Learning to integrate reactivity and deliberation in uncertain planning and scheduling problems

This paper describes an approach to planning and scheduling in uncertain domains. In this approach, a system divides a task on a goal by goal basis into reactive and deliberative components. Initially, a task is handled entirely reactively. When failures occur, the system changes the reactive/deliverative goal division by moving goals into the deliberative component. Because our approach attempts to minimize the number of deliberative goals, we call our approach Minimal Deliberation (MD). Because MD allows goals to be treated reactively, it gains some of the advantages of reactive systems: computational efficiency, the ability to deal with noise and non-deterministic effects, and the ability to take advantage of unforseen opportunities. However, because MD can fall back upon deliberation, it can also provide some of the guarantees of classical planning, such as the ability to deal with complex goal interactions. This paper describes the Minimal Deliberation approach to integrating reactivity and deliberation and describe an ongoing application of the approach to an uncertain planning and scheduling domain

A Decision Support System for Effective Scheduling in an F-16 Pilot Training Squadron

Scheduling of flights for a flight training squadron involves the coordination of time and resources in a dynamic environment. The generation of a daily flight schedule (DFS) requires the proper coordination of resources within established time windows. This research provides a decision support tool to assist in the generation of the DFS. Three different priority rules are investigated for determining an initial ordering of flights and a shifting bottleneck heuristic is used to establish a candidate DFS. A user interface allows a scheduler to interact with the decision support tool during the DFS generation process. Furthermore, the decision support tool provides the capability to produce a weekly schedule for short-term planning purposes as well as the entire flight training program schedule for long- term planning purposes

CloudMedia: When cloud on demand meets video on demand

Internet-based cloud computing is a new computing paradigm aiming to provide agile and scalable resource access in a utility-like fashion. Other than being an ideal platform for computation-intensive tasks, clouds are believed to be also suitable to support large-scale applications with periods of flash crowds by providing elastic amounts of bandwidth and other resources on the fly. The fundamental question is how to configure the cloud utility to meet the highly dynamic demands of such applications at a modest cost. In this paper, we address this practical issue with solid theoretical analysis and efficient algorithm design using Video on Demand (VoD) as the example application. Having intensive bandwidth and storage demands in real time, VoD applications are purportedly ideal candidates to be supported on a cloud platform, where the on-demand resource supply of the cloud meets the dynamic demands of the VoD applications. We introduce a queueing network based model to characterize the viewing behaviors of users in a multichannel VoD application, and derive the server capacities needed to support smooth playback in the channels for two popular streaming models: client-server and P2P. We then propose a dynamic cloud resource provisioning algorithm which, using the derived capacities and instantaneous network statistics as inputs, can effectively support VoD streaming with low cloud utilization cost. Our analysis and algorithm design are verified and extensively evaluated using large-scale experiments under dynamic realistic settings on a home-built cloud platform. © 2011 IEEE.published_or_final_versionThe 31st International Conference on Distributed Computing Systems (ICDCS 2011), Minneapolis, MN., 20-24 June 2011. In Proceedings of 31st ICDCS, 2011, p. 268-27