Beyond the Limits: Flexible and Congestion-Aware Cluster Scheduling for the Cloud

2026-08-10

AI Summary

This work addresses the limitations of existing cloud scheduling approaches, which often oversimplify application resource demands and hardware contention, leading to poor resource utilization and performance instability under shared-resource congestion. To overcome these challenges, the authors propose a scheduling framework that integrates a flexible SLO (Service Level Objective) mechanism with hardware-level resource awareness. By permitting brief, controlled SLO violations to avoid over-provisioning and continuously monitoring last-level cache and memory bandwidth congestion, the framework enables informed, resource-aware scheduling and rescheduling decisions. Experimental results demonstrate that, compared to conventional hard-SLO methods, the proposed approach reduces corrective rescheduling by 49% and decreases node-level resource contention by 8%, significantly enhancing cluster-wide efficiency while maintaining performance stability.

Abstract

Workload scheduling in cloud environments often relies on simplistic assumptions about application resource needs and hardware utilization. Overlooking application-level performance objectives and hardware resource contention that leads to inefficient resource usage and degraded performance. This paper addresses two key limitations of current approaches. First, unnecessarily strict enforcement of service level objectives (SLOs) often leads to resource underutilization and poor energy efficiency. Second, lack of congestion awareness in shared resources such as last-level cache (LLC) and memory bandwidth. In this paper, we propose two complementary strategies to address these limitations: (i) integrating soft SLO limits that allow controlled overcommitment and tolerate minor, transient violations to improve cluster efficiency, and (ii) introducing resource-aware scheduling and rescheduling based on real-time congestion insights for shared resources such as last-level cache (LLC) and memory bandwidth. Our results show that soft SLO limits reduce corrective rescheduling actions by 49% compared to hard-limit approaches while maintaining acceptable performance guarantees. Additionally, resource-aware scheduling decreases node-level congestion by 8% and further mitigates SLO violations, demonstrating the effectiveness of incorporating application-level flexibility and hardware-level insights into scheduling and rescheduling decisions.

Problem

• cluster scheduling
• service level objectives
• resource congestion
• cloud computing
• shared resources

Innovation

• soft SLO
• congestion-aware scheduling
• resource overcommitment
• last-level cache (LLC)
• memory bandwidth