Daniel Sadoc Menasché

On the steady-state of cache networks

Over the past few years Content-Centric Networking, a networking model in which host-to-content communication protocols are introduced, has been gaining much attention. A central component of such an architecture is a large-scale interconnected caching system. To date, the way these Cache Networks operate and perform is still poorly understood. In this work, we demonstrate that certain cache networks are non-ergodic in that their steady-state characterization depends on the initial state of the system. We then establish several important properties of cache networks, in the form of three independently-sufficient conditions for a cache network to comprise a single ergodic component. Each property targets a different aspect of the system - topology, admission control and cache replacement policies. Perhaps most importantly we demonstrate that cache replacement can be grouped into equivalence classes, such that the ergodicity (or lack-thereof) of one policy implies the same property holds for all policies in the class.

A utility optimization approach to network cache design

In any caching system, the admission and eviction policies determine which contents are added and removed from a cache when a miss occurs. Usually, these policies are devised so as to mitigate staleness and increase the hit probability. Nonetheless, the utility of having a high hit probability can vary across contents. This occurs, for instance, when service level agreements must be met, or if certain contents are more difficult to obtain than others. In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. We formulate optimization problems where the objectives are to maximize the sum of utilities over all contents. These problems differ according to the stringency of the cache capacity constraint. Our framework enables us to reverse engineer classical replacement policies such as LRU and FIFO, by computing the utility functions that they maximize. We also develop online algorithms that can be used by service providers to implement various caching policies based on arbitrary utility functions.

Sharing renewable energy in smart microgrids

Renewable energy harvested from the environment is an attractive option for providing green energy to homes. Unfortunately, the intermittent nature of renewable energy results in a mismatch between when these sources generate energy and when homes demand it. This mismatch reduces the efficiency of using harvested energy by either i) requiring batteries to store surplus energy, which typically incurs ~20% energy conversion losses; or ii) using net metering to transmit surplus energy via the electric grids AC lines, which severely limits the maximum percentage of possible renewable penetration. In this paper, we propose an alternative structure wherein nearby homes explicitly share energy with each other to balance local energy harvesting and demand in microgrids. We develop a novel energy sharing approach to determine which homes should share energy, and when, to minimize system-wide efficiency losses. We evaluate our approach in simulation using real traces of solar energy harvesting and home consumption data from a deployment in Amherst, MA. We show that our system i) reduces the energy loss on the AC line by 60% without requiring large batteries, ii) scales up performance with larger battery capacities, and iii) is robust to changes in microgrid topology.

Constrained cost-coupled stochastic games with independent state processes

We study non-cooperative constrained stochastic games in which each player controls its own Markov chain based on its own state and actions. Interactions between players occur through their costs and constraints which depend on the state and actions of all players. We provide an example from wireless communications.

Estimating self-sustainability in peer-to-peer swarming systems

Peer-to-peer swarming is one of the \emph{de facto} solutions for distributed content dissemination in todays Internet. By leveraging resources provided by clients, swarming systems reduce the load on and costs to publishers. However, there is a limit to how much cost savings can be gained from swarming; for example, for unpopular content peers will always depend on the publisher in order to complete their downloads. In this paper, we investigate this dependence. For this purpose, we propose a new metric, namely \emph{swarm self-sustainability}. A swarm is referred to as self-sustaining if all its blocks are collectively held by peers; the self-sustainability of a swarm is the fraction of time in which the swarm is self-sustaining. We pose the following question: how does the self-sustainability of a swarm vary as a function of content popularity, the service capacity of the users, and the size of the file? We present a model to answer the posed question. We then propose efficient solution methods to compute self-sustainability. The accuracy of our estimates is validated against simulation. Finally, we also provide closed-form expressions for the fraction of time that a given number of blocks is collectively held by peers.

Reciprocity and Barter in Peer-to-Peer Systems

This work investigates reciprocity in peer-to-peer systems. The scenario is one where users arrive to the network with a set of contents and content demands. Peers exchange contents to satisfy their demands, following either a direct reciprocity principle (I help you and you help me) or indirect reciprocity principle (I help you and someone helps me). First, we prove that any indirect reciprocity schedule of exchanges, in the absence of relays, can be replaced by a direct reciprocity schedule, provided that users (1) are willing to download undemanded content for bartering purposes and (2) use up to twice the bandwidth they would use under indirect reciprocity. Motivated by the fact that, in the absence of relays, the loss of efficiency due to direct reciprocity is at most two, we study various distributed direct reciprocity schemes through simulations, some of them involving a broker to facilitate exchanges.

An evolutionary game-theoretic approach to congestion control

This paper investigates a system where a set of users sharing a bottleneck link must choose the transmission rate at which multimedia traffic is received. Users are assumed to be self-regarding and make their decisions with the sole goal of maximizing their perceived quality. We are interested in the dynamic process by which users adapt their data rates and the convergence of this process to equilibria. We propose a novel two-layer model to represent this system: the upper layer is an evolutionary game-theoretic model that captures how users adapt their rates; the lower layer model captures the network performance and the quality perceived by the users. Using the model proposed, we demonstrate analytically and numerically several interesting properties of the system equilibria. In particular, we establish the relationship between system states that have non-negligible steady state probabilities and Nash equilibria of the induced game.

Modeling Resource Sharing Dynamics of VoIP Users over a WLAN Using a Game-Theoretic Approach

We consider a scenario in which users share an access point and are mainly interested in VoIP applications. Each user is allowed to adapt to varying network conditions by choosing the transmission rate at which VoIP traffic is received. We denote this adaptation process by end-user congestion control, our object of study. The two questions that we ask are: (1) what are the performance consequences of letting the users to freely choose their rates? and (2) how to explain the adaptation process of the users? We set a controlled lab experiment having students as subject to answer the first question, and we extend an evolutionary game-theoretic model to address the second. Our partial answers are the following: (1) free users with local information can reach an equilibrium which is close to optimal from the system perspective. However, the equilibrium can be unfair; (2) the adaptation of the users can be explained using a game theoretic model. We propose a methodology to parameterize the latter, which involves active network measurements, simulations and an artificial neural network to estimate the QoS perceived by the users in each of the states of the model.

Online estimating the k central nodes of a network

A well known way to find the most central nodes in a network consists of coupling random walk sampling (or one of its variants) with a method to identify the most central nodes in the subgraph induced by the samples. Although it is commonly assumed that degree information is collected during the sampling step, in previous works this information has not been used at the identification step [10], [18]. In this paper, we showed that using degree information at the identification step in a very naive way, namely setting the degree as an alias to other centrality metrics, yields promising results.

Survivability models for the assessment of smart grid distribution automation network designs

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams through different lenses, smart grids require a unified and holistic approach that takes into consideration the interplay of communication reliability, energy backup, distribution automation topology, energy storage and intelligent features such as automated failure detection, isolation and restoration (FDIR) and demand response. In this paper, we present an analytical model and metrics for the survivability assessment of the distribution power grid network. The proposed metrics extend the system average interruption duration index (SAIDI), accounting for the fact that after a failure the energy demand and supply will vary over time during a multi-step recovery process. The analytical model used to compute the proposed metrics is built on top of three design principles: state space factorization, state aggregation and initial state conditioning. Using these principles, we reduce a Markov chain model with large state space cardinality to a set of much simpler models that are amenable to analytical treatment and efficient numerical solution. In the special case where demand response is not integrated with FDIR, we provide closed form solutions to the metrics of interest, such as the mean time to repair a given set of sections. We have evaluated the presented model using data from a real power distribution grid and we have found that survivability of distribution power grids can be improved by the integration of the demand response feature with automated FDIR approaches. Our empirical results indicate the importance of quantifying survivability to support investment decisions at different parts of the power grid distribution network.