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Distributed systems are complex to develop and administer, and performance problem diagnosis is particularly challenging. When performance degrades, the problem might be in any of the system's many components or could be a result of poor interactions among them. Recent research efforts have created tools that automatically localize the problem to a small number of potential culprits, but research is needed to understand what visualization techniques work best for helping distributed systems developers understand and explore their results. This paper compares the relative merits of three well-known visualization approaches (side-by-side, diff, and animation) in the context of presenting the results of one proven automated localization technique called request-flow comparison. Via a 26-person user study, which included real distributed systems developers, we identify the unique benefits that each approach provides for different problem types and usage modes.

Workflow-centric tracing captures the workflow of causally-related events (e.g., work done to process a request) within and among the components of a distributed system. As distributed systems grow in scale and complexity, such tracing is becoming a critical tool for understanding distributed system behavior. Yet, there is a fundamental lack of clarity about how such infrastructures should be designed to provide maximum benefit for important management tasks, such as resource accounting and diagnosis. Without research into this important issue, there is a danger that workflow-centric tracing will not reach its full potential. To help, this paper distills the design space of workflow-centric tracing and describes key design choices that can help or hinder a tracing infrastructures utility for important tasks. Our design space and the design choices we suggest are based on our experiences developing several previous workflow-centric tracing infrastructures.

Vascular adaptation--or structural changes of microvessels in response to physical and metabolic stresses--can influence physiological processes like angiogenesis and hypertension. To better understand the influence of these stresses on adaptation, Pries et al. (1998, 2001a,b, 2005) have developed a computational model for microvascular adaptation. Here, we reformulate this model in a way that is conducive to a dynamical systems analysis. Using th ese analytic methods, we determine the equilibrium geometries of a single vessel under different conditions and classify its type of stability. We demonstrate that our closed-form solution for vessel geometry exhibits the same regions of stability as the numerical predictions of Pries et al. (2005, Remodeling of blood vessels: responses of diameter and wall thickness to hemodynamic and metabolic stimuli. Hypertension, 46, 725-731). Our analytic approach allows us to predict the existence of limit-cycle oscillations and to extend the model to consider a fixed pressure across the vessel in addition to a fixed flow. Under these fixed pressure conditions, we show that the vessel stability is affected and that the multiple equilibria can exist.

Metrics like disk activity and network traffic are widespread sources of diagnosis and monitoring information in datacenters and networks. However, as the scale of these systems increases, examining the raw data yields diminishing insight. We present RainMon, a novel end-to-end approach for mining timeseries monitoring data designed to handle its size and unique characteristics. Our system is able to (a) mine large, bursty, real-world monitoring data, (b) find significant trends and anomalies in the data, (c) compress the raw data effectively, and (d) estimate trends to make forecasts. Furthermore, RainMon integrates the full analysis process from data storage to the user interface to provide accessible long-term diagnosis. We apply RainMon to three real-world datasets from production systems and show its utility in discovering anomalous machines and time periods.

Mobile phone services based on the location of a user have increased in popularity and importance, particularly with the proliferation of feature-rich smartphones. One major obstacle to the widespread use of location-based services is the limited battery life of these mobile devices and the high power costs of many existing approaches.We demonstrate the effectiveness of a localization strategy that performs full localization only when it detects a user has finished moving. We characterize the power use of a smartphone, then verify our strategy using models of long-term walk behavior, recorded data, and device implementation. For the same sample period, our movement-informed strategy reduces power consumption compared to existing approaches by more than 80% with an impact on accuracy of less than 5%. This difference can help achieve the goal of near-continuous localization on mobile devices.