A Commodity-Focused Multi-cloud Marketplace Exemplar Application

Wright, Peter; Harmer, Terence J.; Hawkins, John L.; Sun, Yih Leong

doi:10.1109/cloud.2011.81

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…Figure 5(c) lists the routes of external demands in the optimal ILP solution, where the number above each arrow indicates the traffic load on the corresponding link or switched locally at the same node (e.g., 2↔ 6 2). We can see that most of the demands are served by their closest core switches.…”

Section: A Ilp-based Optimal Solutionmentioning

confidence: 99%

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Scalable Data Center Network Architecture With Distributed Placement of Optical Switches and Racks

Xiao

Jiang

et al. 2014

J. Opt. Commun. Netw.

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Section: A Ilp-based Optimal Solutionmentioning

confidence: 99%

“…T he fast evolution of networking technology is pushing networks to be service-oriented. Most of the emerging services are provided by data center networks (DCNs) [1][2][3], and a network of DCNs is called a cloud [4][5][6]. Traditionally, a DCN is a factory-scale and massively parallel computing and storage resource.…”

mentioning

confidence: 99%

Scalable Data Center Network Architecture With Distributed Placement of Optical Switches and Racks

Xiao

Jiang

et al. 2014

J. Opt. Commun. Netw.

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“…1, an enterprise-level cloud cache can be deployed in a multi-provider cloud environment where a consumer enterprise employs multiple concurrent cloud providers to host its external private clouds that are either independent from each other [13,14] or interconnected as a hybrid cloud [15]; both are the forms of federated cloud [16,17]. There are several benefits of multi-provider cloud deployment such as load balancing [18,19], enabling of planned downtime for system maintenance [17], confidentiality protection [20], risk mitigation (mandated by hospitals, stock markets, air transportation controls, etc.)…”

Section: Introductionmentioning

confidence: 99%

Multi-provider cloud computing network infrastructure optimization

Banditwattanawong

Masdisornchote

Uthayopas

2016

Future Generation Computer Systems

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h i g h l i g h t s• Byte-hit rate and hit rate could be optimal simultaneously in a nonuniform cost model. • i-Cloud outperformed popular LRU, GDSF and LFUDA schemes in a nonuniform cost environment.• i-Cloud's performances were stable and close to those of infinite cache size. • Window size had small performance effect when relative cache sizes were big.• Accounting data-out charge rates improved all performance aspects at small cache sizes. a b s t r a c tCloud-adopting enterprises have been increasingly employing multiple cloud providers concurrently, for example, to consume unique services and to mitigate data lock-in risk. As a consequence, the enterprises must be able to address contrasting quality-of-service degrees offered by the different providers. This paper presents an intelligent cloud cache eviction approach, namely i-Cloud, as the core component of a client-side cloud cache. i-Cloud is capable of reducing public cloud data-out expenses, improving cloud network scalability and lowering cloud service access latencies specifically in multi-provider cloud environments. Trace-driven simulations have shown that i-Cloud outperformed well-known approaches in all performance metrics. In addition, i-Cloud is not only able to achieve optimal performances in all metrics simultaneously but also delivered relatively stable performances across all performance metrics. The results have also indicated that taking the nonuniformity of data-out charge rates into cache eviction decisions improved caching performances in all metrics.

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