A genetic approach for long term virtual organization distribution

Electronic versíon of an article published as International Journal on Artificial Intelligent Tools, Volume 20, issue 2, 2011. 10.1142/S0218213011000152. © World Scientific Publishing Company[EN] An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios. © 2011 World Scientific Publishing Company.This work is supported by TIN2008-04446, TIN2009-13839-C03-01, CSD2007-00022 and FPU grant AP2008-00600 of the Spanish government, and PROMETEO 2008/051 of the Generalitat Valenciana.Sánchez Anguix, V.; Valero Cubas, S.; García Fornes, AM. (2011). A genetic approach for long term virtual organization distribution. International Journal on Artificial Intelligence Tools. 20(2):271-295. https://doi.org/10.1142/S0218213011000152S27129520

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

Relational Cloud: The Case for a Database Service

In this paper, we make the case for â databases as a serviceâ (DaaS), with two target scenarios in mind: (i) consolidation of data management functionality for large organizations and (ii) outsourcing data management to a cloud-based service provider for small/medium organizations. We analyze the many challenges to be faced, and discuss the design of a database service we are building, called Relational Cloud. The system has been designed from scratch and combines many recent advances and novel solutions. The prototype we present exploits multiple dedicated storage engines, provides high-availability via transparent replication, supports automatic workload partitioning and live data migration, and provides serializable distributed transactions. While the system is still under active development, we are able to present promising initial results that showcase the key features of our system. The tests are based on TPC benchmarks and real-world data from epinions.com, and show our partitioning, scalability and balancing capabilities

Capuchin Search Particle Swarm Optimization (CS-PSO) based Optimized Approach to Improve the QoS Provisioning in Cloud Computing Environment

This review introduces the methods for further enhancing resource assignment in distributed computing situations taking into account QoS restrictions. While resource distribution typically affects the quality of service (QoS) of cloud organizations, QoS constraints such as response time, throughput, hold-up time, and makespan are key factors to take into account. The approach makes use of a methodology from the Capuchin Search Particle Large Number Improvement (CS-PSO) apparatus to smooth out resource designation while taking QoS constraints into account. Throughput, reaction time, makespan, holding time, and resource use are just a few of the objectives the approach works on. The method divides the resources in an optimum way using the K-medoids batching scheme. During batching, projects are divided into two-pack assembles, and the resource segment method is enhanced to obtain the optimal configuration. The exploratory association makes use of the JAVA device and the GWA-T-12 Bitbrains dataset for replication. The outrageous worth advancement problem of the multivariable capacity is addressed using the superior calculation. The simulation findings demonstrate that the core (Cloud Molecule Multitude Improvement, CPSO) computation during 500 ages has not reached assembly repeatedly, repeatedly, repeatedly, and repeatedly, respectively.The connection analysis reveals that the developed model outperforms the state-of-the-art approaches. Generally speaking, this approach provides significant areas of strength for a successful procedure for improving resource designation in distributed processing conditions and can be applied to address a variety of resource segment challenges, such as virtual machine setup, work arranging, and resource allocation. Because of this, the capuchin search molecule enhancement algorithm (CSPSO) ensures the success of the improvement measures, such as minimal streamlined polynomial math, rapid consolidation speed, high productivity, and a wide variety of people

Review of SDN-based load-balancing methods, issues, challenges, and roadmap

The development of the Internet and smart end systems, such as smartphones and portable laptops, along with the emergence of cloud computing, social networks, and the Internet of Things, has brought about new network requirements. To meet these requirements, a new architecture called software-defined network (SDN) has been introduced. However, traffic distribution in SDN has raised challenges, especially in terms of uneven load distribution impacting network performance. To address this issue, several SDN load balancing (LB) techniques have been developed to improve efficiency. This article provides an overview of SDN and its effect on load balancing, highlighting key elements and discussing various load-balancing schemes based on existing solutions and research challenges. Additionally, the article outlines performance metrics used to evaluate these algorithms and suggests possible future research directions