioning, energy-aware scheduling. Also, they have to reduce the energy consumption and to improve the resource utilization with the guarantee of quality of service (QoS) while delivering the service.

Reference:

  • [1] Diaby, Tinankoria, and Babak Bashari Rad, “Cloud Computing: A review of the Concepts and Deployment Models,” IJ Information Technology and Computer Science, Vol.6, pp.50-58, 2017.

  • [2]Kaur, Tarandeep, and Inderveer Chana, “Energy efficiency techniques in cloud computing: A survey and taxonomy.” ACM Computing Surveys (CSUR), Vol.48, No.2, pp.22, 2015.

  • [3] Kansal, Nidhi Jain, and Inderveer Chana, “An empirical evaluation of energy-aware load balancing technique for cloud data center,” Cluster Computing, Vol.21, No.2, pp.1311-1329, 2018.

  • [4] Guo, Songtao, Bin Xiao, Yuanyuan Yang, and Yang Yang, “Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing,” 35th Annual IEEE International Conference on INFOCOM -The Computer Communications, IEEE, pp.1-9, 2016.

  • [5] Zhou, Zhou, Jemal Abawajy, Morshed Chowdhury, Zhigang Hu, Keqin Li, Hongbing Cheng, Abdulhameed A. Alelaiwi, and Fangmin Li, “Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms,” Future Generation Computer Systems, Vol.86, pp.836-850, 2018.

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