ECO-KVS: Energy-Aware Compaction Offloading Mechanism for LSM-Tree Based Key-Value Stores in Edge Federation

초록

In recent years, the rise in energy consumption across infrastructure has highlighted the need for more energy-efficient technologies. This is particularly critical in edge computing environments, where resources and power are limited. Consequently, there is increasing interest in improving the energy efficiency of resource-intensive tasks on edge servers. Edge servers commonly use Log-Structured Merge-tree-based Key-Value Store (LSM-KVS), to manage continuous data streams from edge devices. A key operation in LSM-KVS, known as compaction, merges key-value pairs in a CPU-intensive and energy-demanding process. Additionally, delays during compaction can cause write stalls, blocking I/O operations and degrading performance. This creates a significant challenge in balancing energy consumption and system performance. To address these challenges, we propose ECO-KVS, a solution that improves both energy efficiency and performance in LSM-KVS by offloading compaction tasks across edge servers in an edge federation. ECO-KVS leverages a real-time learning model to predict compaction time and energy consumption, reducing write stalls and enhancing overall energy efficiency. Implemented on RocksDB, ECO-KVS achieves up to 21% higher throughput compared to the baseline RocksDB and improves the performance-to-energy efficiency ratio by up to 18% compared to EdgePilot, a state-of-the-art solution for edge environments.

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