A distributed auctioneer for resource allocation in decentralized systems

In decentralized systems, nodes often need to coordinate to access shared resources in a fair manner. One approach to perform such arbitration is to rely on auction mechanisms. Although there is an extensive literature that studies auctions, most of these works assume the existence of a central, trusted auctioneer. Unfortunately, in fully decentralized systems, where the nodes that need to cooperate operate under separate spheres of control, such central trusted entity may not exist. Notable examples of such decentralized systems include community networks, clouds of clouds, cooperative nano data centres, among others. In this paper, we make theoretical and practical contributions to distribute the role of the auctioneer. From the theoretical perspective, we propose a framework of distributed simulations of the auctioneer that are Nash equilibria resilient to coalitions and asynchrony. From the practical perspective, our protocols leverage the distributed nature of the simulations to parallelise the execution. We have implemented a prototype that instantiates the framework for bandwidth allocation in community networks, and evaluated it in a real distributed setting.Peer ReviewedPostprint (author's final draft

Improving quality of service in the internet

The Internet transport technology was designed to be robust, resilient to link or node outages, and with no single point of failure. The resulting connectionless system supports what is called a "best effort datagram delivery service", the perfo rmance of which is often greatly unpredictable. To improve the predictability of IP-based networks, several Quality of Service technologies have been designed over the past decade. The first one, RSVP, based on reservation of resources, is operational but has several major deficiencies, such as scalability difficulties. However, associated to other more recent technologies -RSVP aggregation, Diffserv and MPLS- the combination may result into an appropriate solution for improving Quality of Service guarant ees in a scalable way. This article presents the state of the art on the field in an accurate, yet pedagogical style

Robust Energy Management for Green and Survivable IP Networks

Despite the growing necessity to make Internet greener, it is worth pointing out that energy-aware strategies to minimize network energy consumption must not undermine the normal network operation. In particular, two very important issues that may limit the application of green networking techniques concern, respectively, network survivability, i.e. the network capability to react to device failures, and robustness to traffic variations. We propose novel modelling techniques to minimize the daily energy consumption of IP networks, while explicitly guaranteeing, in addition to typical QoS requirements, both network survivability and robustness to traffic variations. The impact of such limitations on final network consumption is exhaustively investigated. Daily traffic variations are modelled by dividing a single day into multiple time intervals (multi-period problem), and network consumption is reduced by putting to sleep idle line cards and chassis. To preserve network resiliency we consider two different protection schemes, i.e. dedicated and shared protection, according to which a backup path is assigned to each demand and a certain amount of spare capacity has to be available on each link. Robustness to traffic variations is provided by means of a specific modelling framework that allows to tune the conservatism degree of the solutions and to take into account load variations of different magnitude. Furthermore, we impose some inter-period constraints necessary to guarantee network stability and preserve the device lifetime. Both exact and heuristic methods are proposed. Experimentations carried out with realistic networks operated with flow-based routing protocols (i.e. MPLS) show that significant savings, up to 30%, can be achieved also when both survivability and robustness are fully guaranteed

Framework For Effective Resilience Managmenet Of Complex Supply Networks

In today\u27s environment with high global and complex supply chains for engineered products, the ability to assess and manage the resilience of supply chains is not a luxury but a fundamental prerequisite for business continuity and success. This is particularly true for firms with deep-tier supply chains, such as the automotive original equipment manufacturers (OEMs) and their suppliers. Automotive supply networks are particularly facing growing challenges due to their complexity, globalization, economic volatility, rapidly changing technologies, regulations, and environmental/political shocks. These risks and challenges can disrupt and halt operations in any section of the supply network. Given that supply chains have become quite lean in the 21st century with relatively little slack, the COVID-19 pandemic has fully exposed these vulnerabilities. According to Allianz\u27s Business Risk Report from 2014, half of all supply chain disruptions stemming from tier-2 and tier-3 suppliers. However, the industry\u27s supply network assessment practice is primarily limited to immediate (i.e., tier-1 ) suppliers with no real consideration for the deep-tiers. The added complication due to poor supplier relations is that there is no visibility to the upstream deeper-tiers of the supply network, which could lead to severe vulnerabilities and impose massive disruption costs. Our research goal is to enhance the resilience of deep-tier automotive supply networks through improved resilience assessment and management mechanisms. In this collaborative study with a global automotive OEM (Ford Motor Company), we seek to develop methods to assess and manage the resilience of deep-tier supply networks. This research considers the multi-dimensional nature of resilience management, focusing on metrics around cost efficiency, effective inventory management, demand fulfillment, capacity management, and delivery performance. We develop and evaluate our proposed resilience assessment and management framework with a real case study supply network for an automotive climate control system. The supply network contains 20 firms (nodes) located in various global regions and 21 connections (edges) between firms. The network includes three tiers of suppliers with different transportation modes, making the network a rich illustrative example for proposed resilience assessment and management methods and analysis. All inventory and shipping policies with related parameters have been defined and set for each supplier and their connections. The proposed resilience assessment framework relies on discrete-event simulation for effectiveness; computational efficiency is maintained by relying on modern open-source packages for modeling, optimization, and analysis. The framework starts by generating a digital supply network model that includes the focal firm and its suppliers and deeper-tiers based on the available visibility. Disruption scenarios, including disruption sources, frequency, and severity, are then efficiently generated using private and public regional risk sources. For illustrative purposes, we primarily relied on public secondary data sources. The secondary regional risk indices that we relied upon aggregate political, economic, legal, operational, and security risks for the given region. Finally, the digital supply network is simulated with an adequate number of replications for reliable assessment. In this research, discrete-event simulation is implemented using NetworkX and SimPy Python packages. We employ the network analysis techniques combined with discrete-event simulation informed by secondary data sources for improving the assessment framework. Our resilience assessment results confirm that visibility into the deeper-tiers of the supply network (through primary or secondary data sources) leads to a more accurate network resilience assessment. Finally, we offer a global sensitivity analysis procedure to determine the supply network players, parameters, and policies that most influence the network performance. We also propose an effective resilience management framework that efficiently leverages simulation-based optimization. For illustrative purposes, we considered the mitigation strategies typical in the automotive industry, such as dual sourcing, reserve capacities (at primary or secondary suppliers), and contracts with backup suppliers besides carrying safety stock. Sourcing and transportation mode decisions can be easily incorporated into the framework. The method seeks to minimize the cost of risk mitigation strategies while attaining the target resilience. The framework is flexible and can entertain other objectives and constraints. Given that simulation-based optimization methods can be computationally expensive, we employ surrogate models that relate supply network resilience performance to network design parameters within our mathematical programming formulation. Without loss of generality, the surrogate models are based on linear regression models that define the relationship between the focal firm and tier-1 suppliers\u27 resilience levels and network design decision variables. The imperfections of the regression models are accounted for in the formulation through constraints with slack (function of the RMSE of the regression model). We demonstrate that optimal resilience management would stem from jointly allocating safety buffers (e.g., capacity, inventory levels) across the network and not by independently applying a simplistic/static set of rules for all nodes/arcs. Our validation experiments with a real-world case study informed by secondary data from public data sources confirm the effectiveness and efficiency of the proposed supply network resilience management method

Exploiting relocation to reduce network dimensions of resilient optical grids

Optical grids are widely deployed to solve complex problems we are facing today. An important aspect of the supporting network is resiliency i.e. the ability to overcome network failures. In contrast to classical network protection schemes, we will not necessarily provide a back-up path between the source and the original destination. Instead, we will try to relocate the job to another server location if this means that we can provide a backup path which comprises less wavelengths than the one the traditional scheme would suggest. This relocation can be backed up by the grid specific anycast principle: a user generally does not care where his job is executed and is only interested in its results. We present ILP formulations for both resilience schemes and we evaluate them in a case study on an European network topology

Fog Computing Based Radio Access Networks: Issues and Challenges

The fog computing based radio access networks work at the same time as capable worldview commencing to 5G remote transmitting framework give elevated unearthly along with vitality effectiveness. In center thought obtain complete points to interest of neighborhood radio flag preparing, consistent on radio asset administration and disseminated putting away abilities in edge gadgets, which can diminish the substantial weight on front haul. In light of fog computing, the cooperation radio flag handling (CRSP) cannot exclusively accomplish during the unified baseband unit into cloud radio access networks. Unfasten concern into the condition of software defined networking, network function virtualization and edge caching recognized. This paper attempts to minimize the security issues in the performance of edge cashing by using Markov chain model. Simulation results are able to reduce the bandwidth consumption of F_RAN through edge caching in between remote radio heads and user equipments

An insurance paradigm for improving power system resilience via distributed investment

Extreme events, exacerbated by climate change, pose significant risks to the energy system and its consumers. However there are natural limits to the degree of protection that can be delivered from a centralised market architecture. Distributed energy resources provide resilience to the energy system, but their value remains inadequately recognized by regulatory frameworks. We propose an insurance framework to align residual outage risk exposure with locational incentives for distributed investment. We demonstrate that leveraging this framework in large-scale electricity systems could improve consumer welfare outcomes in the face of growing risks from extreme events via investment in distributed energy