Skip to main content

Interference Aware Workload Scheduling for Latency Sensitive Tasks in Cloud Environment

Abstract

The virtualization technology enhances resource utilization and scalability in the cloud environment. Multiple virtual machines with divergent specifications in terms of hardware and software can be run on a single physical machine. Performance of the applications degrades due to interference when multiple applications are executed simultaneously. This performance degradation affects the quality of service and service level agreement in a cloud environment. In this work, we design an interference-aware workload scheduling approach to execute latency-sensitive tasks in the cloud system. Here we built an interference prediction model to manage the interference efficiently and validated that model in a virtualized environment using Xen hypervisor. We further design a resource prediction model to predict the future resource requirement for the set of tasks using modified double exponential smoothing. This prediction model helps to deploy the required number of physical machines for each time duration. Using these two prediction models, we develop an interference-aware workload scheduling approach that minimizes the effect of interference to achieve a better quality of service in the cloud environment. The extensive simulations with Google cluster data show that our proposed approach improves task guarantee ratio and priority guarantee ratio by 3.32% and 3.63% respectively on average, while improving the resource utilization around 17.26% as compared to other state-of-the-art approaches.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. 1.

    Microsoft Azure [Online]. Available: http://azure.microsoft.com/ (2017)

  2. 2.

    Google [Online]. Available: https://cloud.google.com/ (2017)

  3. 3.

    Amazon EC2 [Online]: http://aws.amazon.com/ec2/ (2017)

  4. 4.

    Salesforce [Online]. Available: https://www.salesforce.com/ (2017)

  5. 5.

    Kashif B, Osman K, Aiman E, Khan Samee U (2018) Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers. Comput Netw 130:94–120

    Article  Google Scholar 

  6. 6.

    Welsh T, Benkhelifa E (2020) On resilience in cloud computing: a survey of techniques across the cloud domain. ACM Comput Surv 53(3):59

    Article  Google Scholar 

  7. 7.

    Baktir AC, Ozgovde A, Ersoy C (2017) How can edge computing benefit from software-defined networking: a survey, use cases, and future directions. IEEE Commun Surv Tutorials 19(4):2359–2391

    Article  Google Scholar 

  8. 8.

    Swain CK, Saini N, Sahu A (2020) Reliability aware scheduling of bag of real time tasks in cloud environment. Computing 102:451–475

    MathSciNet  Article  Google Scholar 

  9. 9.

    Li H, Chan KCC, Liang M, Luo X (2016) Composition of resource-service chain for cloud manufacturing. IEEE Trans Ind Inf 12(1):211–219

    Article  Google Scholar 

  10. 10.

    Barham P, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Warfield A (2003) Xen and the art of virtualization. In: Proceedings of the nineteenth ACM symposium on operating systems principles (SOSP ’03). Association for computing machinery, New York, NY, USA, pp 164–177

  11. 11.

    Zhen X, Weijia S, Qi C (2013) Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parallel Distrib Syst 24(6):1107–1117

    Article  Google Scholar 

  12. 12.

    Swain CK, Sahu A (2018) Interference aware scheduling of real time tasks in cloud environment. In: IEEE 20th international conference on high performance computing and communications; IEEE 16th international conference on smart city; IEEE 4th international conference on data science and systems (HPCC/SmartCity/DSS), pp 974–979

  13. 13.

    Tao F, Laili Y, Xu L, Zhang L (2013) FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans Ind Inform 9(4):2023–2033

    Article  Google Scholar 

  14. 14.

    Tao F, Cheng Y, Xu LD, Zhang L, Li BH (2014) CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans Industr Inf 10(2):1435–1442

    Article  Google Scholar 

  15. 15.

    Zheng X, Martin P, Brohman K, Xu LD (2014) Cloud service negotiation in internet of things environment: a mixed approach. IEEE Trans Industr Inf 10(2):1506–1515

    Article  Google Scholar 

  16. 16.

    Swain CK, Gupta B, Sahu A (2020) Constraint aware profit maximization scheduling of tasks in heterogeneous datacenters. Computing 102:2229–2255

    MathSciNet  Article  Google Scholar 

  17. 17.

    Nathuji R, Kansal A, Ghaffarkhah A (2010) Q-clouds: managing performance interference effects for QoS-aware clouds, In: Proceedings of the 5th European conference on computer systems (EuroSys ’10), association for computing machinery, New York, NY, USA, 237–250

  18. 18.

    Govindan S, Liu J, Kansal A, Sivasubramaniam A (2011) Cuanta: quantifying effects of shared on-chip resource interference for consolidated virtual machines. In: Proceeding of ACM symposium on cloud computing

  19. 19.

    Yiduo M, Ling L, Pu X, Sivathanu S, Dong X (2013) Performance analysis of network I/O workloads in virtualized data centers. IEEE Trans Serv Comput 6(1):48–63

    Article  Google Scholar 

  20. 20.

    Chiang RC, Huang HH (2011) TRACON: Interference-aware scheduling for data-intensive applications in virtualized environments. In: Proceedings of international conference for high performance computing, networking, storage and analysis, pp 1–12

  21. 21.

    Pu X, Liu L, Mei Y, Sivathanu S, Koh Y, Pu C (2010) understanding performance interference of i/o workload in virtualized cloud environments. In: IEEE 3rd international conference on cloud computing, pp 51–58

  22. 22.

    Mars J, Tang L, Hundt R, Skadron K, Soffa ML (2011) Bubble-up: increasing utilization in modern warehouse scale computers via sensible co-locations. In: 44th Annual IEEE/ACM international symposium on microarchitecture (MICRO), Porto Alegre, pp 248–259

  23. 23.

    Delimitrou C, Kozyrakis C (2013) Paragon: QoS-aware scheduling for heterogeneous datacenters. In: Proceedings of the eighteenth international conference on Architectural support for programming languages and operating systems, Association for Computing Machinery, New York, NY, USA, 77–88

  24. 24.

    Du J, Sehrawat N, Zwaenepoel W ((2011) Performance profiling of virtual machines. In: Proceedings of ACM SIGPLAN/SIGOPS international conference on virtual execution environments

  25. 25.

    Wood T, Cherkasova L, Ozonat K, Shenoy P (2008) Profiling and modeling resource usage of virtualized applications. In: Proceeding of ACM/IFIP/USENIX international conference on middleware

  26. 26.

    Kundu S, Rangaswami R, Dutta K, Zhao M (2010) Application performance modeling in a virtualized environment. In: The sixteenth international symposium on high-performance computer architecture, Bangalore, pp 1–10

  27. 27.

    Xu F, Liu F, Jin H (2016) Heterogeneity and interference-aware virtual machine provisioning for predictable performance in the cloud. IEEE Trans Comput 65(8):2470–2483

    MathSciNet  Article  Google Scholar 

  28. 28.

    Zhang W, Rajasekaran S, Wood T, Zhu M (2014) MIMP: deadline and interference aware scheduling of hadoop virtual machines. In: 14th IEEE/ACM international symposium on cluster, cloud and grid computing, pp 394–403

  29. 29.

    Goder A, Spiridonov A, Wang Y (2015) Bistro: scheduling data-parallel jobs against live production systems. In: Proceedings of the 2015 usenix annual technical conference, pp 459–471, Santa Clara, CA, USA

  30. 30.

    Lo D, Cheng L, Govindaraju R, Ranganathan P, Kozyrakis C (2015) Heracles: improving resource efficiency at scale. In: Proceedings of the 42nd annual international symposium on computer architecture, pp 450–462, Portland, OR, USA

  31. 31.

    Shuang C, Christina D, José MF (2019) PARTIES:QoS-aware resource partitioning for multiple interactive services. In: Proceedings of the twenty-fourth international conference on architectural support for programming languages and operating systems, Providence, RI, USA, pp 107–120

  32. 32.

    Iorgulescu C, Azimi R, Kwon Y, Elnikety S, Syamala M, Narasayya V, Wang J (2018) PerfIso: performance isolation for commercial latency-sensitive services. In: Proceedings of the 2018 USENIX annual technical conference, pp 519–532, Boston, MA, USA

  33. 33.

    Yunqi Z, George P, Matteo FG, Marcus F, Íñigo G, Ricardo B (2016) History-based harvesting of spare cycles and storage in large-scale datacenters. In: Proceedings of the 12th USENIX conference on operating systems design and implementation, pp 755–770

  34. 34.

    Manohar V, Arpan G, Brandenburg Björn B (2018) Tableau: a high-throughput and predictable VM scheduler for high-density workloads. In: Proceedings of the thirteenth eurosys conference, association for computing machinery, New York, NY, USA, Article 28, 1–16

  35. 35.

    Haishan Z, Mattan E (2016) Dirigent: enforcing QoS for latency-critical tasks on shared multicore systems. In: Proceedings of the twenty-first international conference on architectural support for programming languages and operating systems, pp 33–47, Atlanta, GA, USA

  36. 36.

    Tesfatsion SK, Wadbro E, Tordsson J (2018) PerfGreen: performance and energy aware resource provisioning for heterogeneous clouds. In: Proceedings of IEEE international conference on autonomic computing, pp 81–90, Trento, Italy

  37. 37.

    Shekhar S, Gokhale A (2017) Dynamic resource management across cloud-edge resources for performance-sensitive applications. In: 17th IEEE/ACM international symposium on cluster, cloud and grid computing, pp 707–710

  38. 38.

    Han Z, Tan H, Li X, Jiang SH, Li Y, Lau FCM (2019) OnDisc: online latency-sensitive job dispatching and scheduling in heterogeneous edge-clouds. IEEE/ACM Trans Netw 27(6):2472–2485

    Article  Google Scholar 

  39. 39.

    Tuli S, Ilager S, Ramamohanarao K, Buyya R (2020) Dynamic scheduling for stochastic edge-cloud computing environments using A3C learning and residual recurrent neural networks. In: IEEE transactions on mobile computing, 1, pp 1-1

  40. 40.

    Farahnakian F, Pahikkala T, Liljeberg P, Plosila J, Hieu NT, Tenhunen H (2019) Energy-aware VM consolidation in cloud data centers using utilization prediction model. IEEE Trans Cloud Comput 7(2):524–536

    Article  Google Scholar 

  41. 41.

    Zhu X, Chen Yang LT, Xiang Y (2015) ANGEL: agent-based scheduling for real-time tasks in virtualized clouds. IEEE Trans Comput 64(12):3389–3403

    MathSciNet  Article  Google Scholar 

  42. 42.

    Ye K, Wu Z, Wang C, Zhou BB, Si W, Jiang X, Zomaya A (2015) Profiling-based workload consolidation and migration in virtualized data centers. IEEE Trans Parallel Distrib Syst 26(3):878–890

    Article  Google Scholar 

  43. 43.

    Koh Y, Knauerhase R, Brett P, Bowman M, Wen Z, Pu C (2007) An analysis of performance interference effects in virtual environments. In: IEEE international symposium on performance analysis of systems & software, pp 200–209

  44. 44.

    sysench : https://launchpad.net/sysbench, Nov. 2017

  45. 45.

    Memory bandwidth benchmark: http://manpages.ubuntu.com/manpages/zesty/man1/mbw.1.html

  46. 46.

    Witten IH, Frank E, Mark A (2016) Pal. data mining, fourth edition: practical machine learning tools and techniques. Morgan Kaufmann Publishers Inc

  47. 47.

    Victor C (2014) The business intelligence as a service in the cloud. Futur Gener Comput Syst 37:512–534

    Article  Google Scholar 

  48. 48.

    Garg SK, Toosi Adel N, Srinivasa KG, Buyya R (2014) SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J Netw Comput Appl 45:108–120

    Article  Google Scholar 

  49. 49.

    Google Cluster Data, http://code.google.com/p/googleclusterdata/

  50. 50.

    Delgado P, Didona D, Dinu F, Zwaenepoel W (2016) Job-aware scheduling in eagle: divide and stick to your probes. In: Proceedings of ACM symposium on cloud computing

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Chinmaya Kumar Swain.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 84 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Swain, C.K., Sahu, A. Interference Aware Workload Scheduling for Latency Sensitive Tasks in Cloud Environment. Computing (2021). https://doi.org/10.1007/s00607-021-01014-9

Download citation

Keywords

  • Real Time
  • Cloud computing
  • Scheduling
  • Virtualization
  • Interference

Mathematics Subject Classification

  • 68T20
  • 68W40
  • 68Q15
  • 97K50