The increasing deployment of digital communications in modern society has heightened the need for security and privacy, particularly within the Internet-of-Things (IoT) domain, where cost-effective implementations remain a challenge. Post-quantum cryptography (PQC) schemes based on hard learning problems, such as learning with errors (LWEs) and learning parity with noise (LPN), have gained significant attention due to their robustness against quantum attacks. A key challenge in these cryptographic schemes is the generation of error distributions, which must maintain secrecy and adhere to specific statistical properties. Traditional approaches rely on complex two-phase sampling chains, making hardware implementations both resource-intensive and vulnerable to physical attacks. To address these challenges, inexact or approximate computing has been explored as a means of generating errors. The learning parity with physical noise (LPPN) scheme was introduced as an alternative, leveraging controllable computational inaccuracies instead of explicit error sampling. Initially demonstrated using frequency–voltage Over-Scaling techniques on a 65-nm technology, its viability on advanced semiconductor nodes remains uncertain. In this work, we implement the LPPN technique on a 22-nm fully depleted silicon-on-insulator (FD-SOI) technology, assessing the limitations of conventional voltage–frequency Over-Scaling. Furthermore, we propose the use of back-gate voltage scaling, a unique capability of FD-SOI, to enhance error controllability. Experimental results from both simulations and on-chip measurements demonstrate that back-gate voltage scaling improves the precision of error generation, reducing the sensitivity factor of the error probability by up to four times compared to conventional methods.

This paper addresses a major challenge in traffic engineering: the selection of a set of paths that minimizes routing cost for a random traffic matrix. We introduce the concept of pathbook: a small set of paths to which we restrict routing. The use of pathbook accelerates centralized traffic engineering algorithms, and therefore is appealing for instantiating, configuring, and optimizing large software-based networks. However, restricting routing to a few paths may lead to higher cost or infeasibility. To this end, we introduce the problem of pathbook design, wherein we search for a pathbook of constrained size that minimizes the expected routing cost of the random traffic matrix, which represents a prediction of the future traffic. The pathbook design problem is of combinatorial nature, and we show that it is NP-hard. We then study its convex relaxation for which we propose an optimal algorithm based on the projected subgradient method. For large networks, the subgradient vector is of prohibitive dimensions, hence we propose a coordinate-descent method using the Gauss-Southwell rule, which prescribes a move along the direction of largest subgradient element. We test the performance of our solution on dynamic traffic matrices from GEANT and find that our Gauss-Southwell pathbooks can accelerate standard methods by two orders of magnitude.

Telecommunication networks are converging to a massively distributed cloud infrastructure interconnected with software defined networks. In the envisioned architecture, services will be deployed flexibly and quickly as network slices. Our paper addresses a major bottleneck in this context, namely the challenge of computing the best resource provisioning for network slices in a robust and efficient manner. With tractability in mind, we propose a novel optimization framework which allows fine-grained resource allocation for slices both in terms of network bandwidth and cloud processing. The slices can be further provisioned and auto-scaled optimally based on a large class of utility functions in real-time. Furthermore, by tuning a slice-specific parameter, system designers can trade off traffic-fairness with computing-fairness to provide a mixed fairness strategy. We also propose an iterative algorithm based on the alternating direction method of multipliers (ADMM) that provably converges to the optimal resource allocation and we demonstrate the method's fast convergence in a wide range of quasi-stationary and dynamic settings.

Load-balancing and network optimization in SDN networks require efficient flow splitting during the path computation phase. The way flow splitting is typically implemented in switches is to map the output of an hash function computed on the headers of incoming flows to the content stored in a Ternary Content Addressable Memory (TCAM), a very efficient but scarce resource. Although a large TCAM budget means that the flow distribution can more accurately model a fractional ideal, the distribution of flow volume amongst the paths is constrained in reality to use only a limited number of TCAM rows. In this paper, we present a flow splitting algorithm that maximizes the total number of demands allocated in the network according to the TCAM size constraints and, at the same time, minimize the total routing cost. Although the problem is NP-hard, we show through simulations that we can achieve good approximations of the optimal solution in a reasonable amount of time.

Accurate coverage maps are an important tool for network planning and operation but it is often impossible to obtain these maps completely from measurements. In this paper we describe two new methods that enable operators to minimize the cost for obtaining a complete coverage map at high accuracy. Our first method applies the Singular Value Thresholding (SVT) algorithm to reconstruct a complete map from a sparse matrix of coverage data. We then use the Query by Committee (QbC) rationale to identify the areas where further measurements would maximize accuracy of the completed map. This second method allows operators to plan their drive tests such that a given budget is spent at highest efficiency. Our numerical examples illustrate that our proposed completion technique outperforms relevant state of the art and that QbC further enhances reconstruction accuracy.

Predicting the future location of users in wireless networks has numerous applications, and can help service providers to improve the quality of service perceived by their clients. The location predictors proposed so far estimate the next location of a specific user by inspecting the past individual trajectories of this user. As a consequence, when the training data collected for a given user is limited, the resulting prediction is inaccurate. In this paper, we develop cluster-aided predictors that exploit past trajectories collected from all users to predict the next location of a given user. These predictors rely on clustering techniques and extract from the training data similarities among the mobility patterns of the various users to improve the prediction accuracy. Specifically, we present CAMP (Cluster-Aided Mobility Predictor), a cluster-aided predictor whose design is based on recent non-parametric Bayesian statistical tools. CAMP is robust and adaptive in the sense that it exploits similarities in users' mobility only if such similarities are really present in the training data. We analytically prove the consistency of the predictions provided by CAMP, and investigate its performance using two large-scale datasets. CAMP significantly outperforms existing predictors, and in particular those that only exploit individual past trajectories.

This paper addresses a fundamental limitation for the adoption of caching for wireless access networks due to small population sizes. This shortcoming is due to two main challenges: making timely estimates of varying content popularity and inferring popular content from small samples. We propose a framework which alleviates such limitations. To timely estimate varying popularity in a context of a single cache we propose an Age-Based Threshold (ABT) policy which caches all contents requested more times than a threshold N (τ), where τ is the content age. We show that ABT is asymptotically hit rate optimal in the many contents regime, which allows us to obtain the first characterization of the optimal performance of a caching system in a dynamic context. We then address small sample sizes focusing on L local caches and one global cache. On the one hand we show that the global cache learns L times faster by aggregating all requests from local caches, which improves hit rates. On the other hand, aggregation washes out local characteristics of correlated traffic which penalizes hit rate. This motivates coordination mechanisms which combine global learning of popularity scores in clusters and Least-Recently-Used (LRU) policy with prefetching.

We study a service provisioning system where arriving jobs are routed in an online fashion to any of the available servers; typical applications include datacenters, Internet switches, and cloud computing infrastructures. A common goal in these scenarios is to balance the load across the servers and achieve maximum throughput. For example, the classical online policy Join-the-Shortest-Queue (JSQ) routes an arriving job to the server with the shortest instantaneous queue length. Although JSQ has desirable properties, it requires coordination between the routers and the servers in the form of queue length reports, which prohibits its practical usability in many scenarios. In this paper we study the practical case of “routing with blinkers”, where no coordination is allowed between the routers and the service provisioning system, and the routers act in an individual manner with limited view of the system state. Every router keeps a log of delays of all jobs it has routed in the past; these are delayed estimates of the actual server queue length. Although easy to acquire, such information is a highly inaccurate depiction of the system state and hence it is unclear whether it is enough to achieve maximum performance. Motivated by the fact that a reasonable policy such as Join-the-Shortest-Delay fails to achieve maximum throughput, we propose a novel routing policy that “samples” the servers periodically and achieves maximum throughput, subject to a condition for the service discipline of the server.

This paper is motivated by two applications, namely i) generalizations of cuckoo hashing, a computationally simple approach to assigning keys to objects, and ii) load balancing in content distribution networks, where one is interested in determining the impact of content replication on performance. These two problems admit a common abstraction: in both scenarios, performance is characterized by the maximum weight of a generalization of a matching in a bipartite graph, featuring node and edge capacities. Our main result is a law of large numbers characterizing the asymptotic maximum weight matching in the limit of large bipartite random graphs, when the graphs admit a local weak limit that is a tree. This result specializes to the two application scenarios, yielding new results in both contexts. In contrast with previous results, the key novelty is the ability to handle edge capacities with arbitrary integer values. An analysis of belief propagation algorithms (BP) with multivariate belief vectors underlies the proof. In particular, we show convergence of the corresponding BP by exploiting monotonicity of the belief vectors with respect to the so-called upshifted likelihood ratio stochastic order. This auxiliary result can be of independent interest, providing a new set of structural conditions which ensure convergence of BP.

This paper considers large scale distributed content service platforms, such as peer-to-peer video-on-demand systems. Such systems feature two basic resources, namely storage and bandwidth. Their efficiency critically depends on two factors: (i) content replication within servers, and (ii) how incoming service requests are matched to servers holding requested content. To inform the corresponding design choices, we make the following contributions. We first show that, for underloaded systems, so-called proportional content placement with a simple greedy strategy for matching requests to servers ensures full system efficiency provided storage size grows logarithmically with the system size. However, for constant storage size, this strategy undergoes a phase transition with severe loss of efficiency as system load approaches criticality.

Glauber dynamics is a powerful tool to generate randomized, approximate solutions to combinatorially difficult problems. It has been recently used to design distributed carrier-sense multiple-access (CSMA) scheduling algorithms for multihop wireless networks. In this paper, we derive bounds on the mixing time of a generalization of Glauber dynamics where multiple links update their states in parallel and the fugacity of each link can be different. The results are used to prove that the average queue length (and hence, the delay) under the parallel-Glauber-dynamics-based CSMA grows polynomially in the number of links for wireless networks with bounded-degree interference graphs when the arrival rate lies in a fraction of the capacity region. Other versions of adaptive CSMA can be analyzed similarly. We also show that in specific network topologies, the low-delay capacity region can be further improved.

Glauber dynamics is a powerful tool to generate randomized, approximate solutions to combinatorially difficult problems. It has been recently used to design distributed CSMA scheduling algorithms for multi-hop wireless networks. In this paper, we derive bounds on the mixing time of a generalization of Glauber dynamics where multiple links update their states in parallel and the fugacity of each link can be different. The results are used to prove that the average queue length (and hence, the delay) under the parallel-Glauber-dynamics-based CSMA grows polynomially in the number of links for wireless networks with bounded-degree interference graphs when the arrival rate lies in a fraction of the capacity region. Other versions of adaptive CSMA can be analyzed similarly.

In this paper, we derive new bounds on the throughput efficiency of Greedy Maximal Scheduling (GMS) for wireless networks of arbitrary topology under the generalk-hop interference model. These results improve the known bounds for networks with up to 26 nodes under the 2-hop interference model. We also prove that GMS is throughput-optimal in small networks. In particular, we show that GMS achieves 100% throughput in networks with up to eight nodes under the 2-hop interference model. Furthermore, we provide a simple proof to show that GMS can be implemented using only local neighborhood information in networks of any size.

Due to its low complexity, Greedy Maximal Scheduling (GMS), also known as Longest Queue First (LQF), has been studied extensively for wireless networks. However, GMS can result in degraded throughput performance in general wireless networks. In this paper, we prove that GMS achieves 100% throughput in all networks with eight nodes or less, under the two-hop interference model. Further, we obtain performance bounds that improve upon previous results for larger networks up to a certain size. We also provide a simple proof to show that GMS can be implemented using only local neighborhood information in networks of any size.