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In this paper we address the problem of Data Centres (DCs) integration into the Smart Grid scenario by proposing a technique for scheduling and optimizing their operation allowing them to participate in Smart Demand Response programs. The technique is leveraging on DCs available flexible energy resources, on mechanisms for eliciting this latent flexibility and on an innovative electronic marketplace...
This paper tackles the issue of Data Centers (DCs) energy efficiency by proposing a Self-Adaptive Task Scheduler that proactively allocates incoming tasks to physical servers avoiding the need of consolidation and implicitly avoiding task migration from one server to another. The Self-Adaptive Task Scheduler is based on a MAPE architecture. The monitoring phase aggregates data about resource utilization,...
This paper addresses the problem of Data Centers (DC) energy efficiency by proposing a proactive optimization technique to schedule the day-ahead DC operation to minimize the operational cost. The proactive optimization technique is formalized as a Mixed Integer Optimal Control Problem, known to be NP-hard. Because the time needed for solving this problem by some of the gradient-based solvers depends...
Automatic recommendations based on skill matching techniques can prove to be an important component of an online recruitment platform, being able to lower the costs for employers, ease the process for candidates and increase the hiring quality overall. This is important nowadays, when online recruitment plays a major role in the hiring process. The main challenges in this area consist in providing...
This paper addresses the semantic gap between the domain knowledge and software application engineering by proposing a framework for mapping and integrating multiple heterogeneous data sources with application business logic by means of data semantic enrichment, aggregation, filtering and processing. Based on the main drawbacks identified in the current knowledge enhanced software application architectures,...
This paper addresses the problem of integrating ontological knowledge bases into complex software applications by proposing a library for exposing ontology access and manipulation as web services. The proposed framework is an extension of our previous work, the M2O framework, and enhances it by integrating reasoning techniques and generating a web services layer for performing basic CRUD operations...
This paper presents an analysis of the state of the art solutions for mapping a relational database and an ontology by adding reasoning capabilities and offering the possibility to query the inferred information. We analyzed four approaches: Jena with D2RQ, Jena with R2RML, KAON2 and OWL API. In order to highlight the differences between the four approaches, we used a nutrition diagnostics related...
In this paper we propose an extensible framework over Jena and OWL API that maps complex Java data models onto semantic models based on some custom annotations in order to benefit from the advantages of ontologies in software engineering. Furthermore, it facilitates the implementation of basic CRUD operations for the domain classes and objects, also allowing the definition of new custom operations...
In this paper we address the problem of Data Centers energy efficiency by proposing a methodology which aims at planning the Data Center operation such that the usage of locally produced renewable energy is maximized. We defined a flexibility mechanism and model for Data Center's components (electrical cooling system, IT workload, energy storage and diesel generators) leveraging on optimization actions...
In this paper we propose a new nature inspired algorithm for solving optimization problems: the Crab Mating Optimization Algorithm. The algorithm proposed is inspired from the breeding behavior of crabs in nature. We followed two main steps when developing the Crab mating optimization algorithm: (i) analyzing the breeding behavior of crabs in nature, and modeling the identified concepts and processes...
This paper presents a method for clustering food offers based on the cuckoo search algorithm. The proposed method clusters food offers based on the similarity between their nutritional features (e.g. calcium, vitamins etc.) and/or ingredients. The similarity is evaluated by using the Sorensen-Dice coefficient. To test the clustering method proposed here, we have developed in-house a set of 800 food...
This paper presents an algorithm for optimal spatial coverage of an unknown region by a swarm of agents. The algorithm is based on the Ant Colony Optimization heuristic which is mapped and adapted to solve the current optimization problem. Each agent will leave a virtual pheromone trail during its movement through the unknown region, either attractor or repellent, represented by a positive or negative...
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