Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

SealOS+: A Sealos-based Approach for Adaptive Resource Optimization Under Dynamic Workloads for Securities Trading System

Haojie Jia, Zhenhao Li, Gen Li, Minxian Xu, Kejiang Ye

TL;DR

SealOS+ tackles the challenge of ultra-low-latency, dynamic workloads in modern securities trading systems by integrating a deep reinforcement learning–driven adaptive resource scheduler, a three-level caching architecture, and an LSTM-based load predictor within a Sealos-based container platform. The approach models trading workloads as an MDP, uses a hybrid genetic-algorithm and RL scheduling framework, and combines memory-centric caching with proactive load forecasting to preempt bottlenecks. Real-world deployment demonstrates substantial gains: average CPU utilization of 78%, transaction latency of 105 ms, and peak throughput of 15,000 TPS, along with improved deployment times and scalability compared to VM, Kubernetes, and native Sealos baselines. These results indicate that adaptive scheduling, memory-aware caching, and predictive scaling can significantly enhance performance, reliability, and efficiency in high-frequency trading environments, with practical implications for exchanges seeking microservices-based yet ultra-responsive architectures.

Abstract

As securities trading systems transition to a microservices architecture, optimizing system performance presents challenges such as inefficient resource scheduling and high service response delays. Existing container orchestration platforms lack tailored performance optimization mechanisms for trading scenarios, making it difficult to meet the stringent 50ms response time requirement imposed by exchanges. This paper introduces SealOS+, a Sealos-based performance optimization approach for securities trading, incorporating an adaptive resource scheduling algorithm leveraging deep reinforcement learning, a three-level caching mechanism for trading operations, and a Long Short-Term Memory (LSTM) based load prediction model. Real-world deployment at a securities exchange demonstrates that the optimized system achieves an average CPU utilization of 78\%, reduces transaction response time to 105ms, and reaches a peak processing capacity of 15,000 transactions per second, effectively meeting the rigorous performance and reliability demands of securities trading.

SealOS+: A Sealos-based Approach for Adaptive Resource Optimization Under Dynamic Workloads for Securities Trading System

TL;DR

SealOS+ tackles the challenge of ultra-low-latency, dynamic workloads in modern securities trading systems by integrating a deep reinforcement learning–driven adaptive resource scheduler, a three-level caching architecture, and an LSTM-based load predictor within a Sealos-based container platform. The approach models trading workloads as an MDP, uses a hybrid genetic-algorithm and RL scheduling framework, and combines memory-centric caching with proactive load forecasting to preempt bottlenecks. Real-world deployment demonstrates substantial gains: average CPU utilization of 78%, transaction latency of 105 ms, and peak throughput of 15,000 TPS, along with improved deployment times and scalability compared to VM, Kubernetes, and native Sealos baselines. These results indicate that adaptive scheduling, memory-aware caching, and predictive scaling can significantly enhance performance, reliability, and efficiency in high-frequency trading environments, with practical implications for exchanges seeking microservices-based yet ultra-responsive architectures.

Abstract

As securities trading systems transition to a microservices architecture, optimizing system performance presents challenges such as inefficient resource scheduling and high service response delays. Existing container orchestration platforms lack tailored performance optimization mechanisms for trading scenarios, making it difficult to meet the stringent 50ms response time requirement imposed by exchanges. This paper introduces SealOS+, a Sealos-based performance optimization approach for securities trading, incorporating an adaptive resource scheduling algorithm leveraging deep reinforcement learning, a three-level caching mechanism for trading operations, and a Long Short-Term Memory (LSTM) based load prediction model. Real-world deployment at a securities exchange demonstrates that the optimized system achieves an average CPU utilization of 78\%, reduces transaction response time to 105ms, and reaches a peak processing capacity of 15,000 transactions per second, effectively meeting the rigorous performance and reliability demands of securities trading.

Paper Structure

This paper contains 32 sections, 12 equations, 6 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Scheduling and Load Balancing Techniques
  4. Container Orchestration for Performance Optimization
  5. Machine Learning-Based Resource Management
  6. System Architecture Design
  7. Architecture Overview
  8. System Improvement Design
  9. Performance Bottleneck Analysis of Existing System
  10. Overall Improvement Scheme
  11. System Model and Scheduling Algorithm Design
  12. System Model
  13. Scheduling Algorithm Design
  14. Improvement Scheme for Adaptive Trading System
  15. Adaptive Resource Scheduling Algorithm
  16. ...and 17 more sections

Figures (6)

  • Figure 1: SealOS+ System Architecture Diagram.
  • Figure 2: Trading System Running Status (Response Time).
  • Figure 3: Trading System Running Status (Transaction Volume).
  • Figure 4: SealOS+ performance compared with VM solution, Kubernetes and SealOS
  • Figure 5: End-to-End Response Time.
  • ...and 1 more figures