On the schedulability of deadline-constrained traffic in TDMA Wireless Mesh Networks

In this paper, we evaluate the schedulability of traffic with arbitrary end-to-end deadline constraints in Wireless Mesh Networks (WMNs). We formulate the problem as a mixed integer linear optimization problem, and show that, depending on the flow aggregation policy used in the network, the problem can be either convex or non-convex. We optimally solve the problem in both cases, and prove that the schedulability does depend on the aggregation policy. This allows us to derive rules of thumb to identify which policy improves the schedulability with a given traffic. Furthermore, we propose a heuristic solution strategy that allows good suboptimal solutions to the scheduling problem to be computed in relatively small times, comparable to those required for online admission control in relatively large WMNs

Optimal joint routing and link scheduling for real-time traffic in TDMA Wireless Mesh Networks

We investigate the problem of joint routing and link scheduling in Time-Division Multiple Access (TDMA) Wireless Mesh Networks (WMNs) carrying real-time traffic. We propose a framework that always computes a feasible solution (i.e. a set of paths and link activations) if there exists one, by optimally solving a mixed integer-non linear problem. Such solution can be computed in minutes or tens thereof for e.g. grids of up to 4x4 nodes. We also propose heuristics based on Lagrangian decomposition to compute suboptimal solutions considerably faster and/or for larger WMNs, up to about 50 nodes. We show that the heuristic solutions are near-optimal, and we exploit them to investigate the optimal placement of one or more gateways from a delay bound perspective

Optimal joint routing and link scheduling for real-time traffic in TDMA Wireless Mesh Networks

We investigate the problem of joint routing and link scheduling in Time-Division Multiple Access (TDMA) Wireless Mesh Networks (WMNs) carrying real-time traffic. We propose a framework that always computes a feasible solution (i.e. a set of paths and link activations) if there exists one, by optimally solving a mixed integer-non linear problem. Such solution can be computed in minutes or tens thereof for e.g. grids of up to 4x4 nodes. We also propose heuristics based on Lagrangian decomposition to compute suboptimal solutions considerably faster and/or for larger WMNs, up to about 50 nodes. We show that the heuristic solutions are near-optimal, and we exploit them to gain insight on the schedulability in WMN, i.e. to investigate the optimal placement of one or more gateways from a delay bound perspec-tive, and to investigate how the schedulability is affected by the transmission range

An experimental study on latency-aware and self-adaptive service chaining orchestration in distributed NFV and SDN infrastructures

Network Function Virtualization (NFV) and Software Defined Networking (SDN) changed radically the way 5G networks will be deployed and services will be delivered to vertical applications (i.e., through dynamic chaining of virtualized functions deployed in distributed clouds to best address latency requirements). In this work, we present a service chaining orchestration system, namely LASH-5G, running on top of an experimental set-up that reproduces a typical 5G network deployment with virtualized functions in geographically distributed edge clouds. LASH-5G is built upon a joint integration effort among different orchestration solutions and cloud deployments and aims at providing latency-aware, adaptive and reliable service chaining orchestration across clouds and network resource domains interconnected through SDN. In this paper, we provide details on how this orchestration system has been deployed and it is operated on top of the experimentation infrastructure provided within the Fed4FIRE+ facility and we present performance results assessing the effectiveness of the proposed orchestration approach

The quadratic balanced optimization problem

We introduce the quadratic balanced optimization problem (QBOP) which can be used to model equitable distribution of resources with pairwise interaction. QBOP is strongly NP-hard even if the family of feasible solutions has a very simple structure. Several general purpose exact and heuristic algorithms are presented. Results of extensive computational experiments are reported using randomly generated quadratic knapsack problems as the test bed. These results illustrate the efficacy of our exact and heuristic algorithms. We also show that when the cost matrix is specially structured, QBOP can be solved as a sequence of linear balanced optimization problems. As a consequence, we have several polynomially solvable cases of QBOP