Skip to main content

Advertisement

SpringerLink
Log in

Value of service based resource management for large-scale computing systems

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Task scheduling for large-scale computing systems is a challenging problem. From the users perspective, the main concern is the performance of the submitted tasks, whereas, for the cloud service providers, reducing operation cost while providing the required service is critical. Therefore, it is important for task scheduling mechanisms to balance users’ performance requirements and energy efficiency because energy consumption is one of the major operational costs. We present a time dependent value of service (VoS) metric that will be maximized by the scheduling algorithm that take into consideration the arrival time of a task while evaluating the value functions for completing a task at a given time and the tasks energy consumption. We consider the variation in value for completing a task at different times such that the value of energy reduction can change significantly between peak and non-peak periods. To determine the value of a task completion, we use completion time and energy consumption with soft and hard thresholds. We define the VoS for a given workload to be the sum of the values for all tasks that are executed during a given period of time. Our system model is based on virtual machines, where each task will be assigned a resource configuration characterized by the number of the homogeneous cores and amount of memory. For the scheduling of each task submitted to our system, we use the estimated time to compute matrix and the estimated energy consumption matrix which are created using historical data. We design, evaluate, and compare our task scheduling methods to show that a significant improvement in energy consumption can be achieved when considering time-of-use dependent scheduling algorithms. The simulation results show that we improve the performance and the energy values up to 49% when compared to schedulers that do not consider the value functions. Similar to the simulation results, our experimental results from running our value based scheduling on an IBM blade server show up to 82% improvement in performance value, 110% improvement in energy value, and up to 77% improvement in VoS compared to schedulers that do not consider the value functions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Luo, J., Rao, L., Liu, X.: Eco-idc: trade delay for energy cost with service delay guarantee for internet data centers. In: IEEE International Conference on Cluster Computing (CLUSTER), pp. 4553 (2012)

  2. Greenberg, A., Hamilton, J., Maltz, D.A., Patel, P.: The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput. Commun. Rev. 39(1), 68–73 (2008)

    Article  Google Scholar 

  3. Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S.U., Li, K.: An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Comput. 14(1), 55–74 (2016)

    Article  Google Scholar 

  4. Delforege, P.: Critical action needed to save money and cut pollution. (2015). https://www.nrdc.org/resources/americas-data-centers-consuming-and-wasting-growing-amounts-energy. Accessed May 2016

  5. Delforage, P., Whitney, J.: Scaling up energy efficiency across the data center industry: evaluating key drivers and barriers. Issue Paper, National Research Defense Council (NRDC) (2014)

  6. Koomey, J.: Growth in Data Center Electricity Use 2005 to 2010. Analytics Press. http://www.analyticspress.com/datacenters.html. Accessed May 2016

  7. Top Ten Exascale Resource Challenges, Technical Report, DOE ASCAC (2014). https://science.energy.gov/~/media/ascr/ascac/pdf/meetings/20140210/Top10reportFEB14.pdf. Accessed Aug 2015

  8. Rao, L., Liu, X., Xie, L., Liu, W.: Minimizing electricity cost: optimization of distributed internet data centers in a multi-electricity-market environment. In: IEEE INFOCOM (2010)

  9. Amazon ECE2 Reserved Instances, AWS, Amazon. https://aws.amazon.com/ec2/purchasing-options/reserved-instances. Accessed May 2016

  10. Khokhar, A., Prasanna, V.K., Shaaban, M.E., Wang, C.: Heterogeneous computing: challenges and opportunities. IEEE Comput. 26(6), 18–27 (1993)

    Article  Google Scholar 

  11. Xu, D., Nahrstedt, K., Wichadakul, D.: QoS and contention-aware multi-resource reservation. Clust. Comput. 4(2), 95–107 (2001)

  12. Caron, E., Desprez, F., Muresan, A.: Forecasting for grid and cloud computing on-demand resources based on pattern matching. In: 2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom 2010), pp. 456–463 (2010)

  13. Kosta, S., Aucinas, A., Hui, P., Mortier, R., Zhang, X.: Thinkair: dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: 2012 IEEE Infocom, pp. 945–953 (2012)

  14. Khemka, B., Machovec, D., Blandin, C., Siegel, H.J., Hariri, S., Louri, A., Tunc, C., Fargo, F., Maciejewski, A.A.: Resource management in heterogeneous parallel computing environments with soft and hard deadlines. In: 11th Metaheuristics International Conference (MIC 2015) (2015)

  15. Khemka, B., Friese, R., Briceo, L.D., Siegel, H.J., Maciejewski, A.A., Koenig, G.A., Groer, C., Okonski, G., Hilton, M.M., Rambharos, R., Poole, S.: Utility functions and resource management in an oversubscribed heterogeneous computing environment. IEEE Trans. Comput. 64(8), 2394–2407 (2015)

    Article  MathSciNet  Google Scholar 

  16. Khemka, B., Friese, R., Pasricha, S., Maciejewski, A.A., Siegel, H.J., Koenig, G.A., Powers, S., Hilton, M., Rambharos, R., Poole, S.: Utility maximizing dynamic resource management in an oversubscribed energy-constrained heterogeneous computing system. Sustain. Comput. Inform. Syst. 5, 14–30 (2015)

    Google Scholar 

  17. Wu, H., Ravindran, B., Jensen, E.D.: Energy-efficient, utility accrual real-time scheduling under the unimodal arbitrary arrival model. In: IEEE/ACM Design, Automation and Test in Europe (DATE 2005), pp. 474–479 (2005)

  18. Liu, S., Quan, G., Ren, S.: On-line scheduling of real-time services for cloud computing. In: 6th World Congress Services (SERVICES 2010), pp. 459–464 (2010)

  19. Snir, M., Bader, D.A.: A framework for measuring supercomputer productivity. Int. J. High Perform. Comput. Appl. 18(4), 417–432 (2004)

    Article  Google Scholar 

  20. Bohra, A.E., Chaudhary, V.: VMeter: power modelling for virtualized clouds. In: IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW’10) (2010)

  21. Kansal, A., Zhao, F., Liu, J., Kothari, N., Bhattacharya, A.A.: Virtual machine power metering and provisioning. In: 1st ACM Symposium on Cloud Computing (SoCC 2010), pp. 39–50 (2010)

  22. Perf: Linux profiling with performance counters. https://perf.wiki.kernel.org/index.php/Main_Page. Accessed Nov 2015

  23. Qu, G., Hariri, S., Yousif, M.: A new dependency and correlation analysis for features. IEEE Trans. Knowl. Data Eng. 17(9), 1199–1207 (2005)

    Article  Google Scholar 

  24. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

  25. Fargo, F., Tunc, C., Nashif, Y.A., Hariri, S.: Autonomic performance-per-watt management (APM) of cloud resources and services. In: ACM Cloud and Autonomic Computing Conference (CAC 2013) (2013)

  26. Tunc, C.: Autonomic cloud resource management. PhD Thesis, Electrical and Computer Engineering Deaprtment, University of Arizona, Tucson, Arizona, USA (2015)

  27. Fargo, F., Tunc, C., Al-Nashif, Y., Akoglu, A., Hariri, S.: Autonomic workload and resources management of cloud computing services. In: International Conference on Cloud and Autonomic Computing (ICCAC 2014), pp. 101–110 (2014)

  28. NAS Parallel Benchmarks (NAS-NPB): NASA Advanced Supercomputing. https://www.nas.nasa.gov/publications/npb.html. Accessed Aug 2015

  29. Braun, T., Siegel, H.J., Beck, N., Blni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D., Freund, R.F.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61(6), 810–837 (2001)

    Article  MATH  Google Scholar 

  30. Ibarra, O.H., Kim, C.E.: Heuristic algorithms for scheduling independent tasks on non-identical processors. J. ACM (JACM) 24(2), 280–289 (1977)

    Article  MATH  Google Scholar 

  31. Maheswaran, M., Ali, S., Siegel, H.J., Hensgen, D., Freund, R.F.: Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. J. Parallel Distrib. Comput. 59(2), 107–131 (1999)

    Article  Google Scholar 

  32. Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., Schober, R.: Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid. In: Innovative Smart Grid Technologies (ISGT 2010) (2010)

  33. Ali, S., Siegel, H.J., Maheswaran, M., Hensgen, D., Ali, S.: Representing task and machine heterogeneities for heterogeneous computing systems. Tamkang J. Sci. Eng. 3(3), 195–207 (2000)

    Google Scholar 

  34. Downey, A.B.: Model for speedup of parallel programs, Technical Report UCB/CSD-97-933, Berkeley (1997)

  35. Kivity, A., Kamay, Y., Laor, D., Lublin, U., Liguori, A.: KVM: the Linux virtual machine monitor. Linux Symp. 1, 225–230 (2007)

    Google Scholar 

  36. BladeCenter Advanced Managment Module (User’s Guide). https://publib.boulder.ibm.com/infocenter/bladectr/documentation/topic/com.ibm.bladecenter.advmgtmod.doc/kp1bb_pdf.pdf. Accessed Aug 2015

  37. Plummer, D.: An Ethernet Address Resolution Protocol. RFC 826, MIT-LCS (1982)

  38. Address Resolution Protocol (ARP). http://linux-ip.net/html/ether-arp.html. Accessed Mar 2017

  39. Lifka, D.A.: The ANL/IBM SP scheduling systems. In: Workshop on Job Scheduling Strategies for Parallel Processing (IPPS 1995), pp. 295–303 (1995)

  40. Mu’alem, A.W., Feitelson, D.: Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling. IEEE Trans. Parallel Distrib. Syst. 12(6), 529–543 (2001)

    Article  Google Scholar 

  41. Davis, R., Burns, A.: A survey of hard real-time scheduling for multiprocessor systems. ACM Comput. Surv. 43(4) (2011)

  42. Feitelson, D., Rudolph, L.: Parallel job scheduling: issues and approaches. In: Workshop on Job Scheduling Strategies for Parallel Processing (IPPS 1995) (1995)

  43. Feitelson, D., Rudolph, L., Schwiegelshohn, U., Sevcik, K., Wong, P.: Theory and practice in parallel job scheduling. In: Workshop on Job Scheduling Strategies for Parallel Processing (IPPS 1997) (1997)

  44. Feitelson, D.G., Schwiegelshohn, U., Rudolph, L.: Parallel job scheduling: a status report. In: 10th International Workshop on Job Scheduling Strategies for Parallel Processing (JSPP 2004). Lecture Notes in Computer Science, Springer (2004)

  45. Mishra, A., Mishra, S., Kushwaha, D.S.: An improved backfilling algorithm: SJF-B. Int. J. Recent Trends Eng. Technol. 5(1), 78–81 (2011)

  46. Jensen, E., Locke, C., Tokuda, H.: A time-driven scheduling model for real-time systems. In: IEEE Real-Time Systems Symposium, pp. 112–122 (1985)

  47. Li, P., Ravindran, B., Suhaib, S., Feizabadi, S.: A formally verified application-level framework for real-time scheduling on POSIX realtime operating systems. IEEE Trans. Softw. Eng. 30(9), 613–629 (2004)

    Article  Google Scholar 

  48. Vengerov, D., Mastroleon, L., Murphy, D., Bambos, N.: Adaptive data-aware utility-based scheduling in resource-constrained systems. J. Parallel Distrib. Comput. 70(9), 871–879 (2010)

    Article  MATH  Google Scholar 

  49. Zhou, Z., Lan, Z., Tang, W., Desai, N.: Reducing Energy Costs for IBM Blue Gene/P via Power-Aware Job Scheduling. Job Scheduling Strategies for Parallel Processing (JSSPP 2013). Lecture Notes in Computer Science. Springer 8429, 96–115 (2013)

  50. Kodama, Y., Itoh, S., Shimizu, T., Sekiguchi, S., Nakamura, H., Mori, N.: Imbalance of CPU temperatures in a blade system and its impact for power consumption of fans. Clust. Comput. 16(1), 27–37 (2013)

    Article  Google Scholar 

  51. Laszewski, G.V., Wang, L., Younge, A.J., He, X.: Power-aware scheduling of virtual machines in DVFS-enabled clusters. In: IEEE International Conference on Cluster Computing and Workshops (CLUSTER 2009) (2009)

  52. Ding, Y., Qin, X., Liu, L., Wang, T.: Energy efficient scheduling of virtual machines in cloud with deadline constraint. Future Gener. Comput. Syst. 50, 62–74 (2015)

    Article  Google Scholar 

  53. Goiri, I., Beauchea, R., Le, K., Nguyen, T.D., Haque, M.E., Guitart, J., Bianchini, R.: GreenSlot: Scheduling energy consumption in green datacenters. In: ACM International Conference for High Performance Computing, Networking, Storage and Analysis (2011)

  54. Machovec, D., Khemka, B., Pasricha, S., Maciejewski, A.A., Siegel, H.J., Koenig,G.A., Wright, M., Hilton, M., Rambharos, R., Imam, N.: Dynamic resource management for parallel tasks in an oversubscribed energy-constrained heterogeneous environment. In: 25th Heterogeneity in Computing Workshop (HCW 2016), in 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 67–78 (2016)

  55. Machovec, D., Tunc, C., Kumbhare, N., Khemka, B., Akoglu, A., Hariri, S., Siegel, H.J.: Value-based resource management in high-performance computing systems. In: 7th Workshop on Scientific Cloud Computing (SCIENCECLOUD 2016), pp. 19–26 (2016)

  56. Tunc, C., Kumbhare, N., Akoglu, A., Hariri, S., Machovec, D., Siegel, H.J.: Value of service based task scheduling for cloud computing systems. In: IEEE 2016 International Conference on Cloud and Autonomic Computing (ICCAC 2016) (2016)

Download references

Acknowledgements

A preliminary version of portions of this work appeared in [55, 56]. This work is partly supported by National Science Foundation (NSF) research projects NSF CNS-1624668, SES-1314631, CCF-1302693, and DUE-1303362. Furthermore, this work utilized Colorado State Universitys ISTeC Cray system, which is supported by the NSF under grant number CNS-0923386.

Author information

Authors and Affiliations

  1. NSF Center for Cloud and Autonomic Computing, The University of Arizona, Tucson, AZ, 85721, USA

    Cihan Tunc, Nirmal Kumbhare, Ali Akoglu & Salim Hariri

  2. Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, 80523, USA

    Dylan Machovec, Bhavesh Khemka & Howard Jay Siegel

  3. Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA

    Howard Jay Siegel

Authors
  1. Cihan Tunc
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dylan Machovec
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nirmal Kumbhare
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Akoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Salim Hariri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bhavesh Khemka
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Howard Jay Siegel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cihan Tunc.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tunc, C., Machovec, D., Kumbhare, N. et al. Value of service based resource management for large-scale computing systems. Cluster Comput 20, 2013–2030 (2017). https://doi.org/10.1007/s10586-017-0901-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-0901-9

Keywords

Search

Navigation