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Interactive visualization interfaces enable users to efficiently explore, analyze, and make sense of their datasets. However, as data grows in size, it becomes increasingly challenging to build data interfaces that meet the interface designer’s desired latency expectations and resource constraints. Cloud DBMSs, while optimized for scalability, often fail to meet latency expectations, necessitating complex, bespoke query execution and optimization techniques for data interfaces. This involves manually navigating a huge optimization space that is sensitive to interface design and resource constraints, such as client vs server data and compute placement, choosing which computations are done offline vs online, and selecting from a large library of visualization-optimized data structures. This paper advocates for a Physical Visualization Design (PVD) tool that decouples interface design from system design to provide design independence. Given an interfaces underlying data flow, interactions with latency expectations, and resource constraints, PVD checks if the interface is feasible and, if so, proposes and instantiates a middleware architecture spanning the client, server, and cloud DBMS that meets the expectations. To this end, this paper presents Jade, the first prototype PVD tool that enables design independence. Jade proposes an intermediate representation called Diffplans to represent the data flows, develops cost estimation models that trade off between latency guarantees and plan feasibility, and implements an optimization framework to search for the middleware architecture that meets the guarantees. We evaluate Jade on six representative data interfaces as compared to Mosaic and Azure SQL database. We find Jade supports a wider range of interfaces, makes better use of available resources, and can meet a wider range of data, latency, and resource conditions. 

Bloom Filters are a desirable data structure for distinguishing new values in sequences of data (i.e., messages), due to their space efficiency, their low false positive rates (incorrectly classifying a new value as a repeat), and never producing false negatives (classifying a repeat value as new). However, as the Bloom Filter's bits are filled, false positive rates creep upward. To keep false positive rates below a reasonable threshold, applications periodically "recycle" the Bloom Filter, clearing the memory and then resuming the tracking of data. After a recycle point, subsequent arrivals of recycled messages are likely to be misclassified as new; recycling induces false negatives. Despite numerous applications of recycling, the corresponding false negative rates have never been analyzed. In this paper, we derive approximations, upper bounds, and lower bounds of false negative rates for several variants of recycling Bloom Filters. These approximations and bounds are functions of the size of memory used to store the Bloom Filter and the distributions on new arrivals and repeat messages, and can be efficiently computed on conventional hardware. We show, via comparison to simulation, that our upper bounds and approximations are extremely tight, and can be efficiently computed for megabyte-sized Bloom Filters on conventional hardware. 

Bloom Filters are a space-efficient data structure used for the testing of membership in a set that errs only in the False Positive direction. However, the standard analysis that measures this False Positive rate provides a form of worst case bound that is both overly conservative for the majority of network applications that utilize Bloom Filters, and reduces accuracy by not taking into account the actual state (number of bits set) of the Bloom Filter after each arrival. In this paper, we more accurately characterize the False Positive dynamics of Bloom Filters as they are commonly used in networking applications. In particular, network applications often utilize a Bloom Filter that “recycles”: it repeatedly fills, and upon reaching a certain level of saturation, empties and fills again. In this context, it makes more sense to evaluate performance using the average False Positive rate instead of the worst case bound. We show how to efficiently compute the average False Positive rate of recycling Bloom Filter variants via renewal and Markov models. We apply our models to both the standard Bloom Filter and a “two-phase” variant, verify the accuracy of our model with simulations, and find that the previous analysis’ worst-case formulation leads to up to a 30% reduction in the efficiency of Bloom Filter when applied in network applications, while two-phase overhead diminishes as the needed False Positive rate is tightened. 

WiFi is the dominant means for home Internet access, yet is frequently a performance bottleneck. Without reliable, satisfactory performance at the last hop, end-to-end quality of service (QoS) efforts will fail. Three major reasons for WiFi bottlenecking performance are its: 1) inherent wireless channel characteristics, 2) approach to access control of the shared broadcast channel, and 3) impact on transport layer protocols, such as TCP, that operate end-to-end, and over-react to the loss or delay caused by the single WiFi link. In this paper, we leverage the philosophy of centralization in modern networking and present our cross layer design to address the problem. Specifically, we introduce centralized control at the point of entry/egress into the WiFi network. Based on network conditions measured from buffer sizes, airtime and throughput, flows are scheduled to the optimal utility. Unlike most existing WiFi QoS approaches, {\em our design only relies on transparent modifications, requiring no changes to the network (including link layer) protocols, applications, or user intervention}. Through extensive experimental investigation, we show that our design significantly enhances the reliability and predictability of WiFi performance, providing a ``virtual wire''-like link to the targeted application.