Forever Young: Aging Control For Smartphones In Hybrid Networks

The demand for Internet services that require frequent updates through small messages, such as microblogging, has tremendously grown in the past few years. Although the use of such applications by domestic users is usually free, their access from mobile devices is subject to fees and consumes energy from limited batteries. If a user activates his mobile device and is in range of a service provider, a content update is received at the expense of monetary and energy costs. Thus, users face a tradeoff between such costs and their messages aging. The goal of this paper is to show how to cope with such a tradeoff, by devising \emph{aging control policies}. An aging control policy consists of deciding, based on the current utility of the last message received, whether to activate the mobile device, and if so, which technology to use (WiFi or 3G). We present a model that yields the optimal aging control policy. Our model is based on a Markov Decision Process in which states correspond to message ages. Using our model, we show the existence of an optimal strategy in the class of threshold strategies, wherein users activate their mobile devices if the age of their messages surpasses a given threshold and remain inactive otherwise. We then consider strategic content providers (publishers) that offer \emph{bonus packages} to users, so as to incent them to download updates of advertisement campaigns. We provide simple algorithms for publishers to determine optimal bonus levels, leveraging the fact that users adopt their optimal aging control strategies. The accuracy of our model is validated against traces from the UMass DieselNet bus network.Comment: See also http://www-net.cs.umass.edu/~sadoc/agecontrol

Computing the Hit Rate of Similarity Caching

Similarity caching allows requests for an item $i$ to be served by a similar item $i'$. Applications include recommendation systems, multimedia retrieval, and machine learning. Recently, many similarity caching policies have been proposed, but still we do not know how to compute the hit rate even for the simplest policies, like SIM-LRU and RND-LRU that are straightforward modifications of classical caching algorithms. This paper proposes the first algorithm to compute the hit rate of similarity caching policies under the independent reference model for the request process. In particular, our work shows how to extend the popular TTL approximation from classic caching to similarity caching. The algorithm is evaluated on both synthetic and real world traces

How Often Should I Access My Online Social Networks?

International audienceUsers of online social networks are faced with a conundrum of trying to be always informed without having enough time or attention budget to do so. The retention of users on online social networks has important implications, encompassing economic, psychological and infrastructure aspects. In this paper, we pose the following question: what is the optimal rate at which users should access a social network? To answer this question, we propose an analytical model to determine the value of an access (VoA) to the social network. In the simple setting considered in this paper, VoA is defined as the chance of a user accessing the network and obtaining new content. Clearly, VoA depends on the rate at which sources generate content and on the filtering imposed by the social network. Then, we pose an optimization problem wherein the utility of users grows with respect to VoA but is penalized by costs incurred to access the network. Using the proposed framework, we provide insights on the optimal access rate. Our results are parameterized using Facebook data, indicating the predictive power of the approach

Timelines are Publisher-Driven Caches: Analyzing and Shaping Timeline Networks

International audienceCache networks are one of the building blocks of information centric networks (ICNs). Most of the recent work on cache networks has focused on networks of request driven caches, which are populated based on users requests for content generated by publishers. However, user generated content still poses the most pressing challenges. For such content time-lines are the de facto sharing solution. In this paper, we establish a connection between time-lines and publisher-driven caches. We propose simple models and metrics to analyze publisher-driven caches, allowing for variable-sized objects. Then, we design two efficient algorithms for timeline workload shaping, leveraging admission and price control in order, for instance, to aid service providers to attain prescribed service level agreements

Fundamental Scaling Laws of Covert DDoS Attacks

International audienceBotnets such as Mirai use insecure home devices to conduct distributed denial of service attacks on the Internet infrastructure. Although some of those attacks involve large amounts of traffic, they are generated from a large number of homes, which hampers their early detection. In this paper, our goal is to answer the following question: what is the maximum amount of damage that a DDoS attacker can produce at the network edge without being detected? To that aim, we consider a statistical hypothesis testing approach for attack detection at the network edge. The proposed system assesses the goodness of fit of traffic models based on the ratio of their likelihoods. Under such a model, we show that the amount of traffic that can be generated by a covert attacker scales according to the square root of the number of compromised homes. We evaluate and validate the theoretical results using real data collected from thousands of home-routers connected to a mid-sized ISP

Rejuvenation and the Spread of Epidemics in General Topologies

International audienceEpidemic models have received significant atten-tion in the past few decades to study the propagation of viruses, worms and ideas in computer and social networks. In the case of viruses, the goal is to understand how the topology of the network and the properties of the nodes that comprise the network, together, impact the spread of the epidemics. In this paper, we propose rejuvenation as a way to cope with epidemics. Then, we present a model to study the effect of rejuvenation and of the topology on the steady-state number of infected and failed nodes. We distinguish between a state in which the virus is incubating and in which symptoms might not be visible and yet they may be contagious and infecting other nodes, and a state of failure where symptoms are clear. Sampling costs might be incurred to examine nodes in search for viruses at an early stage. Using the proposed model, we show that the sampling rate admits at most one local minimum greater than zero. Then, we numerically illustrate the impact of different system parameters on the optimal sampling rate, indicating when rejuvenation is more beneficial

Enabling peer-to-peer swarming for multi-commodity dissemination

Peer-to-peer swarming, as used by BitTorrent, is one of the de facto solutions for content dissemination in today\u27s Internet. By leveraging resources provided by users, peer-to-peer swarming is a simple, scalable and efficient mechanism for content distribution. Although peer-to-peer swarming has been widely studied for a decade, prior work has focused on the dissemination of one commodity (a single file). This thesis focuses on the multi-commodity case. We have discovered through measurements that a vast number of publishers currently disseminate multiple files in a single swarm (bundle). The first contribution of this thesis is a model for content availability. We use the model to show that, when publishers are intermittent, bundling K files increases content availability exponentially as function of K. When there is a stable publisher, we consider content availability among peers (excluding the publisher). Our second contribution is the estimate of the dependency of peers on the stable publisher, which is useful for provisioning purposes as well as in deciding how to bundle. To this goal, we propose a new metric, swarm self-sustainability, and present a model that yields swarm self-sustainability as a function of the file size, popularity and service capacity of peers. Then, we investigate reciprocity and the use of barter that occurs among peers. As our third contribution, we prove that the loss of efficiency due to the download of unrequested content to enforce direct reciprocity, as opposed to indirect reciprocity, is at most two in a class of networks without relays. Finally, we study algorithmic and economic problems faced by enterprises who leverage swarming systems and who control prices and bundling strategies. As our fourth contribution, we present two formulations of the optimal bundling problem, and prove that one is NP hard whereas the other is solvable by a greedy strategy. From an economic standpoint, we present conditions for the existence and uniqueness of an equilibrium between publishers and peers

A Backpropagation Approach for Distributed Resource Allocation

Network resource allocation through Network Utility Maximization (NUM) is one of the fundamental problems in the realm of networked systems. In the NUM framework, the network comprises a set of nodes each of which is associated with a utility function, and the goal is to distribute resources across nodes so as to maximize the sum of the nodes utilities. In this paper, we propose a novel backpropagation approach for distributed resource allocation. e internal ow of resources among nodes is governed by the network dynamics, assumed to be captured through a directed acyclic graph (DAG). Control is exercised as an external injection of limited resources at some nodes, where the goal is to determine the optimal amount of resources to be injected at nodes, under the NUM framework. To that aim, we present a novel forward-backward algorithm, inspired by neural network training, wherein ows of resources are transferred during the forward step, and gradients are backpropagated at the backward step. Based on such gradients, the controls are adjusted, considering two variations of the algorithm under synchronous and asynchronous se ings. e proposed algorithms are distributed, in the sense that information in transferred only between neighboring nodes in the network. In addition, they are suitable for continued operation, so that the optimum resource allocation is tracked as conditions gradually change. We formally establish convergence of the proposed algorithms, and numerically compare the speed of convergence under the asynchronous se ing against its synchronous counterpart. Together, our results advance the state-of-the-art in the realm of NUM under nonlinear constraints, indicating how to leverage a backpropagation approach for that matter

A Backpropagation Approach for Distributed Resource Allocation

Network resource allocation through Network Utility Maximization (NUM) is one of the fundamental problems in the realm of networked systems. In the NUM framework, the network comprises a set of nodes each of which is associated with a utility function, and the goal is to distribute resources across nodes so as to maximize the sum of the nodes utilities. In this paper, we propose a novel backpropagation approach for distributed resource allocation. e internal ow of resources among nodes is governed by the network dynamics, assumed to be captured through a directed acyclic graph (DAG). Control is exercised as an external injection of limited resources at some nodes, where the goal is to determine the optimal amount of resources to be injected at nodes, under the NUM framework. To that aim, we present a novel forward-backward algorithm, inspired by neural network training, wherein ows of resources are transferred during the forward step, and gradients are backpropagated at the backward step. Based on such gradients, the controls are adjusted, considering two variations of the algorithm under synchronous and asynchronous se ings. e proposed algorithms are distributed, in the sense that information in transferred only between neighboring nodes in the network. In addition, they are suitable for continued operation, so that the optimum resource allocation is tracked as conditions gradually change. We formally establish convergence of the proposed algorithms, and numerically compare the speed of convergence under the asynchronous se ing against its synchronous counterpart. Together, our results advance the state-of-the-art in the realm of NUM under nonlinear constraints, indicating how to leverage a backpropagation approach for that matter