Task and Resource Allocation for Energy Efficiency in Heterogeneous Cloud Environment

Nahuru Ado Sabongari, Usman Mahmud Ahmed, Musilimat Toyin Ajibade, Poul Adache, Abdulsalam Ya'u Gital

Abstract


Prominent amount the challenges facing heterogeneous cloud environment is resource utilization and alarming increase energy consumption which have attracted series of research attentions. However, efficiency of resource utilization and energy consumption still requires additional research efforts. The current research intends to solve resource utilization and energy consumption challenges in a heterogeneous cloud environment where tasks create enormous quantities of data streams that must be stored. The technique explored a greedy heuristic method for task and resource categorization with the goal of optimizing energy efficiency through creation of energy consumption profile. To classify tasks and resources depending on their intensity levels was proposed.the method was evaluated using CloudSim 3.0.3. version. The result obtained revealed a decrease in energy usage by 1.88% and 24.23% in term of task and resource utilization. Despite the increased resource utilization, the execution time continues to be a significant impediment to achieving optimal energy efficiency. subsequently, this work presents a novel approach to addressing resource utilization and energy consumption challenges in a heterogeneous cloud environment.  The method therefore achieved improved energy efficiency by categorizing tasks and resources based on their intensity levels by using our suggested heuristic method.

Full Text:

PDF

References


Armant Vincent, De Cauwer Milan, Brown Kenneth N, & O’Sullivan, Barry. (2018). Semi-online task assignment policies for workload consolidation in cloud computing systems. Future Generation Computer Systems, 82, 89-103.

Arroba Patricia, Moya José M, Ayala Jose L, & Buyya Rajkumar. (2017). Dynamic Voltage and Frequency Scaling‐aware dynamic consolidation of virtual machines for energy efficient cloud data centers. Concurrency and Computation: Practice and Experience, 29(10), e4067.

Buyya Rajkumar, Srirama Satish Narayana, Casale Giuliano, Calheiros Rodrigo, Simmhan Yogesh, Varghese jBlesson, . . . Netto, Marco AS. (2018). A manifesto for future generation cloud computing: Research directions for the next decade. ACM computing surveys (CSUR), 51(5), 1-38.

Chang Yuan, Huang Runze, Ries Robert J, & Masanet, Eric. (2015). Life-cycle comparison of greenhouse gas emissions and water consumption for coal and shale gas fired power generation in China. Energy, 86, 335-343.

Feng Jie, Pei Qingqi, Yu, F. Richard, Chu Xiaoli, & Shang Bodong. (2019). Computation Offloading and Resource Allocation for Wireless Powered Mobile Edge Computing With Latency Constraint. IEEE Wireless Communications Letters, 8(5), 1320-1323. doi: 10.1109/lwc.2019.2915618.

Gourisaria Mahendra Kumar, Patra SS, & Khilar PM. (2018). Energy saving task consolidation technique in cloud centers with resource utilization threshold Progress in Advanced Computing and Intelligent Engineering (pp. 655-666): Springer.

Hackenberg Daniel, Schöne Robert, Ilsche Thomas, Molka Daniel, Schuchart Joseph, & Geyer Robin. (2015). An energy efficiency feature survey of the intel haswell processor. Paper presented at the 2015 IEEE international parallel and distributed processing symposium workshop.

Hsu Ching-Hsien, Slagter Kenn D, Chen Shih-Chang, & Chung Yeh-Ching. (2014). Optimizing energy consumption with task consolidation in clouds. Information Sciences, 258, 452-462.

Kumar Dilip, Sahoo Bibhudatta, Mondal Bhaskar, & Mandal Tarni. (2015). A genetic algorithmic approach for energy efficient task consolidation in cloud computing. International Journal of Computer Applications, 118(2), 1-6.

Mekala Mahammad Shareef, & Viswanathan Perumal. (2019). Energy-efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT. Computers & Electrical Engineering, 73, 227-244.

Melot Nicolas, Kessler Christoph, & Keller Joerg. (2016). Energy-optimized static scheduling for many-cores with task parallelization, dvfs and core consolidation. Paper presented at the Proceedings of the 19th International Workshop on Software and Compilers for Embedded Systems.

Nasim Robayet, Zola Enrica, & Kassler Andreas J. (2018). Robust optimization for energy-efficient virtual machine consolidation in modern datacenters. Cluster Computing, 21(3), 1681-1709.

Nguyen Ha Huy Cuong, Solanki Vijender Kumar, Van Thang Doan, & Nguyen Thanh Thuy. (2017). Resource allocation for heterogeneous cloud computing. Resource, 9(1-2), 1-15.

Patra Sudhansu Shekhar. (2018). Energy-efficient task consolidation for cloud data center. International Journal of Cloud Applications and Computing (IJCAC), 8(1), 117-142.

Riesinger Christoph, Bakhtiari Arash, Schreiber Martin, Neumann Philipp, & Bungartz Hans-Joachim. (2017). A holistic scalable implementation approach of the lattice Boltzmann method for CPU/GPU heterogeneous clusters. Computation, 5(4), 48.

Sanjeevi P, & Viswanathan P. (2018). DTCF: deadline task consolidation first for energy minimisation in cloud data centres. International Journal of Networking and Virtual Organisations, 19(2-4), 209-233.

Shirvani Mirsaeid Hosseini, Rahmani Amir Masoud, & Sahafi Amir. (2020). A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: taxonomy and challenges. Journal of King Saud University-Computer and Information Sciences, 32(3), 267-286.

Xiong Yonghua, Chen Ya, Jiang Keyuan, & Tang Yongbing. (2017). An energy-aware task consolidation algorithm for cloud computing data centre. International Journal of High Performance Computing and Networking, 10(4-5), 352-358.

Yavari Maede, Rahbar Akbar Ghaffarpour, & Fathi Mohammad Hadi. (2019). Temperature and energy-aware consolidation algorithms in cloud computing. Journal of Cloud Computing, 8(1), 1-16.

Shengchao Xu, Rui & Chen, Huaping. (2020). Energy-efficient scheduling for multi-objective two-stage flow shop using a hybrid ant colony optimisation algorithm. International Journal of Production Research, 58(13), 4103-4120.

Zhong Zhiheng, He Jiabo, Rodriguez Maria A, Erfani Sarah, Kotagiri Ramamohanarao, & Buyya Rajkumar. (2020). Heterogeneous Task Co-location in Containerized Cloud Computing Environments. Paper presented at the 2020 IEEE 23rd International Symposium on Real-Time Distributed Computing (ISORC).

Zhou Zhou, Hu Zhigang, & Li Keqin. (2016). Virtual machine placement algorithm for both energy-awareness and SLA violation reduction in cloud data centers. Scientific Programming, 2016.


Refbacks

  • There are currently no refbacks.