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dc.contributor.advisorGopinath, K
dc.contributor.authorSingla, Priyanka
dc.date.accessioned2021-10-07T06:16:08Z
dc.date.available2021-10-07T06:16:08Z
dc.date.submitted2018
dc.identifier.urihttps://etd.iisc.ac.in/handle/2005/5401
dc.description.abstractCloud computing applications have dynamic workloads, and they often observe spikes in the incoming traffic which might result in system overloads. System overloads are generally handled by various load balancing techniques like replication and data partitioning. These techniques are effective when the incoming bursty traffic is dominated by reads and writes to partitionable data, but they become futile against bursts of writes to a single hot object. Further, the systems which use these load balancing techniques, to provide good performance, often adopt a variant of eventual consistency and do not provide strong guarantees to applications, and programmers. In this work, we propose a new client based consistency model, called social consistency, as a solution to this single object overload problem. Along with handling overloads, the proposed model also provides a stronger set of guarantees within subsets of nodes (socially related), and provides eventual consistency across different subsets. We argue that by using this approach, we can in practice ensure reasonably good consistency among the clients and a concomitant increase in performance. We further describe the design of a prototype system, BLAST, which implements this model. It dynamically adjusts resource utilisation in response to changes in the workload thus ensuring nearly constant latency, and throughput, which scales with the offered load. In particular, the workload spikes for a single hot object are handled by cloning the object and partitioning the clients according to their social connectivity, binding the partitions to different clones, where each partition has a unique view of the object. The clones and the client partitions are recombined when the spike subsides. We compare the performance of BLAST to Cassandra database system, and our experiments show that BLAST handles 1.6X (by performing one split) and 2.4X (by performing three splits) more workload. We also evaluate BLAST against another load balancing system and show that BLAST provides 37% better Quality of Experience (QoE) to the clients.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseries;G29369
dc.rightsI grant Indian Institute of Science the right to archive and to make available my thesis or dissertation in whole or in part in all forms of media, now hereafter known. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertationen_US
dc.subjectCloud computingen_US
dc.subjectoverloadsen_US
dc.subjectBLASTen_US
dc.subjectCassandra database systemen_US
dc.subject.classificationResearch Subject Categories::TECHNOLOGY::Information technology::Computer scienceen_US
dc.titleHandling Overloads with Social Consistencyen_US
dc.typeThesisen_US
dc.degree.nameMSc Enggen_US
dc.degree.levelMastersen_US
dc.degree.grantorIndian Institute of Scienceen_US
dc.degree.disciplineEngineeringen_US


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