Continuous prefetch for interactive data applications

Abstract

Interactive data visualization and exploration (DVE) applications are often network-bottlenecked due to bursty request patterns, large response sizes, and heterogeneous deployments over a range of networks and devices. This makes it difficult to ensure consistently low response times (< 100ms). Khameleon is a framework for DVE applications that uses a novel combination of prefetching and response tuning to dynamically trade-off response quality for low latency. Khameleon exploits DVE's approximation tolerance: immediate lower-quality responses are preferable to waiting for complete results. To this end, Khameleon progressively encodes responses, and runs a server-side scheduler that proactively streams portions of responses using available bandwidth to maximize user-perceived interactivity. The scheduler involves a complex optimization based on available resources, predicted user interactions, and response quality levels; yet, decisions must also be made in real-time. To overcome this, Khameleon uses a fast greedy heuristic that closely approximates the optimal approach. Using image exploration and visualization applications with real user interaction traces, we show that across a wide range of network and client resource conditions, Khameleon outperforms existing prefetching approaches that benefit from perfect prediction models: Khameleon always lowers response latencies (typically by 2--3 orders of magnitude) while keeping response quality within 50--80%.

Studying Early Decision Making with Progressive Bar Charts

“…An important aspect of visual data analysis is the interactive latency [33,50]. Progressive visualization techniques reduce interactive latency, thus improving user experience, by breaking down different stages of the visualization pipeline into iterative steps (e.g., [19,20,37,45,52]). These techniques have also been shown empirically to be effective at mitigating the latency in visual explorations [7,19,58].…”