Caching algorithms are usually described by the eviction method and analyzed using a metric of hit probability. Since contents have different importance (e.g. popularity), the utility of a high hit probability, and the cost of transmission can vary across contents. In this paper, we consider timer-based (TTL) policies across a cache network, where contents have differentiated timers over which we optimize. Each content is associated with a utility measured in terms of the corresponding hit probability. We start our analysis from a linear cache network: we propose a utility maximization problem where the objective is to maximize the sum of utilities and a cost minimization problem where the objective is to minimize the content transmission cost across the network. These frameworks enable us to design online algorithms for cache management, for which we prove achieving optimal performance. Informed by the results of our analysis, we formulate a non-convex optimization problem for a general cache network. We show that the duality gap is zero, hence we can develop a distributed iterative primal-dual algorithm for content management in the network.  Finally, we consider two applications of our cache network model : (i) directly mapping to content distribution and (ii) generalization to wireless sensor network by jointly considering content caching and content compression. We characterize the tradeoff among caching, compression and communication via a nonlinear non-convex optimization problem. We show that it can be transformed into an equivalent convex problem. The obtained numerical results provide us with insights into how to optimize the performance .
Caching algorithms are usually described by the eviction method and analyzed using a metric of hit probability. Since contents have different importance (e.g. popularity), the utility of a high hit probability, and the cost of transmission can vary across contents. In this paper, we consider timer-based (TTL) policies across a cache network, where contents have differentiated timers over which we optimize. Each content is associated with a utility measured in terms of the corresponding hit probability. We start our analysis from a linear cache network: we propose a utility maximization problem where the objective is to maximize the sum of utilities and a cost minimization problem where the objective is to minimize the content transmission cost across the network. These frameworks enable us to design online algorithms for cache management, for which we prove achieving optimal performance. Informed by the results of our analysis, we formulate a non-convex optimization problem for a general cache network. We show that the duality gap is zero, hence we can develop a distributed iterative primal-dual algorithm for content management in the network. Numerical evaluations show that our algorithm significant outperforms path replication with traditional caching algorithms over some network topologies. Finally, we consider a direct application of our cache network model to content distribution .
A
Numerical evaluations show that our algorithm significant outperforms path replication with traditional caching algorithms over some network topologies.
R
two applications
a direct application
D
: (i) directly mapping
D
and (ii) generalization to wireless sensor network by jointly considering content caching and content compression. We characterize the tradeoff among caching, compression and communication via a nonlinear non-convex optimization problem. We show that it can be transformed into an equivalent convex problem. The obtained numerical results provide us with insights into how to optimize the performance