Intra-cloud lightning: Building CDNs in the cloud

Chen, Fangfei; Guo, Katherine; Lin, John C.; Porta, Thomas La

doi:10.1109/infcom.2012.6195782

Cited by 105 publications

(72 citation statements)

References 21 publications

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“…After cloud storage service been used, all content needed is kept in distance data center and users access to it via network [1][2][3][4][5]. So, the cloud storage service may also use Content Delivery Network (CDN) as accelerate manner similar to other Internet services, so as to achieve higher access efficiency and better user experience.…”

Section: Introductionmentioning

confidence: 99%

Towards a Content Delivery Load Balance Algorithm Based on Probability Matching in Cloud Storage

Chen¹,

Tang²,

Zhang³

2015

TOCSJ

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Load balance just reasonably allocate request from users to each edge cloud storage servers. It can avoid overloading or congestion to ensure data transmission efficiency. Referring to idea of ant colony algorithm, the paper brought out Content Delivery Load Balance Algorithm Based on Probability Matching (CDLBA-PM) algorithm. The method calculated access probability of each edge cloud storage peer and selected edge cloud storage peer with probability matching algorithm comprehensively considering about history access costs and real-time access cost of edge cloud storage peers. It fully takes into account current network bandwidth, edge cloud storage peer performance as well as history access cost. Random probability matching was also introduced for cache copy selection in the algorithm. Simulation experiment results with CloudSim show that CDLBA-PM can not only reduce response time of user request, but also effectively balance load of edge cloud storage peer.

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Section: Introductionmentioning

confidence: 99%

Towards a Content Delivery Load Balance Algorithm Based on Probability Matching in Cloud Storage

Chen¹,

Tang²,

Zhang³

2015

TOCSJ

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“…Generally, SLA defines probabilistic QoS guarantees on response time for end-user requests based on geographical regions [1], for example 95% of end-users requesting access to CNN homepage from region A will perceive a latency of no more than 2 seconds [2], or according to Amazon S3 cloud SLA the guaranteed monthly uptime will be 99.9% [3]. We characterize this slack in QoS, as the degree of QoS violation, that is, the difference between QoS achieved and QoS required.…”

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confidence: 99%

“…In W-SNA, we further minimize the degree of QoS violations, that is, maximize QoS for the violations that are within SLA. We compare our results to surrogate selection for CP in CCDNs, using (a) Greedy Site (GS) [1] -employ the ratio of content request over storage cost, and (b) SNA [4] -that uses only BC.…”

mentioning

confidence: 99%

“…Typically, [1], [4], [5]-to mention a few, devise CP heuristics for CCDNs by decomposing the CP strategy into a static pre-allocation for CP, followed by a dynamic adjustment of the resource allocation to cater to changes in content requests and resource utilization. It is crucial to the success of the online dynamic CP strategy, to begin with a good static pre-allocation scheme.…”

mentioning

confidence: 99%

“…In contrast, we focus on push-based CP strategies. CCDNs are intrinsically different from CDNs and CP strategies for CDNs cannot be directly employed in CCDNs [1]. Therefore, in this section we distinguish our push-based CP strategy with relevant CP strategies in CCDNs.…”

mentioning

confidence: 99%

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Social Network Analysis Inspired Content Placement with QoS in Cloud Based Content Delivery Networks

Salahuddin

Elbiaze

Ajib

et al. 2015

2015 IEEE Global Communications Conference (GLOBECOM)

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Abstract-Content Placement (CP) problem in Cloud-based Content Delivery Networks (CCDNs) leverage resource elasticity to build cost effective CDNs that guarantee QoS. In this paper, we present our novel CP model, which optimally places content on surrogates in the cloud, to achieve (a) minimum cost of leasing storage and bandwidth resources for data coming into and going out of the cloud zones and regions, (b) guarantee Service Level Agreement (SLA), and (c) minimize degree of QoS violations. The CP problem is NP-Hard, hence we design a unique push-based heuristic, called Weighted Social Network Analysis (W-SNA) for CCDN providers. W-SNA is based on Betweeness Centrality (BC) from SNA and prioritizes surrogates based on their relationship to the other vertices in the network graph. To achieve our unique objectives, we further prioritize surrogates based on weights derived from storage cost and content requests.We compare our heuristic to current state-of-the-art Greedy Site (GS) and purely Social Network Analysis (SNA) heuristics, which are relevant to our work. We show that W-SNA outperforms GS and SNA in minimizing cost and QoS. Moreover, W-SNA guarantees SLA but also minimizes the degree of QoS violations. To the best of our knowledge, this is the first model and heuristic of its kind, which is timely and gives a fundamental pre-allocation scheme for future online and dynamic resource provision for CCDNs.

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FRP: a fast resource placement algorithm in distributed cloud computing platform

Wei

Zhang

Liu

et al. 2015

Concurrency and Computation

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Summary We consider a large‐scale online service system of placing resources geographically distributed over multiple regional cloud data centers. Service providers need to place the resources in these regions so as to maximize profit, accounting for demand granting revenues minus resource placement costs. The challenge is how to optimally place these resources to fulfill varying demands (e.g., multidimensional and stochastic demands) among these cloud data centers. Considering demand stochasticity will significantly increase time complexity of resource placement algorithm, resulting in inefficiency when handling a large number of resources. We propose a fast resource placement algorithm (FRP) to obtain the maximum resource revenue from distributed cloud systems. Experiments show that in scenarios with general settings, FRP can achieve up to 99.2% revenue of existed best solution while reducing execution time by two orders of magnitude. Therefore, FRP is an effective supplement to existing algorithms under time‐tense scheduling scenarios with a large number of resources. Copyright © 2015 John Wiley & Sons, Ltd.

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Intra-cloud lightning: Building CDNs in the cloud

Cited by 105 publications

References 21 publications

Towards a Content Delivery Load Balance Algorithm Based on Probability Matching in Cloud Storage

Towards a Content Delivery Load Balance Algorithm Based on Probability Matching in Cloud Storage

Social Network Analysis Inspired Content Placement with QoS in Cloud Based Content Delivery Networks

FRP: a fast resource placement algorithm in distributed cloud computing platform

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