29 research outputs found
Online Coded Caching
We consider a basic content distribution scenario consisting of a single
origin server connected through a shared bottleneck link to a number of users
each equipped with a cache of finite memory. The users issue a sequence of
content requests from a set of popular files, and the goal is to operate the
caches as well as the server such that these requests are satisfied with the
minimum number of bits sent over the shared link. Assuming a basic Markov model
for renewing the set of popular files, we characterize approximately the
optimal long-term average rate of the shared link. We further prove that the
optimal online scheme has approximately the same performance as the optimal
offline scheme, in which the cache contents can be updated based on the entire
set of popular files before each new request. To support these theoretical
results, we propose an online coded caching scheme termed coded least-recently
sent (LRS) and simulate it for a demand time series derived from the dataset
made available by Netflix for the Netflix Prize. For this time series, we show
that the proposed coded LRS algorithm significantly outperforms the popular
least-recently used (LRU) caching algorithm.Comment: 15 page
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
A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking
Socially aware networking is an emerging research field that aims to improve the current networking technologies and realize novel network services by applying social network analysis (SNA) techniques. Conducting socially aware networking studies requires knowledge of both SNA and communication networking, but it is not easy for communication networking researchers who are unfamiliar with SNA to obtain comprehensive knowledge of SNA due to its interdisciplinary nature. This paper therefore aims to fill the knowledge gap for networking researchers who are interested in socially aware networking but are not familiar with SNA. This paper surveys three types of important SNA techniques for socially aware networking: identification of influential nodes, link prediction, and community detection. Then, this paper introduces how SNA techniques are used in socially aware networking and discusses research trends in socially aware networking
-Fair Contextual Bandits
Contextual bandit algorithms are at the core of many applications, including
recommender systems, clinical trials, and optimal portfolio selection. One of
the most popular problems studied in the contextual bandit literature is to
maximize the sum of the rewards in each round by ensuring a sublinear regret
against the best-fixed context-dependent policy. However, in many applications,
the cumulative reward is not the right objective - the bandit algorithm must be
fair in order to avoid the echo-chamber effect and comply with the regulatory
requirements. In this paper, we consider the -Fair Contextual Bandits
problem, where the objective is to maximize the global -fair utility
function - a non-decreasing concave function of the cumulative rewards in the
adversarial setting. The problem is challenging due to the non-separability of
the objective across rounds. We design an efficient algorithm that guarantees
an approximately sublinear regret in the full-information and bandit feedback
settings
Weighted Cache Location Problem with Identical Servers
This paper extends the well-known p-CLP with one server to p-CLP with m≥2 identical servers, denoted by (p,m)-CLP. We propose the closest server orienting protocol (CSOP), under which every client connects to the closest server to itself via a shortest route on given network. We abbreviate (p,m)-CLP under CSOP to (p,m)-CSOP CLP and investigate that (p,m)-CSOP CLP on a general network is equivalent to that on a forest and further to multiple CLPs on trees. The case of m=2 is the focus of this paper. We first devise an improved O(ph2+n)-time parallel exact algorithm for p-CLP on a tree and then present a parallel exact algorithm with at most O((4/9)p2n2) time in the worst case for (p,2)-CSOP CLP on a general network. Furthermore, we extend the idea of parallel algorithm to the cases of m>2 to obtain a worst-case O((4/9)(n-m)2((m+p)p/p-1!))-time exact algorithm. At the end of the paper, we first give an example to illustrate our algorithms and then make a series of numerical experiments to compare the running times of our algorithms
Online Caching with no Regret: Optimistic Learning via Recommendations
The design of effective online caching policies is an increasingly important
problem for content distribution networks, online social networks and edge
computing services, among other areas. This paper proposes a new algorithmic
toolbox for tackling this problem through the lens of optimistic online
learning. We build upon the Follow-the-Regularized-Leader (FTRL) framework,
which is developed further here to include predictions for the file requests,
and we design online caching algorithms for bipartite networks with fixed-size
caches or elastic leased caches subject to time-average budget constraints. The
predictions are provided by a content recommendation system that influences the
users viewing activity and hence can naturally reduce the caching network's
uncertainty about future requests. We also extend the framework to learn and
utilize the best request predictor in cases where many are available. We prove
that the proposed {optimistic} learning caching policies can achieve sub-zero
performance loss (regret) for perfect predictions, and maintain the sub-linear
regret bound , which is the best achievable bound for policies that
do not use predictions, even for arbitrary-bad predictions. The performance of
the proposed algorithms is evaluated with detailed trace-driven numerical
tests.Comment: arXiv admin note: substantial text overlap with arXiv:2202.1059
Online Subset Selection using -Core with no Augmented Regret
We consider the problem of sequential sparse subset selections in an online
learning setup. Assume that the set consists of distinct elements. On
the round, a monotone reward function which assigns a non-negative reward to each subset of is
revealed to a learner. The learner selects (perhaps randomly) a subset of elements before the reward function for that round
is revealed . As a consequence of its choice, the learner receives
a reward of on the round. The learner's goal is to
design an online subset selection policy to maximize its expected cumulative
reward accrued over a given time horizon. In this connection, we propose an
online learning policy called SCore (Subset Selection with Core) that solves
the problem for a large class of reward functions. The proposed SCore policy is
based on a new concept of -Core, which is a generalization of the
notion of Core from the cooperative game theory literature. We establish a
learning guarantee for the SCore policy in terms of a new performance metric
called -augmented regret. In this new metric, the power of the offline
benchmark is suitably augmented compared to the online policy. We give several
illustrative examples to show that a broad class of reward functions, including
submodular, can be efficiently learned with the SCore policy. We also outline
how the SCore policy can be used under a semi-bandit feedback model and
conclude the paper with a number of open problems