Now showing 1 - 10 of 29
  • Publication
    Minimizing Network Traffic for Distributed Joins Using Lightweight Locality-Aware Scheduling
    Large computing systems such as data centers are becoming the mainstream infrastructures for big data processing. As one of the key data operators in such scenarios, distributed joins is still challenging current techniques since it always incurs a significant cost on network communication. Various advanced approaches have been proposed to improve the performance, however, most of them just focus on data skew handling, and algorithms designed specifically for communication reduction have received less attention. Moreover, although the state-of-the-art technique can minimize network traffic, it provides fine-grained optimal schedules for all individual join keys, which could result in obvious overhead. In this paper, we propose a new approach called LAS (Lightweight Locality-Aware Scheduling), which targets reducing network communication for large distributed joins in an efficient and effective manner. We present the detailed design and implementation of LAS, and conduct an experimental evaluation using large data joins. Our results show that LAS can effectively reduce scheduling overhead and achieve comparable performance on network reduction compared to the state-of-the-art.
  • Publication
    Bandwidth Allocation By Pricing In ATM Networks
    (Elsevier, 1994-03) ;
    Admission control and bandwidth allocation are important issues in telecommunications networks, especially when there are random fluctuating demands for service and variations in the service rates. In the emerging broadband communications environment these services are likely to be offered via an ATM network. In order to make ATM future safe, methods for controlling the network should not be based on the characteristics of present services. We propose one bandwidth allocation method which has this property . Our proposed approach is based on pricing bandwidth to reflect network utilization, with users competing for resources according to their individual bandwidth valuations. The prices may be components of an actual tariff or they may be used as control signals, as in a private network. Simulation results show the improvement possible with our scheme versus a leaky bucket method in terms of cell loss probability, and confirm that a small queue with pricing can be efficient to multiplex heterogeneous sources.
  • Publication
    The Role of Responsive Pricing in the Internet
    The Internet continues to evolve as it reaches out to a wider user population. The recent introduction of user-friendly navigation and retrieval tools for the World Wide Web has triggered an unprecedented level of interest in the Internet among the media and the general public, as well as in the technical community. It seems inevitable that some changes or additions are needed in the control mechanisms used to allocate usage of Internet resources. In this paper, we argue that a feedback signal in the form of a variable price for network service is a workable tool to aid network operators in controlling Internet traffic. We suggest that these prices should vary dynamically based on the current utilization of network resources. We show how this responsive pricing puts control of network service back where it belongs: with the users.
  • Publication
    National Clinical Review on the Impact of COVID-19 Restrictions on Children and Guidance on Reopening of Schools and the Normalisation of Paediatric Healthcare Services in Ireland
    (Health Service Executive, 2020-08-27) ; ;
    This National Clinical Review Document was developed in May/June 2020 following extensive consultation with Child Health Professionals with a deep and wide understanding of the needs of the child. The document has been updated in August 2020 to reflect more recent developments. It has been written to describe the impact of the COVID-19 lockdown measures on children and what needs to be done now. In essence the document explores where we have come from, where we are now, and where we should be in the coming months. It is our collective opinion that the two key adverse consequences for all children across the State are the prolonged closure of the schools and the curtailment of services to children - specifically paediatric medical services, GP services, and multidisciplinary community support services. We feel that there needs to be a recalibration of how children are catered for in this pandemic. The document provides a template for how best to address children’s needs during this difficult time. At present, children have become invisible despite the fact that they account for 25% of the country’s population. There needs to be more leadership and better co- ordination in both planning and implementation on their behalf. They need strong advocates. Children are not the face of this pandemic, but they risk being among its biggest victims. The short turnaround time between the inception and completion of the document is a reflection of our perceived urgency to set out what next must be done to halt and reverse the adverse effects of the current restrictions.
  • Publication
    Leverage of extended information to enhance the performance of JEE systems
    This paper offers an overview of the performance engineering field, including some of its latest challenges. Then, it briefly describes the research area of enhancing the performance of JEE systems through leveraging its "Extended Information" and some recent investigation trends in that front. Finally some future research ideas are presented.
  • Publication
    Multi-Layer-Mesh: A Novel Topology and SDN-based Path Switching for Big Data Cluster Networks
    Big Data technologies and tools have being used for the past decade to solve several scientific and industry problems, with Hadoop/YARN becoming the ”de facto” standard for these applications, although other technologies run on top of it. As any other distributed application, those big data technologies rely heavily on the network infrastructure to read and move data from hundreds or thousands of cluster nodes. Although these technologies are based on reliable and efficient distributed algorithms, there are scenarios and conditions that can generate bottlenecks and inefficiencies, i.e., when a high number of concurrent users creates data access contention. In this paper, we propose a novel network topology called MultiLayer-Mesh and a path switching algorithm based on SDN, that can increase the performance of a big data cluster while reducing the amount of utilized resources (network equipment), in turn reducing the energy and cooling consumption. A thorough simulation-based evaluation of our algorithms shows an average improvement in performance of 31.77% and an average decrease in resource utilization of 36.03% compared to a traditional SpineLeaf topology, in the selected test scenarios.
      378Scopus© Citations 2
  • Publication
    iVMp: an Interactive VM Placement Algorithm for Agile Capital Allocation
    (Institute of Electrical and Electronic Engineers (IEEE), 2013-06-03) ; ; ; ;
    Server consolidation is an important problem in any enterprise, where capital allocators (CAs) must approve any cost saving plans involving the acquisition or allocation of new assets and the decommissioning of inefficient assets. Our paper describes iVMp an interactive VM placement algorithm, that allows CAs to become 'agile' capital allocators that can interactively propose and update constraints and preferences as placements are recommended by the system. To the best of our knowledge this is the first time that this interactive VM placement recommendation problem has been addressed in the academic literature. Our results show that the proposed algorithm finds near optimal solutions in a highly efficient manner.
  • Publication
    An Energy-efficient Mechanism for Increasing Video Quality of Service in Wireless Mesh Networks
    The continuous growth in user demand for high-quality rich media services puts pressure on Wireless Mesh Network (WMN) resources. Solutions such as those which increase the capacity of the mesh network by equipping mesh routers with additional wireless interfaces provide better Quality of Service (QoS) for video deliveries, but result in higher overall energy consumption for the network. This paper presents LBIS, a distributed solution which combines the benefits of both load-balancing and interface-shifting in order to enhance QoS levels for video services delivered over multi-hop WMNs, while maintaining low energy consumption levels within the network. Simulation-based results show very good performance of our proposed mechanism in terms of QoS metrics (delay, packet loss), Peak Signal-to Noise Ratio (PSNR) and energy consumption in mesh network topologies, and with varying video traffic loads and distributions. The results demonstrate how LBIS can increase the QoS for video deliveries by more than 30% at the cost of an insignificant increase of the overall network energy consumption compared to the WMN with multiple radio interfaces without the LBIS adaptation.
  • Publication
    TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems
    Nowadays, clustered environments are commonly used in high-performance computing and enterprise-level applications to achieve faster response time and higher throughput than single machine environments. Nevertheless, how to effectively manage the workloads in these clusters has become a new challenge. As a load balancer is typically used to distribute the workload among the cluster's nodes, multiple research efforts have concentrated on enhancing the capabilities of load balancers. Our previous work presented a novel adaptive load balancing strategy (TRINI) that improves the performance of a clustered Java system by avoiding the performance impacts of major garbage collection, which is an important cause of performance degradation in Java. The aim of this paper is to strengthen the validation of TRINI by extending its experimental evaluation in terms of generality, scalability and reliability. Our results have shown that TRINI can achieve significant performance improvements, as well as a consistent behaviour, when it is applied to a set of commonly used load balancing algorithms, demonstrating its generality. TRINI also proved to be scalable across different cluster sizes, as its performance improvements did not noticeably degrade when increasing the cluster size. Finally, TRINI exhibited reliable behaviour over extended time periods, introducing only a small overhead to the cluster in such conditions. These results offer practitioners a valuable reference regarding the benefits that a load balancing strategy, based on garbage collection, can bring to a clustered Java system.
      370Scopus© Citations 11
  • Publication
    Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios
    Internet of Things (IoT) systems produce large amounts of raw data in the form of log files. This raw data must then be processed to extract useful information. Machine Learning (ML) has proved to be an efficient technique for such tasks, but there are many different ML algorithms available, each suited to different types of scenarios. In this work, we compare the performance of 22 state-of-the-art supervised ML classification algorithms on different IoT datasets, when applied to the problem of anomaly detection. Our results show that there is no dominant solution, and that for each scenario, several candidate techniques perform similarly. Based on our results and a characterization of our datasets, we propose a recommendation framework which guides practitioners towards the subset of the 22 ML algorithms which is likely to perform best on their data.
      495Scopus© Citations 6