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In-network cache deployment is recognized as an effective technique for reducing content access delay. Caches serve content from multiple content providers, and wish to provide them differentiated services due to monetary incentives and legal obligations. Partitioning is a common approach in providing differentiated storage services. In this paper, we propose a utility-driven cache partitioning approach to cache resource allocation among multiple content providers, where we associate with each content provider a utility that is a function of the hit rate to its content. A cache is partitioned into slices with each partition being dedicated to a particular content provider. We formulate an optimization problem where the objective is to maximize the sum of weighted utilities over all content providers through proper cache partitioning, and mathematically show its convexity. We also give a formal proof that partitioning the cache yields better performance compared to sharing it. We validate the effectiveness of cache partitioning through numerical evaluations, and investigate the impact of various factors (e.g., content popularity, request rate) on the hit rates observed by contending content providers.

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.

In this paper, we consider the problem of allocating cache resources among multiple content providers. The cache can be partitioned into slices and each partition can be dedicated to a particular content provider, or shared among a number of them. It is assumed that each partition employs the LRU policy for managing content. We propose utility-driven partitioning, where we associate with each content provider a utility that is a function of the hit rate observed by the content provider. We consider two scenarios: i) content providers serve disjoint sets of files, ii) there is some overlap in the content served by multiple content providers. In the first case, we prove that cache partitioning outperforms cache sharing as cache size and numbers of contents served by providers go to infinity. In the second case, It can be beneficial to have separate partitions for overlapped content. In the case of two providers it is usually always benefical to allocate a cache partition to serve all overlapped content and separate partitions to serve the non-overlapped contents of both providers. We establish conditions when this is true asymptotically but also present an example where it is not true asymptotically. We develop online algorithms that dynamically adjust partition sizes in order to maximize the overall utility and prove that they converge to optimal solutions, and through numerical evaluations we show they are effective.

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