On the cost of database clusters reconfiguration

Database clusters based on share-nothing replication techniques are currently widely accepted as a practical solution to scalability and availability of the data tier. A key issue when planning such systems is the ability to meet service level agreements when load spikes occur or cluster nodes fail. This translates into the ability to provision and deploy additional nodes. Many current research efforts focus on designing autonomic controllers to perform such reconfiguration, tuned to quickly react to system changes and spawn new replicas based on resource usage and performance measurements. In contrast, we are concerned about the inherent impact of deploying an additional node to an online cluster, considering both the time required to finish such an action as well as the impact on resource usage and performance of the cluster as a whole. If noticeable, such impact hinders the practicability of self-management techniques, since it adds an additional dimension that has to be accounted for. Our approach is to systematically benchmark a number of different reconfiguration scenarios to assess the cost of bringing a new replica online. We consider factors such as: workload characteristics, incremental and parallel recovery, flow control and outdatedness of the recovering replica. As a result, we show that research should be refocused from optimizing the capture and transmition of changes to applying them, which in a realistic setting dominates the cost of the recovery operation.Work supported by the Spanish Government under research grant TIN2006-14738-C02-02

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Developing modular product family using GeMoCURE within an SME

Companies adopt the strategy of producing variety of products to be competitive and responsive to market. Product variation is becoming an important factor in companies' ability to accurately meet customer requirements. Ever increasing consumer options mean that customers have more choices than ever before which put commercial pressures on companies to continue to diversify. This can be a particular problem within Small to Medium Enterprises (SMEs) who do not always have the level of resources to meet these requirements. As such, methods are required that provide means for companies to be able to produce a wide range of products at the lowest cost and shortest time. This paper details a new modular product design methodology that provides a focus on developing modular product families. The methodology's function is described and a case study detailed of how it was used within an SME to define the company's product portfolio and create a new Generic Product Function Structure from which a new family of product variants can be developed. The methodology lends itself to modular re-use which has the potential to support rapid development and configuration of product variants

Moving Ahead: Factors contributing to successful transition of people with intellectual disabilities from congregated to community-based residential options in two regions in Ireland

This report details the findings of a research project entitled Moving Ahead. The project aimed to examine factors that contribute to the successful transfer of people with intellectual disabilities from congregated to community-based living in two regional areas in Ireland. It was undertaken by a team of researchers from Ireland and the UK, led by Dr. Christine Lineha

Understanding the thermal implications of multicore architectures

Multicore architectures are becoming the main design paradigm for current and future processors. The main reason is that multicore designs provide an effective way of overcoming instruction-level parallelism (ILP) limitations by exploiting thread-level parallelism (TLP). In addition, it is a power and complexity-effective way of taking advantage of the huge number of transistors that can be integrated on a chip. On the other hand, today's higher than ever power densities have made temperature one of the main limitations of microprocessor evolution. Thermal management in multicore architectures is a fairly new area. Some works have addressed dynamic thermal management in bi/quad-core architectures. This work provides insight and explores different alternatives for thermal management in multicore architectures with 16 cores. Schemes employing both energy reduction and activity migration are explored and improvements for thread migration schemes are proposed.Peer ReviewedPostprint (published version