Alan Messer

Adaptive offloading for pervasive computing

Delivering a complex application on a resource-constrained mobile device is challenging. An adaptive offloading system enables dynamic partitioning of the application and efficient offloading of part of its execution to a nearby surrogate. To deliver pervasive services without modifying the application or degrading its fidelity, we propose an adaptive offloading system that includes two key parts: a distributed offloading platform and an offloading inference engine. There are two important decision-making problems for adaptive offloading: adaptive offloading triggering and efficient application partitioning.

Towards a distributed platform for resource-constrained devices

Many visions of the future predict a world with pervasive computing, where computing services and resources permeate the environment. In these visions, people will want to execute a service on any available device without worrying about whether the service has been tailored for the device. We believe that it will be difficult to create services that can execute well on the wide variety of devices that are being developed because of problems with diversity and resource constraints. We believe that these problems can be greatly reduced by using an ad-hoc distributed platform to transparently off-load portions of a service from a resource-constrained device to a nearby server. We implemented a preliminary prototype and emulator to study this approach. Our experiments show the beneficial use of nearby resources to relieve both memory and processing constraints, when it is appropriate to do so. We believe that this approach will reduce the burden on developers by masking more device details.

Adaptive offloading inference for delivering applications in pervasive computing environments

Pervasive computing allows a user to access an application on heterogeneous devices continuously and consistently. However it is challenging to deliver complex applications on resource-constrained mobile devices, such as cellular telephones and PDA. Different approaches, such as application-based or system-based adaptations, have been proposed to address the problem. However existing solutions often require degrading application fidelity. We believe that this problem can be overcome by dynamically partitioning the application and offloading part of the application execution to a powerful nearby surrogate. This will enable pervasive application delivery to be realized without significant fidelity degradation or expensive application rewriting. Because pervasive computing environments are highly dynamic, the runtime offloading system needs to adapt to both application execution patterns and resource fluctuations. Using the fuzzy control model, we have developed an offloading inference engine to adaptively solve two key decision-making problems during runtime offloading: (1) timely triggering of adaptive offloading, and (2) intelligent selection of an application partitioning policy. Extensive trace-driven evaluations show the effectiveness of the offloading inference engine.

Susceptibility of commodity systems and software to memory soft errors

It is widely understood that most system downtime is accounted for by programming errors and administration time. However, a growing body of work has indicated an increasing cause of downtime may stem from transient errors in computer system hardware due to external factors, such as cosmic rays. This work indicates that moving to denser semiconductor technologies at lower voltages has the potential to increase these transient errors. In this paper, we investigate the susceptibility of commodity operating systems and applications on commodity PC processors to these soft-errors and we introduce ideas regarding the improved recovery from these transient errors in software. Our results indicate that, for the Linux kernel and a Java virtual machine running sample workloads, many errors are not activated, mostly due to overwriting. In addition, given current and upcoming microprocessor support, our results indicate that those errors activated, which would normally lead to system reboot, need not be fatal to the system if software knowledge is used for simple software recovery. Together, they indicate the benefits of simple memory soft error recovery handling in commodity processors and software.

Increasing relevance of memory hardware errors: a case for recoverable programming models

It is a common belief that most of computer system failures nowadays stem from programming errors. Computer systems are becoming more complex and harder to maintain and administer, making software errors an even more common case, while contemporary computer architectures are optimized for price and performance and not for availability. In this paper, we raise a case for an increasing relevance of memory hardware soft-errors. In particular with the introduction of 64-bit processors, memory scaling is significantly increased, resulting in higher probability for memory errors. At the same time, due to the ubiquitous use of computers, such as at higher altitudes, environmental conditions impact errors (terrestrial cosmic rays). Finally, in shared memory systems, the failure of one nodes memory can take the whole machine down. Current commodity systems do not tolerate memory errors, neither commodity hardware (processors, memories, interconnects) nor software (operating systems, applications, application environments). At the same time, users expect increased reliability. We present the problems of such errors and some solutions for memory error recovery at the processor, operating system and programming model level.

Psi-Pervasive Services Infrastructure

Future systems have been characterized as ubiquitous, pervasive, and invisible. They will consist of devices that are diverse in size, performance, and power consumption. Some of these devices will be mobile, posing additional requirements to system software and applications. The focus will move from technology to deployment and ease of use of services. Consequently, traditional paradigms for reasoning about, designing, and implementing software systems and services will no longer be sufficient. We believe that this future vision will rely on a three-tier infrastructure consisting of back-end servers, infrastructure servers, and front-end clients (mobile or static, handheld or embedded). The critical question for future systems will be how to deliver services on demand from back-end servers to resource-constrained clients. If we can handle the new requirements of these systems, we can enable this computing infrastructure to offer significantly more services to users in a more pervasive way.

SeeNSearch: A Context Directed Search Facilitator for Home Entertainment Devices

The Internet has become an extremely popular source of entertainment and information. But, despite the growing amount of media content, most Web sites today are designed for access via web browsers on the PC, making it difficult for home consumers to access Internet content on their TVs or other devices that lack keyboards. As a result, the Internet is generally restricted to access on the PC or via cumbersome interfaces on non-PC devices. In this paper, we present unobtrusive and assistive technologies enabling home users to easily find and access Internet content related to the TV program they are watching. Using these technologies, the user is now able to access relevant information and video content on the Internet while watching TV.

Mobile Situation-Aware Task Recommendation Application

With more and more applications available on mobile devices, it has become increasingly difficult for users to find a desired application. Although research has been conducted for situation-awarere commendations on mobile devices, none addresses this problem; most research is for media content recommendations. Moreover, existing approaches assume predefined situations and/or user-specified profiles; some require users to intentionally train their devices before using them for recommendations. We believe that what defines a situation and what applications are preferred in the situation not only vary from user to user but also change over time, and therefore these assumptions and requirements are impractical for ordinary consumers. In this paper, we will describe our approach of using unsupervised learning, specifically co-clustering, to derive latent situation-based patterns from usage logs of user interactions with the device and environments and use the patterns for task and communication mode recommendations.

Interplay: a middleware for integration of devices, services and contents in the home networking environment

Current home networking technologies such as UPNP, Jini and HAVi, only focus on connecting devices electronically in a variety of different forms that allow simple device interactions. The result is that devices act in incompatible islands, making their use confusing to users and a nightmare for application developers. To realize a smart home environment, we need to seamlessly integrate devices and services into our every day lives. In this paper, we describe a middleware platform to provide the integration of devices, services and contents in a home. Our middleware facilitates integration of both data (content, preferences, etc.) and applications across home networking technologies to improve consumer and developer usability.

Contextual proactivity for media sharing scenarios in proximity networks

Our mobile computing devices connect with other devices over proximity networks on a daily basis. Due to the availability of protocols like DLNA, applications in connected devices can enable media sharing experiences where content flows among devices in the proximity space. Despite the pervasiveness of DLNA and other similar implementations, average users perceive these connections to be intrinsically complex. This perception is not limited to media sharing and affects other types of N-device experiences. In this paper we propose to avoid the inherent complexity problem with the design of intelligent contextual systems that respond proactively to user intent. Users should be capable of expressing intent, and connected devices should gain the intelligence necessary to proactively satisfy user intent. We introduce a software service with an inference engine to determine when the contextual conditions are such that user intent can be satisfied. Media sharing connections, content, and protocols are proactively configured based on contextual inference in anticipation of user needs. The system has been designed as an Android service so that any media-sharing application that uses the service becomes contextually proactive.