Abstract

It is a challenge to provide detection facilities for large-scale distributed systems running legacy code on hosts that may not allow fault tolerant functions to execute on them. It is tempting to structure the detection in an observer system that is kept separate from the observed system of protocol entities, with the former only having access to the latter's external message exchanges. In this paper, we propose an autonomous self-checking Monitor system, which is used to provide fast detection to underlying network protocols. The Monitor architecture is application neutral and, therefore, lends itself to deployment for different protocols, with the rulebase against which the observed interactions are matched, making it specific to a protocol. To make the detection infrastructure scalable and dependable, we extend it to a hierarchical Monitor structure. The Monitor structure is made dynamic and reconfigurable by designing different interactions to cope with failures, load changes, or mobility. The latency of the Monitor system is evaluated under fault free conditions, while its coverage is evaluated under simulated error injections.

The wide deployment of high-speed computer networks has made distributed systems ubiquitous in today's connected world The machines on which the distributed applications are hosted are heterogeneous in nature, the applications often run legacy code without the availability of their source code, the systems are of very large scales, and often have soft real-time guarantees. In this paper, we target the problem of online detection of disruptions through a generic external entity called Monitor that is able to observe the exchanged messages between the protocol participants and deduce any ongoing disruption by matching against a rule base composed of combinatorial and temporal rules. The Monitor architecture is application neutral, with the rule base making it specific to a protocol. To make the detection infrastructure scalable and dependable, we extend it to a hierarchical Monitor structure. The infrastructure is applied to a streaming video application running on a reliable multicast protocol called TRAM installed on the campus wide network. The evaluation brings out the scalability of the Monitor infrastructure and detection coverage under different kinds of faults for the single level and the hierarchical arrangements.

There is a vast difference in the traditional presentation of AFM data and confocal data. AFM data are presented as surface contours while confocal data are usually visualized using either surface- or volume-rendering techniques. Finding a common meaningful visualization platform is not an easy task. AFM and CLSM technologies are complementary and are more frequently being used to image common biological systems. In order to provide a presentation method that would assist us in evaluating cellular morphology, we propose a simple visualization strategy that is comparative, intuitive, and operates within an open-source environment of ImageJ, SurfaceJ, and VolumeJ applications. In order to find some common ground for AFM-CLSM image comparison, we have developed a plug-in for ImageJ, which allows us to import proprietary image data sets into this application. We propose to represent both AFM and CLSM image data sets as shaded elevation maps with color-coded height. This simple technique utilizes the open source VolumeJ and SurfaceJ plug-ins. To provide an example of this visualization technique, we evaluated the three-dimensional architecture of living chick dorsal root ganglia and sympathetic ganglia measured independently with AFM and CLSM.