A Provably Efficient Online Collaborative Caching Algorithm for Multicell-Coordinated Systems

Abstract

Caching at the base stations brings the contents closer to the users, reduces the traffic through the backhaul links, and reduces the delay experienced by the cellular users. The cellular network operator may charge the content providers for caching their contents. Moreover, content providers may lose their users if the users are not getting their desired quality of service, such as maximum tolerable delay in Video on Demand services. In this paper, we study the collaborative caching problem for a multicell-coordinated system from the point of view of minimizing the total cost paid by the content providers. We formulate the problem as an Integer Linear Program and prove its NP-completeness. We also provide an online caching algorithm that does not require any knowledge about the contents popularities. We prove that the online algorithm achieves a competitive ratio of O(log(n)), and we show that the best competitive ratio that any online algorithm can achieve is Ω( log(n) log log(n) ). Therefore, our proposed caching algorithm is provably efficient. Through simulations, we show that our online algorithm performs very close to the optimal offline collaborative scheme, and can outperform it when contents popularities are not properly estimated.

Cooperative storage by exploiting graph‐based data placement algorithm for edge computing environment

“…16 For mobile computing, cooperative storage allows storage-limited terminals to read data from nearby terminals by exploiting device-device communication 13,17 to reduce the amount of cellular traffic. 14,15,[19][20][21][22][23] Most existing works model the cooperative-storage data placement problems as linear/integer programming problems, needing a global coordinator to solve the linear/integer programming problems. 14,15,[19][20][21][22][23] Most existing works model the cooperative-storage data placement problems as linear/integer programming problems, needing a global coordinator to solve the linear/integer programming problems.…”

“…18 For edge computing, edge-side cooperative storage techniques are used to cache data, thereby reducing network traffic between terminals and Internet datacenters. 14,15,[19][20][21][22][23] Most existing works model the cooperative-storage data placement problems as linear/integer programming problems, needing a global coordinator to solve the linear/integer programming problems. However, in edge computing environments, edge servers are geodistributed, so it is difficult to set up a global coordinator for all the edge servers.…”

“…Edge-side cooperative storage techniques have garnered much attention from the edge computing community, 14,15,[19][20][21][22][23] because edge computing's performance can be constrained by limited edge-server storage size in real-life systems. 37,38 Existing data placement algorithms for edge-side cooperative storage can be classified into two categories that are based on complete priori information and partial information, respectively.…”

“…Thus, Wang et al13 exploited the social impact among users in social network services (SNSs) to predict users' mobility patterns, and then used the pattern to guide content delivery to satisfy all users' delay requirements. We discuss the necessity of applying ECS in edge computing and propose a data placement model for ECS.Edge-side cooperative storage techniques have garnered much attention from the edge computing community,14,15,[19][20][21][22][23] because edge computing's performance can be constrained by limited edge-server storage size in real-life systems 37,38. Another challenge for terminal-side cooperative storage is how to query and retrieve data within a short contact duration of two terminals.…”

Reinforcement Learning for Adaptive Caching With Dynamic Storage Pricing

“…Similar approaches are followed in [23] to relax a non-convex optimization problem to allocate limited caching memory across a network while accounting for the spatio-temporal content popularity together with the rented storage price fluctuations. An online mixedinteger programming formulation has also been investigated in [24].…”