The distribution of real-time GIS (Geographic Information System) data among users is now more important than ever as it becomes increasingly affordable and important for scientific and government agencies to monitor environmental phenomena in real-time. A growing number of sensor networks are being deployed all over the world, but there is a lack of solutions for their effective monitoring. Increasingly, GIS users need access to real-time sensor data from a variety of sources, and the data must be represented in a visually-pleasing way and be easily accessible. In addition, users need to be able to collaborate with each other to share and discuss specific sensor data. The real-time acquisition, analysis, and sharing of sensor data from a large variety of heterogeneous sensor sources is currently difficult due to the lack of a standard architecture to properly represent the dynamic properties of the data and make it readily accessible for collaboration between users. This thesis will present a JEE-based publisher/subscriber architecture that allows real-time sensor data to be displayed collaboratively on the web, requiring users to have nothing more than a web browser and Internet connectivity to gain access to that data. The proposed architecture is evaluated by showing how an AJAX-based and a Flash-based web application are able to represent the real-time sensor data within novel collaborative environments. By using the latest web-based technology and relevant open standards, this thesis shows how map data and GIS data can be made more accessible, more collaborative and generally more useful.

Peer-to-Peer (P2P) computing offers new research challenges in the field of distributed computing. This paradigm can take advantage of a huge number of idle CPU cycles through Internet in order to solve very complex computational problems. All these resources are provided voluntarily by millions of users spread over the world. This means the cost of allocating and maintaining the resources is split and assumed by each owner/peer. For this reason, P2P computing can be seen as a low-cost alternative to expensive super-computers. Obviously, not every kind of parallel application is suitable for a P2P computing environment. Those with high communication requirements between tasks or with high QoS needs should still be performed in a Local Area Networking (LAN) environment. Otherwise, problems with huge computational requirements that can be easily split into millions of independent tasks are suitable for P2P computing, especially as solving these problems with a supercomputer would be extremely expensive. One of the most critical aspects in the design of P2P systems is the development of incentive techniques to enforce cooperation and resource sharing among participants. Incentive policies in P2P distributed computing systems is a new research field that requires specific policies to fight against malicious and selfish behavior by peers. Encouraging peers to collaborate in file-sharing has been widely investigated but, in the P2P computing field, this issue is still at a very early stage of research. Furthermore, the dynamics of peer participation are an inherent property of P2P systems and critical for design and evaluation. This further increases the difficulty of P2P computing. Another critical aspect of P2P computing systems is the development of scheduling techniques to achieve an efficient and scalable management of the computational resources. Unlike file-sharing, based on such immutable resources as files, the mutable ones, such as CPU and Memory are the principal resources involved in P2P computing. Inside the scheduling field, P2P computing can be seen as a particular variant of Grid computing. In a similar way as with the incentive polices, an extensive list of publications can be found that study the scheduling problems for distributed computing, such as Clusters or Grid computing, but few of these focus on P2P computing. For this reason, the scheduling problem in this kind of network is a field that still requires research in depth. In this thesis we propose a Distributed Incentive and Scheduling Integrated Mechanism (DISIM) with a two-level topology and designed to work on largescale distributed computing P2P systems. The low level is formed by associations of peers controlled by super-peers with major responsibilities in managing and gathering information about the state of these groups. Scalability limitations on the first level are avoided by providing the mechanism with an upper level, made up of super-peers interconnected through a logical overlay. Regarding incentives, we propose a mechanism based on credits with a twolevel topology designed to operate on different platforms of shared computing networks. One of the main contributions is a new policy for managing the credits, called Weighted, that increases peer participation significantly. This mechanism reflects P2P user dynamics, penalizes free-riders efficiently and encourages peer participation. Moreover, the use of a popular pricing strategy, called reverse Vickrey Auction, protects the system against malicious peer behavior. Simulation results show that our policy outperforms alternative approaches, maximizing system throughput and limiting free-riding behavior by peers. From the scheduling point of view, the low-level scheduler takes user dynamism into account and is almost optimal since it holds all the status information about the workload and computational power of its constituent peers. Our main contribution at the upper level is to propose three criteria that only use local information for scheduling tasks, providing the overall system with scalability. By setting these criteria, the system can easily, dynamically and rapidly adapt its behavior to very different kinds of parallel jobs in order toachieve an efficient performance. The results obtained proved the efficiency of the overall model and the convergence with the best assignment, achieved with an ideal centralized policy with global information.

This dissertation studies interference in wireless networks. Interference results from multiple simultaneous attempts to communicate, often between unassociated sources and receivers, preventing extensive coordination. Moreover, in practical wireless networks, learning network state is inherently expensive, and nodes often have incomplete and mismatched views of the network. The fundamental communication limits of a network with such views is unknown. To address this, we present a local view model which captures asymmetries in node knowledge. Our local view model does not rely on accurate knowledge of an underlying probability distribution governing network state. Therefore, we can make robust statements about the fundamental limits of communication when the channel is quasi-static or the actual distribution of state is unknown: commonly faced scenarios in modern commercial networks. For each local view, channel state parameters are either perfectly known or completely unknown. While we propose no mechanism for network learning, a local view represents the result of some such mechanism. We apply the local view model to study the two-user Gaussian interference channel: the smallest building block of any interference network. All seven possible local views are studied, and we find that for five of the seven, there exists no policy or protocol that universally outperforms time-division multiplexing (TDM), justifying the orthogonalized approach of many deployed systems. For two of the seven views, TDM-beating performance is possible with use of opportunistic schemes where opportunities are revealed by the local view. We then study how message cooperation --- either at transmitters or receivers --- increases capacity in the local view two-user Gaussian interference channel. The cooperative setup is particularly appropriate for modeling next-generation cellular networks, where costs to share message data among base stations is low relative to costs to learn channel coefficients. For the cooperative setting, we find: (1) opportunistic approaches are still needed to outperform TDM, but (2) opportunities are more abundant and revealed by more local views. For all cases studied, we characterize the capacity region to within some known gap, enabling computation of the generalized degrees of freedom region, a visualization of spatial channel resource usage efficiency.

Monitoring and debugging distributed systems is inherently a difficult problem. Events collected during the execution of distributed systems can enable developers to diagnose and fix faults. Process-time diagrams are normally used to view the relationships between the events and understand the interaction between processes over time. A major difficulty with analyzing these sets of events is that they are usually very large. Therefore, being able to search through the event-data sets can enable users to get to points of interest quickly and find out if patterns in the dataset represent the expected behaviour of the system. A lot of research work has been done to improve the search algorithm for finding event-patterns in large partial-order datasets. In this thesis, we improve on this work by parallelizing the search algorithm. This is useful as many computers these days have more than one core or processor. Therefore, it makes sense to exploit this available computing power as part of an effort to improve the speed of the algorithm. The search problem itself can be modeled as a Constraint Satisfaction Problem (CSP). We develop a simple and efficient way of generating tasks (to be executed by the cores) that guarantees that no two cores will ever repeat the same work-effort during the search. Our approach is generic and can be applied to any CSP consisting of a large domain space. We also implement an efficient dynamic work-stealing strategy that ensures the cores are kept busy throughout the execution of the parallel algorithm. We evaluate the efficiency and scalability of our algorithm through experiments and show that we can achieve efficiencies of up to 80% on a 24-core machine.

The multifaceted utilization of mobile computing devices, including smart phones, PDAs, tablet computers with increasing functionalities and the advances in wireless technologies, has fueled the utilization of collaborative computing (peer-to-peer) technique in mobile environment. Mobile collaborative computing, known as mobile peer-to-peer (MP2P), can provide an economic way of data access among users of diversified applications in our daily life (exchanging traffic condition in a busy high way, sharing price-sensitive financial information, getting the most-recent news), in national security (exchanging information and collaborating to uproot a terror network, communicating in a hostile battle field) and in natural catastrophe (seamless rescue operation in a collapsed and disaster torn area). Nonetheless, data/content dissemination among the mobile devices is the fundamental building block for all the applications in this paradigm. The objective of this research is to propose a data dissemination scheme for mobile distributed systems using an MP2P technique, which maximizes the number of required objects distributed among users and minimizes to object acquisition time. In specific, we introduce a new paradigm of information dissemination in MP2P networks. To accommodate mobility and bandwidth constraints, objects are segmented into smaller pieces for efficient information exchange. Since it is difficult for a node to know the content of every other node in the network, we propose a novel Spatial-Popularity based Information Diffusion (SPID) scheme that determines urgency of contents based on the spatial demand of mobile users and disseminates content accordingly. The segmentation policy and the dissemination scheme can reduce content acquisition time for each node. Further, to facilitate efficient scheduling of information transmission from every node in the wireless mobile networks, we modify and apply the distributed maximal independent set (MIS) algorithm. We also consider neighbor overlap for closely located mobile stations to reduce duplicate transmission to common neighbors. Different parameters in the system such as node density, scheduling among neighboring nodes, mobility pattern, and node speed have a tremendous impact on data diffusion in an MP2P environment. We have developed analytical models for our proposed scheme for object diffusion time/delay in a wireless mobile network to apprehend the interrelationship among these different parameters. In specific, we present the analytical model of object propagation in mobile networks as a function of node densities, radio range, and node speed. In the analysis, we calculate the probabilities of transmitting a single object from one node to multiple nodes using the epidemic model of spread of disease. We also incorporate the impact of node mobility, radio range, and node density in the networks into the analysis. Utilizing these transition probabilities, we construct an analytical model based on the Markov process to estimate the expected delay for diffusing an object to the entire network both for single object and multiple object scenarios. We then calculate the transmission probabilities of multiple objects among the nodes in wireless mobile networks considering network dynamics. Through extensive simulations, we demonstrate that the proposed scheme is efficient for data diffusion in mobile networks.

In distributed systems, dependency among different computations of an application leads to a problem of deciding the locations of computations. Spatial requirements of a computation can be expressed in terms of spatial relationships with other computations. This research presents programming abstractions and language constructs which can be used for specifying spatial coordination requirements for distributed computations. A spatial coordination middleware has been implemented for satisfying spatial coordination requirements of systems implemented using the Actor model of concurrent computation. Our approach abstracts spatial requirements of concurrent computations and provides key programming primitives for specifying these requirements. We have also implemented a number of higher level spatial coordination primitives which can be translated into the basic primitives. Spatial requirements can be specified using these primitives and then the runtime system converts them into a constraint satisfaction problem and satisfies them. Our approach reduces the programming complexity and provides a middleware which separates spatial requirements from functional code and enables the application programmer to change spatial requirements at runtime without effecting application's functionality. We have identified some of the high level primitives and provided a mechanism to develop high level primitives on top of the basic primitives. This thesis presents the rationale, design, implementation, and evaluation of spatial coordination. By comparing programs written with and without our spatial coordination primitives, we show how spatial coordination enables a programmer to specify spatial requirements declaratively and simplify the programming task. Experimental results demonstrate the performance of the approach, as the number of constraints increases.

Long resistive cables used in the operation of remote instrumentation impose fundamental limits on the amount of power delivered and create difficulties in voltage regulation at the remote-end (voltage at the end of the cable) with changing load conditions. This type of power delivery is used in many engineering systems such as in the operation of underwater remotely-operated vehicles, in oil drilling and mining industries, and in highly distributed systems (aircraft, submarines, and space stations, etc.). The focus of this research is to develop new approaches for power delivery in systems that have considerable voltage drops between the local and remote-ends.Two novel methods of power delivery based on state feedback control and parallel operation of switching and linear regulators to enhance stability and increase the power delivered at the remote-end are developed and validated experimentally.A system-level approach is developed to control the remote-end voltage for changing load conditions through the usage of a model inversion technique at the local-end along with a feedback of the local-end variables.

This dissertation presents a novel modeling framework for understanding the dynamics of peer-to-peer (P2P) networks under churn (i.e., random user arrival/departure) and designing systems more resilient against node failure. The proposed models are applicable to general distributed systems under a variety of conditions on graph construction and user lifetimes. The foundation of this work is a new churn model that describes user arrival and departure as a superposition of many periodic (renewal) processes. It not only allows general (non-exponential) user lifetime distributions, but also captures heterogeneous behavior of peers. We utilize this model to analyze link dynamics and the ability of the system to stay connected under churn. Our results offers exact computation of user-isolation and graph-partitioning probabilities for any monotone lifetime distribution, including heavy-tailed cases found in real systems. We also propose an age-proportional random-walk algorithm for creating links in unstructured P2P networks that achieves zero isolation probability as system size becomes infinite. We additionally obtain many insightful results on the transient distribution of in-degree, edge arrival process, system size, and lifetimes of live users as simple functions of the aggregate lifetime distribution. The second half of this work studies churn in structured P2P networks that are usually built upon distributed hash tables (DHTs). Users in DHTs maintain two types of neighbor sets: routing tables and successor/leaf sets. The former tables determine link lifetimes and routing performance of the system, while the latter are built for ensuring DHT consistency and connectivity. Our first result in this area proves that robustness of DHTs is mainly determined by zone size of selected neighbors, which leads us to propose a min-zone algorithm that significantly reduces link churn in DHTs. Our second result uses the Chen-Stein method to understand concurrent failures among strongly dependent successor sets of many DHTs and finds an optimal stabilization strategy for keeping Chord connected under churn.

We present methods for distributed self-assembly that utilize simple rule-of-thumb control and communication schemes providing probabilistic performance guarantees. These methods represents a staunch departure from existing approaches that require more sophisticated control and communication, but provide deterministic guarantees. In particular, we show that even under severe communication restrictions, any assembly described by an acyclic weighted graph can be assembled with a rule set that is linear in the number of nodes contained in the desired assembly graph. We introduce the concept of stochastic stability to the self-assembly problem and show that stochastic stability of desirable configurations can be exploited to provide probabilistic performance guarantees for the process. Relaxation of the communication restrictions allows simple approaches giving deterministic guarantees. We establish a clear relationship between availability of communication and convergence properties. We consider Self-assembly tasks for the cases of many and few agents as well as large and small assembly goals. We analyze sensitivity of the presented process to communication errors as well as ill-intentioned agents. We discuss convergence rates of the presented process and directions for improving them.

State monitoring is a fundamental building block for Cloud services. The demand for providing state monitoring as services (MaaS) continues to grow and is evidenced by CloudWatch from Amazon EC2, which allows cloud consumers to pay for monitoring a selection of performance metrics with coarse-grained periodical sampling of runtime states. One of the key challenges for wide deployment of MaaS is to provide better balance among a set of critical quality and performance parameters, such as accuracy, cost, scalability and customizability. This dissertation research is dedicated to innovative research and development of an elastic framework for providing state monitoring as a service (MaaS). We analyze limitations of existing techniques, systematically identify the need and the challenges at different layers of a Cloud monitoring service platform, and develop a suite of distributed monitoring techniques to support for flexible monitoring infrastructure, cost-effective state monitoring and monitoring-enhanced Cloud management. At the monitoring infrastructure layer, we develop techniques to support multi-tenancy of monitoring services by exploring cost sharing between monitoring tasks and safeguarding monitoring resource usage. To provide elasticity in monitoring, we propose techniques to allow the monitoring infrastructure to self-scale with monitoring demand. At the cost-effective state monitoring layer, we devise several new state monitoring functionalities to meet unique functional requirements in Cloud monitoring. Violation likelihood state monitoring explores the benefits of consolidating monitoring workloads by allowing utility-driven monitoring intensity tuning on individual monitoring tasks and identifying correlations between monitoring tasks. Window based state monitoring leverages distributed windows for the best monitoring accuracy and communication efficiency. Reliable state monitoring is robust to both transient and long-lasting communication issues caused by component failures or cross-VM performance interferences. At the monitoring-enhanced Cloud management layer, we devise a novel technique to learn about the performance characteristics of both Cloud infrastructure and Cloud applications from cumulative performance monitoring data to increase the cloud deployment efficiency.