Stefano Paris

A distributed demand-side management framework for the smart grid

Abstract This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users. In particular, we use a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. We consider two practical cases: (1) a fully distributed approach, where each appliance decides autonomously its own scheduling, and (2) a hybrid approach, where each user must schedule all his appliances. We analyze numerically these two approaches, showing that they are characterized practically by the same performance level in all the considered grid scenarios. We model the proposed system using a non-cooperative game theoretical approach, and demonstrate that our game is a generalized ordinal potential one under general conditions. Furthermore, we propose a simple yet effective best response strategy that is proved to converge in a few steps to a pure Nash Equilibrium, thus demonstrating the robustness of the power scheduling plan obtained without any central coordination of the operator or the customers. Numerical results, obtained using real load profiles and appliance models, show that the system-wide peak absorption achieved in a completely distributed fashion can be reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to meet the growing energy demand.

Cross-Technology Interference Mitigation in Body Area Networks: An Optimization Approach

In recent years, wearable devices and wireless body area networks have gained momentum as a means to monitor peoples behavior and simplify their interaction with the surrounding environment, thus representing a key element of the body-to-body networking (BBN) paradigm. Within this paradigm, several transmission technologies, such as 802.11 and 802.15.4, that share the same unlicensed band (namely, the industrial, scientific, and medical band) coexist, dramatically increasing the level of interference and, in turn, negatively affecting network performance. In this paper, we analyze the cross-technology interference (CTI) caused by the utilization of different transmission technologies that share the same radio spectrum. We formulate an optimization model that considers internal interference, as well as CTI to mitigate the overall level of interference within the system, explicitly taking into account node mobility. We further develop three heuristic approaches to efficiently solve the interference mitigation problem in large-scale network scenarios. Finally, we propose a protocol to compute the solution that minimizes CTI in a distributed fashion. Numerical results show that the proposed heuristics represent efficient and practical alternatives to the optimal solution for solving the CTI mitigation (CTIM) problem in large-scale BBN scenarios.

An Efficient Auction-based Mechanism for Mobile Data Offloading

The opportunistic utilization of third party WiFi access devices to offload customer traffic from the mobile network has recently gained momentum as a promising approach to increase the network capacity and simultaneously reduce the energy consumption of the radio access network (RAN) infrastructure. To foster the opportunistic utilization of unexploited Internet connections, we propose a new and open market where a mobile operator can lease the bandwidth made available by third parties (residential users or private companies) through their access points to increase dynamically (and adaptively) the network capacity. We formulate the offloading problem as a reverse auction considering the most general case of partial covering of the traffic to be offloaded. We discuss the conditions (i) to offload the maximum amount of data traffic according to the capacity made available by third party access devices, (ii) to foster the participation of access point owners (individual rationality), and (iii) to prevent market manipulation (incentive compatibility). Finally, we propose three alternative greedy algorithms that efficiently solve the offloading problem, even for large-size network scenarios.

Efficient and truthful bandwidth allocation in wireless mesh community networks

Nowadays, the maintenance costs of wireless devices represent one of the main limitations to the deployment of wireless mesh networks (WMNs) as a means to provide Internet access in urban and rural areas. A promising solution to this issue is to let the WMN operator lease its available bandwidth to a subset of customers, forming a wireless mesh community network, in order to increase network coverage and the number of residential users it can serve. In this paper, we propose and analyze an innovative marketplace to allocate the available bandwidth of a WMN operator to those customers who are willing to pay the higher price for the requested bandwidth, which in turn can be subleased to other residential users. We formulate the allocation mechanism as a combinatorial truthful auction considering the key features of wireless multihop networks and further present a greedy algorithm that finds efficient and fair allocations even for large-scale, real scenarios while maintaining the truthfulness property. Numerical results show that the greedy algorithm represents an efficient, fair, and practical alternative to the combinatorial auction mechanism.

A power scheduling game for reducing the peak demand of residential users

Smart Grids have recently gained increasing attention as a means to efficiently manage the houses energy consumption in order to reduce their peak absorption, thus improving the performance of power generation and distribution systems. In this paper, we propose a fully distributed Demand Management System especially tailored to reduce the peak demand of a group of residential users. We model such system using a game theoretical approach; in particular, we propose a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. In such scenario, multiple selfish users select the cheapest time slots (minimizing their daily bill) while satisfying their energy requests. We theoretically show that our game is potential, and propose a simple yet effective best response strategy that converges to a Pure Nash Equilibrium, thus proving the robustness of the power scheduling plan obtained without any central coordination of the operator. Numerical results, obtained using real energy consumption traces, show that the system-wide peak absorption achieved in a completely distributed fashion can be reduced up to 20%, thus decreasing the CAPEX necessary to meet the growing energy demand.

Bandwidth and Cache Leasing in Wireless Information-Centric Networks: A Game-Theoretic Study

Information-centric networking (ICN) is a novel paradigm that aims at improving the performance of todays Internet by supporting caching and multicast content delivery on every network device. The main contribution of this paper is to propose a centralized strategy to stimulate third parties to jointly lease the unused bandwidth and storage available on wireless access points (APs) in an ICN. We formulate this problem as a combinatorial reverse auction run by a content provider (CP) willing to increase the number of users reached by its service. We show that the optimal allocation with partial coverage problem is NP-hard, provide greedy heuristics that guarantee the individual rationality and truthfulness properties, and compare their performance numerically. We evaluate the benefits of our proposed mechanisms in terms of the cost savings for the CP obtained by offloading its infrastructure through the caches and the reduced computational time to execute the allocation algorithms. We compare the results obtained in this centralized setting with those that can be observed when the mobile clients autonomously choose which AP they prefer to use, among those activated by the auction mechanism. We model this second scenario as a congestion game, showing that it exhibits the desired properties (i.e., existence and uniqueness of a Nash equilibrium) and by comparing its social welfare with the centralized case.

Controlling flow reconfigurations in SDN

Software-Defined Network (SDN) controllers include mechanisms to globally reconfigure the network in order to respond to a changing environment. While iterative methods are employed to solve flow optimization problems, demands arrive or leave the system changing the optimization instance and requiring further iterations. In this paper, we focus on the general class of iterative solvers considering an exponential decrease over time in the optimality gap. Assuming dynamic arrivals and departures of demands, the computed optimality gap at each iteration Q(t) is described by an auto-regressive stochastic process. At each time slot the controller may choose to apply the current iteration to the network or not. Applying the current iteration improves the optimality gap but requires flow reconfiguration which hurts QoS and system stability. To limit the reconfigurations, we propose two control policies that minimize the flow allocation cost while respecting a network reconfiguration budget. We validate our model by experimenting with a realistic network setting and using standard Linear Programming tools used in the SDN industry. We show that our policies provide a practical means of keeping the optimally gap small within a given reconfiguration constraint.

Optimization Models for Congestion Mitigation in Virtual Networks

Virtualization of network functions and services can significantly reduce capital and operational expenditures of telecommunication operators through the sharing of a single network infrastructure. However, the utilization of the same resources can increase their congestion due to the spatio-temporal correlation of traffic demands and computational loads. In this paper, we propose novel orchestration mechanisms to optimally control and reduce the resource congestion of a physical infrastructure based on the NFV paradigm. In particular, we formulate the network functions composition problem as a nonlinear optimization model to accurately capture the congestion of the physical resources. In order to meet both efficiency and load balancing goals of the physical operator, we introduce two variants of such model to minimize the total and the maximum congestion in the network. Our models allow us to efficiently compute the optimal solution in a short computing time. Numerical results, obtained with real ISP topologies and network instances, show that the proposed approach represents an efficient and practical solution to control the congestion in virtual networks. Furthermore, they indicate that a holistic approach that optimizes the virtual system by jointly considering all elements/components would further improve the performance.

Cross Technology Interference Mitigation in Body-to-Body Area Networks

In recent years, Body-to-Body Networks (BBNs) have gained momentum as a means to monitor people behavior and simplify their interaction with the surrounding environment; thus representing a key element of the Internet of Things (IoT) networking paradigm. Within BBNs, several transmission technologies sharing the same unlicensed band (namely the ISM band) coexist, increasing dramatically the level of interference, which in turn negatively affects the network performance. In this paper, we consider an IoT system composed of several BBNs and we analyze the Cross Technology Interference (CTI) problem caused by the utilization of different transmission technologies that share the same radio spectrum. We formulate an optimization model considering both the Mutual and Cross Technology Interference in order to mitigate the overall level of interference within the IoT system, taking explicitly into account the node mobility. We further develop two heuristic approaches to solve efficiently the interference mitigation problem in large scale network scenarios. Numerical results show that the proposed heuristics represent two efficient and practical alternatives to the optimal solution for solving the CTI mitigation problem in large scale IoT scenarios.

Efficient Orchestration Mechanisms for Congestion Mitigation in NFV: Models and Algorithms

Network Functions Virtualization (NFV) has recently gained momentum among network operators as a means to share their physical infrastructure among virtual operators, which can independently compose and configure their communication services. However, the spatio-temporal correlation of traffic demands and computational loads can result in high congestion and low network performance for virtual operators, thus leading to service level agreement breaches. In this paper, we analyze the congestion resulting from the sharing of the physical infrastructure and propose innovative orchestration mechanisms based on both centralized and distributed approaches, aimed at unleashing the potential of the NFV technology. In particular, we first formulate the network functions composition problem as a non-linear optimization model to accurately capture the congestion of physical resources. To further simplify the network management, we also propose a dynamic pricing strategy of network resources, proving that the resulting system achieves a stable equilibrium in a completely distributed fashion, even when all virtual operators independently select their best network configuration. Numerical results show that the proposed approaches consistently reduce resource congestion. Furthermore, the distributed solution well approaches the performance that can be achieved using a centralized network orchestration system.