Getting the Most Out of Your VNFs: Flexible Assignment of Service Priorities in 5G

Through their computational and forwarding capabilities, 5G networks can support multiple vertical services. Such services may include several common virtual (network) functions (VNFs), which could be shared to increase resource efficiency. In this paper, we focus on the seldom studied VNF-sharing problem, and decide (i) whether sharing a VNF instance is possible/beneficial or not, (ii) how to scale virtual machines hosting the VNFs to share, and (iii) the priorities of the different services sharing the same VNF. These decisions are made with the aim to minimize the mobile operator's costs while meeting the verticals' performance requirements. Importantly, we show that the aforementioned priorities should not be determined a priori on a per-service basis, rather they should change across VNFs since such additional flexibility allows for more efficient solutions. We then present an effective methodology called FlexShare, enabling near-optimal VNF-sharing decisions in polynomial time. Our performance evaluation, using real-world VNF graphs, confirms the effectiveness of our approach, which consistently outperforms baseline solutions using per-service priorities

Beyond swarm intelligence: The Ultraswarm

This paper explores the idea that it may be possible to combine two ideas – UAV flocking, and wireless cluster computing – in a single system, the UltraSwarm. The possible advantages of such a system are considered, and solutions to some of the technical problems are identified. Initial work on constructing such a system based around miniature electric helicopters is described

The edge cloud: A holistic view of communication, computation and caching

The evolution of communication networks shows a clear shift of focus from just improving the communications aspects to enabling new important services, from Industry 4.0 to automated driving, virtual/augmented reality, Internet of Things (IoT), and so on. This trend is evident in the roadmap planned for the deployment of the fifth generation (5G) communication networks. This ambitious goal requires a paradigm shift towards a vision that looks at communication, computation and caching (3C) resources as three components of a single holistic system. The further step is to bring these 3C resources closer to the mobile user, at the edge of the network, to enable very low latency and high reliability services. The scope of this chapter is to show that signal processing techniques can play a key role in this new vision. In particular, we motivate the joint optimization of 3C resources. Then we show how graph-based representations can play a key role in building effective learning methods and devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing: Principles and Applications", P. Djuric and C. Richard Eds., Academic Press, Elsevier, 201

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Are there new models of computation? Reply to Wegner and Eberbach

Wegner and Eberbach[Weg04b] have argued that there are fundamental limitations to Turing Machines as a foundation of computability and that these can be overcome by so-called superTuring models such as interaction machines, the [pi]calculus and the $-calculus. In this paper we contest Weger and Eberbach claims

Addressing the Challenges in Federating Edge Resources

This book chapter considers how Edge deployments can be brought to bear in a global context by federating them across multiple geographic regions to create a global Edge-based fabric that decentralizes data center computation. This is currently impractical, not only because of technical challenges, but is also shrouded by social, legal and geopolitical issues. In this chapter, we discuss two key challenges - networking and management in federating Edge deployments. Additionally, we consider resource and modeling challenges that will need to be addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and Paradigms; Editors Buyya, Sriram

Active architecture for pervasive contextual services

International Workshop on Middleware for Pervasive and Ad-hoc Computing MPAC 2003), ACM/IFIP/USENIX International Middleware Conference (Middleware 2003), Rio de Janeiro, Brazil This work was supported by the FP5 Gloss project IST2000-26070, with partners at Trinity College Dublin and Université Joseph Fourier, and by EPSRC grants GR/M78403/GR/M76225, Supporting Internet Computation in Arbitrary Geographical Locations, and GR/R45154, Bulk Storage of XML Documents.Pervasive services may be defined as services that are available "to any client (anytime, anywhere)". Here we focus on the software and network infrastructure required to support pervasive contextual services operating over a wide area. One of the key requirements is a matching service capable of as-similating and filtering information from various sources and determining matches relevant to those services. We consider some of the challenges in engineering a globally distributed matching service that is scalable, manageable, and able to evolve incrementally as usage patterns, data formats, services, network topologies and deployment technologies change. We outline an approach based on the use of a peer-to-peer architecture to distribute user events and data, and to support the deployment and evolution of the infrastructure itself.Peer reviewe