Optimising and adapting the QoS of a dynamic set of inter-dependent tasks

Due to the growing complexity and adaptability requirements of real-time systems, which often exhibit unrestricted Quality of Service (QoS) inter-dependencies among supported services and user-imposed quality constraints, it is increasingly difficult to optimise the level of service of a dynamic task set within an useful and bounded time. This is even more difficult when intending to benefit from the full potential of an open distributed cooperating environment, where service characteristics are not known beforehand and tasks may be inter-dependent. This paper focuses on optimising a dynamic local set of inter-dependent tasks that can be executed at varying levels of QoS to achieve an efficient resource usage that is constantly adapted to the specific constraints of devices and users, nature of executing tasks and dynamically changing system conditions. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial solution and effectively optimise the rate at which the quality of the current solution improves as the algorithms are given more time to run, with a minimum overhead when compared against their traditional versions

Planning with time limits in BDI agent programming language

This paper provides a theoretical basis for performing time limited planning within Belief-Desire-Intention (BDI) agents. The BDI agent architecture is recognised as one of the most popular architectures for developing agents for complex and dynamic environments, in addition to which they have a strong theoretical foundation. Recent work has extended a BDI agent specification language to include HTN-style planning as a built-in feature. However, the extended semantics assume that agents have an unlimited amount of time available to perform planning, which is often not the case in many dynamic real world environments. We extend previous research by using ideas from anytime algorithms, and allow programmer control over the amount of time the agent spends on planning. We show that the resulting integrated agent specification language has advantages over regular BDI agent reasoning

Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

The scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to perform services with specific Quality of Service constraints, particularly in dynamic distributed environments where the characteristics of the computational load cannot always be predicted in advance. Our work addresses this problem by allowing resource constrained devices to cooperate with more powerful neighbour nodes, opportunistically taking advantage of global distributed resources and processing power. Rather than assuming that the dynamic configuration of this cooperative service executes until it computes its optimal output, the paper proposes an anytime approach that has the ability to tradeoff deliberation time for the quality of the solution. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial solution and effectively optimise the rate at which the quality of the current solution improves at each iteration, with an overhead that can be considered negligible

Data-driven Metareasoning for Collaborative Autonomous Systems

When coordinating their actions to accomplish a mission, the agents in a multi-agent system may use a collaboration algorithm to determine which agent performs which task. This paper describes a novel data-driven metareasoning approach that generates a metareasoning policy that the agents can use whenever they must collaborate to assign tasks. This metareasoning approach collects data about the performance of the algorithms at many decision points and uses this data to train a set of surrogate models that can estimate the expected performance of different algorithms. This yields a metareasoning policy that, based on the current state of the system, estimated the algorithms’ expected performance and chose the best one. For a ship protection scenario, computational results show that one version of the metareasoning policy performed as well as the best component algorithm but required less computational effort. The proposed data-driven metareasoning approach could be a promising tool for developing policies to control multi-agent autonomous systems.This work was supported in part by the U.S. Naval Air Warfare Center-Aircraft Division

Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources

The performance of anytime algorithms can be improved by simultaneously solving several instances of algorithm-problem pairs. These pairs may include different instances of a problem (such as starting from a different initial state), different algorithms (if several alternatives exist), or several runs of the same algorithm (for non-deterministic algorithms). In this paper we present a methodology for designing an optimal scheduling policy based on the statistical characteristics of the algorithms involved. We formally analyze the case where the processes share resources (a single-processor model), and provide an algorithm for optimal scheduling. We analyze, theoretically and empirically, the behavior of our scheduling algorithm for various distribution types. Finally, we present empirical results of applying our scheduling algorithm to the Latin Square problem

Heurísticas para el control deliberativo en una arquitectura de agentes inteligentes de tiempo real

El área de la Inteligencia Artificial está experimentado un gran avance en los últimos tiempos con su aplicación a un mayor número de campos diferentes. Uno de ellos es el de los problemas de tiempo real. Problemas donde no sólo es importante la lógica del cálculo de las soluciones, sino también el instante de tiempo en que son calculadas dichas soluciones. Este acercamiento entre ambas árear es, en principio, provechoso, pues la Inteligencia Artificial puede aportar nuevas posibilidades a los sistemas de tiempo real, como una mayor flexibilidad de adaptación a entornos complejos y dinámicos. Sin embargo esta aproximación ha presentado desde siempre importantes dificultades. Principalmente los sistemas de tiempo real poseen unos requerimientos temporales (predecibilidad de los tiempos de respuesta principalmente) que no suelen ser habituales en las técnicas de Inteligencia Artificial. Entre otras formas de abordar este problema, está el desarrollo de arquitecturas software para el diseño de agentes inteligentes para su uso en entornos de tiempo real. Estas arquitecturas poseen diferentes mecanismos para que los agentes construidos puedan trabajar en entornos de tiempo real . Estas arquitecturas poseen diferentes mecanismos para que los agentes construidos puedan trabajas en entornos de tiempo real ofreciendo comportamientos reactivos (para cumplir los requerimientos temporales) y deliberativos (que hacen uso de técnicas de Inteligencia Artificial para conseguir mejores prestaciones). Una de estas arquitecturas es ARTIS. Esta arquitectura hace uso de una planificación de sus tareas a dos niveles para conseguir complir sus objetivos. Por un lado un planificador de primer nivel garantiza la obtención de respuestas dentro de límites temporales estrictos. Un planificador de segundo nivel se encarga del control de componentes que mejoran la calidad de los resultados. El trabajo presentado se centra en este segundo planificador, desarrollando dos heurísticas, SSS.......Hérnandez López, L. (2004). Heurísticas para el control deliberativo en una arquitectura de agentes inteligentes de tiempo real [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2671Palanci

Exploiting Soft Computing for Real time performance

The classic approach to the design of real time systems is to determine worst-case scenarios for the system statically and manually and then build the system with sufficient resources to meet deadlines and goals. This approach has worked well for traditional real time systems which operate in relatively simple, well-characterized environments. However the emerging generation of complex, dynamic and uncertain real time application domains accentuates the growing need for flexible, adaptable design approaches for real time systems. With the increasing complexity of real time systems, it is becoming infeasible to build systems with sufficient resources to meet the functional and timing requirements of all application tasks at all times. What is becoming increasingly important in the new paradigm of real time computing is the need to meet deadlines with sufficient system solution quality without having to design the system to support worst case program execution. In this thesis, we explore the possibility of exploiting "soft computing" properties of kernels to meet this objective. The chief characteristic of "soft computations" is the fact that they are able to provide cruder results before they complete, or they may execute for a long time refining an already adequate result. In other words, such computations are able to provide useful/incremental results before fully completing execution. More specifically they provide a trade-off between computation time and algorithm solution quality. This thesis addresses the design issues involved in building a system that exploits the "soft computing" properties of kernels to optimize real time performance. In this context, we make the following contributions. Firstly we build a system prototype of a real time situational assessment scenario. We thereafter identify "soft computations" in the system and characterize the computation time/solution quality trade-off opportunities provided by them using performance profiles. Thirdly, we introduce a method to use performance profile based models at run time to determine the optimal composition of different "soft computations" in order to meet real time deadlines with sufficient system solution quality. We quantify the gains from our method both in terms of functional correctness of the system as well as CPU utilization as compared to conventional real time scheduling techniques. We observe that our dynamic scheduling scheme on an average is able to meet the system goals with 39% more accuracy with no missed deadlines as compared to conventional real time scheduling techniques for various design points that do not support worst case behavior. In addition, our method is able to meet the system objective while being highly utilized. Most importantly, our scheme exploits the soft computing properties of kernels to facilitate the design of the system at less aggressive design points while meeting deadlines and system goals at the same level as conventional real time design methodology. Finally, we perform an experimental study to understand the sensitivity of performance profiles to various input data parameters and identify the potential for online learning of performance profiles