Allocating resources to network slices can be achieved by means of solving virtual network embedding problems, whereby virtual nodes are used to reserve computing resources on cloud nodes, and virtual links are used to reserve bandwidth resources on network paths. Since the associated optimization problem is also NP-hard to approximate, in this paper we focus on a natural simplified setting of interest: the case where the tunnels can be embedded with splittable flows. For this problem, we provide a simple proof that it is NP-hard by a reduction from the 3-SAT problem. Further, using the idea of the multipartite graph, we propose a poly-time heuristic for the loose capacity constraint case, based on linear relaxation and randomized rounding. This heuristic is shown to have small optimality gaps in extensive simulations.

The ultimate goal of the Internet of Things (IoT) paradigm is to provide seamless connectivity of low power wireless devices to the Internet Protocol network. Cooperative Beamforming (CB) has been proposed as a technique that can enable low power wireless communication by allowing a collection of devices to transmit data simultaneously to a network aggregator in one hop. Apart from traditional CB approaches where all cooperating devices need to first share the data that need to be transmitted and then cooperate to transmit these data to the network aggregator (in a decode-and-forward (DF) or amplify-and-forward (AF) way), a recently proposed technique allows each device to transmit their own data while still achieving the benefits of cooperation. In this paper, we compare the performance of this disruptive “individual-data” technique against two traditional “same-data” techniques (DF and AF) in terms of required energy to achieve the same data rate and bit error probability over line-of-sight channels corrupted by additive white Gaussian noise. We evaluate the performance of these three CB schemes in a range of realistic system parameters and demonstrate that the “individual-data” technique is not only simpler in implementation but it also achieves higher energy efficiency in many practical scenarios.

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.

Cooperative beamforming (CB) has been proposed as a special case of coordinated multi-point techniques in wireless communications. In wireless sensor networks, CB can enable low power communication by allowing a collection of sensor nodes to transmit data simultaneously to a distant fusion center in one hop. Besides the traditional CB approach where all nodes need to share and transmit the same data, a more recent technique allows each node to transmit its own data while still achieving the benefits of cooperation. However, the intricacies of varying beamforming gains in the direct sequence spread spectrum with binary frequency shift keying multiple access scheme used in this context need to be taken into account when evaluating the performance of this beamforming technique. In this paper, we take the first step towards a more comprehensive understanding of this individual-data CB technique by proposing a best suited decoding scheme and analyzing its bit error rate (BER) performance over an additive white Gaussian noise channel. Through analytical expressions and simulation results BER curves are drawn and the achieved performance improvement offered by the CB gain is quantified.

In this paper we are investigating the capacity of Mobile Ad Hoc Networks under a concurrent transmission constraint imposed by common MAC layer protocols (such as 802.11). This problem is known to be an NP-hard combinatorial problem and a greedy algorithm has been proposed for solving it in polynomial time. We propose an efficient algorithm for solving the exact problem and compare the exact and approximate solutions for a variety of network sizes and node distributions (resulting from commonly used mobility models). Our findings confirm that the greedy solution provides a good approximation of the exact solution even for small network sizes. Based on the exact solution, we also derive an empirical formula that relates the capacity optimizing transmission range to the number of wireless nodes uniformly distributed in a square unit area.

This paper focuses on next generation ubiquitous networks supporting the Future Internet. In this context, it proposes an architecture and an integration framework of wireless and wired network technologies supporting a variety of services with differing service requirements. More specifically the i

A well established principle in the theory of wireless ad hoc networks with randomly moving nodes states that mobility increases the capacity of such networks at the expense of increased packet delivery delays. In this paper we explore the more practical problem of taking advantage of mobility to reduce source-destination path lengths (and therefore increase network throughput) in networks that can tolerate delays up to a certain finite value. We propose an optimal stopping rule policy to select the optimal time to transmit packets based on current shortest path length information and a stochastic model of the connectivity dynamics. Simulation results that illustrate the validity of this approach are presented and the dependence of the performance gain on the network parameters is analyzed.

Although individual node cooperation is necessary for the correct execution of network protocols in mobile ad hoc networks (MANETs), it is not always guaranteed. In this paper, we present a node reputation scheme aiming at reinforcing node cooperation in MANETs with centralized control. This scheme was designed for centralized ad hoc network architecture (CANA), an ad hoc enhancement to the HIPERLAN/2 WLAN standard. Misbehavior detection techniques for protocol attacks in both the cluster formation and data transmission phases of the network operation are developed. Statistical methods for selecting the optimal parameters of the reputation scheme are investigated and their efficiency is illustrated through theoretical analysis and simulation results. Throughout this paper, the specific aspects of CANA that impose particular design decisions are outlined and the applicability of our scheme to other network architectures is discussed.

We develop an importance sampling technique that can be used to speed up the simulation of a model of a buffered communication multiplexer fed by a large number of independent sources. The sources generate traffic according to a periodic function with a random phase. This traffic model accommodates a wide range of situations of practical interest, including ON-OFF periodic traffic models and sequences of bit rates generated by actual variable bit rate sources, such as MPEG video compressors. The simulation seeks to obtain estimates for the buffer overflow probability that in most cases of interest is very small. We use a large deviations result to devise the change of measure used in the importance sampling technique and demonstrate through numerical results that this change of measure leads to a dramatic reduction in the required simulation time over direct Monte Carlo simulation. Possible practical applications include short-term network resource planning and even real-time call admission control.

We propose estimators of the buffer overflow probability in queues fed by a Markov-modulated input process and serviced by an autocorrelated service process. These estimators are based on large-deviations asymptotics for the overflow probability. We demonstrate that the proposed estimators are less likely to underestimate the overflow probability than the estimator obtained by certainty equivalence. As such, they are appropriate in situations where the overflow probability is associated with quality of service (QoS) and we need to provide firm QoS guarantees. We also show that as the number of observations increases to infinity the proposed estimators converge with probability one to the appropriate target, and thus, do not lead to underutilization of the system in this limit.

We adopt a formalism to describe protocols that is close to the human way of thinking and can be easily used to perform reachability analysis of the described protocol in a state-transition format. This formalism allows for an execution tree (ET) to be generated from a set of assertions such that all paths from the root to the leaves are well-defined formulas. We then extend the formalism with regards to real-time properties. Finally, we present a software verification tool, Verify, that implements the above features in the analysis of protocols.