Real-time data processing in large geo-distributed applications, like the Internet of Things (IoT), increasingly shifts computation from the cloud to the network edge to reduce latency and mitigate network congestion. In this setting, minimizing latency while avoiding node overload requires jointly optimizing operator replication and placement of operator instances, a challenge known as the Operator Placement and Replication (OPR) problem. OPR is NP-hard and particularly difficult to solve in large-scale, heterogeneous, and dynamic geo-distributed networks, where solutions must be scalable, resource-aware, and adaptive to changes like node failures. Existing work on OPR has primarily focused on single-stream operators, such as filters and aggregations. However, many latency-sensitive applications, like environmental monitoring and anomaly detection, require efficient regional stream joins near data sources. This paper introduces Nova, an optimization approach designed to address OPR for join operators that are computable on resource-constrained edge devices. Nova relaxes the NP-hard OPR into a convex optimization problem by embedding cost metrics into a Euclidean space and partitioning joins into smaller sub-joins. This new formulation enables linear scalability and efficient adaptation to topological changes through partial re-optimizations. We evaluate Nova through simulations on real-world topologies and on a local testbed, demonstrating up to 39x latency reduction and 4.5x increase in throughput compared to existing edge-centered solutions, while also preventing node overload and maintaining near-constant re-optimization times regardless of topology size.

翻译：在大型地理分布式应用（如物联网）中，实时数据处理日益将计算从云端迁移至网络边缘，以降低延迟并缓解网络拥塞。在此背景下，为最小化延迟同时避免节点过载，需要联合优化算子复制与算子实例放置，这一挑战被称为算子放置与复制问题。该问题是NP难问题，在大规模、异构且动态的地理分布式网络中求解尤为困难，其解决方案必须具备可扩展性、资源感知能力以及对节点故障等变化的适应性。现有关于算子放置与复制的研究主要集中于单流算子，例如过滤器和聚合器。然而，许多延迟敏感型应用（如环境监测与异常检测）需要在数据源附近进行高效的区域流连接。本文提出Nova，一种专门为可在资源受限边缘设备上计算的连接算子解决算子放置与复制问题的优化方法。Nova通过将成本度量嵌入欧几里得空间并将连接划分为更小的子连接，将NP难的算子放置与复制问题松弛为凸优化问题。这一新表述通过部分重优化实现了线性可扩展性以及对拓扑变化的高效适应。我们通过在真实拓扑上的仿真和本地测试平台对Nova进行评估，结果表明：与现有以边缘为中心的解决方案相比，Nova实现了高达39倍的延迟降低和4.5倍的吞吐量提升，同时能够防止节点过载，并保持几乎恒定的重优化时间，与拓扑规模无关。