Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Today's IoT applications exploit the capabilities of three different computation environments: sensors, edge, and cloud. Ensuring fault tolerance at the edge level presents unique challenges due to complex network hierarchies and the presence of resource-constrained computing devices. In contrast to the Cloud, the Edge lacks high availability standards and a persistent upstream backup. To ensure reliability, fault tolerance mechanisms have to be deployed on the edge devices along with processing operators competing for available resources. However, existing operator placement strategies are not aware of fault tolerance resource requirements, and existing fault tolerance approaches are not aware of available resources. This miscommunication in resource-constrained environments like the Edge leads to underprovisioning and failures. In this paper, we present a resource-aware fault-tolerance approach that takes the unique characteristics of the Edge into account to provide reliable stream processing. To this end, we model fault tolerance as an operator placement problem that uses multi-objective optimization to decide where to backup data. As opposed to existing approaches that treat operator placement and fault tolerance as two separate steps, we combine them and showcase that this is especially important for low-end edge devices. Overall, our approach effectively mitigates potential failures and outperforms state-of-the-art fault tolerance approaches by up to an order of magnitude in throughput.
Today's IoT applications exploit the capabilities of three different computation environments: sensors, edge, and cloud. Ensuring fault tolerance at the edge level presents unique challenges due to complex network hierarchies and the presence of resource-constrained computing devices. In contrast to the Cloud, the Edge lacks high availability standards and a persistent upstream backup. To ensure reliability, fault tolerance mechanisms have to be deployed on the edge devices along with processing operators competing for available resources. However, existing operator placement strategies are not aware of fault tolerance resource requirements, and existing fault tolerance approaches are not aware of available resources. This miscommunication in resource-constrained environments like the Edge leads to underprovisioning and failures. In this paper, we present a resource-aware fault-tolerance approach that takes the unique characteristics of the Edge into account to provide reliable stream processing. To this end, we model fault tolerance as an operator placement problem that uses multi-objective optimization to decide where to backup data. As opposed to existing approaches that treat operator placement and fault tolerance as two separate steps, we combine them and showcase that this is especially important for low-end edge devices. Overall, our approach effectively mitigates potential failures and outperforms state-of-the-art fault tolerance approaches by up to an order of magnitude in throughput.
Engineering high-performance query execution engines is a challenging task. Query compilation provides excellent performance, but at the same time introduces significant system complexity, as it makes the engine hard to build, debug, and maintain. To overcome this complexity, we propose Nautilus, a framework that combines the ease of use of query interpretation and the performance of query compilation. On the one hand, Nautilus provides an interpretation-based operator interface that enables engineers to implement operators using imperative C++ code to ensure a familiar developer experience. On the other hand, Nautilus mitigates the performance drawbacks of interpretation by introducing a novel trace-based, multi-backend JIT compiler that translates operators into efficient code. As a result, Nautilus bridges the gap between compilation and interpretation and provides the best of both worlds, achieving high performance without sacrificing the productivity of engineers.
Join ordering and query optimization are crucial for query performance but remain challenging due to unknown or changing characteristics of query intermediates, especially for complex queries with many joins. Over the past two decades, a spectrum of techniques for adaptive query processing (AQP)---including inter-/intra-operator adaptivity and tuple routing---have been proposed to address these challenges. However, commercial database systems in practice do not implement holistic AQP techniques because they increase the system complexity (e.g., intertwined planning and execution) and thus, complicate debugging and testing. Additionally, existing approaches may incur large overheads, leading to problematic performance regressions. In this paper, we introduce POLAR, a simple yet very effective technique for a self-regulating selection of alternative join orderings with bounded overhead. We enhance left-deep join pipelines with alternative join orders, perform regret-bounded tuple routing to find and validate "plans of least resistance", and then process the majority of tuple batches through these plans. We study different join order selection techniques, different routing strategies, and a variety of workload characteristics. Our experiments with a POLAR prototype in DuckDB show runtime improvements of up to 9x and less than 7% overhead for all benchmark queries, while outperforming state-of-the-art AQP systems by up to 15x.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with đź’™ for researchers
Part of the Research Solutions Family.