Multi-commodity network flow models for dynamic energy management – Mathematical formulation

Abstract The evolution of energy infrastructures towards a more distributed, adaptive, predictive and marketbased paradigm implies an effort on combining communication protocols and energy transmission and distribution systems in a common architecture. This architecture should allow decentralized control in order to be able to manage efficiently distributed generation, storage and exchange of energy between sources and sinks. Dynamic energy management models are a part of this "systems thinking" vision that aims to create a new field of applications that is at the intersection of computing science and energy technology. The broader implications associated with them are related with the possibility of creating communities that integrate energy supply and demand within a given region, in order to limit their impact. In order to push intelligence to the energy networks' edges, up to individual sources and sinks, scalable and flexible distributed systems will have to be build. In this sense, data mining techniques and multicommodity network flow models can be combined for pattern detection, forecasting and optimization, which are essential features of dynamic energy management

Integration of decentralized economic models for resource self-management in application layer networks

Resource allocation is one of the challenges for self-management of large scale distributed applications running in a dynamic and heterogeneous environment. Considering Application Layer Networks (ALN) as a general term for such applications including computational Grids, Content Distribution Networks and P2P applications, the characteristics of the ALNs and the environment preclude an efficient resource allocation by a central instance. The approach we propose integrates ideas from decentralized economic models into the architecture of a resource allocation middleware, which allows the scalability towards the participant number and the robustness in very dynamic environments. At the same time, the pursuit of the participants for their individual goals should benefit the global optimization of the application. In this work, we describe the components of this middleware architecture and introduce an ongoing prototype.Peer Reviewe

Adaptive secure network model for dynamic wireless mesh network

University of Technology Sydney. Faculty of Engineering and Information Technology.We as an advanced civilization rely on communication networks for a lot of important tasks. They are used to share information between vital systems, provide us with our pin-point location, access various digital resources and to stay connected with each other. Due to its necessity and enormity, maintaining and securing such a communication medium is an important task. As most communication networks rely on centralized systems, they are bound by the control of a central entity and are unable to keep up with the current growth of the network and advancements in electronic devices. The next step in an inter-connected world requires a decentralized distributed system that can also provide high levels of security. One possible solution is a dynamic distributed wireless mesh network as it provides all the features of a traditional network along with the flexibility of wireless communication and an infrastructure less distributed setup. The network can be created by connecting mobile or stationary devices together using wireless communication devices (such as smartphones, laptops, hot-spots, etc). As the network is created by multiple devices, it would not break-down if some of the devices were disabled. On the contrary, as the network uses hopping for message transmission using dynamic routes, it can self-heal by creating alternate routes if a device was to fail. As the workings and features of a dynamic mesh network differ from the traditional network, it also requires a modified security framework that can provide high levels of security whilst taking benefit of the dynamic mesh network’s unique features. This thesis investigates the problems and limitations linked to secure dynamic wireless mesh networks and how they can be improved upon. In addition to the routing protocols used and how they can be improved upon, the thesis also elaborates on the various security concerns with such networks. As distributed networks aren’t dependent on a central entity, enabling various security features such as authentication are a major challenge. In addition to the decentralized nature of the networks, a single security scheme would not be able to cover the various types of requirements a given scenario in the network might have. Along with authentication, providing end-to-end encryption is also an important component towards ensuring the data travelling through the network is secure and not tampered with. Encryption is also essential in a dynamic wireless mesh network as the data transmitted travels through multiple devices on the network before reaching the destination node and can be easily compromised if not secured. With such an importance of encryption, the network also requires a key management and distribution framework. As traditional network uses a centralized system for maintaining and distributing cryptographic keys in the network, it is a big challenge to implement the same in a distributed network with minimal dependence on a central entity. The key exchange must consider the nature of the network and accordingly incorporate improvements to be able to function in a distributed network. This thesis explores the above areas to propose a new network model for a secure dynamic wireless mesh network including a new routing scheme and a security framework comprising a hybrid encryption scheme, a hybrid authentication scheme and an improved key exchange and management scheme. This thesis demonstrates that our solutions not only strengthen and secure the dynamic wireless mesh networks but also significantly improve the performance and efficiency as compared to existing approaches

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers. The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs. The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market. The focus of this article is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer's level and for improving the resilience of smart grids. Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information. The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark. The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user's tricks

A decentralized framework for cross administrative domain data sharing

Federation of messaging and storage platforms located in remote datacenters is an essential functionality to share data among geographically distributed platforms. When systems are administered by the same owner data replication reduces data access latency bringing data closer to applications and enables fault tolerance to face disaster recovery of an entire location. When storage platforms are administered by different owners data replication across different administrative domains is essential for enterprise application data integration. Contents and services managed by different software platforms need to be integrated to provide richer contents and services. Clients may need to share subsets of data in order to enable collaborative analysis and service integration. Platforms usually include proprietary federation functionalities and specific APIs to let external software and platforms access their internal data. These different techniques may not be applicable to all environments and networks due to security and technological restrictions. Moreover the federation of dispersed nodes under a decentralized administration scheme is still a research issue. This thesis is a contribution along this research direction as it introduces and describes a framework, called \u201cWideGroups\u201d, directed towards the creation and the management of an automatic federation and integration of widely dispersed platform nodes. It is based on groups to exchange messages among distributed applications located in different remote datacenters. Groups are created and managed using client side programmatic configuration without touching servers. WideGroups enables the extension of the software platform services to nodes belonging to different administrative domains in a wide area network environment. It lets different nodes form ad-hoc overlay networks on-the-fly depending on message destinations located in distinct administrative domains. It supports multiple dynamic overlay networks based on message groups, dynamic discovery of nodes and automatic setup of overlay networks among nodes with no server-side configuration. I designed and implemented platform connectors to integrate the framework as the federation module of Message Oriented Middleware and Key Value Store platforms, which are among the most widespread paradigms supporting data sharing in distributed systems

Using Group Management to Tame Mobile Ad Hoc Networks

International audienceMobile ad hoc networks (MANET) offer a convenient basis towards pervasive computing, due to inherent support for anytime, anywhere network access for mobile users. However, the development of applications over MANET still raises numerous challenges. One such challenge relates to accommodating the high dynamics of the network's topology. Group management appears as a promising paradigm to ease the development of distributed applications over dynamic, mobile networks. Specifically, group management takes care of assembling mobile nodes that together allow to meet target functional and nonfunctional properties, and of further making transparent failures due to the mobility of nodes. Various solutions towards group management over MANET have been investigated over the last couple of years, each targeting specific applications. Building upon such an effort, this paper introduces the design and implementation of a group service for MANET, which is generic with respect to the various attributes of relevance. Generic group management allows supporting various applications, as illustrated through groups dedicated to mobile collaborative data sharing

Improving Computer Network Operations Through Automated Interpretation of State

Networked systems today are hyper-scaled entities that provide core functionality for distributed services and applications spanning personal, business, and government use. It is critical to maintain correct operation of these networks to avoid adverse business outcomes. The advent of programmable networks has provided much needed fine-grained network control, enabling providers and operators alike to build some innovative networking architectures and solutions. At the same time, they have given rise to new challenges in network management. These architectures, coupled with a multitude of devices, protocols, virtual overlays on top of physical data-plane etc. make network management a highly challenging task. Existing network management methodologies have not evolved at the same pace as the technologies and architectures. Current network management practices do not provide adequate solutions for highly dynamic, programmable environments. We have a long way to go in developing management methodologies that can meaningfully contribute to networks becoming self-healing entities. The goal of my research is to contribute to the design and development of networks towards transforming them into self-healing entities. Network management includes a multitude of tasks, not limited to diagnosis and troubleshooting, but also performance engineering and tuning, security analysis etc. This research explores novel methods of utilizing network state to enhance networking capabilities. It is constructed around hypotheses based on careful analysis of practical deficiencies in the field. I try to generate real-world impact with my research by tackling problems that are prevalent in deployed networks, and that bear practical relevance to the current state of networking. The overarching goal of this body of work is to examine various approaches that could help enhance network management paradigms, providing administrators with a better understanding of the underlying state of the network, thus leading to more informed decision-making. The research looks into two distinct areas of network management, troubleshooting and routing, presenting novel approaches to accomplishing certain goals in each of these areas, demonstrating that they can indeed enhance the network management experience

Synergies Between Federated Learning and O-RAN: Towards an Elastic Virtualized Architecture for Multiple Distributed Machine Learning Services

Federated learning (FL) is the most popular distributed machine learning technique. However, implementation of FL over modern wireless networks faces key challenges caused by (i) dynamics of the network conditions and (ii) the coexistence of multiple FL services/tasks and other network services in the system, which are not jointly considered in prior works. Motivated by these challenges, we introduce a generic FL paradigm over NextG networks, called dynamic multi-service FL (DMS-FL). We identify three unexplored design considerations in DMS-FL: (i) FL service operator accumulation, (ii) wireless resource fragmentation, and (iii) signal strength fluctuations. We take the first steps towards addressing these design considerations by proposing a novel distributed ML architecture called elastic virtualized FL (EV-FL). EV-FL unleashes the full potential of Open RAN (O-RAN) systems and introduces an elastic resource provisioning methodology to execute FL services. It further constitutes a multi-time-scale FL management system that introduces three dimensions into existing FL architectures: (i) virtualization, (ii) scalability, and (iii) elasticity. Through investigating EV-FL, we reveal a series of open research directions for future work. We finally simulate EV-FL to demonstrate its potential in saving wireless resources and increasing fairness among FL services.Comment: 8 pages, 6 figure