Online List Labeling with Predictions

A growing line of work shows how learned predictions can be used to break through worst-case barriers to improve the running time of an algorithm. However, incorporating predictions into data structures with strong theoretical guarantees remains underdeveloped. This paper takes a step in this direction by showing that predictions can be leveraged in the fundamental online list labeling problem. In the problem, n items arrive over time and must be stored in sorted order in an array of size Theta(n). The array slot of an element is its label and the goal is to maintain sorted order while minimizing the total number of elements moved (i.e., relabeled). We design a new list labeling data structure and bound its performance in two models. In the worst-case learning-augmented model, we give guarantees in terms of the error in the predictions. Our data structure provides strong guarantees: it is optimal for any prediction error and guarantees the best-known worst-case bound even when the predictions are entirely erroneous. We also consider a stochastic error model and bound the performance in terms of the expectation and variance of the error. Finally, the theoretical results are demonstrated empirically. In particular, we show that our data structure has strong performance on real temporal data sets where predictions are constructed from elements that arrived in the past, as is typically done in a practical use case

Design and implementation of a multi-agent opportunistic grid computing platform

Opportunistic Grid Computing involves joining idle computing resources in enterprises into a converged high performance commodity infrastructure. The research described in this dissertation investigates the viability of public resource computing in offering a plethora of possibilities through seamless access to shared compute and storage resources. The research proposes and conceptualizes the Multi-Agent Opportunistic Grid (MAOG) solution in an Information and Communication Technologies for Development (ICT4D) initiative to address some limitations prevalent in traditional distributed system implementations. Proof-of-concept software components based on JADE (Java Agent Development Framework) validated Multi-Agent Systems (MAS) as an important tool for provisioning of Opportunistic Grid Computing platforms. Exploration of agent technologies within the research context identified two key components which improve access to extended computer capabilities. The first component is a Mobile Agent (MA) compute component in which a group of agents interact to pool shared processor cycles. The compute component integrates dynamic resource identification and allocation strategies by incorporating the Contract Net Protocol (CNP) and rule based reasoning concepts. The second service is a MAS based storage component realized through disk mirroring and Google file-system’s chunking with atomic append storage techniques. This research provides a candidate Opportunistic Grid Computing platform design and implementation through the use of MAS. Experiments conducted validated the design and implementation of the compute and storage services. From results, support for processing user applications; resource identification and allocation; and rule based reasoning validated the MA compute component. A MAS based file-system that implements chunking optimizations was considered to be optimum based on evaluations. The findings from the undertaken experiments also validated the functional adequacy of the implementation, and show the suitability of MAS for provisioning of robust, autonomous, and intelligent platforms. The context of this research, ICT4D, provides a solution to optimizing and increasing the utilization of computing resources that are usually idle in these contexts

Extending functional databases for use in text-intensive applications

This thesis continues research exploring the benefits of using functional databases based around the functional data model for advanced database applications-particularly those supporting investigative systems. This is a growing generic application domain covering areas such as criminal and military intelligence, which are characterised by significant data complexity, large data sets and the need for high performance, interactive use. An experimental functional database language was developed to provide the requisite semantic richness. However, heavy use in a practical context has shown that language extensions and implementation improvements are required-especially in the crucial areas of string matching and graph traversal. In addition, an implementation on multiprocessor, parallel architectures is essential to meet the performance needs arising from existing and projected database sizes in the chosen application area. [Continues.

Design and implementation of a multi-agent opportunistic grid computing platform

Opportunistic Grid Computing involves joining idle computing resources in enterprises into a converged high performance commodity infrastructure. The research described in this dissertation investigates the viability of public resource computing in offering a plethora of possibilities through seamless access to shared compute and storage resources. The research proposes and conceptualizes the Multi-Agent Opportunistic Grid (MAOG) solution in an Information and Communication Technologies for Development (ICT4D) initiative to address some limitations prevalent in traditional distributed system implementations. Proof-of-concept software components based on JADE (Java Agent Development Framework) validated Multi-Agent Systems (MAS) as an important tool for provisioning of Opportunistic Grid Computing platforms. Exploration of agent technologies within the research context identified two key components which improve access to extended computer capabilities. The first component is a Mobile Agent (MA) compute component in which a group of agents interact to pool shared processor cycles. The compute component integrates dynamic resource identification and allocation strategies by incorporating the Contract Net Protocol (CNP) and rule based reasoning concepts. The second service is a MAS based storage component realized through disk mirroring and Google file-system’s chunking with atomic append storage techniques. This research provides a candidate Opportunistic Grid Computing platform design and implementation through the use of MAS. Experiments conducted validated the design and implementation of the compute and storage services. From results, support for processing user applications; resource identification and allocation; and rule based reasoning validated the MA compute component. A MAS based file-system that implements chunking optimizations was considered to be optimum based on evaluations. The findings from the undertaken experiments also validated the functional adequacy of the implementation, and show the suitability of MAS for provisioning of robust, autonomous, and intelligent platforms. The context of this research, ICT4D, provides a solution to optimizing and increasing the utilization of computing resources that are usually idle in these contexts

Proceedings of the 4th International Conference on Principles and Practices of Programming in Java

This book contains the proceedings of the 4th international conference on principles and practices of programming in Java. The conference focuses on the different aspects of the Java programming language and its applications

Parent-delivered interventions used at home to improve eating, drinking and swallowing in children with neurodisability: the FEEDS mixed-methods study

This is the final version. Available on open access from the NIHR Journals Library via the DOI in this recordBACKGROUND: Eating, drinking and swallowing difficulties are common in young children with neurodisability. These difficulties may lead to inadequate calorie intake, which affects a child's nutrition, growth and general physical health. OBJECTIVE: To examine which interventions are available that can be delivered at home by parents to improve eating, drinking and swallowing in young children with neurodisability and are suitable for investigation in pragmatic trials. DESIGN: This was a mixed-methods study that included focus groups, surveys, an update of published systematic reviews of interventions, a systematic review of measurement properties of existing tools, evidence mapping, evidence synthesis, a Delphi survey and stakeholder workshops. SETTING: The study was carried out in NHS hospitals, community services, family homes and schools. PARTICIPANTS: Parents of children who had neurodisability and eating, drinking and swallowing difficulties. Professionals from health and education. Young people with eating, drinking and swallowing difficulties or young people who had previously experienced eating, drinking and swallowing difficulties. DATA SOURCES: Literature reviews; national surveys of parents and professionals; focus groups with parents, young people and professionals; and stakeholder consultation workshops. REVIEW METHODS: An update of published systematic reviews of interventions (searched July-August 2017), a mapping review (searched October 2017) and a systematic review of measurement properties using COnsensus-based Standards for the Selection of health status Measurement INstruments (COSMIN) methodology (searched May 2018). RESULTS: Significant limitations of the available research evidence regarding interventions and tools to measure outcomes were identified. A total of 947 people participated: 400 parents, 475 health professionals, 62 education professionals and 10 young people. The survey showed the wide range of interventions recommended by NHS health professionals, with parents and professionals reporting variability in the provision of these interventions. Parents and professionals considered 19 interventions as relevant because they modified eating, drinking and swallowing difficulties. Parents and professionals considered 10 outcomes as important to measure (including Nutrition, Growth and Health/safety); young people agreed that these were important outcomes. Stakeholder consultation workshops identified that project conclusions and recommendations made sense, were meaningful and were valued by parents and professionals. Parents and health professionals were positive about a proposed Focus on Early Eating, Drinking and Swallowing (FEEDS) toolkit of interventions that, through shared decision-making, could be recommended by health professionals and delivered by families. LIMITATIONS: The national surveys included large numbers of parents and professionals but, as expected, these were not representative of the UK population of parents of children with eating, drinking and swallowing difficulties. Owing to the limitations of research evidence, pragmatic decisions were made about interventions that might be included in future research and outcomes that might be measured. For instance, the reviews of research found only weak or poor evidence to support the effectiveness of interventions. The review of outcome measures found only limited low-level evidence about their psychometric properties. CONCLUSIONS: Opportunities and challenges for conducting clinical trials of the effectiveness of the FEEDS toolkit of interventions are described. Parents and professionals thought that implementation of the toolkit as part of usual NHS practice was appropriate. However, this would first require the toolkit to be operationalised through development as a complex intervention, taking account of constituent interventions, delivery strategies, implementation and manualisation. Subsequently, an evaluation of its clinical effectiveness and cost-effectiveness could be undertaken using appropriate research methods. FUTURE WORK: Initial steps include FEEDS toolkit development and evaluation of its use in clinical practice, and identification of the most robust methods to measure valued outcomes, such as Nutrition and Growth. TRIAL REGISTRATION: Current Controlled Trials ISRCTN10454425. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 22. See the NIHR Journals Library website for further project information.National Institute for Health Research (NIHR

Distributed data structure for factored operating systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 151-158).Future computer architectures will likely exhibit increased parallelism through the addition of more processor cores. Architectural trends such as exponentially increasing parallelism and the possible lack of scalable shared memory motivate the reevaluation of operating system design. This thesis work takes place in the context of Factored Operating Systems which leverage distributed system ideas to increase the scalability of multicore processor operating systems. fos, a Factored Operating System, explores a new design point for operating systems where traditional low-level operating system services are fine-grain parallelized while internally only using explicit message passing for communication. fos factors an operating system first by system service and then further parallelizes inside of the system service by splitting the service into a fleet of server processes which communicate via messaging. Constructing parallel low-level operating system services which only internally use messaging is challenging because shared resources must be partitioned across servers and the services must provide scalable performance when met with uneven demand. To ease the construction of parallel fos system services, this thesis develops the dPool distributed data structure. The dPool data structure provides concurrent access to an unordered collection of elements by server processes within a fos fleet. Internal to a single dPool instance, all communication between different portions of a dPool is done via messaging. This thesis uses the dPool data structure within the parallel fos Physical Memory Allocation fleet and demonstrates that it is possible to use a dPool to manage shared state in a factored operating system's physical page allocator. This thesis begins by presenting the design of the prototype fos operating system. In the context of fos system service fleets, this thesis describes the dPool data structure, its design, different implementations, and interfaces. The dPool data structure is shown to achieve scalability across even and uneven micro-benchmark workloads. This thesis shows that common parallel and distributed programming techniques apply to the creation of dPool and that background threads within a dPool can increase performance. Finally, this thesis evaluates different dPool implementations and demonstrates that intelligently pushing elements between dPool parts can increase scalability.by David Wentzlaff.Ph.D

Efficient Resource Management for Deep Learning Clusters

Deep Learning (DL) is gaining rapid popularity in various domains, such as computer vision, speech recognition, etc. With the increasing demands, large clusters have been built to develop DL models (i.e., data preparation and model training). DL jobs have some unique features ranging from their hardware requirements to execution patterns. However, the resource management techniques applied in existing DL clusters have not yet been adapted to those new features, which leads to resource inefficiency and hurts the performance of DL jobs. We observed three major challenges brought by DL jobs. First, data preparation jobs, which prepare training datasets from a large volume of raw data, are memory intensive. DL clusters often over-allocate memory resource to those jobs for protecting their performance, which causes memory underutilization in DL clusters. Second, the execution time of a DL training job is often unknown before job completion. Without such information, existing cluster schedulers are unable to minimize the average Job Completion Time (JCT) of those jobs. Third, model aggregations in Distributed Deep Learning (DDL) training are often assigned with a fixed group of CPUs. However, a large portion of those CPUs are wasted because the bursty model aggregations can not saturate them all the time. In this thesis, we propose a suite of techniques to eliminate the mismatches between DL jobs and resource management in DL clusters. First, we bring the idea of memory disaggregation to enhance the memory utilization of DL clusters. The unused memory in data preparation jobs is exposed as remote memory to other machines that are running out of local memory. Second, we design a two-dimensional attained-service-based scheduler to optimize the average JCT of DL training jobs. This scheduler takes the temporal and spatial characteristics of DL training jobs into consideration and can efficiently schedule them without knowing their execution time. Third, we define a shared model aggregation service to reduce the CPU cost of DDL training. Using this service, model aggregations from different DDL training jobs are carefully packed together and use the same group of CPUs in a time-sharing manner. With these techniques, we demonstrate that huge improvements in resource efficiency and job performance can be obtained when the cluster’s resource management matches with the features of DL jobs.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169955/1/jcgu_1.pd