arXiv subjects

Don Towsley

Publications and source records attributed to Don Towsley.

At least 127 records · Page 7Linked to original sources

arXiv2020

Decentralized gradient methods: does topology matter?

Consensus-based distributed optimization methods have recently been advocated as alternatives to parameter server and ring all-reduce paradigms for large scale training of machine learning models. In this case, each worker maintains a local estimate of the optimal parameter vector and iteratively updates it by averaging the estimates obtained from its neighbors, and applying a correction on the basis of its local dataset. While theoretical results suggest that worker communication topology should have strong impact on the number of epochs needed to converge, previous experiments have shown the opposite conclusion. This paper sheds lights on this apparent contradiction and show how sparse topologies can lead to faster convergence even in the absence of communication delays.

cs.LG↗

arXiv2020

On the Performance Analysis of Epidemic Routing in Non-Sparse Delay Tolerant Networks

We study the behavior of epidemic routing in a delay tolerant network as a function of node density. Focusing on the probability of successful delivery to a destination within a deadline (PS), we show that PS experiences a phase transition as node density increases. Specifically, we prove that PS exhibits a phase transition when nodes are placed according to a Poisson process and allowed to move according to independent and identical processes with limited speed. We then propose four fluid models to evaluate the performance of epidemic routing in non-sparse networks. A model is proposed for supercritical networks based on approximation of the infection rate as a function of time. Other models are based on the approximation of the pairwise infection rate. Two of them, one for subcritical networks and another for supercritical networks, use the pairwise infection rate as a function of the number of infected nodes. The other model uses pairwise infection rate as a function of time, and can be applied for both subcritical and supercritical networks achieving good accuracy. The model for subcritical networks is accurate when density is not close to the percolation critical density. Moreover, the models that target only supercritical regime are accurate.

cs.NI↗

arXiv2020

Resource Allocation in One-dimensional Distributed Service Networks

We consider assignment policies that allocate resources to users, where both resources and users are located on a one-dimensional line. First, we consider unidirectional assignment policies that allocate resources only to users located to their left. We propose the Move to Right (MTR) policy, which scans from left to right assigning nearest rightmost available resource to a user, and contrast it to the Unidirectional Gale-Shapley (UGS) matching policy. While both these policies are optimal among all unidirectional policies, we show that they are equivalent with respect to the expected distance traveled by a request (request distance), although MTR is fairer. Moreover, we show that when user and resource locations are modeled by statistical point processes, and resources are allowed to satisfy more than one user, the spatial system under unidirectional policies can be mapped into bulk service queuing systems, thus allowing the application of a plethora of queuing theory results that yield closed form expressions. As we consider a case where different resources can satisfy different numbers of users, we also generate new results for bulk service queues. We also consider bidirectional policies where there are no directional restrictions on resource allocation and develop an algorithm for computing the optimal assignment which is more efficient than known algorithms in the literature when there are more resources than users. Finally, numerical evaluation of performance of unidirectional and bidirectional allocation schemes yields design guidelines beneficial for resource placement.

cs.PF↗

arXiv2020

LPD Communication: A Sequential Change-Point Detection Perspective

In this paper, we establish a framework for low probability of detection (LPD) communication from a sequential change-point detection (SCPD) perspective, where a transmitter, Alice, wants to hide her signal transmission to a receiver, Bob, under the surveillance of an adversary, Willie. The new framework facilitates to model LPD communication and further evaluate its performance under the condition that Willie has no prior knowledge on when the transmission from Alice starts and that Willie wants to detect the existence of the communication as quickly as possible in real-time manner. We consider three different sequential tests for Willie, i.e., the Shewhart test, the cumulative sum (CUSUM) test, and the Shiryaev-Roberts (SR) test, to model the detection procedure. Communication is said to be covert if it stops before detection by Willie with high probability. Covert probability defined as the probability that Willie is not alerted during the communication procedure is investigated. We formulate an optimization problem aimed at finding the transmit power and transmission duration such that the total amount of information that can be transmitted is maximized subject to a high covert probability. Under Shewhart test, closed-form approximations of the optimal transmit power and transmission duration are derived, which well approximate the solutions obtained from exhaustive search. As for CUSUM and SR tests, we provide an effective algorithm to search the optimal solution. Numeric results are presented to show the performance of LPD communication.

cs.IT↗

arXiv2020

Quickest Detection of Growing Dynamic Anomalies in Networks

The problem of quickest growing dynamic anomaly detection in sensor networks is studied. Initially, the observations at the sensors, which are sampled sequentially by the decision maker, are generated according to a pre-change distribution. At some unknown but deterministic time instant, a dynamic anomaly emerges in the network, affecting a different set of sensors as time progresses. The observations of the affected sensors are generated from a post-change distribution. It is assumed that the number of affected sensors increases with time, and that only the initial and the final size of the anomaly are known by the decision maker. The goal is to detect the emergence of the anomaly as quickly as possible while guaranteeing a sufficiently low frequency of false alarm events. This detection problem is posed as a stochastic optimization problem by using a delay metric that is based on the worst possible path of the anomaly. A detection rule is proposed that is asymptotically optimal as the mean time to false alarm goes to infinity. Finally, numerical results are provided to validate our theoretical analysis.

math.ST↗

arXiv2020

Covert Communication in Continuous-Time Systems

Recent works have considered the ability of transmitter Alice to communicate reliably to receiver Bob without being detected by warden Willie. These works generally assume a standard discrete-time model. But the assumption of a discrete-time model in standard communication scenarios is often predicated on its equivalence to a continuous-time model, which has not been established for the covert communications problem. Here, we consider the continuous-time channel directly and study if efficient covert communication can still be achieved. We assume that an uninformed jammer is present to assist Alice, and we consider additive white Gaussian noise (AWGN) channels between all parties. For a channel with approximate bandwidth W, we establish constructions such that O(WT) information bits can be transmitted covertly and reliably from Alice to Bob in T seconds for two separate scenarios: 1) when the path-loss between Alice and Willie is known; and 2) when the path-loss between Alice and Willie is unknown.

cs.CR↗

arXiv2020

Let's Share: A Game-Theoretic Framework for Resource Sharing in Mobile Edge Clouds

Mobile edge computing seeks to provide resources to different delay-sensitive applications. This is a challenging problem as an edge cloud-service provider may not have sufficient resources to satisfy all resource requests. Furthermore, allocating available resources optimally to different applications is also challenging. Resource sharing among different edge cloud-service providers can address the aforementioned limitation as certain service providers may have resources available that can be ``rented'' by other service providers. However, edge cloud service providers can have different objectives or \emph{utilities}. Therefore, there is a need for an efficient and effective mechanism to share resources among service providers, while considering the different objectives of various providers. We model resource sharing as a multi-objective optimization problem and present a solution framework based on \emph{Cooperative Game Theory} (CGT). We consider the strategy where each service provider allocates resources to its native applications first and shares the remaining resources with applications from other service providers. We prove that for a monotonic, non-decreasing utility function, the game is canonical and convex. Hence, the \emph{core} is not empty and the grand coalition is stable. We propose two algorithms \emph{Game-theoretic Pareto optimal allocation} (GPOA) and \emph{Polyandrous-Polygamous Matching based Pareto Optimal Allocation} (PPMPOA) that provide allocations from the core. Hence the obtained allocations are \emph{Pareto} optimal and the grand coalition of all the service providers is stable. Experimental results confirm that our proposed resource sharing framework improves utilities of edge cloud-service providers and application request satisfaction.

cs.NI↗

arXiv2019

On the Stochastic Analysis of a Quantum Entanglement Switch

We study a quantum entanglement switch that serves $k$ users in a star topology. We model variants of the system using Markov chains and standard queueing theory and obtain expressions for switch capacity and the expected number of qubits stored in memory at the switch. While it is more accurate to use a discrete-time Markov chain (DTMC) to model such systems, we quickly encounter practical constraints of using this technique and switch to using continuous-time Markov chains (CTMCs). Using CTMCs allows us to obtain a number of analytic results for systems in which the links are homogeneous or heterogeneous and for switches that have infinite or finite buffer sizes. In addition, we can model the effects of decoherence of quantum states fairly easily using CTMCs. We also compare the results we obtain from the DTMC against the CTMC in the case of homogeneous links and infinite buffer, and learn that the CTMC is a reasonable approximation of the DTMC. From numerical observations, we discover that decoherence has little effect on capacity and expected number of stored qubits for homogeneous systems. For heterogeneous systems, especially those operating close to stability constraints, buffer size and decoherence can have significant effects on performance metrics. We also learn that in general, increasing the buffer size from one to two qubits per link is advantageous to most systems, while increasing the buffer size further yields diminishing returns.

quant-ph↗

arXiv2019

Planting trees in graphs, and finding them back

In this paper we study detection and reconstruction of planted structures in Erdős-Rényi random graphs. Motivated by a problem of communication security, we focus on planted structures that consist in a tree graph. For planted line graphs, we establish the following phase diagram. In a low density region where the average degree $λ$ of the initial graph is below some critical value $λ_c=1$, detection and reconstruction go from impossible to easy as the line length $K$ crosses some critical value $f(λ)\ln(n)$, where $n$ is the number of nodes in the graph. In the high density region $λ>λ_c$, detection goes from impossible to easy as $K$ goes from $o(\sqrt{n})$ to $ω(\sqrt{n})$, and reconstruction remains impossible so long as $K=o(n)$. For $D$-ary trees of varying depth $h$ and $2\le D\le O(1)$, we identify a low-density region $λ<λ_D$, such that the following holds. There is a threshold $h*=g(D)\ln(\ln(n))$ with the following properties. Detection goes from feasible to impossible as $h$ crosses $h*$. We also show that only partial reconstruction is feasible at best for $h\ge h*$. We conjecture a similar picture to hold for $D$-ary trees as for lines in the high-density region $λ>λ_D$, but confirm only the following part of this picture: Detection is easy for $D$-ary trees of size $ω(\sqrt{n})$, while at best only partial reconstruction is feasible for $D$-ary trees of any size $o(n)$. These results are in contrast with the corresponding picture for detection and reconstruction of {\em low rank} planted structures, such as dense subgraphs and block communities: We observe a discrepancy between detection and reconstruction, the latter being impossible for a wide range of parameters where detection is easy. This property does not hold for previously studied low rank planted structures.

math.PR↗

arXiv2019

A Game-Theoretic Framework for Resource Sharing in Clouds

Providing resources to different users or applications is fundamental to cloud computing. This is a challenging problem as a cloud service provider may have insufficient resources to satisfy all user requests. Furthermore, allocating available resources optimally to different applications is also challenging. Resource sharing among different cloud service providers can improve resource availability and resource utilization as certain cloud service providers may have free resources available that can be ``rented'' by other service providers. However, different cloud service providers can have different objectives or \emph{utilities}. Therefore, there is a need for a framework that can share and allocate resources in an efficient and effective way, while taking into account the objectives of various service providers that results in a \emph{multi-objective optimization} problem. In this paper, we present a \emph{Cooperative Game Theory} (CGT) based framework for resource sharing and allocation among different service providers with varying objectives that form a coalition. We show that the resource sharing problem can be modeled as an $N-$player \emph{canonical} cooperative game with \emph{non-transferable utility} (NTU) and prove that the game is convex for monotonic non-decreasing utilities. We propose an $\mathcal{O}({N})$ algorithm that provides an allocation from the \emph{core}, hence guaranteeing \emph{Pareto optimality}. We evaluate the performance of our proposed resource sharing framework in a number of simulation settings and show that our proposed framework improves user satisfaction and utility of service providers.

cs.MA↗

arXiv2019

Understanding the Signature of Controversial Wikipedia Articles through Motifs in Editor Revision Networks

Wikipedia serves as a good example of how editors collaborate to form and maintain an article. The relationship between editors, derived from their sequence of editing activity, results in a directed network structure called the revision network, that potentially holds valuable insights into editing activity. In this paper we create revision networks to assess differences between controversial and non-controversial articles, as labelled by Wikipedia. Originating from complex networks, we apply motif analysis, which determines the under or over-representation of induced sub-structures, in this case triads of editors. We analyse 21,631 Wikipedia articles in this way, and use principal component analysis to consider the relationship between their motif subgraph ratio profiles. Results show that a small number of induced triads play an important role in characterising relationships between editors, with controversial articles having a tendency to cluster. This provides useful insight into editing behaviour and interaction capturing counter-narratives, without recourse to semantic analysis. It also provides a potentially useful feature for future prediction of controversial Wikipedia articles.

cs.SI↗

arXiv2019

Fundamental Limits of Invisible Flow Fingerprinting

Network flow fingerprinting can be used to de-anonymize communications on anonymity systems such as Tor by linking the ingress and egress segments of anonymized connections. Assume Alice and Bob have access to the input and the output links of an anonymous network, respectively, and they wish to collaboratively reveal the connections between the input and the output links without being detected by Willie who protects the network. Alice generates a codebook of fingerprints, where each fingerprint corresponds to a unique sequence of inter-packet delays and shares it only with Bob. For each input flow, she selects a fingerprint from the codebook and embeds it in the flow, i.e., changes the packet timings of the flow to follow the packet timings suggested by the fingerprint, and Bob extracts the fingerprints from the output flows. We model the network as parallel $M/M/1$ queues where each queue is shared by a flow from Alice to Bob and other flows independent of the flow from Alice to Bob. The timings of the flows are governed by independent Poisson point processes. Assuming all input flows have equal rates and that Bob observes only flows with fingerprints, we first present two scenarios: 1) Alice fingerprints all the flows; 2) Alice fingerprints a subset of the flows, unknown to Willie. Then, we extend the construction and analysis to the case where flow rates are arbitrary as well as the case where not all the flows that Bob observes have a fingerprint. For each scenario, we derive the number of flows that Alice can fingerprint and Bob can trace by fingerprinting.

cs.NI↗

arXiv2019

Fundamental Limits of Covert Packet Insertion

Covert communication conceals the existence of the transmission from a watchful adversary. We consider the fundamental limits for covert communications via packet insertion over packet channels whose packet timings are governed by a renewal process of rate $λ$. Authorized transmitter Jack sends packets to authorized receiver Steve, and covert transmitter Alice wishes to transmit packets to covert receiver Bob without being detected by watchful adversary Willie. Willie cannot authenticate the source of the packets. Hence, he looks for statistical anomalies in the packet stream from Jack to Steve to attempt detection of unauthorized packet insertion. First, we consider a special case where the packet timings are governed by a Poisson process and we show that Alice can covertly insert $\mathcal{O}(\sqrt{λT})$ packets for Bob in a time interval of length $T$; conversely, if Alice inserts $ω(\sqrt{λT})$, she will be detected by Willie with high probability. Then, we extend our results to general renewal channels and show that in a stream of $N$ packets transmitted by Jack, Alice can covertly insert $\mathcal{O}(\sqrt{N})$ packets; if she inserts $ω(\sqrt{N})$ packets, she will be detected by Willie with high probability.

cs.CR↗

arXiv2019

Quantum Networks For Open Science

The United States Department of Energy convened the Quantum Networks for Open Science (QNOS) Workshop in September 2018. The workshop was primarily focused on quantum networks optimized for scientific applications with the expectation that the resulting quantum networks could be extended to lay the groundwork for a generalized network that will evolve into a quantum internet.

quant-ph↗

arXiv2019

Network Cache Design under Stationary Requests: Exact Analysis and Poisson Approximation

The design of caching algorithms to maximize hit probability has been extensively studied. In this paper, we associate each content with a utility, which is a function of either the corresponding content hit rate or hit probability. We formulate a cache optimization problem to maximize the sum of utilities over all contents under stationary and ergodic request processes. This problem is non-convex in general but we reformulate it as a convex optimization problem when the inter-request time (irt) distribution has a non-increasing hazard rate function. We provide explicit optimal solutions for some irt distributions, and compare the solutions of the hit-rate based (HRB) and hit-probability based (HPB) problems. We formulate a reverse engineering based dual implementation of LRU under stationary arrivals. We also propose decentralized algorithms that can be implemented using limited information and use a discrete time Lyapunov technique (DTLT) to correctly characterize their stability. We find that decentralized algorithms that solve HRB are more robust than decentralized HPB algorithms. Informed by these results, we further propose lightweight Poisson approximate decentralized and online algorithms that are accurate and efficient in achieving optimal hit rates and hit probabilities.

cs.NI↗

arXiv2019

On The Scalability of P2P Swarming Systems

One of the fundamental problems in the realm of peer-to-peer systems is that of determining their service capacities. In this paper, we focus on P2P scalability issues and propose models to compute the achievable throughput under distinct policies for selecting both peers and blocks. From these models, we obtain novel insights on the behavior of P2P swarming systems that motivate new mechanisms for publishers and peers to improve the overall performance. In particular, we obtain operational regions for swarm system. In addition, we show that system capacity significantly increases if publishers adopt the most deprived peer selection and peers reduce their service rates when they have all the file blocks but one.

cs.NI↗

arXiv2019

Optimal Energy Consumption with Communication, Computation, Caching and QoI-Guarantee

Energy efficiency is a fundamental requirement of modern data communication systems, and its importance is reflected in much recent work on performance analysis of system energy consumption. However, most works have only focused on communication and computation costs, but do not account for caching costs. Given the increasing interest in cache networks, this is a serious deficiency. {In this paper, we consider the problem of energy consumption in data communication, compression and caching (C$3$) with a Quality of Information (QoI) guarantee in a communication network. {Our goal is to identify the optimal data compression rate and data placement over the network to minimize the overall energy consumption in the network.} he formulated problem is a \emph{Mixed Integer Non-Linear Programming} (MINLP) problem with non-convex functions, which is NP-hard in general. } {We} propose a variant of spatial branch and bound algorithm (V-SBB), that can {provide} the $ε$-global optimal solution to {our problem}. {We numerically show that our C3 optimization framework can improve the energy efficiency up to 88\% compared to any C2 optimization between communication and computation or caching. Furthermore, for our energy consumption problem, V-SBB {provides comparatively better solution than some other MINLP solvers.}}

cs.NI↗

arXiv2018

Utilizing Dynamic Properties of Sharing Bits and Registers to Estimate User Cardinalities over Time

Online monitoring user cardinalities (or degrees) in graph streams is fundamental for many applications. For example in a bipartite graph representing user-website visiting activities, user cardinalities (the number of distinct visited websites) are monitored to report network anomalies. These real-world graph streams may contain user-item duplicates and have a huge number of distinct user-item pairs, therefore, it is infeasible to exactly compute user cardinalities when memory and computation resources are limited.Existing methods are designed to approximately estimate user cardinalities, whose accuracy highly depends on parameters that are not easy to set. Moreover, these methods cannot provide anytime-available estimation, as the user cardinalities are computed at the end of the data stream. Real-time applications such as anomaly detection require that user cardinalities are estimated on the fly. To address these problems, we develop novel bit and register sharing algorithms, which use a bit array and a register array to build a compact sketch of all users' connected items respectively. Compared with previous bit and register sharing methods, our algorithms exploit the dynamic properties of the bit and register arrays (e.g., the fraction of zero bits in the bit array at each time) to significantly improve the estimation accuracy, and have low time complexity (O(1)) to update the estimations each time they observe a new user-item pair. In addition, our algorithms are simple and easy to use, without requirements to tune any parameter. We evaluate the performance of our methods on real-world datasets. The experimental results demonstrate that our methods are several times more accurate and faster than state-of-the-art methods using the same amount of memory.

cs.DS↗