818 research outputs found

    A brokerage system for enhancing wireless access

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    This paper contributes to the management of a network infrastructure formed by distinct wireless access technologies, which are administered by several cooperating mobile operators. These wireless technologies may cover a public area, which at specific times of the day are overwhelmed by a large number of users. A new management solution is proposed that controls the heterogeneous network infrastructure in a distributed way, using policies and metrics, and ensuring a Quality of Service (QoS) level associated with each terminal connection. The QoS level is supported through a novel, vertical and dynamic aggregation of performance information about the wireless access, originated at distinct technologies. A closed innovative control loop among a flexible brokerage service in the network, and agents at the mobile terminals, counteracts any abnormal data load. This allows the terminals to make well-informed decisions about their connections to improve on the QoS offered to the application layer. In this way, depending on the management policies of the brokerage service and the quality metrics, wireless access technologies that by default only offer a best-effort connection service can be enhanced in a very straightforward way. The obtained results highlight the advantages for using this new distributed solution to manage the heterogeneous network infrastructure in several distinct usage scenarios.info:eu-repo/semantics/acceptedVersio

    A Multi-Objective Approach for Multi-Cloud Infrastructure Brokering in Dynamic Markets

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    Cloud Service Brokers (CSBs) simplify complex resource allocation decisions, efficiently linking up the tenant dynamic requirements in to providers dynamic offers, where several objectives should ideally be considered. Nowadays, both demands and offers should be considered in dynamic environments, representing particular challenges in cloud computing markets. This work proposes for the first time a pure multiobjective formulation of a broker-oriented Virtual Machine Placement (VMP) problem for dynamic environments, simultaneously optimizing following objective functions: (1) Total Infrastructure CPU (TICPU), (2) Total Infrastructure Memory (TIMEM) and (3) Total Infrastructure Price (TIP) subject to load balancing across providers. To solve the formulated multi-objective problem, a Multi-Objective Evolutionary Algorithm (MOEA) is proposed. When a change arises in the demands or in the offers, a set of non-dominated solutions is found (usually more than one solution), selection strategies were considered in order to automatically select a solution at each reconfiguration. The proposed MOEA and selection strategies, were compared in different scenarios composed by real data from providers in actual markets. Experimental results demonstrate the good quality of the obtained solutions for the proposed scenarios.Sociedad Argentina de Informática e Investigación Operativa (SADIO

    Peer-to-peer and community-based markets: A comprehensive review

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    The advent of more proactive consumers, the so-called "prosumers", with production and storage capabilities, is empowering the consumers and bringing new opportunities and challenges to the operation of power systems in a market environment. Recently, a novel proposal for the design and operation of electricity markets has emerged: these so-called peer-to-peer (P2P) electricity markets conceptually allow the prosumers to directly share their electrical energy and investment. Such P2P markets rely on a consumer-centric and bottom-up perspective by giving the opportunity to consumers to freely choose the way they are to source their electric energy. A community can also be formed by prosumers who want to collaborate, or in terms of operational energy management. This paper contributes with an overview of these new P2P markets that starts with the motivation, challenges, market designs moving to the potential future developments in this field, providing recommendations while considering a test-case

    On Allocation Policies for Power and Performance

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    With the increasing popularity of Internet-based services and applications, power efficiency is becoming a major concern for data center operators, as high electricity consumption not only increases greenhouse gas emissions, but also increases the cost of running the server farm itself. In this paper we address the problem of maximizing the revenue of a service provider by means of dynamic allocation policies that run the minimum amount of servers necessary to meet user's requirements in terms of performance. The results of several experiments executed using Wikipedia traces are described, showing that the proposed schemes work well, even if the workload is non-stationary. Since any resource allocation policy requires the use of forecasting mechanisms, various schemes allowing compensating errors in the load forecasts are presented and evaluated.Comment: 8 pages, 11 figures, 2010 11th IEEE/ACM International Conference on Grid Computing (GRID), pp 313 - 320 (E2GC2-2010 workshop

    Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

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    The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis

    Spatial Big Data Analytics: The New Boundaries of Retail Location Decision-Making

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    This dissertation examines the current state and evolution of retail location decision-making (RLDM) in Canada. The major objectives are: (i) To explore the type and scale of location decisions that retail firms are currently undertaking; (ii) To identify the availability and use of technology and Spatial Big Data (SBD) within the decision-making process; (iii) To identify the awareness, availability, use, adoption and development of SBD; and, (iv) To assess the implications of SBD in RLDM. These objectives were investigated by using a three stage multi-method research process. First, an online survey of retail location decision makers across a range of sizes and sub-sectors was administered. Secondly, structured interviews were conducted with 24 retail location decision makers, and lastly, three in-depth cases studies were undertaken in order to highlight the changes to RLDM over the last decade and to develop a deeper understanding of RLDM. This dissertation found that within the last decade RLDM changed in three main ways: (i) There has been an increase in the availability and use of technology and SBD within the decision-making process; (ii) The type and scale of location decisions that a firm undertakes remain relatively unchanged even with the growth of new data; and, (iii) The range of location research methods that are employed within retail firms is only just beginning to change given the presence of new data sources and data analytics technology. Traditional practices still dominate the RLDM process. While the adoption of SBD applications is starting to appear within retail planning, they are not widespread. Traditional data sources, such as those highlighted in past studies by Hernandez and Emmons (2012) and Byrom et al. (2001) are still the most commonly used data sources. It was evident that at the heart of SBD adoption is a data environment that promotes transparency and a clear corporate strategy. While most retailers are aware of the new SBD techniques that exist, they are not often adopted and routinized
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