An Agent Architecture to fulfill Real-Time Requirements

In this paper we present AMSIA, an agent architecture that combines the possibility of using di erent reasoning methods with a mechanism to control the resources needed by the agent to ful ll its high level objectives. The architecture is based on the blackboard paradigm which o ers the possibility of combining di erent reasoning techniques and opportunistic behavior. The AMSIA architecture adds a representation of plans of objectives allowing di erent reasoning activities to create plans to guide the future behavior of the agent. The opportunism is in the acquisition of high-level objectives and in the modi cation of the predicted activity when something doesn't happen as expected. A control mechanism is responsible for the translation of plans of objectives to concrete activities, considering resource-boundedness. To do so, all the activity in the agent (including control) is explicitly scheduled, but allowing the necessary exibility to make changes in the face of contingencies that are expected in dynamic environments. Experimental work is also presented

Investigations into Elasticity in Cloud Computing

The pay-as-you-go model supported by existing cloud infrastructure providers is appealing to most application service providers to deliver their applications in the cloud. Within this context, elasticity of applications has become one of the most important features in cloud computing. This elasticity enables real-time acquisition/release of compute resources to meet application performance demands. In this thesis we investigate the problem of delivering cost-effective elasticity services for cloud applications. Traditionally, the application level elasticity addresses the question of how to scale applications up and down to meet their performance requirements, but does not adequately address issues relating to minimising the costs of using the service. With this current limitation in mind, we propose a scaling approach that makes use of cost-aware criteria to detect the bottlenecks within multi-tier cloud applications, and scale these applications only at bottleneck tiers to reduce the costs incurred by consuming cloud infrastructure resources. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness by studying the behaviour of an example electronic commerce site application. Furthermore, we consider the characteristics of the algorithm for implementing the business logic of cloud applications, and investigate the elasticity at the algorithm level: when dealing with large-scale data under resource and time constraints, the algorithm's output should be elastic with respect to the resource consumed. We propose a novel framework to guide the development of elastic algorithms that adapt to the available budget while guaranteeing the quality of output result, e.g. prediction accuracy for classification tasks, improves monotonically with the used budget.Comment: 211 pages, 27 tables, 75 figure

Assessing the performance of human-automation collaborative planning systems

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 215-221).Planning and Resource Allocation (P/RA) Human Supervisory Control (HSC) systems utilize the capabilities of both human operators and automated planning algorithms to schedule tasks for complex systems. In these systems, the human operator and the algorithm work collaboratively to generate new scheduling plans, each providing a unique set of strengths and weaknesses. A systems engineering approach to the design and assessment of these P/RA HSC systems requires examining each of these aspects individually, as well as examining the performance of the system as a whole in accomplishing its tasks. An obstacle in this analysis is the lack of a standardized testing protocol and a standardized set of metric classes that define HSC system performance. An additional issue is the lack of a comparison point for these revolutionary systems, which must be validated with respect to current operations before implementation. This research proposes a method for the development of test metrics and a testing protocol for P/RA HSC systems. A representative P/RA HSC system designed to perform high-level task planning for deck operations on United States Naval aircraft carriers is utilized in this testing program. Human users collaborate with the planning algorithm to generate new schedules for aircraft and crewmembers engaged in carrier deck operations. A metric class hierarchy is developed and used to create a detailed set of metrics for this system, allowing analysts to detect variations in performance between different planning configurations and to depict variations in performance for a single planner across levels of environment complexity. In order to validate this system, these metrics are applied in a testing program that utilizes three different planning conditions, with a focus on validating the performance of the combined Human-Algorithm planning configuration. Experimental result analysis revealed that the experimental protocol was successful in providing points of comparison for planners within a given scenario while also being able to explain the root causes of variations in performance between planning conditions. The testing protocol was also able to provide a description of relative performance across complexity levels. The results demonstrate that the combined Human-Algorithm planning condition performed poorly for simple and complex planning conditions, due to errors in the recognition of a transient state condition and in modeling the effects of certain actions, respectively. The results also demonstrate that Human planning performance was relatively consistent as complexity increased, while combined Human-Algorithm planning was effective only in moderate complexity levels. Although the testing protocol used for these scenarios and this planning algorithm was effective, several limiting factors should be considered. Further research must address how the effectiveness of the defined metrics and the test methodology changes as different types of planning algorithms are utilized and as a larger number of human test subjects are incorporated.by Jason C. Ryan.S.M