PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Multilayered autoscaling performance evaluation: Can virtual machines and containers co-scale?

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The wide adoption of cloud computing by businesses is due to several reasons, among which the elasticity of the cloud virtual infrastructure is the definite leader. Container technology allows increasing the flexibility of an application by adding another layer of virtualization. The containers can be dynamically created and terminated, and also moved from one host to another. A company can achieve a significant cost reduction and increase the manageability of its applications by allowing the running of containerized microservice applications in the cloud. Scaling for such solutions is conducted on both the virtual infrastructure layer and the container layer. Scaling on both layers needs to be synchronized so that, for example, the virtual machine is not terminated with containers still running on it. The synchronization between layers is enabled by multilayered cooperative scaling, implying that the autoscaling solution of the virtual infrastructure layers is aware of the decisions of the autoscaling solution on the container layer and vice versa. In this paper, we introduce the notion of cooperative multilayered scaling and the performance of multilayered autoscaling solutions evaluated using the approach implemented in ScaleX (previously known as Autoscaling Performance Measurement Tool, APMT). We provide the results of the experimental evaluation of multilayered autoscaling performance for the combination of virtual infrastructure autoscaling of AWS, Microsoft Azure and Google Compute Engine with pods horizontal autoscaling of Kubernetes by using ScaleX with four distinct load patterns. We also discuss the effect of the Docker container image size and its pulling policy on the scaling performance.
Rocznik
Strony
227--244
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
  • Chair of Computer Architecture and Parallel Systems, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
  • Chair of Computer Architecture and Parallel Systems, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
  • Chair of Computer Architecture and Parallel Systems, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
Bibliografia
  • [1] Abedi, A. and Brecht, T. (2017). Conducting repeatable experiments in highly variable cloud computing environments, Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE’17, L’Aquila, Italy, pp. 287–292.
  • [2] Al-Dhuraibi, Y., Paraiso, F., Djarallah, N. and Merle, P. (2017). Autonomic vertical elasticity of docker containers with elasticdocker, 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), Honolulu, HI, USA, pp. 472–479.
  • [3] Bauer, A., Herbst, N. and Kounev, S. (2017). Design and evaluation of a proactive, application-aware auto-scaler: Tutorial paper, Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE’17, L’Aquila, Italy, pp. 425–428.
  • [4] Bondi, A.B. (2000). Characteristics of scalability and their impact on performance, Proceedings of the 2nd International Workshop on Software and Performance, WOSP’00, Ottawa, Canada, pp. 195–203.
  • [5] Evangelidis, A., Parker, D. and Bahsoon, R. (2017). Performance modelling and verification of cloud-based auto-scaling policies, Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid’17, Madrid, Spain, pp. 355–364.
  • [6] Guo, Y., Stolyar, A. and Walid, A. (2018). Online VM auto-scaling algorithms for application hosting in a cloud, IEEE Transactions on Cloud Computing, pp. 1–1, (early access), https://ieeexplore.ieee.org/document/8351912.
  • [7] Herbst, N.R., Kounev, S. and Reussner, R. (2013). Elasticity in cloud computing: What it is, and what it is not, Proceedings of the 10th International Conference on Autonomic Computing (ICAC 13), San Jose, CA, USA , pp. 23–27.
  • [8] Hwang, K., Bai, X., Shi, Y., Li, M., Chen, W.G. and Wu, Y. (2016). Cloud performance modeling with benchmark evaluation of elastic scaling strategies, IEEE Transactions on Parallel and Distributed Systems 27(1): 130–143.
  • [9] Ilyushkin, A., Ali-Eldin, A., Herbst, N., Papadopoulos, A.V., Ghit, B., Epema, D. and Iosup, A. (2017). An experimental performance evaluation of autoscaling policies for complex workflows, Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE’17, L’Aquila, Italy, pp. 75–86.
  • [10] Jakobik, A., Grzonka, D. and Kolodziej, J. (2017). Security supportive energy aware scheduling and scaling for cloud environments, European Conference on Modelling and Simulation, ECMS 2017, Budapest, Hungary, pp. 583–590.
  • [11] Jindal, A., Podolskiy, V. and Gerndt, M. (2017). Multilayered cloud applications autoscaling performance estimation, 2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2), Kanazawa, Japan, pp. 24–31.
  • [12] Versluis, L. and Neacsu, A.I. (2017). A trace-based performance study of autoscaling workloads of workflows in datacenters, Technical Report 1711.08993v1, Vrije Universiteit Amsterdam, Amsterdam.
  • [13] Liu, Y., Rameshan, N., Monte, E., Vlassov, V. and Navarro, L. (2015). Prorenata: Proactive and reactive tuning to scale a distributed storage system, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Shenzen, China, pp. 453–464.
  • [14] Lloyd, W., Ramesh, S., Chinthalapati, S., Ly, L. and Pallickara, S. (2018). Serverless computing: An investigation of factors influencing microservice performance, 2018 IEEE International Conference on Cloud Engineering (IC2E), Orlando, FL, USA, pp. 159–169.
  • [15] Moore, L.R., Bean, K. and Ellahi, T. (2013). Transforming reactive auto-scaling into proactive auto-scaling, Proceedings of the 3rd International Workshop on Cloud Data and Platforms, CloudDP’13, Prague, Czech Republic, pp. 7–12.
  • [16] Nikravesh, A.Y., Ajila, S.A. and Lung, C.-H. (2015). Towards an autonomic auto-scaling prediction system for cloud resource provisioning, Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS’15, Florence, Italy, pp. 35–45.
  • [17] Papadopoulos, A.V., Ali-Eldin, A., Arzen, K.-E., Tordsson, J. and Elmroth, E. (2016). PEAS: A performance evaluation framework for auto-scaling strategies in cloud applications, ACM Transactions on Modeling and Performance Evaluation of Computing Systems 1(4): 15:1–15:31.
  • [18] Roy, N., Dubey, A. and Gokhale, A. (2011). Efficient autoscaling in the cloud using predictive models for workload forecasting, 2011 IEEE 4th International Conference on Cloud Computing, Washington, DC, USA, pp. 500–507.
  • [19] Sotomayor, B., Montero, R.S., Llorente, I.M. and Foster, I. (2009a). Resource leasing and the art of suspending virtual machines, Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications, HPCC’09, Seoul, South Korea, pp. 59–68.
  • [20] Sotomayor, B., Montero, R.S., Llorente, I.M. and Foster, I. (2009b). Virtual infrastructure management in private and hybrid clouds, IEEE Internet Computing 13(5): 14–22.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-73201d45-773e-40eb-8e46-dcd6eb0986ad
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.