Archana Ganapathi

Learn More
MapReduce systems face enormous challenges due to increasing growth, diversity, and consolidation of the data and computation involved. Provisioning, configuring, and managing large-scale MapReduce clusters require realistic, workload-specific performance insights that existing MapReduce benchmarks are ill-equipped to supply. In this paper, we build the(More)
We describe the architecture, operational practices, and failure characteristics of three very large-scale Internet services. Our research on architecture and operational practices took the form of interviews with architects and operations staff at those (and several other) services. Our research on component and service failure took the form of examining(More)
One of the most challenging aspects of managing a very large data warehouse is identifying how queries will behave before they start executing. Yet knowing their performance characteristics --- their runtimes and resource usage --- can solve two important problems. First, every database vendor struggles with managing unexpectedly long-running queries. When(More)
System designers in industry are often overwhelmed by large scale data, while researchers in academic often confront a lack of publicly available production data. In this paper, we analyze a large scale production workload trace recently made publicly available by Google. We offer a statistical profile of the data, with several interesting discoveries(More)
A recent trend for data-intensive computations is to use pay-as-you-go execution environments that scale transparently to the user. However, providers of such environments must tackle the challenge of configuring their system to provide maximal performance while minimizing the cost of resources used. In this paper, we use statistical models to predict(More)
Cloud computing has given rise to a variety of distributed applications that rely on the ability to harness commodity resources for large scale computations. The inherent performance variability in these applications’ workload coupled with the system’s heterogeneity render ineffective heuristics-based design decisions such as system configuration,(More)
Most modern systems generate abundant and diverse log data. With dwindling storage costs, there are fewer reasons to summarize or discard data. However, the lack of tools to efficiently store and cross-correlate heterogeneous datasets makes it tedious to mine the data for analytic insights. In this paper, we present Splunk, a semistructured time series(More)
Reliability is a rapidly growing concern in contemporary personal computer (PC) industry, both for computer users as well as product developers. To improve dependability, systems designers and programmers must consider failure and usage data for operating systems as well as applications. In this paper, we discuss our experience with crash and usage data(More)