Abstract
Online applications that serve global workload have become a norm and those applications are experiencing not only temporal but also spatial workload variations. In addition, more applications are hosting their backend tiers separately for benefits such as ease of management. To provision for such applications, traditional elasticity approaches that only consider temporal workload dynamics and assume well-provisioned backends are insufficient. Instead, in this article, we propose a new type of provisioning mechanisms—geo-elasticity, by utilizing distributed clouds with different locations. Centered on this idea, we build a system called DBScale that tracks geographic variations in the workload to dynamically provision database replicas at different cloud locations across the globe. Our geo-elastic provisioning approach comprises a regression-based model that infers database query workload from spatially distributed front-end workload, a two-node open queueing network model that estimates the capacity of databases serving both CPU and I/O-intensive query workloads and greedy algorithms for selecting best cloud locations based on latency and cost. We implement a prototype of our DBScale system on Amazon EC2’s distributed cloud. Our experiments with our prototype show up to a 66% improvement in response time when compared to local elasticity approaches.
- Daniel Abadi. 2012. Consistency tradeoffs in modern distributed database system design: CAP is only part of the story. Computer 45, 2 (Feb. 2012), 37--42. Google Scholar
Digital Library
- Sharad Agarwal, John Dunagan, Navendu Jain, Stefan Saroiu, Alec Wolman, and Harbinder Bhogan. 2010. Volley: Automated data placement for geo-distributed cloud services. In Proceedings of the Conference on Networked Systems Design and Implementation (NSDI’10). 17--32. Google Scholar
Digital Library
- Amazon Global Infrastructure 2016. Amazon Global Infrastructure. Retrieved from http://aws.amazon.com/about-aws/global-infrastructure/.Google Scholar
- Amazon Route 53 2015. Amazon Route 53: Choosing a Routing Policy. Retrieved from http://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html.Google Scholar
- Yair Amir, Claudiu Danilov, Michal Miskin-Amir, Jonathan Stanton, and Ciprian Tutu. 2003. On the Performance of Consistent Wide-area Database Replication. Technical Report.Google Scholar
- Martin F. Arlitt and Carey L. Williamson. 1997. Internet web servers: Workload characterization and performance implications. IEEE/ACM Transactions on Networking 5, 5 (1997), 631--645. Google Scholar
Digital Library
- Robert Birke, Lydia Y. Chen, and Evgenia Smirni. 2012. Usage patterns in multi-tenant data centers: A temporal perspective. In Proceedings of the 2015 IEEE International Conference on Autonomic Computing (ICAC’12). Google Scholar
Digital Library
- George Edward Pelham Box and Gwilym Jenkins. 1990. Time Series Analysis, Forecasting and Control. Google Scholar
Digital Library
- O. J. Boxma, R. D. van der Mei, J. A. C. Resing, and K. M. C. van Wingerden. 2005. Sojourn time approximations in a two-node queueing network. In Proceedings of the International Trade Commission (ITC’05).Google Scholar
- G. Casale, Ningfang Mi, L. Cherkasova, and E. Smirni. 2012. Dealing with burstiness in multi-tier applications: Models and their parameterization. IEEE Transactions on Software Engineering 38, 5 (2012), 1040--1053. Google Scholar
Digital Library
- Emmanuel Cecchet, Rahul Singh, Upendra Sharma, and Prashant Shenoy. 2011. Dolly: Virtualization-driven database provisioning for the cloud. In Proceedings of the Conference on Virtual Execution Environments (VEE’11). Google Scholar
Digital Library
- Ludmila Cherkasova and Rob Gardner. 2005. Measuring CPU overhead for I/O processing in the xen virtual machine monitor. In Proceedings of the Annual Technical Conference (ATEC’05). Google Scholar
Digital Library
- Ron C. Chiang and H. Howie Huang. 2011. TRACON: Interference-aware scheduling for data-intensive applications in virtualized environments. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis. ACM, 47. Google Scholar
Digital Library
- Cisco Global Cloud Index 2016. Cisco Global Cloud Index:Forecast and Methodology, 2015--2020. Retrieved from http://www.cisco.com/c/dam/en/us/solutions/collateral/service-provider/global-cloud-index-gci/white-paper-c11-738085.pdf.Google Scholar
- James C. Corbett, Jeffrey Dean, Michael Epstein, Andrew Fikes, Christopher Frost, J. J. Furman, Sanjay Ghemawat, Andrey Gubarev, Christopher Heiser, Peter Hochschild, Wilson Hsieh, Sebastian Kanthak, Eugene Kogan, Hongyi Li, Alexander Lloyd, Sergey Melnik, David Mwaura, David Nagle, Sean Quinlan, Rajesh Rao, Lindsay Rolig, Yasushi Saito, Michal Szymaniak, Christopher Taylor, Ruth Wang, and Dale Woodford. 2012. Spanner: Google’s globally-distributed database. In Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation (OSDI’12). USENIX Association, Berkeley, CA, 251--264. Retrieved from http://dl.acm.org/citation.cfm?id=2387880.2387905. Google Scholar
Digital Library
- Carlo Curino, Evan P. C. Jones, Samuel Madden, and Hari Balakrishnan. 2011a. Workload-aware database monitoring and consolidation. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’11). Google Scholar
Digital Library
- Carlo Curino, Evan Jones, Raluca Ada Popa, Nirmesh Malviya, Eugene Wu, Samuel Madden, Hari Balakrishnan, and Nickolai Zeldovich. 2011b. Relational cloud: A database-as-a-service for the cloud. In proceedings of the 5th Biennial Conference on Innovative Data Systems Research.Google Scholar
- G. DeCandia, D. Hastorun, and M. Jampani. 2007. Dynamo: Amazon’s highly available key-value store. ACM SIGOPS Operat. (2007). Google Scholar
Digital Library
- Aaron J. Elmore, Sudipto Das, Divyakant Agrawal, and Amr El Abbadi. 2011. Zephyr: Live migration in shared nothing databases for elastic cloud platforms. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’11). Google Scholar
Digital Library
- Global Cloud Infrastructure 2016. Regions Beyond Regions: Global Cloud Infrastructure Expansions. Retrieved from https://blog.fugue.co/2016-04-12-regions-beyond-regions-global-cloud-infrastructure-expansions.html.Google Scholar
- Chuanxiong Guo, Lihua Yuan, Dong Xiang, Yingnong Dang, Ray Huang, Dave Maltz, Zhaoyi Liu, Vin Wang, Bin Pang, Hua Chen, Zhi-Wei Lin, and Varugis Kurien. 2015. Pingmesh: A large-scale system for data center network latency measurement and analysis. In Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication (SIGCOMM’15). ACM, New York, NY, 139--152. Google Scholar
Digital Library
- T. Guo and P. Shenoy. 2015. Model-driven geo-elasticity in database clouds. In Proceedings of the 2015 IEEE International Conference on Autonomic Computing (ICAC’15). Google Scholar
Digital Library
- Tian Guo, Prashant Shenoy, and Hakan Hacigümüş. 2016. GeoScale: Providing geo-elasticity in distributed clouds. In Proceedings of the IEEE International Conference on Cloud Engineering (IC2E’16).Google Scholar
Cross Ref
- Keqiang He, Alexis Fisher, Liang Wang, Aaron Gember, Aditya Akella, and Thomas Ristenpart. 2013. Next stop, the cloud: Understanding modern web service deployment in EC2 and azure. In Proceedings of the Internet Measurement Conference (IMC’13). Google Scholar
Digital Library
- Melanie Kambadur, Tipp Moseley, Rick Hank, and Martha A. Kim. 2012. Measuring interference between live datacenter applications. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society Press, 51. Google Scholar
Digital Library
- S. Kundu, R. Rangaswami, K. Dutta, and Ming Zhao. 2010. Application performance modeling in a virtualized environment. In Proceedings of the Conference on High Performance Computer Architecture (HPCA’10).Google Scholar
Cross Ref
- John D. C. Little. 1961. A proof for the queuing formula: L = λW. Operat. Res. 9, 3 (1961), 383--387. Google Scholar
Digital Library
- Guoxin Liu, Haiying Shen, and Harrison Chandler. 2013. Selective data replication for online social networks with distributed datacenters. In Proceedings of the 2013 21st IEEE International Conference on Network Protocols (ICNP’13). IEEE, 1--10.Google Scholar
Cross Ref
- Maxmind GeoIP Service 2016. Maximind GeoIP Service. Retrieved from https://www.maxmind.com/en/home.Google Scholar
- Memcached 2015. Memcached. Retrieved from http://memcached.org/.Google Scholar
- Xiaoqiao Meng, Canturk Isci, Jeffrey Kephart, Li Zhang, Eric Bouillet, and Dimitrios Pendarakis. 2010. Efficient resource provisioning in compute clouds via VM multiplexing. In Proceedings of the 2015 IEEE International Conference on Autonomic Computing (ICAC’10). 10. Google Scholar
Digital Library
- Ningfang Mi, Giuliano Casale, Ludmila Cherkasova, and Evgenia Smirni. 2008. Burstiness in multi-tier applications: Symptoms, causes, and new models. In Proceedings of the Conference on Middleware. Google Scholar
Digital Library
- Barzan Mozafari, Carlo Curino, and Samuel Madden. 2013. DBSeer: Resource and performance prediction for building a next generation database cloud. In Proceedings of teh Conference on Innovative Data Systems Research (CIDR’13).Google Scholar
- Mohamed N. Bennani and Daniel A. Menasce. 2005. Resource allocation for autonomic data centers using analytic performance models. In Proceedings of the 2015 IEEE International Conference on Autonomic Computing (ICAC’05). Google Scholar
Digital Library
- Ripal Nathuji, Aman Kansal, and Alireza Ghaffarkhah. 2010. Q-clouds: Managing performance interference effects for QoS-aware clouds. In Proceedings of EuroSys. Google Scholar
Digital Library
- Faisal Nawab, Vaibhav Arora, Divyakant Agrawal, and Amr El Abbadi. 2015. Minimizing commit latency of transactions in geo-replicated data stores. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’15). ACM, New York, NY, 1279--1294. Google Scholar
Digital Library
- Hiep Nguyen, Zhiming Shen, Xiaohui Gu, Sethuraman Subbiah, and John Wilkes. 2013. Agile: Elastic distributed resource scaling for infrastructure-as-a-service. In Proceedings of the USENIX International Conference on Automated Computing (ICAC’13).Google Scholar
- P. N. Shankaranarayanan, Ashiwan Sivakumar, Sanjay Rao, and Mohit Tawarmalani. 2014. Performance sensitive replication in geo-distributed cloud datastores. In Proceedings of the Conference on Dependable Systems and Networks (DSN’14). Google Scholar
Digital Library
- Percona Xtrabackup. 2015. Percona Xtrabackup (2015). Retrieved from: https://www.percona.com/software/mysql-database/percona-xtrabackup.Google Scholar
- Fan Ping, Xiaohu Li, Christopher McConnell, Rohini Vabbalareddy, and Jeong-Hyon Hwang. 2011. Towards optimal data replication across data centers. In Proceedings of the 2011 31st International Conference on Distributed Computing Systems Workshops (ICDCSW’11). IEEE, 66--71. Google Scholar
Digital Library
- Raluca Ada Popa, Catherine Redfield, Nickolai Zeldovich, and Hari Balakrishnan. 2011. CryptDB: Protecting confidentiality with encrypted query processing. In Proceedings of the 23rd ACM Symposium on Operating Systems Principles. ACM, 85--100. Google Scholar
Digital Library
- S. Sakr and A. Liu. 2012. SLA-based and consumer-centric dynamic provisioning for cloud databases. In Proceedings of the IEEE International Conference on Cloud Computing (CLOUD’12). Google Scholar
Digital Library
- Ankit Singla, Balakrishnan Chandrasekaran, P. Brighten Godfrey, and Bruce Maggs. 2014. The internet at the speed of light. In Proceedings of the 13th ACM Workshop on Hot Topics in Networks (HotNets-XIII’14). ACM, New York, NY, Article 1, 7 pages. Google Scholar
Digital Library
- Yair Sovran, Russell Power, Marcos K. Aguilera, and Jinyang Li. 2011. Transactional storage for geo-replicated systems. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles (SOSP’11). ACM. 385--400. Google Scholar
Digital Library
- The ObjectWeb TPC-W implementation 2005. The ObjectWeb TPC-W implementation. Retrieved from http://jmob.ow2.org/tpcw.html.Google Scholar
- B. Urgaonkar, P. Shenoy, A. Chandra, and P. Goyal. 2005. Dynamic provisioning of multi-tier internet applications. In Proceedings of the 2015 IEEE International Conference on Autonomic Computing (ICAC’05). Google Scholar
Digital Library
- Daniel Villela, Prashant Pradhan, and Dan Rubenstein. 2007. Provisioning servers in the application tier for e-commerce systems. In Proceedings of TOIT (2007).Google Scholar
Digital Library
- Website Latency With and Without a Content Delivery Network 2016. Website Latency With and Without a Content Delivery Network. Retrieved from https://www.keycdn.com/blog/website-latency/.Google Scholar
- Timothy Wood, Ludmila Cherkasova, Kivanc Ozonat, and Prashant Shenoy. 2008. Profiling and modeling resource usage of virtualized applications. In Proceedings of the Conference on Middleware. Google Scholar
Digital Library
- P. Xiong, Y. Chi, S. Zhu, H. J. Moon, C. Pu, and H. Hacigümüş. 2011. Intelligent management of virtualized resources for database systems in cloud environment. In Proceedings of the 2011 IEEE 27th International Conference on Data Engineering. 87--98. Google Scholar
Digital Library
- Qiang Xu, Jeffrey Erman, Alexandre Gerber, Zhuoqing Mao, Jeffrey Pang, and Shobha Venkataraman. 2011. Identifying diverse usage behaviors of smartphone apps. In Proceedings of the ACM Internet Measurement Conference (IMC’11). Google Scholar
Digital Library
- Qi Zhang, Ludmila Cherkasova, and Evgenia Smirni. 2007. A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In Proceedings of the 2015 IEEE International Conference on Autonomic Computing (ICAC’07). Google Scholar
Digital Library
- Qian Zhu and Teresa Tung. 2012. A performance interference model for managing consolidated workloads in QoS-aware clouds. In Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing (CLOUD’12). IEEE, 170--179. Google Scholar
Digital Library
Index Terms
Performance and Cost Considerations for Providing Geo-Elasticity in Database Clouds
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