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With the proliferation of Internet of Things (IoT) devices, real-time stream processing at the edge of the network has gained significant attention. However, edge stream processing systems face substantial challenges due to the heterogeneity and constraints of computational and network resources and the intricacies of multi-tenant application hosting. An optimized placement strategy for edge application topology becomes crucial to leverage the advantages offered by Edge computing and enhance the throughput and end-to-end latency of data streams. This paper presents Beaver, a resource scheduling framework designed to efficiently deploy stream processing topologies across distributed edge nodes. Its core is a novel scheduler that employs a synergistic integration of graph partitioning within application topologies and a two-sided matching technique to optimize the strategic placement of stream operators. Beaver aims to achieve optimal performance by minimizing bottlenecks in the network, memory, and CPU resources at the edge. We implemented a prototype of Beaver using Apache Storm and Kubernetes orchestration engine and evaluated its performance using an open-source real-time IoT benchmark (RIoTBench). Compared to state-of-the-art techniques, experimental evaluations demonstrate at least 1.6× improvement in the number of tuples processed within a one-second deadline under varying network delay and bandwidth scenarios.
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DART: A Scalable and Adaptive Edge Stream Processing Engine
Many Internet of Things (IoT) applications are time-critical and dynamically changing. However, traditional data processing systems (e.g., stream processing systems, cloud-based IoT data processing systems, wide-area data analytics systems) are not well-suited for these IoT applications. These systems often do not scale well with a large number of concurrently running IoT applications, do not support low-latency processing under limited computing resources, and do not adapt to the level of heterogeneity and dynamicity commonly present at edge environments. This suggests a need for a new edge stream processing system that advances the stream processing paradigm to achieve efficiency and flexibility under the constraints presented by edge computing architectures. We present \textsc{Dart}, a scalable and adaptive edge stream processing engine that enables fast processing of a large number of concurrent running IoT applications’ queries in dynamic edge environments. The novelty of our work is the introduction of a dynamic dataflow abstraction by leveraging distributed hash table (DHT) based peer-to-peer (P2P) overlay networks, which can automatically place, chain, and scale stream operators to reduce query latency, adapt to edge dynamics, and recover from failures. We show analytically and empirically that DART outperforms Storm and EdgeWise on query latency and significantly improves scalability and adaptability when processing a large number of real-world IoT stream applications' queries. DART significantly reduces application deployment setup times, becoming the first streaming engine to support DevOps for IoT applications on edge platforms.
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- Award ID(s):
- 1919181
- PAR ID:
- 10298610
- Date Published:
- Journal Name:
- USENIX Annual Technical Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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