Application of Web Services to a Simulation Framework

The Joint Semi-Automated Forces (JSAF) simulator is an excellent tool for military training and a great testbed for new SAF behaviors. However, it has the drawback that behaviors must be ported into its own Finite State Machine (FSM) language. Web Services is a growing technology that seamlessly connects service providers to service consumers. This work attempts to merge these two technologies by modeling SAF behaviors as web services. The JSAF simulator is then modeled as a web service consumer. This approach allows new Semi-Automated Forces (SAF) behaviors to be developed independently of the simulator, which provides the developer with greater flexibility when choosing a programming language, development environment, and development platform. In addition to new SAF behaviors, this approach also supports any external component that can be modeled as a web service. Furthermore, these services are often run over a network, which distributes the computational load across several computers. Finally, hosting copies of a single service on several machines, a concept similar to file-sharing mirrors, offers an environment for load-balancing. This means if several entities are running the same behavior, a single server does not perform the computation for every entity. Instead, each entity is assigned to a specific server, increasing the quality of service seen by the system. A Web Services framework linking JSAF with several services is designed and implemented. Suppression of Enemy Air Defense (SEAD) behaviors written in MATLAB and a behavior recognition system are integrated with JSAF. These behaviors and the recognition tool were developed by other researchers, independent of this work. Results show that offloading computation to other machines is beneficial, especially when the simulation system is under heavy load. Preliminary results also indicate that load-balancing performs much better than using a single server

Cross-Layer Peer-to-Peer Track Identification and Optimization Based on Active Networking

P2P applications appear to emerge as ultimate killer applications due to their ability to construct highly dynamic overlay topologies with rapidly-varying and unpredictable traffic dynamics, which can constitute a serious challenge even for significantly over-provisioned IP networks. As a result, ISPs are facing new, severe network management problems that are not guaranteed to be addressed by statically deployed network engineering mechanisms. As a first step to a more complete solution to these problems, this paper proposes a P2P measurement, identification and optimisation architecture, designed to cope with the dynamicity and unpredictability of existing, well-known and future, unknown P2P systems. The purpose of this architecture is to provide to the ISPs an effective and scalable approach to control and optimise the traffic produced by P2P applications in their networks. This can be achieved through a combination of different application and network-level programmable techniques, leading to a crosslayer identification and optimisation process. These techniques can be applied using Active Networking platforms, which are able to quickly and easily deploy architectural components on demand. This flexibility of the optimisation architecture is essential to address the rapid development of new P2P protocols and the variation of known protocols

Using cooperation to improve the experience of web services consumers

Web Services (WS) are one of the most promising approaches for building loosely coupled systems. However, due to the heterogeneous and dynamic nature of the WS environment, ensuring good QoS is still non-trivial. While WS tend to scale better than tightly coupled systems, they introduce a larger communication overhead and are more susceptible to server/resource latency. Traditionally this problem has been addressed by relying on negotiated Service Level Agreement to ensure the required QoS, or the development of elaborate compensation handlers to minimize the impact of undesirable latency. This research focuses on the use of cooperation between consumers and providers as an effective means of optimizing resource utilization and consumer experiences. It introduces a novel cooperative approach to implement the cooperation between consumers and providers

Service Level Agreement Driven Adaptive Resource Management For Web Applications on Heterogeneous Compute Clouds

Cloud computing is an emerging topic in the field of parallel and distributed computing. Many IT giants such as IBM, Sun, Amazon, Google, and Microsoft are promoting and offering various storage and compute clouds. Clouds provide services such as high performance computing, storage, and application hosting. Cloud providers are expected to ensure Quality of Service (QoS) through a Service Level Agreement (SLA) between the provider and the consumer. In this research, I develop a heterogeneous testbed compute cloud and investigate adaptive management of resources for Web applications to satisfy a SLA that enforces specific response time requirements. I develop a system on top of EUCALYTPUS framework that actively monitors the response time of the compute resources assign to a Web application and dynamically allocates the resources required by the application to satisfy the specific response time requirements

DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning

Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human intervention to adapt to workload fluctuations. However, autoscaling microservice is challenging due to various factors. In particular, complex, time-varying service dependencies are difficult to quantify accurately and can lead to cascading effects when allocating resources. This paper presents DeepScaler, a deep learning-based holistic autoscaling approach for microservices that focus on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. DeepScaler employs (i) an expectation-maximization-based learning method to adaptively generate affinity matrices revealing service dependencies and (ii) an attention-based graph convolutional network to extract spatio-temporal features of microservices by aggregating neighbors' information of graph-structural data. Thus DeepScaler can capture more potential service dependencies and accurately estimate the resource requirements of all services under dynamic workloads. It allows DeepScaler to reconfigure the resources of the interacting services simultaneously in one resource provisioning operation, avoiding the cascading effect caused by service dependencies. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice that not only allocates resources accurately but also adapts to dependencies changes, significantly reducing SLA violations by an average of 41% at lower costs.Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

A service-oriented Grid environment with on-demand QoS support

Grid Computing entstand aus der Vision für eine neuartige Recheninfrastruktur, welche darauf abzielt, Rechenkapazität so einfach wie Elektrizität im Stromnetz (power grid) verfügbar zu machen. Der entsprechende Zugriff auf global verteilte Rechenressourcen versetzt Forscher rund um den Globus in die Lage, neuartige Herausforderungen aus Wissenschaft und Technik in beispiellosem Ausmaß in Angriff zu nehmen. Die rasanten Entwicklungen im Grid Computing begünstigten auch Standardisierungsprozesse in Richtung Harmonisierung durch Service-orientierte Architekturen und die Anwendung kommerzieller Web Services Technologien. In diesem Kontext ist auch die Sicherung von Qualität bzw. entsprechende Vereinbarungen über die Qualität eines Services (QoS) wichtig, da diese vor allem für komplexe Anwendungen aus sensitiven Bereichen, wie der Medizin, unumgänglich sind. Diese Dissertation versucht zur Entwicklung im Grid Computing beizutragen, indem eine Grid Umgebung mit Unterstützung für QoS vorgestellt wird. Die vorgeschlagene Grid Umgebung beinhaltet eine sichere Service-orientierte Infrastruktur, welche auf Web Services Technologien basiert, sowie bedarfsorientiert und automatisiert HPC Anwendungen als Grid Services bereitstellen kann. Die Grid Umgebung zielt auf eine kommerzielle Nutzung ab und unterstützt ein durch den Benutzer initiiertes, fallweises und dynamisches Verhandeln von Serviceverträgen (SLAs). Das Design der QoS Unterstützung ist generisch, jedoch berücksichtigt die Implementierung besonders die Anforderungen von rechenintensiven und zeitkritischen parallelen Anwendungen, bzw. Garantien f¨ur deren Ausführungszeit und Preis. Daher ist die QoS Unterstützung auf Reservierung, anwendungsspezifische Abschätzung und Preisfestsetzung von Ressourcen angewiesen. Eine entsprechende Evaluation demonstriert die Möglichkeiten und das rationale Verhalten der QoS Infrastruktur. Die Grid Infrastruktur und insbesondere die QoS Unterstützung wurde in Forschungs- und Entwicklungsprojekten der EU eingesetzt, welche verschiedene Anwendungen aus dem medizinischen und bio-medizinischen Bereich als Services zur Verfügung stellen. Die EU Projekte GEMSS und Aneurist befassen sich mit fortschrittlichen HPC Anwendungen und global verteilten Daten aus dem Gesundheitsbereich, welche durch Virtualisierungstechniken als Services angeboten werden. Die Benutzung von Gridtechnologie als Basistechnologie im Gesundheitswesen ermöglicht Forschern und Ärzten die Nutzung von Grid Services in deren Arbeitsumfeld, welche letzten Endes zu einer Verbesserung der medizinischen Versorgung führt.Grid computing emerged as a vision for a new computing infrastructure that aims to make computing resources available as easily as electric power through the power grid. Enabling seamless access to globally distributed IT resources allows dispersed users to tackle large-scale problems in science and engineering in unprecedented ways. The rapid development of Grid computing also encouraged standardization, which led to the adoption of a service-oriented paradigm and an increasing use of commercial Web services technologies. Along these lines, service-level agreements and Quality of Service are essential characteristics of the Grid and specifically mandatory for Grid-enabling complex applications from certain domains such as the health sector. This PhD thesis aims to contribute to the development of Grid technologies by proposing a Grid environment with support for Quality of Service. The proposed environment comprises a secure service-oriented Grid infrastructure based on standard Web services technologies which enables the on-demand provision of native HPC applications as Grid services in an automated way and subject to user-defined QoS constraints. The Grid environment adopts a business-oriented approach and supports a client-driven dynamic negotiation of service-level agreements on a case-by-case basis. Although the design of the QoS support is generic, the implementation emphasizes the specific requirements of compute-intensive and time-critical parallel applications, which necessitate on-demand QoS guarantees such as execution time limits and price constraints. Therefore, the QoS infrastructure relies on advance resource reservation, application-specific resource capacity estimation, and resource pricing. An experimental evaluation demonstrates the capabilities and rational behavior of the QoS infrastructure. The presented Grid infrastructure and in particular the QoS support has been successfully applied and demonstrated in EU projects for various applications from the medical and bio-medical domains. The EU projects GEMSS and Aneurist are concerned with advanced e-health applications and globally distributed data sources, which are virtualized by Grid services. Using Grid technology as enabling technology in the health domain allows medical practitioners and researchers to utilize Grid services in their clinical environment which ultimately results in improved healthcare

Connecting Vehicles to the Internet - Strategic Data Transmission for Mobile Nodes using Heterogeneous Wireless Networks

With the advent of autonomous driving, the driving experience for users of connected vehicles changes, as they may enjoy their travel time with entertainment, or work productively. In our modern society, both require a stable Internet access. However, future mobile networks are not expected to be able to satisfy application Quality of Service (QoS) requirements as needed, e.g. during rush hours. To address this problem, this dissertation investigates data transmission strategies that exploit the potential of using a heterogeneous wireless network environment. To this end, we combine two so far distinct concepts, firstly, network selection and, secondly, transmission time selection, creating a joint time-network selection strategy. It allows a vehicle to plan delay-tolerant data transmissions ahead, favoring transmission opportunities with the best prospective flow-network matches. In this context, our first contribution is a novel rating model for perceived transmission quality, which assesses transmission opportunities with respect to application QoS requirement violations, traded off by monetary cost. To enable unified assessment of all data transmissions, it generalizes existing specialized rating models from network selection and transmission time selection and extends them with a novel throughput requirement model. Based on that, we develop a novel joint time-network selection strategy, Joint Transmission Planning (JTP), as our second contribution, planning optimized data transmissions within a defined time horizon. We compare its transmission quality to that of three predominant state-of-the-art transmission strategies, revealing that JTP outperforms the others significantly by up to 26%. Due to extensive scenario variation, we discover broad stability of JTP reaching 87-91% of the optimum. As JTP is a planning approach relying on prediction data, the transmission quality is strongly impaired when executing its plans under environmental changes. To mitigate this impact, we develop a transmission plan adaptation as our third contribution, modifying the planned current transmission online in order to comply with the changes. Even under strong changes of the vehicle movement and the network environment, it sustains 57%, respectively 36%, of the performance gain from planning. Finally, we present our protocol Mobility management for Vehicular Networking (MoVeNet), pooling available network resources of the environment to enable flexible packet dispatching without breaking connections. Its distributed architecture provides broad scalability and robustness against node failures. It complements control mechanisms that allow a demand-based and connection-specific trade-off between overhead and latency. Less than 9 ms additional round trip time in our tests, instant handover and 0 to 4 bytes per-packet overhead prove its efficiency. Employing the presented strategies and mechanisms jointly, users of connected vehicles and other mobile devices can significantly profit from the demonstrated improvements in application QoS satisfaction and reduced monetary cost

Actas da 10ª Conferência sobre Redes de Computadores

Universidade do MinhoCCTCCentro AlgoritmiCisco SystemsIEEE Portugal Sectio

Applications of agent architectures to decision support in distributed simulation and training systems

This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool. The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees. The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests