A novel approach to minimizing the risks of soft errors at modeling level of mobile and ubiquitous systems is outlined. From a pure dependability viewpoint, critical components, whose failure is likely to impact on system functionality, attract more attention of protection/prevention mechanisms (against soft errors) than others do. Tolerating soft errors can be much improved if critical components can be identified at an early design phase and measures are taken to lower their criticalities at that stage. This improvement is achieved by presenting a criticality ranking (among the components) formed by combining a prediction of soft errors, consequences of them, and a propagation of failures at system modeling phase; and pointing out the ways to apply changes in the model to minimize the risks of degradation of desired functionalities. Case study results are given to illustrate and validate the approach.
Hardware software co-design seeks to meet performance objectives via a combination of hardware and software modules. One difficulty in reaching these objectives lies in lack of cohesion and increased coupling amongst the implemented modules that results in an increased inter module communication cost. While most of the traditional partitioning approaches are initiated in the post-coding phase, we suggest the design stage may be a better focus of attention in addressing this problem.In this paper, we propose a novel approach that uses information from sequence diagrams in UML designs to help ease the partitioning problem.
Tremendous growth in interest of Service oriented Architectures (SOA) triggers a substantial amount of research in its reliability assurances. To minimize the risks of these types of systems’ failure, it is a requirement to flag those components of SOA that are likely to have higher faults. Clearly, the degree of protection or prevention of faults mechanism is not same for all components. This chapter proposes the usage of metrics that are simply heuristics and are used to scan the system model and flag complex components where faults are more likely to take place. Thus the metric output is some priority or it is a measure of likelihood of faults in a component. This chapter then suggests the designers for possible changes in the design if there remains any risk(s) of degradation of desired functionalities.
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