The increasing complexity of modern computer systems makes fault detection and localization prohibitively expensive, and therefore fast recovery from failures is becoming more and more important. A significant fraction of failures can be cured by executing specific repair actions, e.g. rebooting, even when the exact root causes are unknown. However, designing reasonable recovery policies to effectively schedule potential repair actions could be difficult and error prone. In this paper, we present a novel approach to automate recovery policy generation with reinforcement learning techniques. Based on the recovery history of the original user-defined policy, our method can learn a new, locally optimal policy that outperforms the original one. In our experimental work on data from a real cluster environment, we found that the automatically generated policy can save 10% of machine downtime.

Computer problem diagnosis remains a serious challenge to users and support professionals. Traditional troubleshooting methods relying heavily on human intervention make the process inefficient and the results inaccurate even for solved problems, which contribute significantly to user's dissatisfaction. We propose to use system behavior information such as system event traces to build correlations with solved problems, instead of using only vague text descriptions as in existing practices. The goal is to enable automatic identification of the root cause of a problem if it is a known one, which would further lead to its resolution. By applying statistical learning techniques to classifying system call sequences, we show our approach can achieve considerable accuracy of root cause recognition by studying four case examples.

As dynamic content becomes increasingly dominant, it becomes an important research topic as how the edge resources such as client-side proxies, which are otherwise underutilized for such content, can be put into use. However, it is unclear what will be the best strategy, and the design/deployment trade offs lie therein. In this paper, using one representative e-commerce benchmark, we report our experience of an extensive investigation of different offloading and caching options. Our results point out that, while great benefits can be reached in general, advanced offloading strategies can be overly complex and even counterproductive. In contrast, simple augmentation at proxies to enable fragment caching and page composition achieves most of the benefit without compromising important considerations such as security. We also present proxy+ architecture which supports such capabilities for existing Web applications with minimal reengineering effort.

Abstract—As dynamic content becomes increasingly dominant, it becomes an important research topic as how the edge resources such as client-side proxies, which are otherwise underutilized for such content, can be put into use. However, it is unclear what will be the best strategy, and the design/deployment trade offs lie therein. In this paper, using one representative e-commerce benchmark, we report our experience of an extensive investigation of different offloading and caching options. Our results point out that, while great benefits can be reached in general, advanced offloading strategies can be overly complex and even counterproductive. In contrast, simple augmentation at proxies to enable fragment caching and page composition achieves most of the benefit without compromising important considerations such as security. We also present Proxy+ architecture which supports such capabilities for existing Web applications with minimal reengineering effort. Index Terms—Edge caching, offloading, dynamic content, fragment caching, page composition. 1