Cross-middleware Interoperability in Distributed Concurrent Engineering

Secure, distributed collaboration between different organizations is a key challenge in Grid computing today. The GDCD project has produced a Grid-based demonstrator Virtual Collaborative Facility (VCF) for the European Space Agency. The purpose of this work is to show the potential of Grid technology to support fully distributed concurrent design, while addressing practical considerations including network security, interoperability, and integration of legacy applications. The VCF allows domain engineers to use the concurrent design methodology in a distributed fashion to perform studies for future space missions. To demonstrate the interoperability and integration capabilities of Grid computing in concurrent design, we developed prototype VCF components based on ESA’s current Excel-based Concurrent Design Facility (a non-distributed environment), using a STEP-compliant database that stores design parameters. The database was exposed as a secure GRIA 5.1 Grid service, whilst a .NET/WSE3.0-based library was developed to enable secure communication between the Excel client and STEP database

Coordinating complex decision support activities across distributed applications

Knowledge-based technologies have been applied successfully to automate planning and scheduling in many problem domains. Automation of decision support can be increased further by integrating task-specific applications with supporting database systems, and by coordinating interactions between such tools to facilitate collaborative activities. Unfortunately, the technical obstacles that must be overcome to achieve this vision of transparent, cooperative problem-solving are daunting. Intelligent decision support tools are typically developed for standalone use, rely on incompatible, task-specific representational models and application programming interfaces (API's), and run on heterogeneous computing platforms. Getting such applications to interact freely calls for platform independent capabilities for distributed communication, as well as tools for mapping information across disparate representations. Symbiotics is developing a layered set of software tools (called NetWorks! for integrating and coordinating heterogeneous distributed applications. he top layer of tools consists of an extensible set of generic, programmable coordination services. Developers access these services via high-level API's to implement the desired interactions between distributed applications

An informatics model for guiding assembly of telemicrobiology workstations for malaria collaborative diagnostics using commodity products and open-source software

<p>Abstract</p> <p>Background</p> <p>Deficits in clinical microbiology infrastructure exacerbate global infectious disease burdens. This paper examines how commodity computation, communication, and measurement products combined with open-source analysis and communication applications can be incorporated into laboratory medicine microbiology protocols. Those commodity components are all now sourceable globally. An informatics model is presented for guiding the use of low-cost commodity components and free software in the assembly of clinically useful and usable telemicrobiology workstations.</p> <p>Methods</p> <p>The model incorporates two general principles: 1) collaborative diagnostics, where free and open communication and networking applications are used to link distributed collaborators for reciprocal assistance in organizing and interpreting digital diagnostic data; and 2) commodity engineering, which leverages globally available consumer electronics and open-source informatics applications, to build generic open systems that measure needed information in ways substantially equivalent to more complex proprietary systems. Routine microscopic examination of Giemsa and fluorescently stained blood smears for diagnosing malaria is used as an example to validate the model.</p> <p>Results</p> <p>The model is used as a constraint-based guide for the design, assembly, and testing of a functioning, open, and commoditized telemicroscopy system that supports distributed acquisition, exploration, analysis, interpretation, and reporting of digital microscopy images of stained malarial blood smears while also supporting remote diagnostic tracking, quality assessment and diagnostic process development.</p> <p>Conclusion</p> <p>The open telemicroscopy workstation design and use-process described here can address clinical microbiology infrastructure deficits in an economically sound and sustainable manner. It can boost capacity to deal with comprehensive measurement of disease and care outcomes in individuals and groups in a distributed and collaborative fashion. The workstation enables local control over the creation and use of diagnostic data, while allowing for remote collaborative support of diagnostic data interpretation and tracking. It can enable global pooling of malaria disease information and the development of open, participatory, and adaptable laboratory medicine practices. The informatic model highlights how the larger issue of access to generic commoditized measurement, information processing, and communication technology in both high- and low-income countries can enable diagnostic services that are much less expensive, but substantially equivalent to those currently in use in high-income countries.</p

A middleware service for fault-tolerant group communications

PhD ThesisMany distributed applications require multicast group communication services, enabling an entity to interact with a group of other entities. Providing the reliability and ordering guarantees required by group based applications is not a trivial task in distributed systems where computation and communication delays might not be known accurately. Furthermore, the approaches available to support these guarantees are diverse. The choice of approach may significantly effect the performance of an application and/or may not be suitable for some application types. Nowadays, distributed applications are frequently built as a Middleware service. The Thesis develops techniques for providing group communication support in Middleware environments. A group communication service has been designed and implemented in such a way as not to hinder the interoperability/portability of applications built using it. The service provides a variety of functions that may be tailored to suit many different types of applications. Group communication protocols are presented that ensure reliability and ordering guarantees. Furthermore, the reliability and ordering guarantees of such protocols may be tailored to suit a wide variety of applications. Mechanisms that provide a variety of approaches to inter-member and inter-group interactions that are suitable for satisfying the requirements of many different types of applications (e.g., fault- tolerant, collaborative) are also supported. The service can work over local and wide area networks (Internet).Hewlett Packard laboratories Engineering and Physical Science Research Counci

A Topology-Aware Approach for Distributed Data Reconciliation in P2P Networks

International audienceA growing number of collaborative applications are being built on top of Peer-to-Peer (P2P) networks which provide scalability and support dynamic behavior. However, the distributed algorithms used by these applications typically introduce multiple communications and interactions between nodes. This is because P2P networks are constructed independently of the underlying topology, which may cause high latencies and communication overheads. In this paper, we propose a topology-aware approach that exploits physical topology information to perform P2P distributed data reconciliation, a major function for collaborative applications. Our solution (P2P-Reconciler-TA) relies on dynamically selecting nodes to execute specific steps of the algorithm, while carefully placing relevant data. We show that P2P-Reconciler-TA introduces a gain of 50% compared to P2P-Reconciler and still scales up

A Topology-Aware Approach for Distributed Data Reconciliation in P2P Networks

International audienceA growing number of collaborative applications are being built on top of Peer-to-Peer (P2P) networks which provide scalability and support dynamic behavior. However, the distributed algorithms used by these applications typically introduce multiple communications and interactions between nodes. This is because P2P networks are constructed independently of the underlying topology, which may cause high latencies and communication overheads. In this paper, we propose a topology-aware approach that exploits physical topology information to perform P2P distributed data reconciliation, a major function for collaborative applications. Our solution (P2P-Reconciler-TA) relies on dynamically selecting nodes to execute specific steps of the algorithm, while carefully placing relevant data. We show that P2P-Reconciler-TA introduces a gain of 50% compared to P2P-Reconciler and still scales up

Uncertainty Estimation in Multi-Agent Distributed Learning

Traditionally, IoT edge devices have been perceived primarily as low-power components with limited capabilities for autonomous operations. Yet, with emerging advancements in embedded AI hardware design, a foundational shift paves the way for future possibilities. Thus, the aim of the KDT NEUROKIT2E project is to establish a new open-source framework to further facilitate AI applications on edge devices by developing new methods in quantization, pruning-aware training, and sparsification. These innovations hold the potential to expand the functional range of such devices considerably, enabling them to manage complex Machine Learning (ML) tasks utilizing local resources and laying the groundwork for innovative learning approaches. In the context of 6G's transformative potential, distributed learning among independent agents emerges as a pivotal application, attributed to 6G networks' support for ultra-reliable low-latency communication, enhanced data rates, and advanced edge computing capabilities. Our research focuses on the mechanisms and methodologies that allow edge network-enabled agents to engage in collaborative learning in distributed environments. Particularly, one of the key issues within distributed collaborative learning is determining the degree of confidence in the learning results, considering the spatio-temporal locality of data sets perceived by independent agents.Comment: Poster for SAL Symposium on 6G. 22 November 2023 - 23 November 2023 Linz, Austri

Development of mobile agent framework in wireless sensor networks for multi-sensor collaborative processing

Recent advances in processor, memory and radio technology have enabled production of tiny, low-power, low-cost sensor nodes capable of sensing, communication and computation. Although a single node is resource constrained with limited power, limited computation and limited communication bandwidth, these nodes deployed in large number form a new type of network called the wireless sensor network (WSN). One of the challenges brought by WSNs is an efficient computing paradigm to support the distributed nature of the applications built on these networks considering the resource limitations of the sensor nodes. Collaborative processing between multiple sensor nodes is essential to generate fault-tolerant, reliable information from the densely-spatial sensing phenomenon. The typical model used in distributed computing is the client/server model. However, this computing model is not appropriate in the context of sensor networks. This thesis develops an energy-efficient, scalable and real-time computing model for collaborative processing in sensor networks called the mobile agent computing paradigm. In this paradigm, instead of each sensor node sending data or result to a central server which is typical in the client/server model, the information processing code is moved to the nodes using mobile agents. These agents carry the execution code and migrate from one node to another integrating result at each node. This thesis develops the mobile agent framework on top of an energy-efficient routing protocol called directed diffusion. The mobile agent framework described has been mapped to collaborative target classification application. This application has been tested in three field demos conducted at Twentynine palms, CA; BAE Austin, TX; and BBN Waltham, MA

On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent Gaussian random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this paper, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201