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Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks

Sabrina Müller, Onur Atan, Mihaela van der Schaar, Anja Klein

arXiv:1606.04236v2cs.NIcs.LG

TL;DR

Wireless caching must place content without prior popularity knowledge while serving fluctuating users whose preferences depend on context. The paper proposes an online context-aware proactive caching algorithm with service differentiation, derives sublinear regret and convergence to the optimal cache placement strategy, and reports improved cache hits on real-world data.

  • Problem

    Limited storage requires cache placement, but content popularity is not known a priori and can fluctuate with users and their contexts.

  • Method

    The paper proposes a contextual multi-armed bandit algorithm that learns context-specific content popularity online and supports customer prioritization.

  • Results

    The analysis derives a sublinear regret bound and proves convergence to the optimal cache content placement strategy maximizing expected cache hits.

  • Takeaways & Limitations

    Context-aware online learning provides a caching strategy that adapts to connected users while allowing service differentiation.

Abstract

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Content caching in small base stations or wireless infostations is considered to be a suitable approach to improve the efficiency in wireless content delivery. Placing the optimal content into local caches is crucial due to storage limitations, but it requires knowledge about the content popularity distribution, which is often not available in advance. Moreover, local content popularity is subject to fluctuations since mobile users with different interests connect to the caching entity over time. Which content a user prefers may depend on the user's context. In this paper, we propose a novel algorithm for context-aware proactive caching. The algorithm learns context-specific content popularity online by regularly observing context information of connected users, updating the cache content and observing cache hits subsequently. We derive a sublinear regret bound, which characterizes the learning speed and proves that our algorithm converges to the optimal cache content placement strategy in terms of maximizing the number of cache hits. Furthermore, our algorithm supports service differentiation by allowing operators of caching entities to prioritize customer groups. Our numerical results confirm that our algorithm outperforms state-of-the-art algorithms in a real world data set, with an increase in the number of cache hits of at least 14%.

I. INTRODUCTION

The paper addresses cache placement under unknown, changing, and context-dependent local popularity, while incorporating operator priorities. It proposes an online context-aware algorithm with theoretical convergence and empirical gains.

  • I. INTRODUCTION: Limited cache storage makes intelligent content placement necessary, but future and local content popularity may be unknown or differ from global popularity.The caching entity must learn local popularity rather than rely on externally available estimates.
  • I. INTRODUCTION: Changing user populations and context-dependent preferences require proactive placement to account for diversity across users and contexts.Relevant contexts include location, personal characteristics, mood, and device characteristics.
  • I. INTRODUCTION: The proposed algorithm jointly learns popularity online, models user-level context and preference diversity, and supports service differentiation through prioritization.It can proactively cache content for currently connected users based on what it has learned previously.
  • I. INTRODUCTION: The algorithm uses contextual multi-armed bandit optimization, supports prioritization, and is evaluated numerically against reference algorithms on a real-world data set.The paper also presents extensions involving multicast transmissions and user ratings.
  • I. INTRODUCTION: A sublinear regret bound characterizes learning speed and proves convergence to the optimal placement strategy maximizing expected cache hits.The algorithm is analytically compared with an oracle having a priori popularity knowledge.

II. RELATED WORK

Prior caching work uses replacement rules, popularity learning, collaborative filtering, coded caching, or user clustering. The proposed approach combines online learning with user diversity, context, fluctuating arrivals, and service differentiation.

  • II. RELATED WORK: Earlier approaches include LRU and LFU replacement, collaborative filtering with training data, multi-armed bandits, coded caching, and multicast-aware schemes.These methods target objectives such as request satisfaction, served traffic, delay, energy, or backhaul avoidance.
  • II. RELATED WORK: The proposed algorithm does not require a training phase and learns content popularity online.This contrasts with approaches that estimate fixed global popularity from a known training set.
  • II. RELATED WORK: Unlike methods assuming a specific popularity distribution, the proposed algorithm is model-free and makes no such a priori distribution assumption.The paper identifies distributional assumptions as restrictive when the distribution type is unknown.
  • II. RELATED WORK: Compared with prior online bandit approaches, it accounts for population-wide popularity diversity and exploits users’ context information.It also reacts to arbitrary arrivals of users preferring different content.
  • II. RELATED WORK: Relative to the authors’ previous work, the paper uses single-user context rather than averaging context across connected users and adds service differentiation and extensions.The stated extensions include multicast transmission and caching decisions based on user ratings.
  • II. RELATED WORK: The framework extends contextual bandits to multiple context arrivals per time slot and weighted rewards, enabling prioritization of users and files.The caching entity selects a subset of files to maximize expected weighted cache-hit rewards.

III. SYSTEM MODEL

The system models a wireless local caching entity with limited storage serving dynamically changing connected users. Cache hits are served locally, whereas misses use the macro and backhaul networks.

  • A. Wireless Local Caching Entity: The caching entity may be an SBS or wireless infostation with limited storage, and it can store up to m equal-sized files from a finite library.At most Umax users can be simultaneously connected.
  • A. Wireless Local Caching Entity: Users connect within the coverage area, and the connected-user set changes dynamically over time because of mobility.The entity periodically informs connected users about available cached files.
  • A. Wireless Local Caching Entity: Requests for cached files are served through localized communication, avoiding additional load on the macro cellular and backhaul networks.The cache therefore serves traffic locally when requested content is stored.
  • A. Wireless Local Caching Entity: Requests for uncached files go through an MBS, which downloads them from the core network over its backhaul connection.Such misses place load on both the macro cellular and backhaul networks.
  • A. Wireless Local Caching Entity: The caching entity observes requests for cached files, or cache hits, but cannot observe requests for non-cached files, or cache misses.This wireless-caching restriction makes popularity learning more difficult, although it avoids congestion from cache misses.
  • A. Wireless Local Caching Entity: The placement objective is to maximize traffic served locally, corresponding to maximizing cache hits, so the entity must learn file popularity over time.This objective is tied to reducing load on the macro cellular and backhaul networks.

B. Service Differentiation

The caching architecture supports service differentiation by assigning higher value to cache hits from prioritized users or content providers. It also incorporates context information into periodic cache monitoring, refreshment, and learning.

  • User prioritization: Service differentiation assigns higher value to cache hits generated by prioritized users than by regular users.Operators may prioritize users based on pricing, subscriptions, advertising importance, reputation, or usage patterns.
  • Service weights: Each service type s has a fixed known weight v_s ≥ 1 representing the value of one cache hit.The model defines a finite set of service types and v_max as the largest service weight.
  • Objective: The service-differentiated objective is to maximize the number of weighted cache hits, while ordinary cache-hit maximization is a special case with all weights equal to one.This special case uses one service type with v_s = 1 and w_f = 1 for every file.
  • Scope: Service differentiation takes effect only when users have heterogeneous content preferences across the population.The paper also describes differentiation by a third-party operator prioritizing content providers.
  • Content prioritization: Content-provider prioritization associates each file with a fixed known weight w_f ≥ 1, selectable individually or per content provider.The operator may prioritize certain content providers by caching their content.
  • Caching workflow: The architecture periodically gathers user and optional external context, updates cache decisions, refreshes local content, serves requests, and records cache-hit information for learning.Its components include a Context Monitor, Context and Learning Databases, Decision Engine, Cache Controller, Storage Interface, and Request Handler.

E. Formal Problem Formulation

The formal problem models caching in discrete time with changing user contexts and unknown context-dependent demands. At each slot, the entity chooses limited cache contents, observes requests, and learns toward the oracle placement.

  • Time model: The system operates over discrete time slots t = 1, 2, ..., T, where T is a finite time horizon.
  • Slot operations: In each slot, the algorithm observes connected users’ contexts and service types before refreshing the cache.The refreshed cache depends on contexts, service types, service weights, file weights, and knowledge from prior slots.
  • Demand observation: Requests for cached files are observed during each slot, including the number of cache hits for every cached file.A user may request the same file repeatedly within one time slot.
  • Optimization objective: The objective is to maximize expected cumulative weighted cache hits through the finite horizon T.Binary variable y_t,f indicates whether file f is cached in slot t.
  • Oracle benchmark: With known expected demands, each slot reduces to selecting the m highest-ranked files by weighted expected demand.The resulting unit-weight knapsack subproblem can be solved in O(|F| log(|F|)).
  • Learning formulation: Without prior popularity knowledge, the caching entity learns expected demands from observed contextual requests while balancing exploration and exploitation.Learning regret is measured against the oracle solution using the history of cache choices and observed demands.

IV. A CONTEXT-AWARE PROACTIVE CACHING ALGORITHM

m-CAC partitions user contexts, learns context-specific file demand online, and alternates exploration with exploitation to update proactive cache placements.

  • Online learning: After caching, the algorithm observes requests for cached files and updates their estimated demands and counters.The estimated demand for each file-context pair is based on observed demands when that file was cached.
  • Context observation: At each time slot, the algorithm observes connected users, their contexts and service types, then maps each context vector to a partition set.These mapped sets determine the context-specific demand estimates used for caching decisions.
  • Context partitioning: m-CAC partitions the context space into uniformly sized hypercubes and associates each file with each context set.The partition contains (hT)^D sets, with hT controlling its granularity.
  • Exploration and exploitation: During exploration, m-CAC caches under-explored files randomly, while filling remaining cache capacity with files ranked by estimated weighted demand.This allows exploitation even when fewer than m files remain under-explored.
  • Exploration and exploitation: During exploitation, m-CAC caches the m files with the highest estimated weighted demands for the observed context and service types.Ties between equally ranked file sets are broken arbitrarily.

V. ANALYSIS OF THE REGRET

The regret analysis shows that m-CAC learns an optimal cache placement strategy with sublinear regret under a similarity assumption on context-dependent demands. The bound also applies to finite horizons and preserves the regret order under service differentiation.

  • Similarity assumption: m-CAC assumes expected file demands are similar for users with similar contexts, formalized through a Hölder condition.The assumption is realistic when user context influences content preferences and supports segmenting users by similar contexts.
  • Regret bound: R(T) = O(T^γ) with γ < 1, establishing sublinear regret for m-CAC.The bound characterizes the algorithm’s learning speed over the time horizon T.
  • Convergence: Sublinear regret proves that m-CAC converges to the optimal cache content placement strategy maximizing expected cache hits.The theorem provides a finite-horizon bound on the loss incurred across cache placement phases.
  • Service differentiation: Under service differentiation, the regret bound is a constant multiple of the bound for the special case without service differentiation.The special case sets vmax = 1 and wmax = 1, while retaining the same regret order.

VI. MEMORY REQUIREMENTS

m-CAC’s memory use is determined mainly by counters maintained for context-partition sets and files. The theoretical requirement is sublinear in T, while practical storage depends on observed context heterogeneity.

  • Counter storage: m-CAC maintains counters N_f,p and ˆd_f,p for each partition set p and file f.The number of files is |F|, and these counters primarily determine runtime memory requirements.
  • Memory bound: |F|2^D T^(D/(3α+D)) bounds the required memory, making storage sublinear in the time horizon T.This follows from the theorem’s upper bound on the number of context-partition sets.
  • Practical storage: For practical operation, the algorithm keeps counters only for partition sets containing at least one observed user context.The required memory therefore depends on the heterogeneity of connected users’ context vectors and can be much smaller than the upper bound.

VII. EXTENSIONS

The extensions adapt m-CAC to multicast delivery and rating-aware caching while retaining context-based learning. Missing ratings increase regret by a factor tied to rating availability but do not change its time order or convergence guarantee.

  • Multicast extension: m-CAC can be extended to multicast by accumulating same-file requests within time-slot intervals before transmission.Low-demand files may still use unicast to prevent unnecessary waiting when further requests are not expected.
  • Service differentiation: High-priority users can be served by unicast transmissions so service differentiation does not increase their delay through multicast waiting times.The extension combines multicast efficiency with differentiated treatment of priority groups.
  • Rating availability: With always-available ratings, Theorem 1 provides a regret bound for rating-based caching using the combined demand measure.The bound includes vmax, wmax, rmax, mUmax, Rmax, |F|, and T.
  • Rating-based caching: Rating-aware caching combines file demand and user ratings, allowing the content provider to choose a trade-off between ratings and cache hits.The resulting demand measure is learned with m-CAC, and the rating range controls the trade-off.
  • Missing ratings: 1/q scales regret under missing ratings, but the regret’s time order remains unchanged and m-CAC still converges to optimal cache placement.Here q is the probability that a user reveals a rating; missing ratings can require more exploration phases.
  • Missing ratings: Theorem 2 formalizes the regret bound for rating-based caching when ratings are missing.Its comparison with Theorem 1 identifies the scaling factor and preserves the convergence conclusion.
  • Missing ratings: m-CAC remains robust to users refusing to rate requested content because it still converges to the optimal cache placement strategy.The robustness claim concerns convergence despite incomplete rating feedback.

C. Asynchronous User Arrival

The evaluation uses MovieLens ratings as wireless content requests, organized into hourly slots with user context, and compares m-CAC against reference algorithms. The section also describes extensions for asynchronous arrivals and overlapping caching entities.

  • C. Asynchronous User Arrival: For asynchronous arrivals, m-CAC excludes users who disconnect without requesting but updates counters using requests from users arriving after cache placement.Arriving users’ contexts are omitted from the placement decision, while their requests contribute to faster learning.
  • C. Asynchronous User Arrival: With overlapping coverage, m-CACao extends m-CAC by overhearing neighboring cache hits and updating counters for files associated with those hits.This extension is intended to learn content popularity faster without requiring centralized cache coordination.
  • A. Description of the Data Set: The first year is divided into 8760 hourly time slots, with requests and user contexts assigned according to timestamps.The cache is assumed to update hourly, and each request is interpreted as coming from a separate user.
  • B. Reference Algorithms: The evaluation compares m-CAC with an omniscient oracle, m-UCB, m-ϵ-Greedy, m-Myopic, and Random.The oracle selects files using exact future demands, whereas the other algorithms use varying degrees of learning or randomization.

C. Performance Measures

Performance is assessed using cache-hit counts and cache efficiency across time, cache sizes, service priorities, and overlapping coverage areas. The results consistently favor learning methods that exploit demand history and context, while overlap changes the relative behavior of m-CAC and m-CACao.

  • C. Performance Measures: Cache efficiency is the percentage of requests served by cached files, calculated as cache hits divided by total demand.Absolute performance is also measured through per-time-slot and cumulative cache-hit counts.
  • D. Results: 1.146, 1.377, 3.985 and 5.506 times: m-CAC’s cumulative cache hits exceed m-ϵ-Greedy, m-UCB, m-Myopic and Random, respectively.The comparison is made at the time horizon T.
  • D. Results: 28.4% versus 25.3%, 21.4%, 7.76% and 5.69%: m-CAC’s average cache efficiency exceeds m-ϵ-Greedy, m-UCB, m-Myopic and Random across cache sizes.All algorithms’ cache efficiency increases as cache size increases.
  • D. Results: 1.156, 1.219, 3.914 and 5.362 times: with service differentiation, m-CAC’s weighted cache hits exceed m-ϵ-Greedy, m-UCB, m-Myopic and Random, respectively.Service types use weights v1 = 5 and v2 = 1, prioritizing service type 1.
  • D. Results: For overlap o ≤0.25, m-CACao outperforms m-CAC; for o > 0.25, m-CAC yields higher cache-hit counts.Large overlap causes m-CACao caches to learn similar popularity distributions and store similar files, whereas m-CAC maintains greater cache diversity.

IX. CONCLUSION

The paper presents a context-aware proactive caching algorithm that learns fluctuating, context-specific popularity online and adapts cache placement. It derives sublinear regret and reports convergence to optimal placement, while numerical studies show superior performance on MovieLens and support customer prioritization.

  • IX. CONCLUSION: The algorithm uses contextual multi-armed bandits to learn context-specific content popularity from connected users’ contexts, cache updates, and subsequent cache hits.This enables proactive adaptation to fluctuating local popularity without requiring a training phase.
  • IX. CONCLUSION: A sublinear regret bound characterizes learning speed and proves convergence to the cache placement strategy maximizing expected cache hits.The result formalizes the algorithm’s learning behavior over the time horizon.
  • IX. CONCLUSION: The algorithm supports customer prioritization and can be combined with multicast transmissions and rating-based caching decisions.These are stated as additional service and deployment capabilities.
  • IX. CONCLUSION: Numerical studies on a real-world data set show that exploiting context information lets the algorithm outperform state-of-the-art algorithms.The conclusion reports the comparison without specifying a numerical margin here.
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