The increasing availability of heterogeneous ambient sensing systems challenges the according information processing systems to analyse and compare a variety of different systems in a single scenario. For instance, localization of objects can be performed by image processing systems as well as by radio based localization. If such systems are utilized to localize the same objects, synergy of the outputs is important to enable comparable and meaningful analysis. This demo showcases the practical deployment and challenges of such an example system.

In this paper, we put forward the notion that systems management for large masses of virtual machines in data centers is going to be done differently in the short to medium term future-through smart phones and through controlled crowdsourcing to a variety of experts within an organization, rather than dedicated system administrators alone. We lay out the research and practitioner challenges this model raises and give some preliminary solution directions that are being developed, here at IBM and elsewhere.

Debugging today's large-scale distributed applications is complex. Traditional debugging techniques such as breakpoint-based debugging and performance profiling require a substantial amount of domain knowledge and do not automate the process of locating bugs and performance anomalies. We present Orion, a framework to automate the problem-localization process in distributed applications. From a large set of metrics, Orion intelligently chooses important metrics and models the application's runtime behavior through pair wise correlations of those metrics in the system, within multiple non-overlapping time windows. When correlations deviate from those of a learned correct model due to a bug, our analysis pinpoints the metrics and code regions (class and method within it) that are most likely associated with the failure. We demonstrate our framework with several real-world failure cases in distributed applications such as: HBase, Hadoop DFS, a campus-wide Java application, and a regression testing framework from IBM. Our results show that Orion is able to pinpoint the metrics and code regions that developers need to concentrate on to fix the failures.

CHAOS arc is an object-based multiprocessor operating system kernel that provides primitives with which programmers may easily construct objects of differing types and object invocations of differing semantics, targeting multiprocessor systems, and real-time applications. The CHAOS arc can guarantee desired performance and functionality levels of selected computations in real-time applications. Such guarantees can be made despite possible uncertainty in execution environments by allowing programs to adapt in performance and functionality to varying operating conditions. This paper reviews the primitives offered by CHAOS arc and demonstrates how the required elements of the CHAOS arc real-time kernel are constructed with those primitives.

The authors present a family of object-based real-time operating system kernels that address portability, extensibility, and customizability for low-level and subsystem-level operations. The family is extensible in that new abstractions and functionalities can be added easily and efficiently, thereby maintaining uniform kernel interfaces and permitting the implementation of domain or target machine specific features while preserving some given kernel interface for existing programs. It also provides an environment for experimenting with and prototyping of new operating system constructs and policies. The family is customizable in that existing kernel abstractions and functions can be modified easily, facilitating changes to an operating system for uses with different target architectures or application domains. The family is portable in that its implementation is based on the Mach C threads standard as a base layer for uniprocessors and parallel architectures.>