BIG Cache Abstraction for Cache Networks

Ramadan, Eman; Narayanan, Arvind; Li, Runhui; Zhang, Gong

doi:10.1109/icdcs.2017.306

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…1 Fig. 1 illustrates an example of such a network, which is consistent with the YouTube video delivery system [4]. We use C to denote the set of network caches, S to denote the set of users, and L to denote the set of communication links that connect the network caches, routers, and users.…”

Section: System Modelmentioning

confidence: 99%

“…We now discuss the content placement (CoP) problem, which entails deciding p c,v , the indicator function that network cache c stores video version v, for all c and v. As discussed in Section II, a major challenge to our optimization problem (1a)-(1f) is that the vector p needs to be updated much less frequently than the vector z. To address this challenge, we introduce pseudo-variables z := [z s,c,v ] and p := [p s,c,v ], which can be updated much more frequently than p, to replace z and p. 4 We only update p, the real content placement, after p converges. Also, we relax (1f) by allowing p c,v and z s,c,v to be any real number between 0 and 1.…”

Section: The Content Placement Problem (Cop)mentioning

confidence: 99%

“…1 for evaluation. Same as in [4], the topology of network caches follows the three-tier hierarchy of the YouTube video delivery system. There are 15 network routers with caches in total, including the root node and 8 edge caches.…”

Section: Evaluationsmentioning

confidence: 99%

“…Content caching is crucial for practical adaptive video streaming in wireless edge networks. Considering distributed caches, Ramadan et al [4] proposed the abstraction of "BIG" cache to effectively utilize the resources. Applegate et al [13] studied optimal content placement of videos with a focus on scalability.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Cache-Version Selection and Content Placement for Adaptive Video Streaming in Wireless Edge Networks

Sasikumar¹,

Zhao²,

Hou³

et al. 2019

Preprint

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Wireless edge networks are promising to provide better video streaming services to mobile users by provisioning computing and storage resources at the edge of wireless network. However, due to the diversity of user interests, user devices, video versions or resolutions, cache sizes, network conditions, etc., it is challenging to decide where to place the video contents, and which cache and video version a mobile user device should select. In this paper, we study the joint optimization of cache-version selection and content placement for adaptive video streaming in wireless edge networks. We propose practical distributed algorithms that operate at each user device and each network cache to maximize the overall network utility. In addition to proving the optimality of our algorithms, we implement our algorithms as well as several baseline algorithms on ndnSIM, an ns-3 based Named Data Networking simulator. Simulation evaluations demonstrate that our algorithms significantly outperform conventional heuristic solutions.

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Section: System Modelmentioning

confidence: 99%

Section: The Content Placement Problem (Cop)mentioning

confidence: 99%

Section: Evaluationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Cache-Version Selection and Content Placement for Adaptive Video Streaming in Wireless Edge Networks

Sasikumar¹,

Zhao²,

Hou³

et al. 2019

Preprint

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“…Furthermore, caches can be cooperative or non-cooperative, where cooperative caching requires state exchange (and traffic if necessary) between caches. "Big Cache" [28], for example, abstracts the multiple caches into one single big storage and caches work collaboratively to improve system performance. Finally, it is worthy to note that other factors from real world such as delay, bandwidth constraints also play important roles in designing a practical caching system, which makes the problem even more complicated.…”

Section: A Design Space Of In-network Caching Systemsmentioning

confidence: 99%

Joint cache resource allocation and request routing for in-network caching services

et al. 2018

Self Cite

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In-network caching is recognized as an effective solution to offload content servers and the network. A cache service provider (SP) always has incentives to better utilize its cache resources by taking into account diverse roles that content providers (CPs) play, e.g., their business models, traffic characteristics, preferences. In this paper, we study the cache resource allocation problem in a Multi-Cache Multi-CP environment. We propose a cache partitioning approach, where each cache can be partitioned into slices with each slice dedicated to a content provider. We propose a content-oblivious request routing algorithm, to be used by individual caches, that optimizes the routing strategy for each CP. We associate with each content provider a utility that is a function of its content delivery performance, and formulate an optimization problem with the objective to maximize the sum of utilities over all content providers. We establish the biconvexity of the problem, and develop decentralized (online) algorithms based on convexity of the subproblem. The proposed model is further extended to bandwidth-constrained and minimum-delay scenarios, for which we prove fundamental properties, and develop efficient algorithms. Finally, we present numerical results to show the efficacy of our mechanism and the convergence of our algorithms.

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A TTL-based Approach for Content Placement in Edge Networks

Panigrahy

Zafari

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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BIG Cache Abstraction for Cache Networks

Cited by 11 publications

References 11 publications

Cache-Version Selection and Content Placement for Adaptive Video Streaming in Wireless Edge Networks

Cache-Version Selection and Content Placement for Adaptive Video Streaming in Wireless Edge Networks

Joint cache resource allocation and request routing for in-network caching services

A TTL-based Approach for Content Placement in Edge Networks

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