Work

Mako: a low-pause, high-throughput evacuating collector for memory-disaggregated datacenters

Year: 2022

Type: conference-paper

Abstract: Resource disaggregation has gained much traction as an emerging datacenter architecture, as it improves resource utilization and simplifies hardware adoption. Under resource disaggregation, different types of resources (memory, CPUs, etc.) are disaggregated into dedicated servers connected by high-speed network fabrics. Memory disaggregation brings efficiency challenges to concurrent garbage collection (GC), which is widely used for latency-sensitive cloud applications, because GC and mutator threads simultaneously run and constantly compete for memory and swap resources.

Source: ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)

DOI: 10.1145/3519939.3523441

Authors: Haoran Ma, Shi Liu, Chenxi Wang, Yifan Qiao, Michael D. Bond, Stephen M. Blackburn, Miryung Kim, Guoqing Harry Xu

Institutions: University of California, Los Angeles, The Ohio State University, Australian National University

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Cisco Systems, Samsung, Australian Research Council, Office of Naval Research

Awards: CSR: Small: Improved Memory Management for Object-Oriented Big Data Systems, G2709462027, Collaborative Research: CNS Core: Medium: Reinvented Data Plane for Memory-Disaggregated Datacenters, XPS: FULL: Collaborative Research: Rethinking Architecture Support for Memory Consistency, SaTC: CORE: Medium: Sentinel: Constructing Secure Smart Home IoT Systems via Managed Communications, CNS Core: Small: Semeru: A memory-disaggregated managed runtime, SHF: Medium: Interactive Debegging for Big Data Analytics, Next generation garbage collection: discovery, design, and development, Collaborative Research: SHF: Medium: Reinventing Fuzz Testing for Data and Compute Intensive Systems, CAPA: Collaborative Research: A Multi-Paradigm Programming Infrastructure for Heterogeneous Architectures, CNS Core: Small: Offline Inference for Ultra-Efficient Memory Management, CNS Core: Small: Rethinking High-Performance Persistent Transactions, CNS Core: Small: Not All Cameras are Created Equal: Systems Support for Highly Adaptive Video Analytics Pipelines, CHS: Medium: Collaborative Research: Code demography: Addressing information needs at scale for programming interface users and designers, G8876996369

OpenAlex ID: W4281715971

FWCI: 2.981

Cites: 36

Cited by: 19

Related to: 10