11 research outputs found
Soft-TTL: Time-Varying Fractional Caching
Standard Time-to-Live (TTL) cache management prescribes the storage of entire
files, or possibly fractions thereof, for a given amount of time after a
request. As a generalization of this approach, this work proposes the storage
of a time-varying, diminishing, fraction of a requested file. Accordingly, the
cache progressively evicts parts of the file over an interval of time following
a request. The strategy, which is referred to as soft-TTL, is justified by the
fact that traffic traces are often characterized by arrival processes that
display a decreasing, but non-negligible, probability of observing a request as
the time elapsed since the last request increases. An optimization-based
analysis of soft-TTL is presented, demonstrating the important role played by
the hazard function of the inter-arrival request process, which measures the
likelihood of observing a request as a function of the time since the most
recent request
On Optimal Geographical Caching in Heterogeneous Cellular Networks
In this work we investigate optimal geographical caching in heterogeneous
cellular networks where different types of base stations (BSs) have different
cache capacities. Users request files from a content library according to a
known probability distribution. The performance metric is the total hit
probability, which is the probability that a user at an arbitrary location in
the plane will find the content that it requires in one of the BSs that it is
covered by.
We consider the problem of optimally placing content in all BSs jointly. As
this problem is not convex, we provide a heuristic scheme by finding the
optimal placement policy for one type of base station conditioned on the
placement in all other types. We demonstrate that these individual optimization
problems are convex and we provide an analytical solution. As an illustration,
we find the optimal placement policy of the small base stations (SBSs)
depending on the placement policy of the macro base stations (MBSs). We show
how the hit probability evolves as the deployment density of the SBSs varies.
We show that the heuristic of placing the most popular content in the MBSs is
almost optimal after deploying the SBSs with optimal placement policies. Also,
for the SBSs no such heuristic can be used; the optimal placement is
significantly better than storing the most popular content. Finally, we show
that solving the individual problems to find the optimal placement policies for
different types of BSs iteratively, namely repeatedly updating the placement
policies, does not improve the performance.Comment: The article has 6 pages, 7 figures and is accepted to be presented at
IEEE Wireless Communications and Networking Conference (WCNC) 2017, 19 - 22
March 2017, San Francisco, CA, US
A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints
We consider caching in cellular networks in which each base station is
equipped with a cache that can store a limited number of files. The popularity
of the files is known and the goal is to place files in the caches such that
the probability that a user at an arbitrary location in the plane will find the
file that she requires in one of the covering caches is maximized.
We develop distributed asynchronous algorithms for deciding which contents to
store in which cache. Such cooperative algorithms require communication only
between caches with overlapping coverage areas and can operate in asynchronous
manner. The development of the algorithms is principally based on an
observation that the problem can be viewed as a potential game. Our basic
algorithm is derived from the best response dynamics. We demonstrate that the
complexity of each best response step is independent of the number of files,
linear in the cache capacity and linear in the maximum number of base stations
that cover a certain area. Then, we show that the overall algorithm complexity
for a discrete cache placement is polynomial in both network size and catalog
size. In practical examples, the algorithm converges in just a few iterations.
Also, in most cases of interest, the basic algorithm finds the best Nash
equilibrium corresponding to the global optimum. We provide two extensions of
our basic algorithm based on stochastic and deterministic simulated annealing
which find the global optimum.
Finally, we demonstrate the hit probability evolution on real and synthetic
networks numerically and show that our distributed caching algorithm performs
significantly better than storing the most popular content, probabilistic
content placement policy and Multi-LRU caching policies.Comment: 24 pages, 9 figures, presented at SIGMETRICS'1
Energy-delay trade-off of wireless data collection in the plane
We analyze the Pareto front of the delay of collecting data from wireless devices located in the plane according to a Poisson process and the energy needed by the devices to transmit their observations. Fundamental bounds on the energy-delay trade-off over the space of all achievable scheduling strategies are provided
Distributed Cooperative Caching for VoD with Geographic Constraints
International audienceWe consider caching of video streams in a cellular network in which each base station is equipped with a cache. Video streams are partitioned into multiple substreams and the goal is to place substreams in caches such that the residual backhaul load is minimized. We consider two coding mechanisms for the substreams: Layered coding (LC) mechanism and multiple description coding (MDC). We develop a distributed asynchronous algorithm for deciding which files to store in which cache to minimize the residual bandwidth, i.e., the cost for downloading the missing substreams of the user's requested video with a certain video quality from the gateway (i.e., the main server). We show that our algorithm converges rapidly. Finally, we show that MDC partitioning is better than the LC mechanism when the most popular content is stored in caches; however, our algorithm enables to use the LC mechanism as well without any performance loss
Optimization of Caching Devices with Geometric Constraints
International audienceIt has been recently advocated that in large communication systems it is beneficial both for the users and for the network as a whole to store content closer to users. One particular implementation of such an approach is to co-locate caches with wireless base stations. In this paper we study geographically distributed caching of a fixed collection of files. We model cache placement with the help of stochastic geometry and optimize the allocation of storage capacity among files in order to minimize the cache miss probability. We consider both per cache capacity constraints as well as an average capacity constraint over all caches. The case of per cache capacity constraints can be efficiently solved using dynamic programming, whereas the case of the average constraint leads to a convex optimization problem. We demonstrate that the average constraint leads to significantly smaller cache miss probability. Finally, we suggest a simple LRU-based policy for geographically distributed caching and show that its performance is close to the optimal
Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014
Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem