Decision-centric resource-efficient semantic information management

For the past few decades, we have put significant efforts in building tools that extend our senses and enhance our perceptions, be it the traditional sensor networks, or the more recent Internet-of-Things. With such systems, the lasting strives for efficiency and effectiveness have driven research forces in the community to keep seeking smarter ways to manage bigger data with lower resource consumptions, especially resource-poor environments such as post-disaster response and recovery scenarios. In this dissertation, we base ourselves on the state-of-the-arts studies, and build a set of techniques as well as a holistic information management system that not only account for data level characteristics, but, more importantly, take advantage of the higher information semantic level features as well as the even higher level decision logic structures in achieving effective and efficient data acquisition and dissemination. We first introduce a data prioritization algorithm that accounts for overlaps among data sources to maximize information delivery. We then build a set of techniques that directly optimize the efficiency of decision making, as opposed to only focusing on traditional, lower-level communication optimizations, such as total network throughput or average latency. In developing these algorithms, we view decisions as choices of a course of action, based on several logical predicates. Making a decision is thus reduced to evaluating a Boolean expression on these predicates; for example, "if it is raining, I will carry an umbrella." To evaluate a predicate, evidence is needed (e.g., a picture of the weather). Data objects, retrieved from sensors, supply the needed evidence for predicate evaluation. By using a decision-making model, our retrieval algorithms are able to take into consideration historical/domain knowledge, logical dependencies among data items, as well as information freshness decays, in order to prioritize data transmission to minimize overhead of transferring information needed by a variety of decision makers, while at the same time coping with query level timeliness requirements, environment dynamics, and system resource limitations. Finally we present the architecture for a distributed semantic-aware information management system, which we call Athena. We discuss its key design choices, and how we incorporate various techniques, such as interest book-keeping and label sharing, to improve information dissemination efficiency in realistic scenarios. For all the components as well as the whole Athena system, we will discuss our implementations and evaluations under realistic settings. Results show that our techniques improve the efficiency of information gathering and delivery in support of post-disaster situation assessment and decision making in the face of various environmental and systems constraints

Data Storage and Dissemination in Pervasive Edge Computing Environments

Nowadays, smart mobile devices generate huge amounts of data in all sorts of gatherings. Much of that data has localized and ephemeral interest, but can be of great use if shared among co-located devices. However, mobile devices often experience poor connectivity, leading to availability issues if application storage and logic are fully delegated to a remote cloud infrastructure. In turn, the edge computing paradigm pushes computations and storage beyond the data center, closer to end-user devices where data is generated and consumed. Hence, enabling the execution of certain components of edge-enabled systems directly and cooperatively on edge devices. This thesis focuses on the design and evaluation of resilient and efficient data storage and dissemination solutions for pervasive edge computing environments, operating with or without access to the network infrastructure. In line with this dichotomy, our goal can be divided into two specific scenarios. The first one is related to the absence of network infrastructure and the provision of a transient data storage and dissemination system for networks of co-located mobile devices. The second one relates with the existence of network infrastructure access and the corresponding edge computing capabilities. First, the thesis presents time-aware reactive storage (TARS), a reactive data storage and dissemination model with intrinsic time-awareness, that exploits synergies between the storage substrate and the publish/subscribe paradigm, and allows queries within a specific time scope. Next, it describes in more detail: i) Thyme, a data storage and dis- semination system for wireless edge environments, implementing TARS; ii) Parsley, a flexible and resilient group-based distributed hash table with preemptive peer relocation and a dynamic data sharding mechanism; and iii) Thyme GardenBed, a framework for data storage and dissemination across multi-region edge networks, that makes use of both device-to-device and edge interactions. The developed solutions present low overheads, while providing adequate response times for interactive usage and low energy consumption, proving to be practical in a variety of situations. They also display good load balancing and fault tolerance properties.Resumo Hoje em dia, os dispositivos móveis inteligentes geram grandes quantidades de dados em todos os tipos de aglomerações de pessoas. Muitos desses dados têm interesse loca- lizado e efêmero, mas podem ser de grande utilidade se partilhados entre dispositivos co-localizados. No entanto, os dispositivos móveis muitas vezes experienciam fraca co- nectividade, levando a problemas de disponibilidade se o armazenamento e a lógica das aplicações forem totalmente delegados numa infraestrutura remota na nuvem. Por sua vez, o paradigma de computação na periferia da rede leva as computações e o armazena- mento para além dos centros de dados, para mais perto dos dispositivos dos utilizadores finais onde os dados são gerados e consumidos. Assim, permitindo a execução de certos componentes de sistemas direta e cooperativamente em dispositivos na periferia da rede. Esta tese foca-se no desenho e avaliação de soluções resilientes e eficientes para arma- zenamento e disseminação de dados em ambientes pervasivos de computação na periferia da rede, operando com ou sem acesso à infraestrutura de rede. Em linha com esta dico- tomia, o nosso objetivo pode ser dividido em dois cenários específicos. O primeiro está relacionado com a ausência de infraestrutura de rede e o fornecimento de um sistema efêmero de armazenamento e disseminação de dados para redes de dispositivos móveis co-localizados. O segundo diz respeito à existência de acesso à infraestrutura de rede e aos recursos de computação na periferia da rede correspondentes. Primeiramente, a tese apresenta armazenamento reativo ciente do tempo (ARCT), um modelo reativo de armazenamento e disseminação de dados com percepção intrínseca do tempo, que explora sinergias entre o substrato de armazenamento e o paradigma pu- blicação/subscrição, e permite consultas num escopo de tempo específico. De seguida, descreve em mais detalhe: i) Thyme, um sistema de armazenamento e disseminação de dados para ambientes sem fios na periferia da rede, que implementa ARCT; ii) Pars- ley, uma tabela de dispersão distribuída flexível e resiliente baseada em grupos, com realocação preventiva de nós e um mecanismo de particionamento dinâmico de dados; e iii) Thyme GardenBed, um sistema para armazenamento e disseminação de dados em redes multi-regionais na periferia da rede, que faz uso de interações entre dispositivos e com a periferia da rede. As soluções desenvolvidas apresentam baixos custos, proporcionando tempos de res- posta adequados para uso interativo e baixo consumo de energia, demonstrando serem práticas nas mais diversas situações. Estas soluções também exibem boas propriedades de balanceamento de carga e tolerância a faltas

Design of Overlay Networks for Internet Multicast - Doctoral Dissertation, August 2002

Multicast is an efficient transmission scheme for supporting group communication in networks. Contrasted with unicast, where multiple point-to-point connections must be used to support communications among a group of users, multicast is more efficient because each data packet is replicated in the network – at the branching points leading to distinguished destinations, thus reducing the transmission load on the data sources and traffic load on the network links. To implement multicast, networks need to incorporate new routing and forwarding mechanisms in addition to the existing are not adequately supported in the current networks. The IP multicast are not adequately supported in the current networks. The IP multicast solution has serious scaling and deployment limitations, and cannot be easily extended to provide more enhanced data services. Furthermore, and perhaps most importantly, IP multicast has ignored the economic nature of the problem, lacking incentives for service providers to deploy the service in wide area networks. Overlay multicast holds promise for the realization of large scale Internet multicast services. An overlay network is a virtual topology constructed on top of the Internet infrastructure. The concept of overlay networks enables multicast to be deployed as a service network rather than a network primitive mechanism, allowing deployment over heterogeneous networks without the need of universal network support. This dissertation addresses the network design aspects of overlay networks to provide scalable multicast services in the Internet. The resources and the network cost in the context of overlay networks are different from that in conventional networks, presenting new challenges and new problems to solve. Our design goal are the maximization of network utility and improved service quality. As the overall network design problem is extremely complex, we divide the problem into three components: the efficient management of session traffic (multicast routing), the provisioning of overlay network resources (bandwidth dimensioning) and overlay topology optimization (service placement). The combined solution provides a comprehensive procedure for planning and managing an overlay multicast network. We also consider a complementary form of overlay multicast called application-level multicast (ALMI). ALMI allows end systems to directly create an overlay multicast session among themselves. This gives applications the flexibility to communicate without relying on service provides. The tradeoff is that users do not have direct control on the topology and data paths taken by the session flows and will typically get lower quality of service due to the best effort nature of the Internet environment. ALMI is therefore suitable for sessions of small size or sessions where all members are well connected to the network. Furthermore, the ALMI framework allows us to experiment with application specific components such as data reliability, in order to identify a useful set of communication semantic for enhanced data services

3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)

This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Online Modeling and Tuning of Parallel Stream Processing Systems

Writing performant computer programs is hard. Code for high performance applications is profiled, tweaked, and re-factored for months specifically for the hardware for which it is to run. Consumer application code doesn\u27t get the benefit of endless massaging that benefits high performance code, even though heterogeneous processor environments are beginning to resemble those in more performance oriented arenas. This thesis offers a path to performant, parallel code (through stream processing) which is tuned online and automatically adapts to the environment it is given. This approach has the potential to reduce the tuning costs associated with high performance code and brings the benefit of performance tuning to consumer applications where otherwise it would be cost prohibitive. This thesis introduces a stream processing library and multiple techniques to enable its online modeling and tuning. Stream processing (also termed data-flow programming) is a compute paradigm that views an application as a set of logical kernels connected via communications links or streams. Stream processing is increasingly used by computational-x and x-informatics fields (e.g., biology, astrophysics) where the focus is on safe and fast parallelization of specific big-data applications. A major advantage of stream processing is that it enables parallelization without necessitating manual end-user management of non-deterministic behavior often characteristic of more traditional parallel processing methods. Many big-data and high performance applications involve high throughput processing, necessitating usage of many parallel compute kernels on several compute cores. Optimizing the orchestration of kernels has been the focus of much theoretical and empirical modeling work. Purely theoretical parallel programming models can fail when the assumptions implicit within the model are mis-matched with reality (i.e., the model is incorrectly applied). Often it is unclear if the assumptions are actually being met, even when verified under controlled conditions. Full empirical optimization solves this problem by extensively searching the range of likely configurations under native operating conditions. This, however, is expensive in both time and energy. For large, massively parallel systems, even deciding which modeling paradigm to use is often prohibitively expensive and unfortunately transient (with workload and hardware). In an ideal world, a parallel run-time will re-optimize an application continuously to match its environment, with little additional overhead. This work presents methods aimed at doing just that through low overhead instrumentation, modeling, and optimization. Online optimization provides a good trade-off between static optimization and online heuristics. To enable online optimization, modeling decisions must be fast and relatively accurate. Online modeling and optimization of a stream processing system first requires the existence of a stream processing framework that is amenable to the intended type of dynamic manipulation. To fill this void, we developed the RaftLib C++ template library, which enables usage of the stream processing paradigm for C++ applications (it is the run-time which is the basis of almost all the work within this dissertation). An application topology is specified by the user, however almost everything else is optimizable by the run-time. RaftLib takes advantage of the knowledge gained during the design of several prior streaming languages (notably Auto-Pipe). The resultant framework enables online migration of tasks, auto-parallelization, online buffer-reallocation, and other useful dynamic behaviors that were not available in many previous stream processing systems. Several benchmark applications have been designed to assess the performance gains through our approaches and compare performance to other leading stream processing frameworks. Information is essential to any modeling task, to that end a low-overhead instrumentation framework has been developed which is both dynamic and adaptive. Discovering a fast and relatively optimal configuration for a stream processing application often necessitates solving for buffer sizes within a finite capacity queueing network. We show that a generalized gain/loss network flow model can bootstrap the process under certain conditions. Any modeling effort, requires that a model be selected; often a highly manual task, involving many expensive operations. This dissertation demonstrates that machine learning methods (such as a support vector machine) can successfully select models at run-time for a streaming application. The full set of approaches are incorporated into the open source RaftLib framework