发表机构
National Technical University of Athens（雅典国立技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Memoryless，一种碎片感知的无服务器资源管理器，通过弹性内存局部性匹配实例形状与碎片化节点容量，减少节点使用率高达40%并降低CPU stranded 44%。
AI 中文摘要
无服务器平台通常依赖于捆绑的、以内存为中心的配置，其中CPU容量跟随指定的内存大小。资源解耦减少了这种浪费，但可能通过产生多样的CPU-内存形状而导致外部碎片化，这些形状使剩余容量 stranded 在节点上。内存分解和分层可以通过启用弹性内存局部性将这些资源空洞转化为可用容量，其中实例使用本地和远程内存的不同组合。在本文中，我们提出了Memoryless，一个碎片感知的资源管理器，将内存局部性暴露为无服务器控制平面原语。Memoryless联合选择并放置函数变体，这些变体在计算分配和内存局部性比率上有所不同。这允许调度器将实例形状匹配到碎片化的节点容量，同时保持SLO。我们在Knative之上实现了Memoryless，并使用跟踪驱动的工作负载进行评估。与最先进的无服务器放置框架相比，Memoryless在稳态下将节点使用率降低了高达40%，在峰值时降低了46%，同时将SLO违规保持在3%以内。它还将CPU stranded 减少了44%，表明弹性内存局部性可以将碎片化的节点容量转化为可用资源。
英文摘要
Serverless platforms commonly rely on bundled, memory-centric configurations, where CPU capacity follows the specified memory size. Resource decoupling reduces this waste, but can create external fragmentation by producing diverse CPU-memory shapes that leave residual capacity stranded across nodes. Memory disaggregation and tiering can turn these resource holes into usable capacity by enabling elastic memory locality, where instances use different mixes of local and remote memory. In this paper, we present Memoryless, a fragmentation-aware resource manager that exposes memory locality as a serverless control-plane primitive. Memoryless jointly selects and places function variants that differ in their compute allocation and memory-locality ratio. This allows the scheduler to match instance shapes to fragmented node capacity while preserving SLOs. We implement Memoryless on top of Knative and evaluate it with trace-driven workloads. Compared to state-of-the-art serverless placement frameworks, Memoryless reduces node usage by up to 40% in steady state and 46% at peak, while keeping SLO violations within 3%. It also reduces CPU stranding by 44%, showing that elastic memory locality can convert fragmented node capacity into usable resources.
