Abstract
In Data Centers (DCs), elastic clusters are introduced to cut down the huge energy cost. In elastic clusters, the number of working nodes can be manipulated based on the intensity of workloads. However, affected by the way of distributing workloads to working nodes, the required number of working nodes is different to meet the Service Level Agreement (SLA) of workloads. Workloads consist of several requests which come from clients. In general, workloads are queued and served with N-N queues. The first N means that multiple requests can be queued in the service queue maintained by cluster managers. In addition, the second N means that the service queue of each working node can also queue multiple requests. With N-N queues, requests are first received to the service queue maintained by cluster managers, and then are distributed to appropriate service queues of working nodes. According to queueing theory, a fact is that the service efficiency of N-N queues is lower than that of N-1 queues. Here, N-1 queues mean that the service queue maintained by cluster managers can queue multiple requests, while no request is allowed to be queued in working nodes. Motivated by this fact, we propose an N-1 queueing method to make all service queues work in the form of N-1 queues. Thus under same workloads, fewer working nodes are required to meet a same SLA. As a result, without suffering performance degradation, the energy cost of an elastic cluster can be significantly reduced.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Please visit http://iotta.snia.org/traces/3378 for the details of deasna2.
References
Anagnostopoulos, I., Zeadally, S., Exposito, E.: Handling big data: research challenges and future directions. J. Supercomput. 72(4), 1494–1516 (2016)
Biondi, A., Natale, M.D., Buttazzo, G.: Response-time analysis of engine control applications under fixed-priority scheduling. IEEE Trans. Comput. 67(5), 687–703 (2018)
Deng, Y., Hu, Y., Meng, X., Zhu, Y., Zhang, Z., Han, J.: Predictively booting nodes to minimize performance degradation of a power-aware web cluster. Cluster Comput. 17(4), 1309–1322 (2014)
Detti, A., Bracciale, L., Loreti, P., Rossi, G., Melazzi, N.B.: A cluster-based scalable router for information centric networks. Comput. Netw. 142, 24–32 (2018)
Entrialgo, J., Medrano, R., García, D.F., García, J.: Autonomic power management with self-healing in server clusters under qos constraints. Computing 98(9), 871–894 (2016)
Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., Khan, S.U., Zomaya, A.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016)
Hu, C., Deng, Y.: Fast resource scaling in elastic clusters with an agile method for demand estimation. Sustain. Comput. Inf. Syst. 19, 165–173 (2018)
Hu, C., Deng, Y.: Aggregating correlated cold data to minimize the performance degradation and power consumption of cold storage nodes. J. Supercomput. 75(2), 662–687 (2019)
Hu, C., Deng, Y., Min, G., Huang, P., Qin, X.: Qos promotion in energy-efficient datacenters through peak load scheduling. IEEE Trans. Cloud Comput. (2018). https://doi.org/10.1109/TCC.2018.2886187,
Hu, C., Deng, Y., Yang, L.T., Zhao, Y.: Estimating the resource demand in power-aware clusters by regressing a linearly dependent relation. IEEE Trans. Sustain. Comput. (2019). https://doi.org/10.1109/TSUSC.2019.2894708
Iritani, M., Yokota, H.: Effects on performance and energy reduction by file relocation based on file-access correlations. In: Proceedings of the 2012 Joint EDBT/ICDT Workshops, EDBT-ICDT 2012, pp. 79–86. ACM (2012)
Krioukov, A., Mohan, P., Alspaugh, S., Keys, L., Culler, D., Katz, R.: NapSAC: design and implementation of a power-proportional web cluster. ACM SIGCOMM Comput. Commun. Rev. 41(1), 102–108 (2011)
Lu, L., Varman, P., Doshi, K.: Graduated QoS by decomposing bursts: don’t let the tail wag your server. In: Proceedings of the 2009 29th IEEE International Conference on Distributed Computing Systems, ICDCS 2009, pp. 12–21. IEEE Computer Society, Washington, DC (2009)
Lu, L., Varman, P.J., Doshi, K.: Decomposing workload bursts for efficient storage resource management. IEEE Trans. Parallel Distrib. Syst. 22(5), 860–873 (2011)
Mardukhi, F., NematBakhsh, N., Zamanifar, K., Barati, A.: Qos decomposition for service composition using genetic algorithm. Appl. Soft Comput. 13(7), 3409–3421 (2013)
Messaoudi, F., Ksentini, A., Simon, G., Bertin, P.: Performance analysis of game engines on mobile and fixed devices. ACM Trans. Multimedia Comput. Commun. Appl. 13(4), 57:1–57:28 (2017)
Smart, E., Brown, D.D.J., Borges, K.T., Granger-Brown, N.: Reducing energy usage in drive storage clusters through intelligent allocation of incoming commands. Appl. Soft Comput. 52, 673–686 (2017)
Stallings, W.: Operating Systems: Internals and Design Principles, 9th edn. Pearson, Upper Saddle River (2017)
Xu, F., Liu, F., Jin, H.: Heterogeneity and interference-aware virtual machine provisioning for predictable performance in the cloud. IEEE Trans. Comput. 65(8), 2470–2483 (2016)
Yang, L., Deng, Y., Yang, L.T., Lin, R.: Reducing the cooling power of data centers by intelligently assigning tasks. IEEE Internet Things J. 5(3), 1667–1678 (2018)
Zhang, L., Deng, Y., Zhu, W., Zhou, J., Wang, F.: Skewly replicating hot data to construct a power-efficient storage cluster. J. Netw. Comput. Appl. 50, 168–179 (2015)
Zhang, Y., Wei, Q., Chen, C., Xue, M., Yuan, X., Wang, C.: Dynamic scheduling with service curve for qos guarantee of large-scale cloud storage. IEEE Trans. Comput. 67(4), 457–468 (2018)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (61976061), and the School-level Characteristics and Technological Innovation Project, Guangdong University of Foreign Studies (18TS21).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hu, C., Tang, M. (2020). Reduce the Energy Cost of Elastic Clusters by Queueing Workloads with N-1 Queues. In: Zheng, Z., Dai, HN., Tang, M., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2019. Communications in Computer and Information Science, vol 1156. Springer, Singapore. https://doi.org/10.1007/978-981-15-2777-7_23
Download citation
DOI: https://doi.org/10.1007/978-981-15-2777-7_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2776-0
Online ISBN: 978-981-15-2777-7
eBook Packages: Computer ScienceComputer Science (R0)