CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing

核心概念

CoRaiS is a lightweight real-time scheduler designed to optimize multi-edge cooperative computing systems by minimizing response times and balancing workloads.

摘要

CoRaiS introduces a system-level state evaluation model to manage heterogeneous resources and redefine service capabilities. It utilizes an integer linear programming model for optimal request dispatching and a learning-based scheduler to minimize response times. CoRaiS successfully learns to balance loads, perceive real-time states, and recognize heterogeneity while scheduling.

統計資料

Evaluation results verify that CoRaiS can make high-quality scheduling decisions in real time.
The system consists of Q edges and Z requests.
The training dataset includes backlogs for each edge with varying input data sizes.

引述

"CoRaiS embeds the real-time states of multi-edge system and request information."
"Evaluation results verify that CoRaiS can make a high-quality scheduling decision in real time."
"Characteristic validation demonstrates that CoRaiS successfully learns to balance loads, perceive real-time state, and recognize heterogeneity while scheduling."

從以下內容提煉的關鍵洞見

CoRaiS

by Yujiao Hu,Qi... 於 arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.09671.pdf

深入探究

How does CoRaiS handle the challenges of modeling system states in multi-edge cooperative computing

CoRaiS addresses the challenges of modeling system states in multi-edge cooperative computing by introducing a system-level state evaluation model. This model includes two key components: service-oriented performance estimation and service-oriented workload evaluation.

Service-Oriented Performance Estimation: CoRaiS utilizes functions to estimate the computation time required for processing data packets at each edge, taking into account the size of the input data. By establishing these relationships through historical data, CoRaiS can predict response times on any edge based on the input data size without needing to analyze code or know specific computation numbers in advance.

Service-Oriented Workload Evaluation: The system evaluates workloads by considering factors such as required computing time for completing tasks locally, transmitting data from other edges, and completing tasks transferred from other edges. These evaluations help determine the real-time service capacity of each edge and inform scheduling decisions based on current workload conditions.
By incorporating these models into its architecture, CoRaiS can effectively shield complex hardware configurations, redefine service capabilities at heterogeneous edges, and make informed scheduling decisions that optimize response times across all requests while balancing loads and recognizing heterogeneity within the system.

What are the implications of using synthetic data for training CoRaiS compared to using real-world datasets

Using synthetic data for training CoRaiS offers several advantages compared to using real-world datasets:

Flexibility: Synthetic data allows for greater flexibility in creating diverse scenarios with varying numbers of edges, requests, and services. This flexibility enables comprehensive testing under different conditions that may not be readily available in real-world datasets.

Generalization: Training on synthetic data helps improve generalization abilities by exposing CoRaiS to a wide range of simulated environments. This broader exposure enhances its adaptability when faced with unseen applications or network configurations during deployment.

Controlled Environment: Synthetic datasets provide a controlled environment where specific parameters can be adjusted systematically to study their impact on scheduling outcomes. This controlled setting aids in understanding how different factors influence decision-making processes.
While real-world datasets offer insights into actual operational scenarios, synthetic data complements this by offering versatility and control over training conditions essential for developing robust scheduling algorithms like CoRaiS.

How does the lightweight design of CoRaiS contribute to its efficiency in making real-time scheduling decisions

The lightweight design of CoRaiS contributes significantly to its efficiency in making real-time scheduling decisions due to several key factors:

Computational Efficiency: The lightweight architecture ensures fast computations even when dealing with large-scale multi-edge cooperative systems. This efficiency is crucial for making quick decisions necessary for real-time operations without compromising accuracy or quality.

Scalability: The design allows CoRaiS to scale effectively as the number of edges or requests increases without significant computational overhead. It can handle larger workloads efficiently while maintaining high-performance standards.

Real-Time Responsiveness: The streamlined structure enables rapid decision-making processes that align with stringent timing requirements typical in multi-edge cooperative computing systems. By minimizing processing delays inherent in heavier architectures, CoRais ensures timely responses across all requests regardless of system scales or complexities.
Overall, the lightweight nature of CoRaIS enhances its agility and responsiveness in handling dynamic scheduling challenges inherent in multi-edge cooperative computing environments while maintaining optimal performance levels under varying workloads and constraints.