Livio B. Soares

On fault resilience of OpenStack

Cloud-management stacks have become an increasingly important element in cloud computing, serving as the resource manager of cloud platforms. While the functionality of this emerging layer has been constantly expanding, its fault resilience remains under-studied. This paper presents a systematic study of the fault resilience of OpenStack---a popular open source cloud-management stack. We have built a prototype fault-injection framework targeting service communications during the processing of external requests, both among OpenStack services and between OpenStack and external services, and have thus far uncovered 23 bugs in two versions of OpenStack. Our findings shed light on defects in the design and implementation of state-of-the-art cloud-management stacks from a fault-resilience perspective.

Pointy: a hybrid pointer prefetcher for managed runtime systems

This work proposes Pointy, a software assisted hardware pointer prefetcher for Java applications. Pointy exploits the strengths of both software and hardware. Its runtime software component communicates points-to relationships between objects to the underlying hardware. This points-to information is maintained and tracked in any managed runtime that implements automatic garbage collection. Pointy stores the object connectivity information in a separate hardware structure and uses it to generate timely pointer prefetches. To achieve a low-cost hardware implementation, Pointy spills the object metadata to the conventional memory hierarchy and retrieves it only when needed. Taking advantage of its hybrid design, Pointy can selectively communicate points-to metadata to the hardware based on class profiling that is readily available at the runtime level, while impractical to extract at the hardware level. Experimental results show that Pointy improves performance for pointer intensive benchmarks even in the presence of conventional prefetchers. When used in conjunction with traditional prefetchers, such as striding and next line, Pointy improves application performance by 53% for SpecJBB 2005 and by 72% on average1, which represents a speedup of 19% and 18%, respectively, compared to traditional prefetchers.

Watson Concept Insights: A Conceptual Association Framework

Watson Concept Insights (WCI) is a service that was recently made publicly available by IBM. WCI provides an information retrieval framework that is designed to facilitate search and exploration of text documents, and is particularly effective on sparse data sets. Its methodology consists of first defining a dictionary of concepts which are interconnected in a concept graph and then modeling a document by predicting its relevance to any given concept in the concept graph using the concepts that are directly mentioned in the document itself. This technique in effect increases the document recall for any given query, even for very sparse data sets, exposing the user to a variety of connections between their query and a data set of interest.