Vahe Poladian

Task-based adaptation for ubiquitous computing

An important domain for autonomic systems is the area of ubiquitous computing: users are increasingly surrounded by technology that is heterogeneous, pervasive, and variable. In this paper we describe our work in developing self-adapting computing infrastructure that automates the configuration and reconfiguration of such environments. Focusing on the engineering issues of self-adaptation in the presence of heterogeneous platforms, legacy applications, mobile users, and resource variable environments, we describe a new approach based on the following key ideas: 1) explicit representation of user tasks allows us to determine what service qualities are required of a given configuration; 2) decoupling task and preference specification from the lower level mechanisms that carry out those preferences provides a clean engineering separation of concerns between what is needed and how it is carried out; and 3) efficient algorithms allow us to calculate in real time near-optimal resource allocations and reallocations for a given task

Dynamic configuration of resource-aware services

An important emerging requirement for computing systems is the ability to adapt at run time, taking advantage of local computing devices, and coping with dynamically changing resources. Three specific technical challenges in satisfying this requirement are to (1) select an appropriate set of applications or services to carry out a users task, (2) allocate (possibly scarce) resources among those applications, and (3) reconfigure the applications or resource assignments if the situation changes. In this paper, we show how to provide a shared infrastructure that automates configuration decisions given a specification of the users task. The heart of the approach is an analytical model and an efficient algorithm that can be used at run time to make near-optimal (re)configuration decisions. We validate this approach both analytically and by applying it to a representative scenario.

Leveraging Resource Prediction for Anticipatory Dynamic Configuration

Self-adapting systems based on multiple concurrent applications must decide how to allocate scarce resources to applications and how to set the quality parameters of each application to best satisfy the user. Past work has made those decisions with analytic models that used current resource availability information: they react to recent changes in resource availability as they occur, rather than anticipating future availability. These reactive techniques may model each local decision optimally, but the accumulation of decisions over time nearly always becomes less than optimal. In this paper, we propose an approach to self- adaptation, called anticipatory configuration that leverages predictions of future resource availability to improve utility for the user over the duration of the task. The approach solves the following technical challenges: (1) how to express resource availability prediction, (2) how to combine prediction from multiple sources, and (3) how to leverage predictions continuously while improving utility to the user. Our experiments show that when certain adaptation operations are costly, anticipatory configuration provides better utility to the user than reactive configuration, while being comparable in resource demand.

Task-based self-adaptation

Recently there has been increasing interest in developing systems that can adapt dynamically to cope with changing environmental conditions and unexpected system errors. Most efforts for achieving self-adaptation have focused on the mechanisms for detecting opportunities for improvement and then taking appropriate action. However, such mechanisms beg the question: what is the system trying to achieve? In a given situation there may be many possible adaptations, and knowing which one to pick is a difficult question. In this paper we advocate the use of explicit representation of user task as a critical element in addressing this missing link.

Improving Architecture-Based Self-Adaptation through Resource Prediction

An increasingly important concern for modern systems design is how best to incorporate self-adaptation into systems so as to improve their ability to dynamically respond to faults, resource variation, and changing user needs. One promising approach is to use architectural models as a basis for monitoring, problem detection, and repair selection. While this approach has been shown to yield positive results, current systems use a reactive approach: they respond to problems only when they occur. In this paper we argue that self-adaptation can be improved by adopting an anticipatory approach in which predictions are used to inform adaptation strategies. We show how such an approach can be incorporated into an architecture-based adaptation framework and demonstrate the benefits of the approach.

uDesign: End-User Design Applied to Monitoring and Control Applications for Smart Spaces

This paper introduces an architectural style for enabling end-users to quickly design and deploy software systems in domains characterized by highly personalized and dynamic requirements. The style offers an intuitive metaphor based on boxes, pipes, and wires, but retains enough preciseness that systems can be automatically assembled and dynamically reconfigured based on uDesign descriptions. uDesign was primarily motivated and validated within monitoring and control applications for smart spaces, but we envision possible extensions to other domains. Our contribution differs from early attempts at end- user programming by dealing with higher level software architectural abstractions rather than programming, and by addressing run-time descriptions rather than code structures. The paper presents validation of uDesign along the following aspects: (a) expressiveness, by means of two case studies, one in health care, and one in home security, (b) soundness, by providing uDesigns formal semantics, and (c) implementability, by describing a mapping of uDesign to an existing software infrastructure: the Aura infrastructure.

Character Formulas for q -Rook Monoid Algebras

AbstractThe q-rook monoid Rn(q) is a semisimple ℂ(q)-algebra that specializes when q → 1 to ℂ[Rn], where Rn is the monoid of n × n matrices with entries from {0, 1} and at most one nonzero entry in each row and column. We use a Schur-Weyl duality between Rn(q) and the quantum general linear group

Anticipatory configuration of resource-aware applications

U_q {\mathfrak{g}}{\mathfrak{l}}(r)