Scientific Workflow Scheduling in Cloud Considering Cold Start and Variable Pricing Model

2025-04-30

AI Summary

This paper addresses the joint optimization challenge faced by Scientific Cloud Service Providers (SCSPs) in scheduling scientific workflows: mitigating cold-start latency while dynamically coordinating pricing across heterogeneous virtual machine types—reserved, on-demand, and spot instances. We propose the first hybrid scheduling framework integrating historical prediction with real-time adaptivity. Our approach jointly models cold-start latency and spot-price volatility within dependency-aware task scheduling decisions. Key innovations include a reserved-plus-spot pre-provisioning strategy, runtime elastic scaling, and a cold-start-aware scheduling algorithm. Evaluated on real-world benchmark datasets, our method reduces cold-start overhead by 20% and increases pricing-model revenue by 15% compared to state-of-the-art approaches. These improvements significantly enhance SCSP profitability and deadline satisfaction rates.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Cloud computing has become a pivotal platform for executing scientific workflows due to its scalable and cost-effective infrastructure. Scientific Cloud Service Providers (SCSPs) act as intermediaries that rent virtual machines (VMs) from Infrastructure-as-a-Service (IaaS) providers to meet users' workflow execution demands. The SCSP earns profit from the execution of scientific workflows if it completes the execution of the workflow before the specified deadline of the workflow. This paper addresses two key challenges that impact the profitability of SCSPs: the cold start problem and the efficient management of diverse VM pricing models, namely reserved, on-demand, and spot instances. We propose a hybrid scheduling framework that integrates initial planning based on historical data with real-time adaptations informed by actual workload variations. In the initial phase, VMs are provisioned using reserved pricing based on predicted workloads and spot instances. During execution, the system dynamically adjusts by provisioning additional VMs through on-demand or spot instances to accommodate unexpected bursts in task arrivals. Our framework also incorporates a dependency-aware task scheduling strategy that accounts for cold start delays and spot pricing volatility. Experimental results on real-world benchmark datasets demonstrate that our approach outperforms state-of-the-art methods, achieving up to 20% improvement over cold-start-focused techniques and 15% over pricing-model-based VM provisioning strategies.

Problem

• Optimizing VM scheduling to reduce cold start delays
• Managing hybrid pricing models for cost efficiency
• Enhancing workflow deadline compliance for SCSP profitability

Innovation

• Hybrid scheduling framework with historical data
• Dynamic VM adjustment for workload bursts
• Dependency-aware task scheduling strategy