Technology Category
Planning, Routing, and Scheduling: Scheduling under UncertaintyMachine Learning: Hardware-aware MLConstraint Satisfaction and Optimization: Distributed CSP/Optimization
Monk: Opportunistic Scheduling to Delay Horizontal Scaling
🤖 AI Summary
To address elevated latency in Java servers caused by CPU contention between garbage collection (GC) threads and application threads, this paper introduces an opportunistic scheduling mechanism—first implemented in ZGC—that dynamically schedules GC work exclusively during CPU idle periods, thereby prioritizing application thread execution. The approach preserves ZGC’s core algorithm and requires only lightweight modifications: an extension to the Linux CFS scheduler and minimal changes to ZGC’s source code—ensuring low overhead and high compatibility. Evaluation on SPECjbb2015 shows a 15% throughput improvement under a 25 ms latency bound; Hazelcast benchmarks demonstrate a 40% reduction in mean latency. Across multi-workload scenarios, service responsiveness and throughput stability are significantly enhanced. This work provides a novel, scale-free SLA optimization path for latency-sensitive Java services operating under moderate load.
Technology Category
Application Category
📝 Abstract
In modern server computing, efficient CPU resource usage is often traded for latency. Garbage collection is a key aspect of memory management in programming languages like Java, but it often competes with application threads for CPU time, leading to delays in processing requests and consequent increases in latency. This work explores if opportunistic scheduling in ZGC, a fully concurrent garbage collector (GC), can reduce application latency on middle-range CPU utilization, a topical deployment, and potentially delay horizontal scaling. We implemented an opportunistic scheduling that schedules GC threads during periods when CPU resources would otherwise be idle. This method prioritizes application threads over GC workers when it matters most, allowing the system to handle higher workloads without increasing latency. Our findings show that this technique can significantly improve performance in server applications. For example, in tests using the SPECjbb2015 benchmark, we observed up to a 15% increase in the number of requests processed within the target 25ms latency. Additionally, applications like Hazelcast showed a mean latency reduction of up to 40% compared to ZGC without opportunistic scheduling. The feasibility and effectiveness of this approach were validated through empirical testing on two widely used benchmarks, showing that the method consistently improves performance under various workloads. This work is significant because it addresses a common bottleneck in server performance -- how to manage GC without degrading application responsiveness. By improving how GC threads are scheduled, this research offers a pathway to more efficient resource usage, enabling higher performance and better scalability in server applications.
Problem
Research questions and friction points this paper is trying to address.
Reduces application latency by optimizing garbage collection scheduling.
Improves CPU resource usage during idle periods for better performance.
Delays horizontal scaling needs by enhancing server application efficiency.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Opportunistic scheduling reduces application latency.
GC threads scheduled during CPU idle periods.
Prioritizes application threads over GC workers.
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