Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.This work has received funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE (IT1294-19), EMAITEK and ELK ARTEK programs. D. Camacho also acknowledges support from the Spanish Ministry of Science and Education under PID2020-117263GB-100 grant (FightDIS), the Comunidad Autonoma de Madrid under S2018/TCS-4566 grant (CYNAMON), and the CHIST ERA 2017 BDSI PACMEL Project (PCI2019-103623, Spain)

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Complex Query Operators on Modern Parallel Architectures

Identifying interesting objects from a large data collection is a fundamental problem for multi-criteria decision making applications.In Relational Database Management Systems (RDBMS), the most popular complex query operators used to solve this type of problem are the Top-K selection operator and the Skyline operator.Top-K selection is tasked with retrieving the k-highest ranking tuples from a given relation, as determined by a user-defined aggregation function.Skyline selection retrieves those tuples with attributes offering (pareto) optimal trade-offs in a given relation.Efficient Top-K query processing entails minimizing tuple evaluations by utilizing elaborate processing schemes combined with sophisticated data structures that enable early termination.Skyline query evaluation involves supporting processing strategies which are geared towards early termination and incomparable tuple pruning.The rapid increase in memory capacity and decreasing costs have been the main drivers behind the development of main-memory database systems.Although the act of migrating query processing in-memory has created many opportunities to improve the associated query latency, attaining such improvements has been very challenging due to the growing gap between processor and main memory speeds.Addressing this limitation has been made easier by the rapid proliferation of multi-core and many-core architectures.However, their utilization in real systems has been hindered by the lack of suitable parallel algorithms that focus on algorithmic efficiency.In this thesis, we study in depth the Top-K and Skyline selection operators, in the context of emerging parallel architectures.Our ultimate goal is to provide practical guidelines for developing work-efficient algorithms suitable for parallel main memory processing.We concentrate on multi-core (CPU), many-core (GPU), and processing-in-memory architectures (PIM), developing solutions optimized for high throughout and low latency.The first part of this thesis focuses on Top-K selection, presenting the specific details of early termination algorithms that we developed specifically for parallel architectures and various types of accelerators (i.e. GPU, PIM).The second part of this thesis, concentrates on Skyline selection and the development of a massively parallel load balanced algorithm for PIM architectures.Our work consolidates performance results across different parallel architectures using synthetic and real data on variable query parameters and distributions for both of the aforementioned problems.The experimental results demonstrate several orders of magnitude better throughput and query latency, thus validating the effectiveness of our proposed solutions for the Top-K and Skyline selection operators

Computer Science and Technology Series : XV Argentine Congress of Computer Science. Selected papers

CACIC'09 was the fifteenth Congress in the CACIC series. It was organized by the School of Engineering of the National University of Jujuy. The Congress included 9 Workshops with 130 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2009 was organized following the traditional Congress format, with 9 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 267 submissions. An average of 2.7 review reports were collected for each paper, for a grand total of 720 review reports that involved about 300 different reviewers. A total of 130 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Air Force Institute of Technology Research Report 1999

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

Proceedings, MSVSCC 2013

Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia

ENERGY-AWARE OPTIMIZATION FOR EMBEDDED SYSTEMS WITH CHIP MULTIPROCESSOR AND PHASE-CHANGE MEMORY

Over the last two decades, functions of the embedded systems have evolved from simple real-time control and monitoring to more complicated services. Embedded systems equipped with powerful chips can provide the performance that computationally demanding information processing applications need. However, due to the power issue, the easy way to gain increasing performance by scaling up chip frequencies is no longer feasible. Recently, low-power architecture designs have been the main trend in embedded system designs. In this dissertation, we present our approaches to attack the energy-related issues in embedded system designs, such as thermal issues in the 3D chip multiprocessor (CMP), the endurance issue in the phase-change memory(PCM), the battery issue in the embedded system designs, the impact of inaccurate information in embedded system, and the cloud computing to move the workload to remote cloud computing facilities. We propose a real-time constrained task scheduling method to reduce peak temperature on a 3D CMP, including an online 3D CMP temperature prediction model and a set of algorithm for scheduling tasks to different cores in order to minimize the peak temperature on chip. To address the challenging issues in applying PCM in embedded systems, we propose a PCM main memory optimization mechanism through the utilization of the scratch pad memory (SPM). Furthermore, we propose an MLC/SLC configuration optimization algorithm to enhance the efficiency of the hybrid DRAM + PCM memory. We also propose an energy-aware task scheduling algorithm for parallel computing in mobile systems powered by batteries. When scheduling tasks in embedded systems, we make the scheduling decisions based on information, such as estimated execution time of tasks. Therefore, we design an evaluation method for impacts of inaccurate information on the resource allocation in embedded systems. Finally, in order to move workload from embedded systems to remote cloud computing facility, we present a resource optimization mechanism in heterogeneous federated multi-cloud systems. And we also propose two online dynamic algorithms for resource allocation and task scheduling. We consider the resource contention in the task scheduling

Efficient and elastic management of computing infrastructures

Tesis por compendio[EN] Modern data centers integrate a lot of computer and electronic devices. However, some reports state that the mean usage of a typical data center is around 50% of its peak capacity, and the mean usage of each server is between 10% and 50%. A lot of energy is destined to power on computer hardware that most of the time remains idle. Therefore, it would be possible to save energy simply by powering off those parts from the data center that are not actually used, and powering them on again as they are needed. Most data centers have computing clusters that are used for intensive computing, recently evolving towards an on-premises Cloud service model. Despite the use of low consuming components, higher energy savings can be achieved by dynamically adapting the system to the actual workload. The main approach in this case is the usage of energy saving criteria for scheduling the jobs or the virtual machines into the working nodes. The aim is to power off idle servers automatically. But it is necessary to schedule the power management of the servers in order to minimize the impact on the end users and their applications. The objective of this thesis is the elastic and efficient management of cluster infrastructures, with the aim of reducing the costs associated to idle components. This objective is addressed by automating the power management of the working nodes in a computing cluster, and also proactive stimulating the load distribution to achieve idle resources that could be powered off by means of memory overcommitment and live migration of virtual machines. Moreover, this automation is of interest for virtual clusters, as they also suffer from the same problems. While in physical clusters idle working nodes waste energy, in the case of virtual clusters that are built from virtual machines, the idle working nodes can waste money in commercial Clouds or computational resources in an on-premises Cloud.[ES] En los Centros de Procesos de Datos (CPD) existe una gran concentración de dispositivos informáticos y de equipamiento electrónico. Sin embargo, algunos estudios han mostrado que la utilización media de los CPD está en torno al 50%, y que la utilización media de los servidores se encuentra entre el 10% y el 50%. Estos datos evidencian que existe una gran cantidad de energía destinada a alimentar equipamiento ocioso, y que podríamos conseguir un ahorro energético simplemente apagando los componentes que no se estén utilizando. En muchos CPD suele haber clusters de computadores que se utilizan para computación de altas prestaciones y para la creación de Clouds privados. Si bien se ha tratado de ahorrar energía utilizando componentes de bajo consumo, también es posible conseguirlo adaptando los sistemas a la carga de trabajo en cada momento. En los últimos años han surgido trabajos que investigan la aplicación de criterios energéticos a la hora de seleccionar en qué servidor, de entre los que forman un cluster, se debe ejecutar un trabajo o alojar una máquina virtual. En muchos casos se trata de conseguir equipos ociosos que puedan ser apagados, pero habitualmente se asume que dicho apagado se hace de forma automática, y que los equipos se encienden de nuevo cuando son necesarios. Sin embargo, es necesario hacer una planificación de encendido y apagado de máquinas para minimizar el impacto en el usuario final. En esta tesis nos planteamos la gestión elástica y eficiente de infrastructuras de cálculo tipo cluster, con el objetivo de reducir los costes asociados a los componentes ociosos. Para abordar este problema nos planteamos la automatización del encendido y apagado de máquinas en los clusters, así como la aplicación de técnicas de migración en vivo y de sobreaprovisionamiento de memoria para estimular la obtención de equipos ociosos que puedan ser apagados. Además, esta automatización es de interés para los clusters virtuales, puesto que también sufren el problema de los componentes ociosos, sólo que en este caso están compuestos por, en lugar de equipos físicos que gastan energía, por máquinas virtuales que gastan dinero en un proveedor Cloud comercial o recursos en un Cloud privado.[CA] En els Centres de Processament de Dades (CPD) hi ha una gran concentració de dispositius informàtics i d'equipament electrònic. No obstant això, alguns estudis han mostrat que la utilització mitjana dels CPD està entorn del 50%, i que la utilització mitjana dels servidors es troba entre el 10% i el 50%. Estes dades evidencien que hi ha una gran quantitat d'energia destinada a alimentar equipament ociós, i que podríem aconseguir un estalvi energètic simplement apagant els components que no s'estiguen utilitzant. En molts CPD sol haver-hi clusters de computadors que s'utilitzen per a computació d'altes prestacions i per a la creació de Clouds privats. Si bé s'ha tractat d'estalviar energia utilitzant components de baix consum, també és possible aconseguir-ho adaptant els sistemes a la càrrega de treball en cada moment. En els últims anys han sorgit treballs que investiguen l'aplicació de criteris energètics a l'hora de seleccionar en quin servidor, d'entre els que formen un cluster, s'ha d'executar un treball o allotjar una màquina virtual. En molts casos es tracta d'aconseguir equips ociosos que puguen ser apagats, però habitualment s'assumix que l'apagat es fa de forma automàtica, i que els equips s'encenen novament quan són necessaris. No obstant això, és necessari fer una planificació d'encesa i apagat de màquines per a minimitzar l'impacte en l'usuari final. En esta tesi ens plantegem la gestió elàstica i eficient d'infrastructuras de càlcul tipus cluster, amb l'objectiu de reduir els costos associats als components ociosos. Per a abordar este problema ens plantegem l'automatització de l'encesa i apagat de màquines en els clusters, així com l'aplicació de tècniques de migració en viu i de sobreaprovisionament de memòria per a estimular l'obtenció d'equips ociosos que puguen ser apagats. A més, esta automatització és d'interés per als clusters virtuals, ja que també patixen el problema dels components ociosos, encara que en este cas estan compostos per, en compte d'equips físics que gasten energia, per màquines virtuals que gasten diners en un proveïdor Cloud comercial o recursos en un Cloud privat.Alfonso Laguna, CD. (2015). Efficient and elastic management of computing infrastructures [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/57187Compendi