1,795 research outputs found
Dynamic Balanced Graph Partitioning
This paper initiates the study of the classic balanced graph partitioning
problem from an online perspective: Given an arbitrary sequence of pairwise
communication requests between nodes, with patterns that may change over
time, the objective is to service these requests efficiently by partitioning
the nodes into clusters, each of size , such that frequently
communicating nodes are located in the same cluster. The partitioning can be
updated dynamically by migrating nodes between clusters. The goal is to devise
online algorithms which jointly minimize the amount of inter-cluster
communication and migration cost.
The problem features interesting connections to other well-known online
problems. For example, scenarios with generalize online paging, and
scenarios with constitute a novel online variant of maximum matching. We
present several lower bounds and algorithms for settings both with and without
cluster-size augmentation. In particular, we prove that any deterministic
online algorithm has a competitive ratio of at least , even with significant
augmentation. Our main algorithmic contributions are an -competitive deterministic algorithm for the general setting with
constant augmentation, and a constant competitive algorithm for the maximum
matching variant
Stitching IC Images
Image stitching software is used in many areas such as photogrammetry, biomedical imaging, and even amateur digital photography. However, these algorithms require relatively large image overlap, and for this reason they cannot be used to stitch the integrated circuit (IC) images, whose overlap is typically less than 60 pixels for a 4096 by 4096 pixel image.
In this paper, we begin by using algorithmic graph theory to study optimal patterns for adding IC images one at a time to a grid. In the remaining sections we study ways of stitching all the images simultaneously using different optimisation approaches: least squares methods, simulated annealing, and nonlinear programming
A flexible receiver-driven cache replacement scheme for continuous media objects in best-effort networks
In this paper, we investigate the potential of caching to improve quality of reception (QoR) in the context of continuous media applications over best-effort networks. Specifically, we investigate the influence of parameters such as loss rate, jitter, delay and area in determining a proxy\u27s cache contents. We propose the use of a flexible cost function in caching algorithms and develop a framework for benchmarking continuous media caching algorithms. The cost function incorporates parameters in which, an administrator and or a client can tune to influence a proxy\u27s cache. Traditional caching systems typically base decisions around static schemes that do not take into account the interest of their receiver pool. Based on the flexible cost function, an improvised Greedy Dual (GD) algorithm called GD-multi has been developed for layered multiresolution multimedia streams. The effectiveness of the proposed scheme is evaluated by simulation-based performance studies. Performance of several caching schemes are evaluated and compared with those of the proposed scheme. Our empirical results indicate GD-multi performs well despite employing a generalized caching policy
Asymptotically-Optimal Incentive-Based En-Route Caching Scheme
Content caching at intermediate nodes is a very effective way to optimize the
operations of Computer networks, so that future requests can be served without
going back to the origin of the content. Several caching techniques have been
proposed since the emergence of the concept, including techniques that require
major changes to the Internet architecture such as Content Centric Networking.
Few of these techniques consider providing caching incentives for the nodes or
quality of service guarantees for content owners. In this work, we present a
low complexity, distributed, and online algorithm for making caching decisions
based on content popularity, while taking into account the aforementioned
issues. Our algorithm performs en-route caching. Therefore, it can be
integrated with the current TCP/IP model. In order to measure the performance
of any online caching algorithm, we define the competitive ratio as the ratio
of the performance of the online algorithm in terms of traffic savings to the
performance of the optimal offline algorithm that has a complete knowledge of
the future. We show that under our settings, no online algorithm can achieve a
better competitive ratio than , where is the number of
nodes in the network. Furthermore, we show that under realistic scenarios, our
algorithm has an asymptotically optimal competitive ratio in terms of the
number of nodes in the network. We also study an extension to the basic
algorithm and show its effectiveness through extensive simulations
Optimistic No-regret Algorithms for Discrete Caching
We take a systematic look at the problem of storing whole files in a cache
with limited capacity in the context of optimistic learning, where the caching
policy has access to a prediction oracle (provided by, e.g., a Neural Network).
The successive file requests are assumed to be generated by an adversary, and
no assumption is made on the accuracy of the oracle. In this setting, we
provide a universal lower bound for prediction-assisted online caching and
proceed to design a suite of policies with a range of performance-complexity
trade-offs. All proposed policies offer sublinear regret bounds commensurate
with the accuracy of the oracle. Our results substantially improve upon all
recently-proposed online caching policies, which, being unable to exploit the
oracle predictions, offer only regret. In this pursuit, we
design, to the best of our knowledge, the first comprehensive optimistic
Follow-the-Perturbed leader policy, which generalizes beyond the caching
problem. We also study the problem of caching files with different sizes and
the bipartite network caching problem. Finally, we evaluate the efficacy of the
proposed policies through extensive numerical experiments using real-world
traces.Comment: Accepted to ACM SIGMETRICS 202
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
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