Abstract: 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: (a) Explicit representation of user tasks allows us to determine what service qualities are required of a given configuration; (b) 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 (c) Efficient algorithms allow us to calculate in real time near-optimal resource allocations and reallocations for a given task.

Abstract: The most precious resource in a computer system is no longer its processor, memory, disk, or network, but rather human attention. Aura aims to minimize distractions on a user's attention, creating an environment that adapts to the user's context and needs.

Abstract: This chapter introduces a new way of thinking about software systems for supporting the activities of end-users. In this approach, models of user activities are promoted to first class entities, and software systems are assembled and configured dynamically based on activity models. This constitutes a fundamental change of perspective over traditional applications: activities take the main stage and may be long-lived, whereas the agents that carry them out are plentiful and interchangeable. The core of the chapter describes a closed-loop control design that enables activity-oriented systems to become self-aware and self-configurable, and to adapt to dynamic changes both in the requirements of user activities and in the environment resources. The chapter discusses how that design addresses challenges such as user mobility, resolving conflicts in accessing scarce resources, and robustness in the broad sense of responding adequately to user expectations, even in unpredictable situations, such as random failures, erroneous user input, and continuously changing resources. The chapter further summarizes challenges and ongoing work related to managing activities where humans and automated agents collaborate, human-computer interactions for managing activities, and privacy and security aspects.

Abstract: This paper presents a framework for engineering resource-adaptive software targeted at small mobile devices. Rather than building a solution from scratch, we extend and integrate existing work on software infrastructures for ubiquitous computing, and on resource-adaptive applications. This paper addresses two research questions: first, is it feasibility to coordinate resource allocation and adaptation policies among several applications in a way that is both effective and efficient. And second, can end-users understand and control such adaptive behaviors dynamically, depending on user-defined goals for each activity. The evaluation covered both the systems and the usability perspectives, the latter by means of a user study. The contributions of this work are: first, a set of design guidelines, including APIs for integrating new applications; second, a concrete infrastructure that implements the guidelines. And third, a way to model quality of service tradeoffs based on utility theory, which our research indicates end-users with diverse backgrounds are able to leverage for guiding the adaptive behaviors towards activity-specific quality goals.

Abstract: Context is not simply the state of a predefined environment with a fixed set of interaction resources. It's part of a process of interacting with an ever-changing environment composed of reconfigurable, migratory, distributed, and multiscale resources.

Abstract: Internet of Things (IoT) allows for cyber-physical applications to be created and composed to provide intelligent support or automation of end-user tasks. For many of such tasks, human participation is crucial to the success and the quality of the tasks. The cyber systems should proactively request help from the humans to accomplish the tasks when needed. However, the outcome of such system-human synergy may be affected by factors external to the systems. Failure to consider those factors when involving human participants in the tasks may result in suboptimal performance and negative experience on the humans. In this paper, we propose an approach for automated generation of control strategies of cyber-human systems. We investigate how explicit modeling of human participant can be used in automated planning to generate cooperative strategy of human and system to achieve a given task, by means of which best and appropriately utilize the human. Specifically, our approach consists of: (1) a formal framework for modeling cooperation between cyber system and human, and (2) a formalization of system-human cooperative task planning as strategy synthesis of stochastic multiplayer game. We illustrate our approach through an example of indoor air quality control in smart homes.

Abstract: 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 in the level of abstraction, software architecture rather than programming, and in the subject of description: run-time rather than code structures. To validate the approach, the paper presents (a) two case studies, one in health care and one in home security, (b) the formal semantics of uDesign’s primitives, and (c) a mapping of those primitives to an existing software infrastructure: the Aura infrastructure.

Abstract: Dynamic adaptive systems based on multiple concurrent applications typically employ optimization models to decide how to allocate scarce resources among the applications and how to tune their runtime settings for optimal quality-of-service according to the preferences of an end user.

Traditionally, such systems have avoided dealing with uncertainty by assuming that current snapshots of the relevant inputs are precise and by solving for an optimal system point. To achieve dynamic behavior, a system performs an optimization loop upon discovering changes in the input variables (e.g. changes in the available level of resources) and adapts the applications according to the new optimal solution. Unfortunately, when certain adaptation actions incur costs, such reactive adaptation strategies suffer from a significant shortcoming: several locally optimal decisions over time may often be less than optimal globally.

By using predictive information about the future values of the problem inputs, we can model and implement an anticipatory adaptation strategy that helps improve the global behavior of the system in many situations. However, modeling predictions requires representing and dealing with uncertainty from different sources. In this paper, we describe our proposed approach to represent multiple sources of uncertainty and outline algorithms for solving the anticipatory configuration problem with predictive inputs.

Abstract: 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.

Abstract: Computers support more and more tasks in the personal and professional activities of users. Such user tasks increasingly span large periods of time and many locations across the enterprise space and beyond. Recently there has been a growing interest in developing applications that can cope with the specific environmental conditions at each location, and adapt to dynamic changes in system resources. However, in a given situation there may be many possible configuration solutions, and an awareness of the user's intent for each task is a critical element in knowing which one to pick. In this paper, we discuss the limitations of building such awareness into applications, and propose to factor the awareness of user tasks into a common software layer. That however, brings up the problem of coordinating the system-wide adaptation performed by such a layer with fine-grain adaptation performed by resource-aware applications. We summarize the main features of an architectural framework that incorporates such a layer, and distill some of the lessons learned in implementing the framework.

Abstract: 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 user s 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 user s 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.

Abstract: Ubiquitous computing poses a number of challenges for software architecture. One of the most important is the ability to design software systems that accommodate dynamically-changing resources. Resource variability arises naturally in a ubiquitous computing setting through user mobility (a user moves from one computing environment to another), and through the need to exploit time-varying resources in a given environment (such as wireless bandwidth). Traditional approaches to handling resource variability in applications attempt to address the problem by imposing uniformity on the environment. We argue that those approaches are inadequate, and describe an alternative architectural framework that is better matched to the needs of ubiquitous computing. A key feature of the architecture is that user tasks become first class entities. User proxies, or Auras, use models of user tasks to set up, monitor and adapt computing environments proactively. The architectural framework has been implemented and currently being used as a central component of Project Aura, a campus-wide ubiquitous computing effort.

Abstract: An important requirement for pervasive computing systems is the ability to adapt at runtime to handle varying resources, user mobility, changing user needs, and system faults. In this paper we describe an approach in which dynamic adaptation is supported by the use of software architectural models to monitor an application and guide dynamic changes to it. The use of externalized models permits one to make reconfiguration decisions based on a global per-spective of the running system, apply analytic models to determine correct re-pair strategies, and gauge the effectiveness of repair through continuous system monitoring. We illustrate the application of this idea to pervasive computing systems, focusing on the need to adapt based on performance-related criteria and models.

Abstract: Being able to find, adapt, and incorporate disparate components to form working, reliable applications is the goal of component-based software engineering. To date, there has been a lot of research, among other things, on locating components, reasoning about component compatibility, and methods for interoperability. Pervasive computing raises a number of new challenges for component-based software engineering that heretofore have been given little attention, such as mobility, adaptability, and resource awareness. In this paper we motivate and discuss the need for research in these areas, and discuss how our work in the Aura group at Carnegie Mellon University helps to address these issues.

Abstract: The use of largely isolated workstations and laptops as the primary carrier of personal computing is giving way to a world of pervasive computing populated by ubiquitous networks of heterogeneous computing and information facilities. In such a world the boundaries between a user’s personal computation space and other people’s is no longer well defined. As a result, collaboration becomes the dominant mode of computing – in essence, all systems become the basis for on-going computer-supported cooperative work.

Abstract: Advances in computing and networking are prompting users to change their expectations about the availability of computing. Instead of making primary use of a single machine, users may expand their computer-supported tasks across multiple locations, and they may work on some tasks for days or even months. It is well known that such tasks typically involve several applications and information resources, making it a chore to rebuild the state of devices and software for resuming a task interrupted somewhere else or sometime ago. Unfortunately, current systems offer little support for scaling task management in space and in time, and consequently users are torn between taking advantage of increasingly pervasive computing systems, and the price (in attention and skill) that they have to pay for using them. This dissertation describes a new approach to the scalability of task management in space, across heterogeneous environments, and in time, allowing users to recover tasks interrupted long ago. The approach is based on high-level models of what users need from the computing environment for each of their tasks. Such models are exploited at run-time by an infrastructure that automatically configures the computing environment, on demand, on behalf of users. We present an architectural framework that grounds our approach, and that embodies new system design principles that hold independently of the particular infrastructure implementing the framework. As part of the framework, we present a utility-theoretic model that enables finding the best match between user needs and the capabilities and resources in the environment. We evaluate our research from three perspectives. First, from a user’s perspective, we validate that the infrastructure: (a) delivers the capabilities for scaling task management in space and in time; (b) that it reconciles the competing requirements of sparing users from routine configuration chores, while enabling them to take full advantage of the surrounding computing environments; and (c) that it is usable by non-experts. Second, from a software architect’s perspective, we evaluate the benefits and limitations of the architectural framework supporting our approach. And third, from a systems perspective, we validate that the infrastructure exhibits a performance that makes it usable on a daily basis.

Abstract: Addressing resource variation plays an increasingly important role in engineering today’s software sys- tems. Research in resource-adaptive applications takes an important step towards addressing this problem. However, existing solutions stop short of addressing the fact that different user tasks often have specific goals of quality of service, and that such goals often entail multiple aspects of quality of service. This paper presents a framework for engineering software systems capable of adapting to resource variations in ways that are specific to the quality goals of each user task. For that, users are empowered to specify their task-specific preferences with respect to multiple aspects of quality of service. Such prefer- ences are then exploited to both coordinate resource usage across the applications supporting the task, and to dynamically control the resource adaptation polices of those applications. A user study validates that non-expert users can use our framework to successfully control the behavior of such adaptive systems.

Abstract: Computing environments of the future should enable mobile users to take full advantage of the computing capabilities available at each location, while allowing them to focus on their real tasks, rather than being distracted by dealing with the configuration and reconfiguration of computer systems to support those tasks. The Aura infrastructure performs automatic configuration and reconfiguration of Ubicomp environments, according to the user s task and intent. This report describes the software architecture of the Aura infrastructure, and discusses the underlying rational. It describes the architecture from a layered perspective, detailing the partition of responsibility and shared assumptions, as well as from a component-connector perspective, detailing the protocols of interaction between the components (APIs and sequencing). The contents and format of the exchanged messages is extensively discussed, as well as the details pertaining service interconnection and decomposition. This report proposes a utility-based approach for modeling user preferences, and details how such models can be exploited for both coarse-grain automatic (re)configuration, and fine-grain adaptation to resource change.