ESPS: Energy Saving Power Spectrum-Aware Scheduling to Leverage Differences in Power Ratings of Physical Hosts in Datacenters
Abstract
Cloud Computing has seen massive growth over the past couple of decades, leading to exponential growth in energy consumption at data centres. Data centres consuming high amounts of energy leave a carbon footprint of the same scale, hence Cloud Service Providers (CSPs) have been looking for energy-efficient solutions to task scheduling in cloud to reduce the amount of carbon dioxide emission. Saving energy not only helps reduce the carbon footprint datacentres have on the environment, but also helps cover the costs of running multiple datacentres on the CSP’s end. In this paper, we propose an energy saving task scheduling heuristic for heterogeneous cloud systems which selects the optimal physical host containing virtual machines with the additional consideration of the utilization of any incoming task on that particular virtual machine. We compare the energy efficiency of our proposed heuristic with recent algorithms including ECTC, MaxUtil, Random, and FCFS on several benchmark and synthetic datasets to display its superiority in energy-efficient task scheduling in heterogeneous cloud environments. Our proposed heuristic, namely Energy Saving Power Spectrum-Aware Scheduling (ESPS) minimizes energy consumption in a heterogeneous cloud environment by about 38.65%, 33.59%, 53.02%, and 46.96% when compared to FCFS, MaxUtil, Random and ECTC respectively.
Full Text:
PDFReferences
Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, Vol. 25, No. 6, pp. 599–616. doi:10.1016/j.future.2008.12.001
Gombiner, J. (2011). Carbon Footprinting the Internet. Consilience, (5), 119-124. Retrieved July 7, 2020, from www.jstor.org/stable/26167805
Krug, L., Shackleton, M., & Saffre, F. (2014). Understanding the environmental costs of fixed line networking. Proceedings of the 5th International Conference on Future Energy Systems - e-Energy ’14. doi:10.1145/2602044.2602057
Ullman, J. D. (1975). NP-complete scheduling problems. Journal of Computer and System Sciences, 10(3), 384–393. doi:10.1016/s0022-0000(75)80008-0
SPEC, Fujitsu FUJITSU Server PRIMERGY RX1330 M1, https://www.spec.org/power_ssj2008/results/res2014q3/power_ssj2008-20140804-00662.html.
SPEC, Inspur Corporation NF5280M4, https://www.spec.org/power_ssj2008/results/res2014q4/power_ssj2008-20140905-00673.html.
SPEC, Dell Inc. PowerEdge R820 (Intel Xeon E5-4650 v2 2.40 GHz), https://www.spec.org/power_ssj2008/results/res2014q2/power_ssj2008-20140401-00654.html.
SPEC, IBM Corporation IBM NeXtScale nx360 M4 (Intel Xeon E5-2660 v2), https://www.spec.org/power_ssj2008/results/res2014q2/power_ssj2008-20140421-00657.html.
Lee, Y. C., & Zomaya, A. Y. (2010). Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, Vol. 60, No. 2, pp. 268–280. doi:10.1007/s11227-010-0421-3
Gourisaria, M. K., Gupta, P., GM, H., Patra, S. S., Khilar, P. M. (2020). A Comparative Study of Various Task Scheduling Algorithms in Cloud Computing. International Journal of Control and Automation, Vol. 13, No. 4, pp. 1152-1169.
Hsu, C.-H., Chen, S.-C., Lee, C.-C., Chang, H.-Y., Lai, K.-C., Li, K.-C., & Rong, C. (2011). Energy-Aware Task Consolidation Technique for Cloud Computing. 2011 IEEE Third International Conference on Cloud Computing Technology and Science. Doi:10.1109/cloudcom.2011.25
Meisner, D., Gold, B. T., & Wenisch, T. F. (2009). PowerNap. ACM SIGARCH Computer Architecture News, Vol. 37, No. 1, pp. 205. doi:10.1145/2528521.1508269
Ismail, L., & Materwala, H. (2018). EATSVM: Energy-Aware Task Scheduling on Cloud Virtual Machines. Procedia Computer Science, Vol. 135, pp. 248–258. doi:10.1016/j.procs.2018.08.172
Panda, S. K., & Jana, P. K. (2018). An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems. Cluster Computing. doi:10.1007/s10586-018-2858-8
Wu, C.-M., Chang, R.-S., & Chan, H.-Y. (2014). A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Generation Computer Systems, Vol. 37, pp. 141–147. doi:10.1016/j.future.2013.06.009
Kong, J., Choi, J., Choi, L., & Chung, S. W. (2008). Low-cost application-aware DVFS for multi-core architecture. In 2008 Third International Conference on Convergence and Hybrid Information Technology, Vol. 2, pp. 106-111.
Kimura, H., Sato, M., Imada, T., & Hotta, Y. (2008). Runtime DVFS control with instrumented code in power-scalable cluster system. In 2008 IEEE International Conference on Cluster Computing, pp. 354-359.
Genser, A., Bachmann, C., Steger, C., Weiss, R., & Haid, J. (2010). Power emulation based DVFS efficiency investigations for embedded systems. In 2010 International Symposium on System on Chip, pp. 173-178.
Khan, M. I., & Rinner, B. (2014). Energy-aware task scheduling in wireless sensor networks based on cooperative reinforcement learning. 2014 IEEE International Conference on Communications Workshops (ICC). doi:10.1109/iccw.2014.6881310
Wen, G., Hong, J., Xu, C., Balaji, P., Feng, S., & Jiang, P. (2011). Energy-aware hierarchical scheduling of applications in large scale data centers. In 2011 International Conference on Cloud and Service Computing, pp. 158-165.
Mishra, S. K., Sahoo, S., Sahoo, B., & Jena, S. K. (2019). Energy-Efficient Service Allocation Techniques in Cloud: A Survey. IETE Technical Review, pp. 1–14. doi:10.1080/02564602.2019.1620648
Fan, X., Weber, W.-D., & Barroso, L. A. (2007). Power provisioning for a warehouse-sized computer. ACM SIGARCH Computer Architecture News, Vol. 35, No. 2, pp. 13. doi:10.1145/1273440.1250665
Braun, T. D., Siegel, H. J., Beck, N., Bölöni, L. L., Maheswaran, M., Reuther, A. I., … Freund, R. F. (2001). A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. Journal of Parallel and Distributed Computing, Vol. 61, No. 6, pp. 810–837. doi:10.1006/jpdc.2000.1714
Ali, S., Siegel, H. J., Maheswaran, M., Hensgen, D., & Ali, S. (n.d.). Task execution time modeling for heterogeneous computing systems. Proceedings 9th Heterogeneous Computing Workshop (HCW 2000) (Cat. No.PR00556). doi:10.1109/hcw.2000.843743
Zhang, Y., Cheng, X., Chen, L., & Shen, H. (2018). Energy-efficient Tasks Scheduling Heuristics with Multi-constraints in Virtualized Clouds. Journal of Grid Computing, Vol. 16, No. 3, pp. 459–475. doi:10.1007/s10723-018-9426-6
Quan D. M., Somov, A., & Dupont, C. (2012). Energy usage and carbon emission optimization mechanism for federated data centers. In Energy Efficient Data Centres. Lecture Notes in Computer Science, Vol. 7396. Springer, Berlin, 129–140. DOI:http://dx.doi.org/10.1007/978-3-642-33645-4_12
Kliazovich, D., Bouvry, P., & Khan, S. U. (2010). DENS: Data Center Energy-Efficient Network-Aware Scheduling. 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing. doi:10.1109/greencom-cpscom.2010.31
Gourisaria, M.K., Patra, S.S., & Khilar, P.M. (2016). Minimizing Energy Consumption by Task Consolidation in Cloud Centers with Optimized Resource Utilization. International Journal of Electrical and Computer Engineering, Vol. 6, No. 6, pp. 3283-3292.
Gourisaria, M. K., Patra, S. S., & Khilar, P. M. (2018, December). Energy saving task consolidation technique in cloud centers with resource utilization threshold. International Conference on Advanced Computing and Intelligent Engineering. In Progress in Advanced Computing and Intelligent Engineering, pp. 655-666. Springer, Singapore. Bhubaneswar, https://doi.org/10.1007/978-981-10-6872-0_63
Gourisaria, M. K., Samanta, A., Saha, A., Patra, S. S., & Khilar, P. M. (2020, March). An Extensive Review on Cloud Computing. 3rd International Conference on Data Engineering and Communication Technology, In Data Engineering and Communication Technology, pp. 53-78. Springer, Singapore. https://doi.org/10.1007/978-981-15-1097-7_6
Sharma, R., Gourisaria, M. K., Patra, S. S. (2021). Cloud Computing—Security, Issues, and Solutions. Lecture Notes in Networks and Systems, Vol. 134, pp. 687–700.
Bhardwaj, A. K., Gajpal, Y., Surti, C., & Gill, S. S. (2020). HEART: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta‐heuristic algorithms. Software: Practice and Experience, Vol. 50, No. 12, pp. 2231-2251.
Mohialdeen, I. A. (2013). Comparative study of scheduling al-gorithms in cloud computing environment. Journal of Computer Science, Vol. 9, No. 2, pp. 252-263.
Ruan, X., Chen H., Tian Y., & Yin, S. (2019). Virtual machine allocation and migration based on performance-to-power ratio in energy-efficient clouds. Future Generation Computer Systems, Vol. 100, pp. 380-394.
Lin, W., Wang, W., Wu, W., Pang, X., Liu, B., & Zhang, Y. (2018). A heuristic task scheduling algorithm based on server power efficiency model in cloud environments. Sustainable computing: informatics and systems, Vol. 20, pp. 56-65.
DOI: https://doi.org/10.31449/inf.v45i6.3458
This work is licensed under a Creative Commons Attribution 3.0 License.