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In modern Stream Processing Engines (SPEs), numerous diverse applications, which can differ in aspects such as cost, criticality or latency sensitivity, can co-exist in the same computing node. When these differences need to be considered to control the performance of each application, custom scheduling of operators to threads is of key importance (e.g., when a smart vehicle needs to ensure that safety-critical applications always have access to computational power, while other applications are given lower, variable priorities). Many solutions have been proposed regarding schedulers that allocate threads to operators to optimize specific metrics (e.g., latency) but there is still lack of a tool that allows arbitrarily complex scheduling strategies to be seamlessly plugged on top of an SPE. We propose Haren to fill this gap. More specifically, we (1) formalize the thread scheduling problem in stream processing in a general way, allowing to define ad-hoc scheduling policies, (2) identify the bottlenecks and the opportunities of scheduling in stream processing, (3) distill a compact interface to connect Haren with SPEs, enabling rapid testing of various scheduling policies, (4) illustrate the usability of the framework by integrating it into an actual SPE and (5) provide a thorough evaluation. As we show, Haren makes it is possible to adapt the use of computational resources over time to meet the goals of a variety of scheduling policies. CCS CONCEPTS • Information systems → Online analytical processing engines; • Software and its engineering → Scheduling; Middleware.

Fine-grained data provenance in data streaming allows linking each result tuple back to the source data that contributed to it, something beneficial for many applications (e.g., to find the conditions triggering a security-or safety-related alert). Further, when data transmission or storage has to be minimized, as in edge computing and cyber-physical systems, it can help in identifying the source data to be prioritized. The memory and processing costs of fine-grained data provenance, possibly afforded by high-end servers, can be prohibitive for the resource-constrained devices deployed in edge computing and cyber-physical systems. Motivated by this challenge, we present GeneaLog, a novel fine-grained data provenance technique for data streaming applications. Leveraging the logical dependencies of the data, GeneaLog takes advantage of cross-layer properties of the software stack and incurs a minimal, constant size per-tuple overhead. Furthermore, it allows for a modular and efficient algorithmic implementation using only standard data streaming operators. This is particularly useful for distributed streaming applications since the provenance processing can be executed at separate nodes, orthogonal to the data processing. We evaluate an implementation of GeneaLog using vehicular and smart grid applications, confirming it efficiently captures fine-grained provenance data with minimal overhead.

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In data streaming, why-provenance can explain why a given outcome is observed but offers no help in understanding why an expected outcome is missing. Explaining missing answers has been addressed in DBMSs, but these solutions are not directly applicable to the streaming setting, because of the extra challenges posed by limited storage and by the unbounded nature of data streams. With our framework, Erebus , we tackle the unaddressed challenges behind explaining missing answers in streaming applications. Erebus allows users to define expectations about the results of a query, verifying at runtime if such expectations hold, and also providing explanations when expected and observed outcomes diverge (missing answers). To the best of our knowledge, Erebus is the first such solution in data streaming. Our thorough evaluation on real data shows that Erebus can explain the (missing) answers with small overheads, both in low- and higher-end devices, even when large portions of the processed data are part of such explanations.