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Kimmo Raatikainen Chiara Braghin Stefano Campadello Heikki Helin Oskari Koskimies

Kimmo Raatikainen Chiara Braghin Stefano Campadello Heikki Helin Oskari Koskimies Pauli Misikangas Mikko Mäkelä Sampo Pyysalo Sasu Tarkoma. University of Helsinki, Finland. Overview of Presentation. Monads project Goals Architecture Predicting Quality of Service

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Kimmo Raatikainen Chiara Braghin Stefano Campadello Heikki Helin Oskari Koskimies

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  1. Kimmo Raatikainen Chiara Braghin Stefano Campadello Heikki Helin Oskari Koskimies Pauli Misikangas Mikko Mäkelä Sampo Pyysalo Sasu Tarkoma University of Helsinki, Finland

  2. Overview of Presentation • Monads project • Goals • Architecture • Predicting Quality of Service • Other Research Issues • Agent Communication Layers • Wireless Java RMI • Partitioning Applications with Agents

  3. Monads Research Project • Areas of research • usefulness of agents in mobile computing? • (Dis)Connection Management • Bandwidth Optimisation • Easy to use API • Supporting Communication with legacy software • agent communication in wireless environments • adaptability to available resources • short-term predictions of available resources

  4. The Nomadic environment • A mobile computer connected to a network with a wireless connection • Characteristics of wireless networks • low throughput, highly variable delays, sudden disconnections, ... • Nomadic users need special support • Intelligent and adaptable applications • Optimized communication

  5. Monads Adaptation Model Adaptability Adaptability User Interface Agent Service Agent Service Terminal Equipment Co-operation Data Comm. Agent Adaptability Communications Infrastructure

  6. Monads layered Architecture

  7. Monads System Services

  8. Predicting QOS • Minimal Adaptation • Detect changes in the Quality-of-Service • Adapt to the current QoS • Adaptation may come too late • Uses of Prediction • Scheduling decisions, Data Prefetching, Connection Management • Examples • What is the expected data transfer within next ‘t’ seconds • What is the expected time to transfer the ‘x’ KB of data

  9. Learning to Predict QOS • Predictions can be achieved by learning how certain quantities affect the QoS • location of the terminal • time of day • day of week • recent QoS values • Division into sub-problems: • predicting terminal movement • predicting QoS at given location and time

  10. System Architecture

  11. QOS Prediction - System Components • Location Service • Reads location information from a GPS device • Builds a Waypoint Map • Represents the current location as a point on a straight between two waypoints • QOS Management Service • Provides information about the current QoS • Perception Service • Centralized collection of observable values

  12. System components (contd..) • Route Modeler Agent • Actively learns the regular routes of a user • Provides predictions about user movement • given a sequence of waypoints, which will be the following waypoints? • QOS Modeler Agent • Actively learns the characteristics of the QoS on the routes traveled • Provides predictions about the QoS at given location and time

  13. System components (contd..) • QOS Prediction Agent • An intelligent agent that provides QoS predictions • combines predictions made by the modeler agents • Selects between alternative ways to predict • Informs other agents when the QoS is about to change significantly

  14. Communication Layers • Interaction Protocol • FIPA-query, FIPA-request • Message Coupling • Communication Language • KQML, FIPA-ACL • Binary Encoding of Message/Content • Message Transport • HTTP,RMI,IIOP • Transport Protocol • SMS,MDCP,TCP

  15. Related Research Results • Optimized RMI • Adaptation through Application Partitioning • terminal characteristics • wireless link quality

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