Prasad, Abhinandan S.

[["dc.bibliographiccitation.firstpage","639"],["dc.bibliographiccitation.journal","Future Generation Computer Systems"],["dc.bibliographiccitation.lastpage","650"],["dc.bibliographiccitation.volume","87"],["dc.contributor.author","Prasad, Abhinandan S."],["dc.contributor.author","Koll, David"],["dc.contributor.author","Iglesias, Jesus Omana"],["dc.contributor.author","Aroca, Jordi Arjona"],["dc.contributor.author","Hilt, Volker"],["dc.contributor.author","Fu, Xiaoming"],["dc.date.accessioned","2019-07-30T10:03:48Z"],["dc.date.available","2019-07-30T10:03:48Z"],["dc.date.issued","2018"],["dc.description.abstract","Optimal deployment of complex services in a virtualized environment is still an open problem. These services typically consist of a set of connected components, and each component may consist of multiple instances. Each instance can in turn be run in different virtual flavors, while the service constructed by the combination of these instances must satisfy a customer Service Level Objective (SLO). While there have been efforts to answer the questions of when to provision additional resources in a running service, and how many resources are needed, the question of what (i.e., which combination of instances) should be provisioned has not been investigated yet. In this work, we offer to service providers the first system that automatically deploys component instances for complex services such that the resource utilization at the providers premises is maximized in the presence of customer constraints. Our system consists of two key technologies (RConf and RConfPD), both of which build on an analytical model based on robust queuing theory to accurately model arbitrary components. With the help of this model, RConf proposes an algorithm to ultimately find the optimal combination of component instances. Our real-world experiments show that, compared to greedy approaches, RConf provisions 20% less resources in the first place, and can reduce resource wastage on live resources by up to 50%. At the same time, RConfPD trades-off some of the optimality of RConf for a computational expense 1–2 orders of magnitude below that of RConf to provision time-sensitive services. Based on a primal–dual algorithm framework RConfPD relaxes the optimality constraints of RConf and removes dominated combinations to determine an approximation for the optimal solution. Our evaluation shows that RConfPD allows for fast decisions (in many cases <1ms), while maintaining 80%–99% of the solution quality of RConf."],["dc.identifier.doi","10.1016/j.future.2018.02.027"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62190"],["dc.language.iso","en"],["dc.relation.issn","0167-739X"],["dc.title","RConf(PD): Automated resource configuration of complex services in the cloud"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","17"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","IEEE Transactions on Computers"],["dc.bibliographiccitation.lastpage","30"],["dc.bibliographiccitation.volume","63"],["dc.contributor.author","Prasad, Abhinandan S."],["dc.contributor.author","Rao, Shrisha"],["dc.date.accessioned","2019-07-30T12:50:45Z"],["dc.date.available","2019-07-30T12:50:45Z"],["dc.date.issued","2014"],["dc.description.abstract","We present a cloud resource procurement approach which not only automates the selection of an appropriate cloud vendor but also implements dynamic pricing. Three possible mechanisms are suggested for cloud resource procurement: C-DSIC, C-BIC and C-OPT. C-DSIC is dominant strategy incentive compatible, based on the VCG mechanism, and is a low-bid Vickrey auction. C-BIC is Bayesian incentive compatible, which achieves budget balance. C-BIC does not satisfy individual rationality. In C-DSIC and C-BIC, the cloud vendor who charges the lowest cost per unit QoS is declared the winner. In C-OPT, the cloud vendor with the least virtual cost is declared the winner. C-OPT overcomes the limitations of both C-DSIC and C-BIC. C-OPT is not only Bayesian incentive compatible, but also individually rational. Our experiments indicate that the resource procurement cost decreases with increase in number of cloud vendors irrespective of the mechanisms. We also propose a procurement module for a cloud broker which can implement C-DSIC, C-BIC or C-OPT to perform resource procurement in a cloud computing context. A cloud broker with such a procurement module enables users to automate the choice of a cloud vendor among many with diverse offerings, and is also an essential first step towards implementing dynamic pricing in the cloud."],["dc.identifier.doi","10.1109/TC.2013.106"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62201"],["dc.language.iso","en"],["dc.relation.issn","0018-9340"],["dc.title","A Mechanism Design Approach to Resource Procurement in Cloud Computing"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","904"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IEEE Transactions on Cloud Computing"],["dc.bibliographiccitation.lastpage","914"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Prasad, G. Vinu"],["dc.contributor.author","Prasad, Abhinandan S."],["dc.contributor.author","Rao, Shrisha"],["dc.date.accessioned","2020-12-10T18:26:16Z"],["dc.date.available","2020-12-10T18:26:16Z"],["dc.date.issued","2018"],["dc.description.abstract","In hybrid cloud computing, cloud users have the ability to procure resources from multiple cloud vendors, and furthermore also the option of selecting different combinations of resources. The problem of procuring a single resource from one of many cloud vendors can be modeled as a standard winner determination problem, and there are mechanisms for single item resource procurement given different QoS and pricing parameters. There however is no compatible approach that would allow cloud users to procure arbitrary bundles of resources from cloud vendors. We design the CLOUD-CABOB algorithm to solve the multiple resource procurement problem in hybrid clouds. Cloud users submit their requirements, and in turn vendors submit bids containing price, QoS and their offered sets of resources. The approach is scalable, which is necessary given that there are a large number of cloud vendors, with more continually appearing. We perform experiments for procurement cost and scalability efficacy on the CLOUD-CABOB algorithm using various standard distribution benchmarks like random, uniform, decay and CATS. Simulations using our approach with prices procured from several cloud vendors' datasets show its effectiveness at multiple resource procurement."],["dc.identifier.doi","10.1109/TCC.2016.2541150"],["dc.identifier.eissn","2168-7161"],["dc.identifier.eissn","2372-0018"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76021"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.issn","2168-7161"],["dc.relation.issn","2372-0018"],["dc.title","A Combinatorial Auction Mechanism for Multiple Resource Procurement in Cloud Computing"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]