238 research outputs found
Distributed Weight Selection in Consensus Protocols by Schatten Norm Minimization
In average consensus protocols, nodes in a network perform an iterative
weighted average of their estimates and those of their neighbors. The protocol
converges to the average of initial estimates of all nodes found in the
network. The speed of convergence of average consensus protocols depends on the
weights selected on links (to neighbors). We address in this paper how to
select the weights in a given network in order to have a fast speed of
convergence for these protocols. We approximate the problem of optimal weight
selection by the minimization of the Schatten p-norm of a matrix with some
constraints related to the connectivity of the underlying network. We then
provide a totally distributed gradient method to solve the Schatten norm
optimization problem. By tuning the parameter p in our proposed minimization,
we can simply trade-off the quality of the solution (i.e. the speed of
convergence) for communication/computation requirements (in terms of number of
messages exchanged and volume of data processed). Simulation results show that
our approach provides very good performance already for values of p that only
needs limited information exchange. The weight optimization iterative procedure
can also run in parallel with the consensus protocol and form a joint
consensus-optimization procedure.Comment: N° RR-8078 (2012
How to Network in Online Social Networks
In this paper, we consider how to maximize users' influence in Online Social
Networks (OSNs) by exploiting social relationships only. Our first contribution
is to extend to OSNs the model of Kempe et al. [1] on the propagation of
information in a social network and to show that a greedy algorithm is a good
approximation of the optimal algorithm that is NP-hard. However, the greedy
algorithm requires global knowledge, which is hardly practical. Our second
contribution is to show on simulations on the full Twitter social graph that
simple and practical strategies perform close to the greedy algorithm.Comment: NetSciCom 2014 - The Sixth IEEE International Workshop on Network
Science for Communication Networks (2014
Implicit Coordination of Caches in Small Cell Networks under Unknown Popularity Profiles
We focus on a dense cellular network, in which a limited-size cache is
available at every Base Station (BS). In order to optimize the overall
performance of the system in such scenario, where a significant fraction of the
users is covered by several BSs, a tight coordination among nearby caches is
needed. To this end, this pape introduces a class of simple and fully
distributed caching policies, which require neither direct communication among
BSs, nor a priori knowledge of content popularity. Furthermore, we propose a
novel approximate analytical methodology to assess the performance of
interacting caches under such policies. Our approach builds upon the well known
characteristic time approximation and provides predictions that are
surprisingly accurate (hardly distinguishable from the simulations) in most of
the scenarios. Both synthetic and trace-driven results show that the our
caching policies achieve excellent performance (in some cases provably
optimal). They outperform state-of-the-art dynamic policies for interacting
caches, and, in some cases, also the greedy content placement, which is known
to be the best performing polynomial algorithm under static and perfectly-known
content popularity profiles
Similarity Caching: Theory and Algorithms
This paper focuses on similarity caching systems, in which a user request for an object o that is not in the cache can be (partially) satisfied by a similar stored object o 0 , at the cost of a loss of user utility. Similarity caching systems can be effectively employed in several application areas, like multimedia retrieval, recommender systems, genome study, and machine learning training/serving. However, despite their relevance, the behavior of such systems is far from being well understood. In this paper, we provide a first comprehensive analysis of similarity caching in the offline, adversarial, and stochastic settings. We show that similarity caching raises significant new challenges, for which we propose the first dynamic policies with some optimality guarantees. We evaluate the performance of our schemes under both synthetic and real request traces
Elastic Provisioning of Cloud Caches: a Cost-aware TTL Approach
We consider elastic resource provisioning in the cloud, focusing on in-memory
key-value stores used as caches. Our goal is to dynamically scale resources to
the traffic pattern minimizing the overall cost, which includes not only the
storage cost, but also the cost due to misses. In fact, a small variation on
the cache miss ratio may have a significant impact on user perceived
performance in modern web services, which in turn has an impact on the overall
revenues for the content provider that uses those services. We propose and
study a dynamic algorithm for TTL caches, which is able to obtain
close-to-minimal costs. Since high-throughput caches require low complexity
operations, we discuss a practical implementation of such a scheme requiring
constant overhead per request independently from the cache size. We evaluate
our solution with real-world traces collected from Akamai, and show that we are
able to obtain a 17% decrease in the overall cost compared to a baseline static
configuration
A\c{C}AI: Ascent Similarity Caching with Approximate Indexes
Similarity search is a key operation in multimedia retrieval systems and
recommender systems, and it will play an important role also for future machine
learning and augmented reality applications. When these systems need to serve
large objects with tight delay constraints, edge servers close to the end-user
can operate as similarity caches to speed up the retrieval. In this paper we
present A\c{C}AI, a new similarity caching policy which improves on the state
of the art by using (i) an (approximate) index for the whole catalog to decide
which objects to serve locally and which to retrieve from the remote server,
and (ii) a mirror ascent algorithm to update the set of local objects with
strong guarantees even when the request process does not exhibit any
statistical regularity
Complexity Analysis of Optimal Recharge Scheduling for Electric Vehicles
IEEE early access articleInternational audienceThe massive introduction of Electric Vehicles (EVs) will make fleet managers spend a significant amount of money to buy electric energy. If energy price changes over time, accurate scheduling of recharging times may result in significant savings. In this paper we evaluate the complexity of the optimal scheduling problem considering a scenario with a fleet manager having full knowledge of the customers’ traveling needs at the beginning of the scheduling horizon. We prove that the problem has polynomial complexity and provide complexity lower and upperbounds. Moreover, we propose an online sub-optimal scheduling heuristic that schedules the EVs’ recharge based on historical travelling data. We compare the performance of the optimal and sub-optimal methods to a benchmark online approach that does not rely on any prior knowledge of the customers’ requests, in order to evaluate whether the additional complexity required by the proposed strategies is worth the achieved economicadvantages. Numerical results show up to of 35% cost savings with respect to the benchmark approach
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