A Framework for Evaluating Caching Policies in A Hierarchical Network of Caches

Ramadan, Eman; Babaie, Pariya

doi:10.23919/ifipnetworking.2018.8697030

Cited by 4 publications

(6 citation statements)

References 25 publications

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“…In the search for an ideal, how might caching 'quality' be quantified? The inadequacy of conventional measures and tools to evaluate and compare caching policies recently motivated frameworks to assess ICN performance [6]. However, in our wider investigations, we find no means by which to compare caching policies and placements specifically for adaptive streaming.…”

Section: Introductionmentioning

confidence: 78%

“…As HTTP-based paradigms evolve to meet the demands of UHD and live video [1], Information-Centric Network (ICN) developments promise responsive and dynamic in-network caching [2], [3]. In this context the characterization challenges of ICN caching systems extend beyond individual cache hits and misses [4], [5], yet the performance evaluation toolset is both stale and elusive [6]. The challenges are further compounded by adaptive video streaming, where cache performance is affected by dynamics between consumer adaptations and caching systems that are poorly understood.…”

Section: Introductionmentioning

confidence: 99%

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A Cache-Level Quality of Experience Metric to Characterize ICNs for Adaptive Streaming

Fayed

Oteafy

et al. 2019

IEEE Commun. Lett.

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Adaptive streaming has motivated Information-Centric Network (ICN) designs to improve end-user quality of experience (QoE). However, their management and evaluation rely either on conventional cache-level metrics that are poor representations of QoE, or consumer-side indicators that are opaque to network services. This paper proposes a measure to bridge the gap between cache performance and consumer QoE. We introduce Maximal Sustainable Bitrate (MSB), defined as the highest bitrate deliverable in time to be in time to meet a given request without buffering. Based on our observations, we posit that QoE is maximal when requested bitrates match a cache's MSB for that content. We design a cache-level reward function as a benchmark metric that measures the difference between requested bitrates and MSB. We hypothesize that aggregated rewards are an indicator of overall system performance. Performance evaluations show high correlation between the sum of cache rewards and consumer QoE.

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Section: Introductionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

A Cache-Level Quality of Experience Metric to Characterize ICNs for Adaptive Streaming

Fayed

Oteafy

et al. 2019

IEEE Commun. Lett.

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“…Indeed, today's content delivery networks such as Akamai [29], have tree network structures. Accounting for the hierarchy of caches has become a common practice in recent works; see also [30], [31], [32], [33]. Specifically, a general framework to evaluate caching policies in a hierarchical network of caches was developed in [30].…”

Section: A Prior Art On Cachingmentioning

confidence: 99%

“…Prompted by the recent interest in hierarchical caching, this paper focuses on a two-level network caching, where a parent node is connected to multiple leaf nodes to serve end-user file requests. Such a two-level network constitutes the building block of the popular tree hierarchical cache networks in e.g., [30], [29]. To model the interaction between caching decisions of parent and leaf nodes along with the space-time evolution of file requests, a scalable DRL approach based on hyper deep Qnetworks (DQNs) is developed.…”

Section: B This Workmentioning

confidence: 99%

Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks

Sadeghi

Wang

Giannakis

2019

IEEE Trans. Cogn. Commun. Netw.

112

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Caching is envisioned to play a critical role in nextgeneration content delivery infrastructure, cellular networks, and Internet architectures. By smartly storing the most popular contents at the storage-enabled network entities during off-peak demand instances, caching can benefit both network infrastructure as well as end users, during on-peak periods. In this context, distributing the limited storage capacity across network entities calls for decentralized caching schemes. Many practical caching systems involve a parent caching node connected to multiple leaf nodes to serve user file requests. To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning (RL) framework is put forth. To handle the large continuous state space, a scalable deep RL approach is pursued. The novel approach relies on a hyper-deep Q-network to learn the Q-function, and thus the optimal caching policy, in an online fashion. Reinforcing the parent node with ability to learnand-adapt to unknown policies of leaf nodes as well as spatiotemporal dynamic evolution of file requests, results in remarkable caching performance, as corroborated through numerical tests.

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“…One could consider this network as part of a larger hierarchical caching system, where the parent node is connected to a higher level caching node instead of the cloud, as depicted in Fig. 7 (right); see also [53,54]. All nodes in this network store files to serve file requests.…”

Section: Modeling and Problem Statementmentioning

confidence: 99%

Reinforcement Learning for Caching with Space-Time Popularity Dynamics

Sadeghi¹,

Giannakis²,

Wang³

et al. 2020

Preprint

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With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks. To intelligently prefetch and store contents, a cache node should be able to learn what and when to cache. Considering the geographical and temporal content popularity dynamics, the limited available storage at cache nodes, as well as the interactive influence of caching decisions in networked caching settings, developing effective caching policies is practically challenging. In response to these challenges, this chapter presents a versatile reinforcement learning based approach for near-optimal caching policy design, in both single-node and network caching settings under dynamic space-time popularities. The herein presented policies are complemented using a set of numerical tests, which showcase the merits of the presented approach relative to several standard caching policies.

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A Framework for Evaluating Caching Policies in A Hierarchical Network of Caches

Cited by 4 publications

References 25 publications

A Cache-Level Quality of Experience Metric to Characterize ICNs for Adaptive Streaming

A Cache-Level Quality of Experience Metric to Characterize ICNs for Adaptive Streaming

Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks

Reinforcement Learning for Caching with Space-Time Popularity Dynamics

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