Continuous prefetch for interactive data applications
Continuous prefetch for interactive data applications
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DOI：
10.14778/3407790.3407826
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发表时间：
2020-05
影响因子：
2.5
通讯作者：
Haneen Mohammed
中科院分区：
文献类型：
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作者：
Haneen Mohammed
交互式数据可视化和探索（DVE）应用程序通常是由于爆发的请求模式，较大的响应大小以及在一系列网络和设备上的异质部署而被网络 - 底线。这使得难以确保持续较低的响应时间（<100ms）。 Khameleon是DVE应用程序的框架，该框架使用了预取和响应调整的新型组合，以使低潜伏期的动态折衷响应质量。 Khameleon利用了DVE的近似公差：立即的低质量响应比等待完整的结果更可取。为此，Khameleon逐渐编码响应，并运行服务器端调度程序，该调度程序可以使用可用的带宽主动地流式传输响应的部分，以最大程度地提高用户感知的交互性。调度程序涉及基于可用资源，预测用户交互和响应质量水平的复杂优化；但是，也必须实时做出决定。为了克服这一点，Khameleon使用了一种快速贪婪的启发式方法，该启发式距离接近最佳方法。使用图像探索和可视化应用程序具有真实的用户交互跟踪，我们表明，在各种网络和客户端资源条件下，Khameleon优于现有的预摘要方法，这些方法受益于完美的预测模型：Khameleon始终降低响应潜伏期（通常是2----3数量级），同时将响应质量保持在50--80％之内。
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%.