Online Optimization in Cloud Resource Provisioning

Due to mainstream adoption of cloud computing and its rapidly increasing usage of energy, the efficient management of cloud computing resources has become an important issue. A key challenge in managing the resources lies in the volatility of their demand. While there have been a wide variety of online algorithms (e.g. Receding Horizon Control, Online Balanced Descent) designed, it is hard for cloud operators to pick the right algorithm. In particular, these algorithms vary greatly on their usage of predictions and performance guarantees. This paper aims at studying an automatic algorithm selection scheme in real time. To do this, we empirically study the prediction errors from real-world cloud computing traces. Results show that prediction errors are distinct from different prediction algorithms, across virtual machines, and over the time horizon. Based on these observations, we propose a simple prediction error model and prove upper bounds on the dynamic regret of several online algorithms. We then apply the empirical and theoretical results to create a simple online meta-algorithm that chooses the best algorithm on the fly. Numerical simulations demonstrate that the performance of the designed policy is close to that of the best algorithm in hindsight.

[1]  Lachlan L. H. Andrew,et al.  Online algorithms for geographical load balancing , 2012, 2012 International Green Computing Conference (IGCC).

[2]  Shahin Shahrampour,et al.  Distributed Online Optimization in Dynamic Environments Using Mirror Descent , 2016, IEEE Transactions on Automatic Control.

[3]  Manfred K. Warmuth,et al.  The Weighted Majority Algorithm , 1994, Inf. Comput..

[4]  David A. Patterson,et al.  A Case For Adaptive Datacenters To Conserve Energy and Improve Reliability , 2008 .

[5]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

[6]  L H AndrewLachlan,et al.  Dynamic right-sizing for power-proportional data centers , 2013 .

[7]  Martin Zinkevich,et al.  Online Convex Programming and Generalized Infinitesimal Gradient Ascent , 2003, ICML.

[8]  Jing Xu,et al.  Adaptive virtual resource management with fuzzy model predictive control , 2011, ICAC '11.

[9]  Ricardo Bianchini,et al.  Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms , 2017, SOSP.

[10]  Omar Besbes,et al.  Non-Stationary Stochastic Optimization , 2013, Oper. Res..

[11]  Steven Hand,et al.  Self-adaptive and self-configured CPU resource provisioning for virtualized servers using Kalman filters , 2009, ICAC '09.

[12]  Allan Borodin,et al.  An optimal on-line algorithm for metrical task system , 1992, JACM.

[13]  Zongpeng Li,et al.  Proactive VNF provisioning with multi-timescale cloud resources: Fusing online learning and online optimization , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[14]  A. Wierman,et al.  Optimality, fairness, and robustness in speed scaling designs , 2010, SIGMETRICS '10.

[15]  Vladimir Vlassov,et al.  ElastMan: elasticity manager for elastic key-value stores in the cloud , 2013, CAC.

[16]  Lachlan L. H. Andrew,et al.  Online Convex Optimization Using Predictions , 2015, SIGMETRICS.

[17]  Jinhui Huang,et al.  Resource prediction based on double exponential smoothing in cloud computing , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).

[18]  D. Mayne,et al.  Robust receding horizon control of constrained nonlinear systems , 1993, IEEE Trans. Autom. Control..

[19]  Jing Xu,et al.  Fuzzy Modeling Based Resource Management for Virtualized Database Systems , 2011, 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems.

[20]  Austin Donnelly,et al.  Sierra: a power-proportional, distributed storage system , 2009 .

[21]  Adam Wierman,et al.  Smoothed Online Convex Optimization in High Dimensions via Online Balanced Descent , 2018, COLT.

[22]  Rebecca Willett,et al.  Online Convex Optimization in Dynamic Environments , 2015, IEEE Journal of Selected Topics in Signal Processing.

[23]  Nandini Mukherjee,et al.  Optimizing the utilization of virtual resources in Cloud environment , 2010, 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems.

[24]  Aniruddha S. Gokhale,et al.  Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[25]  Yue Tan,et al.  An Adaptive Learning Approach for Efficient Resource Provisioning in Cloud Services , 2015, PERV.

[26]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

[27]  Eddy Caron,et al.  Forecasting for Cloud computing on-demand resources based on pattern matching , 2010 .

[28]  Lachlan L. H. Andrew,et al.  Power-Aware Speed Scaling in Processor Sharing Systems , 2009, IEEE INFOCOM 2009.

[29]  Gang Yin,et al.  Online Self-Reconfiguration with Performance Guarantee for Energy-Efficient Large-Scale Cloud Computing Data Centers , 2010, 2010 IEEE International Conference on Services Computing.

[30]  Shaoquan Zhang,et al.  Proactive Serving Decreases User Delay Exponentially: The Light-Tailed Service Time Case , 2017, IEEE/ACM Transactions on Networking.

[31]  Kathryn Bean,et al.  A Coordinated Reactive and Predictive Approach to Cloud Elasticity , 2013, CLOUD 2013.

[32]  Marko Bacic,et al.  Model predictive control , 2003 .

[33]  Chuanqi Kan,et al.  DoCloud: An elastic cloud platform for Web applications based on Docker , 2016, 2016 18th International Conference on Advanced Communication Technology (ICACT).

[34]  Lachlan L. H. Andrew,et al.  A tale of two metrics: simultaneous bounds on competitiveness and regret , 2013, SIGMETRICS '13.

[35]  Shahin Shahrampour,et al.  Online Optimization : Competing with Dynamic Comparators , 2015, AISTATS.

[36]  Adam Wierman,et al.  Using Predictions in Online Optimization: Looking Forward with an Eye on the Past , 2016, SIGMETRICS.

[37]  Timothy Grance,et al.  Cloud Computing Synopsis and Recommendations , 2012 .

[38]  Allan Borodin,et al.  An optimal online algorithm for metrical task systems , 1987, STOC.

[39]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[40]  Danilo Ardagna,et al.  A Receding Horizon Approach for the Runtime Management of IaaS Cloud Systems , 2014, 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

[41]  Allan Borodin,et al.  Online computation and competitive analysis , 1998 .

[42]  Philippe Merle,et al.  Elasticity in Cloud Computing: State of the Art and Research Challenges , 2018, IEEE Transactions on Services Computing.

[43]  Peter J. Denning,et al.  The Operational Analysis of Queueing Network Models , 1978, CSUR.

[44]  Gustavo de Veciana,et al.  Performance evaluation and asymptotics for Content Delivery Networks , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[45]  Marcos José Santana,et al.  Providing IaaS resources automatically through prediction and monitoring approaches , 2014, 2014 IEEE Symposium on Computers and Communications (ISCC).

[46]  Adam Wierman,et al.  Greening multi-tenant data center demand response , 2015, Perform. Evaluation.

[47]  Zhenhuan Gong,et al.  PRESS: PRedictive Elastic ReSource Scaling for cloud systems , 2010, 2010 International Conference on Network and Service Management.

[48]  Paul Marshall,et al.  Elastic Site: Using Clouds to Elastically Extend Site Resources , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[49]  Zhenhua Liu,et al.  Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms , 2019, Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems.

[50]  Kevin Lee,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012, Future Gener. Comput. Syst..

[51]  Ivan Porres,et al.  CRAMP: Cost-efficient Resource Allocation for Multiple web applications with Proactive scaling , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.