Given limited resource and time before software release, development-site testing and debugging become more and more insufficient to ensure satisfactory software performance. As a counterpart for debugging in the large pioneered by the Microsoft Windows Error Reporting (WER) system focusing on crashing/hanging bugs, performance debugging in the large has emerged thanks to available infrastructure support to collect execution traces with performance issues from a huge number of users at the deployment sites. However, performance debugging against these numerous and complex traces remains a significant challenge for performance analysts. In this paper, to enable performance debugging in the large in practice, we propose a novel approach, called StackMine, that mines callstack traces to help performance analysts effectively discover highly impactful performance bugs (e.g., bugs impacting many users with long response delay). As a successful technology-transfer effort, since December 2010, StackMine has been applied in performance-debugging activities at a Microsoft team for performance analysis, especially for a large number of execution traces. Based on real-adoption experiences of StackMine in practice, we conducted an evaluation of StackMine on performance debugging in the large for Microsoft Windows 7. We also conducted another evaluation on a third-party application. The results highlight substantial benefits offered by StackMine in performance debugging in the large for large-scale software systems.
Abstract-As sensor networking technologies continue to develop, the notion of adding large-scale mobility into sensor networks is becoming feasible by crowd-sourcing data collection to personal mobile devices. However, tasking such networks at fine granularity becomes problematic because the sensors are heterogeneous, owned by the crowd and not the network operators. In this paper, we present Zoom, a multi-resolution tasking framework for crowdsourced geo-spatial sensor networks. Zoom allows users to define arbitrary sensor groupings over heterogeneous, unstructured and mobile networks and assign different sensing tasks to each group. The key idea is the separation of the task information ( what task a particular sensor should perform ) from the task implementation ( code ). Zoom consists of (i) a map, an overlay on top of a geographic region, to represent both the sensor groups and the task information, and (ii) adaptive encoding of the map at multiple resolutions and region-of-interest cropping for resource-constrained devices, allowing sensors to zoom in quickly to a specific region to determine their task. Simulation of a realistic traffic application over an area of 1 sq. km with a task map of size 1.5 KB shows that more than 90 % of nodes are tasked correctly. Zoom also outperforms Logical Neighborhoods, the state-of-the-art tasking protocol in task information size for similar tasks. Its encoded map size is always less than 50% of Logical Neighborhood's predicate size.
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