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Serverless computing is an increasingly attractive paradigm in the cloud due to its ease of use and fine-grained pay-for-what-you-use billing. However, serverless computing poses new challenges to system design due to its short-lived function execution model. Our detailed analysis reveals that memory management is responsible for a major amount of function execution cycles. This is because functions pay the full critical-path costs of memory management in both userspace and the operating system without the opportunity to amortize these costs over their short lifetimes. To address this problem, we propose Memento, a new hardware-centric memory management design based upon our insights that memory allocations in serverless functions are typically small, and either quickly freed after allocation or freed when the function exits. Memento alleviates the overheads of serverless memory management by introducing two key mechanisms: (i) a hardware object allocator that performs in-cache memory allocation and free operations based on arenas, and (ii) a hardware page allocator that manages a small pool of physical pages used to replenish arenas of the object allocator. Together these mechanisms alleviate memory management overheads and bypass costly userspace and kernel operations. Memento naturally integrates with existing software stacks through a set of ISA extensions that enable seamless integration with multiple languages runtimes. Finally, Memento leverages the newly exposed memory allocation semantics in hardware to introduce a main memory bypass mechanism and avoid unnecessary DRAM accesses for newly allocated objects. We evaluate Memento with full-system simulations across a diverse set of containerized serverless workloads and language runtimes. The results show that Memento achieves function execution speedups ranging between 8–28% and 16% on average. Furthermore, Memento hardware allocators and main memory bypass mechanisms drastically reduce main memory traffic by 30% on average. The combined effects of Memento reduce the pricing cost of function execution by 29%. Finally, we demonstrate the applicability of Memento beyond functions, to major serverless platform operations and long-running data processing applications.
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This content will become publicly available on April 28, 2026
Making Serverless Pay-For-Use a Reality with Leopard
Serverless computing has gained traction due to its event-driven architecture and “pay for use” (PFU) billing model. However, our analysis reveals that current billing practices do not align with true resource consumption. This paper challenges the prevailing SLIM (static, linear, interactive-only model) assumptions that underpin existing billing models, demonstrating that current billing does not realize PFU for realistic workloads. We introduce the Nearly Pay-for-Use (NPFU) billing model, which accommodates varying CPU and memory demands, spot cores, and preemptible memory. We also introduce Leopard, an NPFU-based serverless platform that integrates billing awareness into several major subsystems: CPU scheduler, OOM killer, admission controller, and cluster scheduler. Experimental results indicate that Leopard benefits both providers and users, increasing throughput by more than 2x and enabling cost reductions.
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- Award ID(s):
- 2402859
- PAR ID:
- 10583616
- Editor(s):
- USENIX
- Publisher / Repository:
- USENIX NSDI
- Date Published:
- Subject(s) / Keyword(s):
- cloud, serverless
- Format(s):
- Medium: X
- Location:
- Philadelphia, PA
- Sponsoring Org:
- National Science Foundation
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