Counter Pools: Counter Representation for Efficient Stream Processing

2025-02-20

AI Summary

In stream processing, counters incur substantial memory overhead; fixed-bit-width designs struggle to balance precision and density—narrow widths risk overflow, while wide widths limit the number of counters per memory unit. Method: This paper introduces the *Counter Pool* abstraction, departing from the fixed-bit-per-counter paradigm. It enables dynamic, fine-grained, bit-level sharing and adaptive allocation of counters within a fixed-size memory pool (e.g., 64-bit words). The approach integrates memory-pooled encoding, hardware-friendly compact layout, and a lightweight Sketch integration framework, supporting high-density counting with controllable overflow. Contribution/Results: Applied to mainstream streaming algorithms—including Count-Min and Heavy-Hitters—the Counter Pool reduces memory consumption by 30–70% and improves throughput by up to 2.1×, while strictly preserving the original accuracy guarantees.

Technology Category

• Data Mining & Knowledge Management: Data Stream Mining
• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Due to the large data volume and number of distinct elements, space is often the bottleneck of many stream processing systems. The data structures used by these systems often consist of counters whose optimization yields significant memory savings. The challenge lies in balancing the size of the counters: too small, and they overflow; too large, and memory capacity limits their number. In this work, we suggest an efficient encoding scheme that sizes each counter according to its needs. Our approach uses fixed-sized pools of memory (e.g., a single memory word or 64 bits), where each pool manages a small number of counters. We pay special attention to performance and demonstrate considerable improvements for various streaming algorithms and workload characteristics.

Problem

• Optimizes counter size for stream processing
• Balances memory usage and counter overflow
• Proposes efficient encoding with fixed-sized memory pools

Innovation

• Efficient counter encoding scheme
• Fixed-sized memory pools management
• Performance optimization for streaming algorithms