Constructing a Social Content Delivery Network for eScience

Kugler, Kai; Chard, Kyle; Caton, Simon; Rana, Omer F.

doi:10.1109/escience.2013.52

Cited by 6 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A master worker scenario similar to that discussed in [28] could also be constructed. Finally, peer-to-peer like social content delivery networks (see [29], [30]) for the sharing or distribution of large scientific data could be implemented. A core aspect of our future work is the construction of a Seattle-based toolkit for social cloud applications.…”

Section: Discussionmentioning

confidence: 99%

A Social Compute Cloud: Allocating and Sharing Infrastructure Resources via Social Networks

Caton

Haas

Chard

et al. 2014

IEEE Trans. Serv. Comput.

Self Cite

View full text Add to dashboard Cite

Social network platforms have rapidly changed the way that people communicate and interact. They have enabled the establishment of, and participation in, digital communities as well as the representation, documentation and exploration of social relationships. We believe that as 'apps' become more sophisticated, it will become easier for users to share their own services, resources and data via social networks. To substantiate this, we present a social compute cloud where the provisioning of cloud infrastructure occurs through "friend" relationships. In a social compute cloud, resource owners offer virtualized containers on their personal computer(s) or smart device(s) to their social network. However, as users may have complex preference structures concerning with whom they do or do not wish to share their resources, we investigate, via simulation, how resources can be effectively allocated within a social community offering resources on a best effort basis. In the assessment of social resource allocation, we consider welfare, allocation fairness, and algorithmic runtime. The key findings of this work illustrate how social networks can be leveraged in the construction of cloud computing infrastructures and how resources can be allocated in the presence of user sharing preferences.

show abstract

Section: Discussionmentioning

confidence: 99%

A Social Compute Cloud: Allocating and Sharing Infrastructure Resources via Social Networks

Caton

Haas

Chard

et al. 2014

IEEE Trans. Serv. Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the case of Netflix their CDN is a collection of servers placed within their own Autonomous System (AS), servers hosted by third parties and caches placed in ISP's networks [17]. CDN have been recently studied in relation to very diverse applications such as social networking in [18] or cloud storage in [19].…”

Section: Related Workmentioning

confidence: 99%

Caching over-the-top services, the Netflix case

Jensen

Gutiérrez

2015

2015 International Conference on Computing, Networking and Communications (ICNC)

View full text Add to dashboard Cite

Over-the-top (OTT) traffic represents around 67% of the downstream Internet data volume during peak periods in the US. OTT content providers have been following the strategy of deploying caching servers within the ISPs' own networks. This work presents a method for deciding where these caches for OTT services can be conveniently deployed to provide significant traffic offload to ISPs' back-haul links. The procedure followed is to find suitable caching locations that minimize the maximum link traffic load. The problem is defined and solved as a Link Load Balanced Capacitated Facility Location Problem (LLB-CFL). The solution search processes are implemented based on Genetic Algorithms (GA), designing genetic operators highly targeted towards this specific problem. The proposed methods are applied to a case study focusing on the demand and cache specifications of Netflix, and framed into a real geographical area, the island of Bornholm, Denmark. The results cover a thorough analysis of the cost-fitness trade-off of the search method and an evaluation of both cache and gateway traffic flowing in the network in relation to the solutions found using different network topologies.

show abstract

“…In this paper, which is an extension of our previous work , we describe our S‐CDN model, present our prototype implementation, and investigate via simulation how social sharing can improve data access performance, increase data availability, and reduce data access latency via user‐contributed infrastructure. We present and analyze socially derived replication algorithms to show that a social sharing model is feasible and beneficial.…”

Section: Introductionmentioning

confidence: 99%

A social content delivery network for e‐Science

Chard

Caton

Kugler

et al. 2016

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Summary We are in the midst of a scientific data explosion in which the rate of data growth is rapidly increasing. While large‐scale research projects have developed sophisticated data distribution networks to share their data with researchers globally, there is no such support for the many millions of research projects generating data of interest to much smaller audiences (as exemplified by the long tail scientist). In data‐oriented research, every aspect of the research process is influenced by data access. However, sharing and accessing data efficiently as well as lowering access barriers are difficult. In the absence of dedicated large‐scale storage, many have noted that there is an enormous storage capacity available via connected peers, none more so than the storage resources of many research groups. With widespread usage of the content delivery network model for disseminating web content, we believe a similar model can be applied to distributing, sharing, and accessing long tail research data in an e‐Science context. We describe the vision and architecture of a social content delivery network – a model that leverages the social networks of researchers to automatically share and replicate data on peers' resources based upon shared interests and trust. Using this model, we describe a simulator and investigate how aspects such as user activity, geographic distribution, trust, and replica selection algorithms affect data access and storage performance. From these results, we show that socially informed replication strategies are comparable with more general strategies in terms of availability and outperform them in terms of spatial efficiency. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Constructing a Social Content Delivery Network for eScience

Cited by 6 publications

References 14 publications

A Social Compute Cloud: Allocating and Sharing Infrastructure Resources via Social Networks

A Social Compute Cloud: Allocating and Sharing Infrastructure Resources via Social Networks

Caching over-the-top services, the Netflix case

A social content delivery network for e‐Science

Contact Info

Product

Resources

About