Experience Availability: Tail-Latency Oriented Availability in Software-Defined Cloud Computing
- 85 Downloads
Resource sharing, multi-tenant interference and bursty workloads in cloud computing lead to high tail-latency that severely affects user quality of experience (QoE), where response latency is a critical factor. A lot of research efforts are dedicated to reducing high tail-latency and improving user QoE, such as software-defined cloud computing (SDC). However, the traditional availability analysis of cloud computing captures the pure failure-repair behavior with user QoE ignored. In this paper, we propose a conceptual framework, experience availability, to properly assess the effectiveness of SDC while taking into account both availability and response latency simultaneously. We review the related work on availability models and methods of cloud systems, and discuss open problems for evaluating experience availability in SDC. We also show some of our preliminary results to demonstrate the feasibility of our ideas.
Keywordscloud computing software-defined cloud computing (SDC) availability tail-latency
Unable to display preview. Download preview PDF.
- Grandl R, Chen Y, Khalid J, Yang S, Anand A, Benson T, Akella A. Harmony: Coordinating network, compute, and storage in software-defined clouds. In Proc. the 4th Annual Symposium on Cloud Computing (poster), Oct. 2013.Google Scholar
- Buyya R, Calheiros R N, Son J, Dastjerdi A V, Yoon Y. Software-defined cloud computing: Architectural elements and open challenges. In Proc. International Conference on Advances in Computing, Communications and Informatics, Sept. 2014.Google Scholar
- Amazon EC2 service level agreement. 2013. http://aws.amazon.com/ec2/sla/, Feb. 2017.
- App engine service level agreement (SLA). https://developers.google.com/appengine/sla, Feb. 2017.
- Microsoft. Service level agreements. https://azure.microsoft.com/en-us/support/legal/sla/. Feb. 2017.
- Neamtiu I, Dumitraş T. Cloud software upgrades: Challenges and opportunities. In Proc. International Workshop on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems, Sept. 2011.Google Scholar
- Lu Q, Xu X, Zhu L, Bass L, Li Z, Sakr S, Bannerman P L, Liu A. Incorporating uncertainty into in-cloud application deployment decisions for availability. In Proc. IEEE International Conference on Cloud Computing, Jun. 2013, pp.454-461.Google Scholar
- Ghosh R, Trivedi K S, Naik V K, Kim D S. End-to-end performability analysis for Infrastructure-as-a-Service cloud: An interacting stochastic models approach. In Proc. the 16th IEEE Pacific Rim International Symposium on Dependable Computing, Dec. 2010, pp.125-132.Google Scholar
- Wei B, Lin C, Kong X. Dependability modeling and analysis for the virtual data center of cloud computing. In Proc. High Performance Computing and Communications, Sept. 2011, pp.784-789.Google Scholar
- Wang Y, Luo C, Liu Z. Reliability analysis of multi-node SDDC using fault tree. In Proc. International Industrial Informatics and Computer Engineering Conference, Jan. 2015, pp.1155-1158.Google Scholar
- Trivedi K S. Probability and Statistics with Reliability, Queuing and Computer Science Applications. John Wiley & Sons, 2008.Google Scholar
- Ivanchenko O, Kharchenko V. Semimarkov availability models for an Infrastructure as a Service cloud with multiple pools. In Proc. International Conference on ICT in Education, Research, and Industrial Applications, Nov. 2016, pp.349-360.Google Scholar
- Longo F, Ghosh R, Naik V K, Trivedi K S. A scalable availability model for Infrastructure-as-a-Service cloud. In Proc. the 41st IEEE/IFIP International Conference on Dependable Systems & Networks, Jun. 2011, pp.335-346.Google Scholar
- Wei B, Lin C, Kong X. Dependability modeling and analysis for the virtual clusters. In Proc. International Conference on Computer Science and Network Technology, Volume 4, Dec. 2011, pp.2316-2320.Google Scholar
- Dantas J, Matos R, Araujo J, Maciel P. Models for dependability analysis of cloud computing architectures for eucalyptus platform. International Transactions on Systems Science and Applications, 2012, 8: 13-25.Google Scholar
- Cooper B F, Silberstein A, Tam E, Ramakrishnan R, Sears R. Benchmarking cloud serving systems with YCSB. In Proc. the 1st ACM Symposium on Cloud Computing, Jun. 2010, pp.143-154.Google Scholar
- Iosup A, Prodan R, Epema D. IaaS cloud benchmarking: Approaches, challenges, and experience. In Cloud Computing for Data-Intensive Applications, Li X, Qiu J (eds.), Springer, 2014, pp.83-104.Google Scholar
- Varghese B, Subba L T, Thai L T, Barker A D. DocLite: A Docker-based lightweight cloud benchmarking tool. In Proc. the 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2016), May. 2016, pp.213-222.Google Scholar
- Fujita H, Matsuno Y, Hanawa T, Sato M, Kato S, Ishikawa Y. DS-Bench Toolset: Tools for dependability bench-marking with simulation and assurance. In Proc. IEEE/IFIP International Conference on Dependable Systems and Networks, Jun. 2012.Google Scholar
- Sangroya A, Serrano D, Bouchenak S. Benchmarking dependability of MapReduce systems. In Proc. the 31st IEEE Symposium on Reliable Distributed Systems, Feb. 2012, pp.21-30.Google Scholar