Academic Research - Revision history

Radostin at 02:40, 26 February 2025

2025-02-26T02:40:14Z

Wenhuizhang at 02:55, 18 December 2024

2024-12-18T02:55:32Z

Wenhuizhang at 23:49, 10 December 2024

2024-12-10T23:49:47Z

Wenhuizhang at 23:43, 10 December 2024

2024-12-10T23:43:49Z

Wenhuizhang: add 24 works

2024-12-10T23:20:58Z

add 24 works

Wenhuizhang at 23:09, 23 January 2024

2024-01-23T23:09:51Z

Wenhuizhang at 01:40, 11 March 2023

2023-03-11T01:40:42Z

Wenhuizhang: Replaced content with "* Optimization of CRIU * CRIU for Migration [VEE'22] Portkey: hypervisor-assisted container migration in nested cloud environments * CRIU for Security [ATC'22] RRC..."

2023-03-11T01:36:46Z

Replaced content with "* Optimization of CRIU * CRIU for Migration [VEE'22] Portkey: hypervisor-assisted container migration in nested cloud environments * CRIU for Security [ATC'22] RRC..."

Wenhuizhang at 01:35, 11 March 2023

2023-03-11T01:35:47Z

Wenhuizhang at 01:15, 11 March 2023

2023-03-11T01:15:18Z

← Older revision		Revision as of 02:40, 26 February 2025
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	* GPU CRIU		* GPU CRIU
		+
		+	[arXiv:2502.16631 '25] CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

	[SoCC '24] On-demand and Parallel Checkpoint/Restore for GPU Applications		[SoCC '24] On-demand and Parallel Checkpoint/Restore for GPU Applications

@@ Line 2: / Line 2: @@
 [SoCC '24] On-demand and Parallel Checkpoint/Restore for GPU Applications
 [arXiv '23] PARALLELGPUOS: A Concurrent OS-level GPU Checkpoint and Restore System using Validated Speculation
 [SC-W '23] Checkpoint/Restart for CUDA Kernels
@@ Line 22: / Line 35: @@
 [Cloud '24] FastMig: Leveraging FastFreeze to Establish Robust Service Liquidity in Cloud 2.0
 [TOCS '22] H-Container: Enabling Heterogeneous-ISA Container Migration in Edge Computing
 [VEE '22] Portkey: hypervisor-assisted container migration in nested cloud environments
@@ Line 37: / Line 114: @@
 [ATC '22] RRC: Responsive Replicated Containers
@@ Line 44: / Line 127: @@
 [VLDB '23] Async-fork: Mitigating Query Latency Spikes Incurred by the Fork-based Snapshot Mechanism from the OS Level
 [EuroSys '21] On-demand-fork: a microsecond fork for memory-intensive and latency-sensitive applications

← Older revision		Revision as of 23:49, 10 December 2024
Line 2:		Line 2:

	[SoCC '24] On-demand and Parallel Checkpoint/Restore for GPU Applications		[SoCC '24] On-demand and Parallel Checkpoint/Restore for GPU Applications
		+
		+	[arXiv '23] PARALLELGPUOS: A Concurrent OS-level GPU Checkpoint and Restore System using Validated Speculation

	[SC-W '23] Checkpoint/Restart for CUDA Kernels		[SC-W '23] Checkpoint/Restart for CUDA Kernels
		+

@@ Line 1: / Line 1: @@
 * GPU CRIU
-[SoCC'24] On-demand and Parallel Checkpoint/Restore for GPU Applications
+[SoCC '24] On-demand and Parallel Checkpoint/Restore for GPU Applications
 * CRIU for Migration
-[APNet'24] Software-based Live Migration for Containerized RDMA
+[APNet '24] Software-based Live Migration for Containerized RDMA
-[SEATED'24] Live Migration of Multi-Container Kubernetes Pods in Multi-Cluster Serverless Edge Systems
+[ICT '24] Packet Buffering to Minimize Service Downtime and Packet Loss During Redundancy Switchover
-[ICT'24] Packet Buffering to Minimize Service Downtime and Packet Loss During Redundancy Switchover
+[SIGMOD/PODS '24] Demonstration of ElasticNotebook: Migrating Live Computational Notebook States
-[ICDCS'24] Dapper: A Lightweight and Extensible Framework for Live Program State Rewriting
+[ICDCS '24] Dapper: A Lightweight and Extensible Framework for Live Program State Rewriting
-[Cloud'24] FastMig: Leveraging FastFreeze to Establish Robust Service Liquidity in Cloud 2.0
+[Cloud '24] FastMig: Leveraging FastFreeze to Establish Robust Service Liquidity in Cloud 2.0
-[TOCS'22] H-Container: Enabling Heterogeneous-ISA Container Migration in Edge Computing
+[TOCS '22] H-Container: Enabling Heterogeneous-ISA Container Migration in Edge Computing
-[VEE'22] Portkey: hypervisor-assisted container migration in nested cloud environments
+[VEE '22] Portkey: hypervisor-assisted container migration in nested cloud environments
 * CRIU Security
-[APSys'24] Towards Efficient End-to-End Encryption for Container Checkpointing Systems
+[APSys '24] Towards Efficient End-to-End Encryption for Container Checkpointing Systems
-[eBPF'24] Custom Page Fault Handling With eBPF
+[eBPF '24] Custom Page Fault Handling With eBPF
-[ARES'24] Don't, Stop, Drop, Pause: Forensics of CONtainer CheckPOINTs (ConPoint)
+[ARES '24] Don't, Stop, Drop, Pause: Forensics of CONtainer CheckPOINTs (ConPoint)
-[ATC'22] RRC: Responsive Replicated Containers
+[ATC '22] RRC: Responsive Replicated Containers
 * CRIU for Database
-[Journal of Cloud Computing'24] MDB-KCP: persistence framework of in-memory database with CRIU-based container checkpoint in Kubernetes
+[Journal of Cloud Computing '24] MDB-KCP: persistence framework of in-memory database with CRIU-based container checkpoint in Kubernetes
-[VLDB'23] Async-fork: Mitigating Query Latency Spikes Incurred by the Fork-based Snapshot Mechanism from the OS Level
+[VLDB '23] Async-fork: Mitigating Query Latency Spikes Incurred by the Fork-based Snapshot Mechanism from the OS Level
-[EuroSys'21] On-demand-fork: a microsecond fork for memory-intensive and latency-sensitive applications
+[EuroSys '21] On-demand-fork: a microsecond fork for memory-intensive and latency-sensitive applications

@@ Line 1: / Line 1: @@
 * CRIU for Migration
 [TOCS'22] H-Container: Enabling Heterogeneous-ISA Container Migration in Edge Computing
@@ Line 7: / Line 21: @@
-* CRIU for Security
+* CRIU Security
 [ATC'22] RRC: Responsive Replicated Containers
@@ Line 13: / Line 33: @@
 * CRIU for Database
 [VLDB'23] Async-fork: Mitigating Query Latency Spikes Incurred by the Fork-based Snapshot Mechanism from the OS Level
 [EuroSys'21] On-demand-fork: a microsecond fork for memory-intensive and latency-sensitive applications

← Older revision		Revision as of 23:09, 23 January 2024
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	* CRIU for Migration		* CRIU for Migration
		+
		+	[TOCS'22] H-Container: Enabling Heterogeneous-ISA Container Migration in Edge Computing

	[VEE'22] Portkey: hypervisor-assisted container migration in nested cloud environments		[VEE'22] Portkey: hypervisor-assisted container migration in nested cloud environments

@@ Line 1: / Line 1: @@
 * Optimization of CRIU
 * CRIU for Migration
 * CRIU for Security
 * CRIU for Database
@@ Line 16: / Line 61: @@
 Fork has long been the process creation system call for Unix. At its inception, fork was hailed as an efficient system call due to its use of copy-on-write on memory shared between parent and child processes. However, application memory demand has increased drastically since the early days and the cost incurred by fork to simply set up virtual memory (e.g., copy page tables) is now a concern, even for applications that only require hundreds of MBs of memory. In practice, fork performance already holds back system efficiency and latency across a range of uses cases that fork large processes, such as fault-tolerant systems, serverless frameworks, and testing frameworks.
 This paper proposes On-demand-fork, a fast implementation of the fork system call specifically designed for applications with large memory footprints. On-demand-fork relies on the observation that copy-on-write can be generalized to page tables, even on commodity hardware. On-demand-fork executes faster than the traditional fork implementation by additionally sharing page tables between parent and child at fork time and selectively copying page tables in small chunks, on-demand, when handling page faults. On-demand-fork is a drop-in replacement for fork that requires no changes to applications or hardware.
 We evaluated On-demand-fork on a range of micro-benchmarks and real-world workloads. On-demand-fork significantly reduces the fork invocation time and has improved scalability. For processes with 1 GB of allocated memory, On-demand-fork has a 65× performance advantage over Fork. We also evaluated On-demand-fork on testing, fuzzing, and snapshotting workloads of well-known applications, obtaining execution throughput improvements between 59% and 226% and up to 99% invocation latency reduction.

@@ Line 5: / Line 5: @@
 [VLDB'23] Async-fork: Mitigating Query Latency Spikes Incurred by the Fork-based Snapshot Mechanism from the OS Level
 Abstract
 In-memory key-value stores (IMKVSes) serve many online applications. They generally adopt the fork-based snapshot mechanism to support data backup. However, this method can result in query latency spikes because the engine is out-of-service for queries during the snapshot. In contrast to existing research optimizing snapshot algorithms, we address the problem from the operating system (OS) level, while keeping the data persistent mechanism in IMKVSes unchanged. Specifically, we first study the impact of the fork operation on query latency. Based on findings in the study, we propose Async-fork, which performs the fork operation asynchronously to reduce the out-of-service time of the engine. Async-fork is implemented in the Linux kernel and deployed into the online Redis database in public clouds. Our experiment results show that Async-fork can significantly reduce the tail latency of queries during the snapshot.
 [EuroSys'21] On-demand-fork: a microsecond fork for memory-intensive and latency-sensitive applications
-ABSTRACT
 Fork has long been the process creation system call for Unix. At its inception, fork was hailed as an efficient system call due to its use of copy-on-write on memory shared between parent and child processes. However, application memory demand has increased drastically since the early days and the cost incurred by fork to simply set up virtual memory (e.g., copy page tables) is now a concern, even for applications that only require hundreds of MBs of memory. In practice, fork performance already holds back system efficiency and latency across a range of uses cases that fork large processes, such as fault-tolerant systems, serverless frameworks, and testing frameworks.