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2020 | OriginalPaper | Buchkapitel

Towards Interference-Aware Dynamic Scheduling in Virtualized Environments

verfasst von : Vinícius Meyer, Uillian L. Ludwig, Miguel G. Xavier, Dionatrã F. Kirchoff, Cesar A. F. De Rose

Erschienen in: Job Scheduling Strategies for Parallel Processing

Verlag: Springer International Publishing

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Abstract

Our previous work shows that multiple applications contending for shared resources in virtualized environments are susceptible to cross-application interference, which can lead to significant performance degradation and consequently an increase in the number of broken SLAs. Nevertheless, state of the art in resource scheduling in virtualized environments still relies mainly on resource capacity, adopting heuristics such as bin packing, overlooking this source of overhead. However, in recent years interference-aware scheduling has gained traction, with the investigation of ways to classify applications regarding their interference levels and the proposal of static cost models and policies for scheduling co-hosted cloud applications. Preliminary results in this area already show a considerable improvement on resource usage and in the reduction of broken SLAs, but we strongly believe that there are still opportunities for improvement in the areas of application classification and pro-active dynamic scheduling strategies. This paper presents the state of the art in interference-aware scheduling for virtualized environments and the challenges and advantages of a dynamic scheme.

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Nächstes Kapitel Towards Hybrid Isolation for Shared Multicore Systems
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Literatur
1.
Chen, X., et al.: CloudScope: diagnosing and managing performance interference in multi-tenant clouds. In: IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, pp. 164–173 (2015)
2.
Gollapudi, S.: Practical Machine Learning. Packt Publishing Ltd., Birmingham (2016)
3.
Herdrich, A.: Cache Qos: from concept to reality in the intel® xeon® processor e5–2600 v3 product family. In: 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 657–668. IEEE (2016)
4.
Huang, S., Huang, J., Dai, J., Xie, T., Huang, B.: The HiBench benchmark suite: characterization of the MapReduce-based data analysis. In: Agrawal, D., Candan, K.S., Li, W.-S. (eds.) New Frontiers in Information and Software as Services. LNBIP, vol. 74, pp. 209–228. Springer, Heidelberg (2011). https://​doi.​org/​10.​1007/​978-3-642-19294-4_​9CrossRef
5.
Iqbal, W., Erradi, A., Mahmood, A.: Dynamic workload patterns prediction for proactive auto-scaling of web applications. J. Netw. Comput. Appl. 124, 94–107 (2018)CrossRef
6.
Javadi, S.A., Gandhi, A.: Dial: reducing tail latencies for cloud applications via dynamic interference-aware load balancing. In: IEEE International Conference on Autonomic Computing (ICAC), pp. 135–144 (2017)
7.
Jersak, L.C., Ferreto, T.: Performance-aware server consolidation with adjustable interference levels. In: 31st ACM Symposium on Applied Computing, pp. 420–425 (2016)
8.
Keller, A., Ludwig, H.: The WSLA framework: specifying and monitoring service level agreements for web services. J. Netw. Syst. Manag. 11, 57–81 (2003)CrossRef
9.
Kirchoff, F.D., Xavier, M.G., Mastella, J., De Rose, C.A.: A preliminary study of machine learning workload prediction techniques for cloud applications. In: 27th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 253–260 (2019)
10.
Kougkas, A., Devarajan, H., Sun, X., Lofstead, J.: Harmonia: an interference-aware dynamic I/O scheduler for shared non-volatile burst buffers. In: IEEE International Conference on Cluster Computing (CLUSTER), pp. 290–301 (2018)
11.
Krzywda, J., et al.: Modeling and simulation of QoS-aware power budgeting in cloud data centers. In: 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 88–93 (2020)
12.
Kumar, J., Singh, A.K.: Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Gener. Comput. Syst. 81, 41–52 (2018)CrossRef
13.
Kumar, R., Setia, S.: Interface aware scheduling of tasks on cloud. In: 4th International Conference on Signal Processing, Computing and Control (ISPCC), pp. 654–658 (2017)
14.
15.
Lu, K., et al.: Fault-tolerant service level agreement lifecycle management in clouds using actor system. Future Gener. Comput. Syst. 54, 247–259 (2016)CrossRef
16.
Ludwig, U.L., Xavier, M.G., Kirchoff, D.F., Cezar, I.B., De Rose, C.A.F.: Optimizing multi-tier application performance with interference and affinity-aware placement algorithms. Concurr. Comput. Pract. Exper. 31, e5098 (2019)
17.
Meyer, V., Kirchoff, D.F., Da Silva, M.L., De Rose, C.A.F.: An interference-aware application classifier based on machine learning to improve scheduling in clouds. In: 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 80–87 (2020)
18.
Meyer, V., Xavier, M.G., Kirchoff, D.F., da R. Righi, R., De Rose, C.A.F.: Performance and cost analysis between elasticity strategies over pipeline-structured applications. In: International Conference on Cloud Computing and Services Science (CLOSER), pp. 404–411 (2019)
19.
Nathuji, R., Kansal, A., Ghaffarkhah, A.: Q-clouds: managing performance interference effects for QoS-aware clouds. In: Proceedings of the 5th European Conference on Computer Systems, pp. 237–250 (2010)
20.
Potter, K.H.: Dynamic addressing mapping to eliminate memory resource contention in a symmetric multiprocessor system, uS Patent 6,505,269, 7 January 2003
21.
22.
Scheepers, M.J.: Virtualization and containerization of application infrastructure: a comparison. In: 21st Twente Student Conference on IT, pp. 1–7 (2014)
23.
Shah, A., Wolf, F., Zhumatiy, S., Voevodin, V.: Capturing inter-application interference on clusters. In: IEEE International Conference on Cluster Computing, pp. 1–5 (2013)
24.
Shekhar, S., Abdel-Aziz, H., Bhattacharjee, A., Gokhale, A., Koutsoukos, X.: Performance interference-aware vertical elasticity for cloud-hosted latency-sensitive applications. In: 2018 IEEE 11th International Conference on Cloud Computing, pp. 82–89 (2018)
25.
26.
Somani, G., Khandelwal, P., Phatnani, K.: VUPIC: virtual machine usage based placement in IaaS cloud. arXiv preprint arXiv:​1212.​0085 (2012)
27.
Su, K., Xu, L., Chen, C., Chen, W., Wang, Z.: Affinity and conflict-aware placement of virtual machines in heterogeneous data centers. In: IEEE Twelfth International Symposium on Autonomous Decentralized Systems (ISADS), pp. 289–294 (2015)
28.
Terpstra, D., Jagode, H., You, H., Dongarra, J.: Collecting performance data with PAPI-C. In: Müller, M.S., Resch, M.M., Schulz, A., Nagel, W.E. (eds.) Tools for High Performance Computing 2009, pp. 157–173 (2010)
29.
30.
Tosatto, A., Ruiu, P., Attanasio, A.: Container-based orchestration in cloud: state of the art and challenges. In: 2015 Ninth International Conference on Complex, Intelligent, and Software Intensive Systems, pp. 70–75 (2015)
31.
Urgaonkar, B., Shenoy, P., Roscoe, T.: Resource overbooking and application profiling in shared hosting platforms. SIGOPS Oper. Syst. Rev. 36, 239–254 (2003)
32.
Vavilapalli, V.K., et al.: Apache hadoop yarn: yet another resource negotiator. In: 4th Symposium on Cloud Computing (2013)
33.
34.
Xavier, M.G.: Data processing with cross-application interference control via system-level instrumentation. Ph.D. thesis, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil (2019)
35.
Zhang, F., Tang, X., Li, X., Khan, S.U., Li, Z.: Quantifying cloud elasticity with container-based autoscaling. Future Gener. Comput. Syst. 98, 672–681 (2019)CrossRef
36.
37.
Zhang, W., Rajasekaran, S., Wood, T., Zhu, M.: MIMP: deadline and interference aware scheduling of Hadoop virtual machines. In: 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 394–403 (2014)
38.
Zhuravlev, S., Blagodurov, S., Fedorova, A.: Addressing shared resource contention in multicore processors via scheduling. ACM SIGARCH Comput. Architect. News 45, 129–142 (2010)CrossRef
Metadaten
Titel
Towards Interference-Aware Dynamic Scheduling in Virtualized Environments
verfasst von
Vinícius Meyer
Uillian L. Ludwig
Miguel G. Xavier
Dionatrã F. Kirchoff
Cesar A. F. De Rose
Copyright-Jahr
2020
Buch
Job Scheduling Strategies for Parallel Processing

Print ISBN: 978-3-030-63170-3

Electronic ISBN: 978-3-030-63171-0

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-63171-0_1