Self-optimising data farming for web applications

Many Web applications walk the thin line between the need for dynamic data and the need to meet user performance expectations. In environments where funds are not available to constantly upgrade hardware inline with user demand, alternative approaches need to be considered. This paper introduces a ‘Data farming’ model whereby dynamic data, which is ‘grown’ in operational applications, is ‘harvested’ and ‘packaged’ for various consumer markets. Like any well managed agricultural operation, crops are harvested according to historical and perceived demand as inferred by a self-optimising process. This approach aims to make enhanced use of available resources through better utlilisation of system downtime - thereby improving application performance and increasing the availability of key business data.

On the persistence of computer dreams: an application framework for robust adaptive deployment

The anticipated rewards of adaptive approaches will only be fully realised when autonomic algorithms can take configuration and deployment decisions that match and exceed those of human engineers. Such decisions are typically characterised as being based on a foundation of experience and knowledge. In humans, these underpinnings are themselves founded on the ashes of failure, the exuberance of courage and (sometimes) the outrageousness of fortune. In this paper we describe an application framework that will allow the incorporation of similarly risky, error prone and downright dangerous software artefacts into live systems – without undermining the certainty of correctness at application level. We achieve this by introducing the notion of application dreaming.

Generic support for policy-based self-adaptive systems

This paper presents a policy definition language which forms part of a generic policy toolkit for autonomic computing systems in which the policies themselves can be modified dynamically and automatically. Targeted enhancements to the current state of practice include: policy self-adaptation where the policy itself is dynamically modified to match environmental conditions; improved support for non autonomics-expert developers; and facilitating easy deployment of adaptive policies into legacy code. The policy definition language permits powerful expression of self-managing behaviours and facilitates a diverse policy behaviour space. Features include support for multiple versions of a given policy type, multiple configuration templates, and meta policies to dynamically select between policy instances. An example deployment scenario illustrates advanced functionality in the context of a multi policy stock trading system which is sensitive to environmental volatility.

Exploring adaptation & self-adaptation in autonomic computing systems

This panel paper sets out to discuss what self-adaptation means, and to explore the extent to which current autonomic systems exhibit truly self-adaptive behaviour. Many of the currently cited examples are clearly adaptive, but debate remains as to what extent they are simply following prescribed adaptation rules within preset bounds, and to what extent they have the ability to truly learn new behaviour. Is there a standard test that can be applied to differentiate? Is adaptive behaviour sufficient anyway? Other autonomic computing issues are also discussed.

Web-based course support systems: an essential tool for supporting flexible learning environment

In Higher Education web-based course support systems are essential for supporting flexible learning environments. They provide tools to enable the interaction between student and tutor to reinforce transfer of theory to understanding particularly in an academic environment, therefore this paper will examine issues associated with the use of curriculum and learning resources within Web-based course support systems and the effectiveness of the resulting flexible learning environments This paper is a general discussion about flexible learning and in this case how it was applied to one of the courses at undergraduate level one. The first section will introduce what is flexible learning and the importance of flexible learning in Higher Education followed by the description of the course and why the flexible learning concepts is important in such a course and finally, how the flexibility was useful for this particular instance.

Measuring inconsistency in network intrusion rules

In this preliminary case study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. Finally, we measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the informativeness of these measures. We conclude that such measures are useful for the network intrusion domain assuming that incorporating domain knowledge for correlation of rules is feasible.

Serum 25-hydroxyvitamin D and insulin resistance in people at high risk of cardiovascular disease: a euglycaemic hyperinsulinaemic clamp study

CONTEXT: In observational studies low serum 25-hydroxyvitamin D (25-OHD) concentration is associated with an increased risk of type 2 diabetes mellitus (DM). Increasing serum 25-OHD may have beneficial effects on insulin resistance or beta-cell function. Cross-sectional studies utilising sub-optimal methods for assessment of insulin sensitivity and serum 25-OHD concentration provide conflicting results.

OBJECTIVE: This study examined the relationship between serum 25-OHD concentration and insulin resistance in healthy overweight individuals at increased risk of cardiovascular disease, using optimal assessment techniques.

METHODS: 92 subjects (mean age 56.0, SD 6.0 years), who were healthy but overweight (mean BMI 30.9, SD 2.3 kg/m(2) ) underwent assessments of insulin sensitivity (two-step euglycaemic hyperinsulinaemic clamp, HOMA2-IR), beta-cell function (HOMA2%B), serum 25-OHD concentration and body composition (DEXA).

RESULTS: Mean total 25-OHD concentration was 32.2, range 21.8 - 46.6 nmol/L. No association was demonstrated between serum 25-OHD concentration and insulin resistance.

CONCLUSIONS: In this study using optimal assessment techniques to measure 25-OHD concentration, insulin sensitivity and body composition, there was no association between serum 25-OHD concentration and insulin resistance in healthy, overweight individuals at high risk of developing cardiovascular disease. This study suggests the documented inverse association between serum 25-OHD concentration and risk of type 2 DM is not mediated by a relationship between serum 25-OHD concentration and insulin resistance. 

Remuneration and tariffs in the context of virtual power players

Power systems have been through deep changes in recent years, namely with the operation of competitive electricity markets in the scope and the increasingly intensive use of renewable energy sources and distributed generation. This requires new business models able to cope with the new opportunities that have emerged. Virtual Power Players (VPPs) are a new player type which allows aggregating a diversity of players (Distributed Generation (DG), Storage Agents (SA), Electrical Vehicles, (V2G) and consumers), to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players` benefits. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. This paper proposes a model to implement fair and strategic remuneration and tariff methodologies, able to allow efficient VPP operation and VPP goals accomplishment in the scope of electricity markets.

Multi-agent simulation of continental, regional, and micro electricity markets

Renewable based power generation has significantly increased over the last years. However, this process has evolved separately from electricity markets, leading to an inadequacy of the present market models to cope with huge quantities of renewable energy resources, and to take full advantage of the presently existing and the increasing envisaged renewable based and distributed energy resources. This paper proposes the modelling of electricity markets at several levels (continental, regional and micro), taking into account the specific characteristics of the players and resources involved in each level and ensuring that the proposed models accommodate adequate business models able to support the contribution of all the resources in the system, from the largest to the smaller ones. The proposed market models are integrated in MASCEM (Multi- Agent Simulator of Competitive Electricity Markets), using the multi agent approach advantages for overcoming the current inadequacy and significant limitations of the presently existing electricity market simulators to deal with the complex electricity market models that must be adopted.

Adaptive tool for automatic data collection of real electricity markets

The study of electricity markets operation has been gaining an increasing importance in last years, as result of the new challenges that the electricity markets restructuring produced. This restructuring increased the competitiveness of the market, but with it its complexity. The growing complexity and unpredictability of the market’s evolution consequently increases the decision making difficulty. Therefore, the intervenient entities are forced to rethink their behaviour and market strategies. Currently, lots of information concerning electricity markets is available. These data, concerning innumerous regards of electricity markets operation, is accessible free of charge, and it is essential for understanding and suitably modelling electricity markets. This paper proposes a tool which is able to handle, store and dynamically update data. The development of the proposed tool is expected to be of great importance to improve the comprehension of electricity markets and the interactions among the involved entities.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. However, it is still necessary to adequately optimize the player’s portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

Multi-agent Simulation of Bilateral Contracting in Competitive Electricity Markets

Traditional vertically integrated power utilities around the world have evolved from monopoly structures to open markets that promote competition among suppliers and provide consumers with a choice of services. Market forces drive the price of electricity and reduce the net cost through increased competition. Electricity can be traded in both organized markets or using forward bilateral contracts. This article focuses on bilateral contracts and describes some important features of an agent-based system for bilateral trading in competitive markets. Special attention is devoted to the negotiation process, demand response in bilateral contracting, and risk management. The article also presents a case study on forward bilateral contracting: a retailer agent and a customer agent negotiate a 24h-rate tariff.

Realistic Multi-Agent Simulation of Competitive Electricity Markets

The dynamism and ongoing changes that the electricity markets sector is constantly suffering, enhanced by the huge increase in competitiveness, create the need of using simulation platforms to support operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents an enhanced electricity market simulator, based on multi-agent technology, which provides an advanced simulation framework for the study of real electricity markets operation, and the interactions between the involved players. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) uses real data for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations bring to different countries. Also, the development of an upper-ontology to support the communication between participating agents, provides the means for the integration of this simulator with other frameworks, such as MAN-REM (Multi-Agent Negotiation and Risk Management in Electricity Markets). A case study using the enhanced simulation platform that results from the integration of several systems and different tools is presented, with a scenario based on real data, simulating the MIBEL electricity market environment, and comparing the simulation performance with the real electricity market results.