SpotMon: Enabling General Hotspot Monitoring in Key-Value Stores

Zhengyan Zhou, Jinhan Zu, Enhao Huang, Zizhao Wang, Haifeng Zhou, Dong Zhang, Xiang Chen, Chunming Wu

Abstract: Key-value stores are essential to online services such as e-commerce. In key-value stores, a hotspot (i.e., frequently accessed items) may cause severe load imbalances, high response latency, and Service Level Agreement (SLA) violations. However, existing works only focus on specific types of hotspots, thus overlooking other types of hotspots and leading to blind spots. In this paper, we propose SpotMon, a system that enables general hotspot monitoring in key-value stores. Specifically, we (1) systematically identify the generality requirements of hotspot monitoring from existing works, (2) formulate general hotspot monitoring as an arbitrary partial spot query problem, (3) measure the hotness of hotspot candidates with a new vector expression, (4) propose hotspot encoding, filtering, decoding, and querying to support general queries without focusing on specific hotspots, (5) leverage the in-network visibility of programmable switches to identify system-wide hotspots. Our extensive experiments indicate that SpotMon provides high accuracy (e.g., F1 score from 0.88 to 1) and enables efficient hotspot mitigations (e.g., up to $4.03 \times$ MQPS).

External IDs:dblp:conf/icnp/ZhouZHWZZC024