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Fast, byte-addressable persistent memory (PM) is becoming a reality in products. However, porting legacy kernel file systems to fully support PM requires substantial effort and encounters the challenge of bridging the gap between block-based access granularity and byte-addressability. Moreover, new PM-specific file systems remain far from production-ready, preventing them from being widely used. In this paper, we propose P2CACHE, a novel in-kernel caching mechanism to explore how legacy kernel file systems can effectively evolve in the face of fast, byte-addressable PM. P2CACHE exploits a read/write-distinguishable memory hierarchy upon a tiered memory system involving both PM and DRAM. P2CACHE leverages PM to serve all write requests for instant data durability and strong crash consistency while using DRAM to serve most read I/Os for high I/O performance. Further, P2CACHE employs a simple yet effective synchronization model between PM and DRAM by leveraging device-level parallelism. Our evaluation shows that P2CACHE can significantly increase the performance of legacy kernel file systems -- e.g., by 200x for RocksDB on Ext4 -- meanwhile equipping them with instant data durability and strong crash consistency, similar to PM-specialized file systems. 

In cloud-native environments, containers are often deployed within lightweight virtual machines (VMs) to ensure strong security isolation and privacy protection. With the growing demand for customized cloud services, third-party vendors are turning to infrastructure-as-a-service (IaaS) cloud providers to build their own cloud-native platforms, necessitating the need to run a VM or a guest that hosts containers inside another VM instance leased from an IaaS cloud. State-of-the-art nested virtualization in the x86 architecture relies heavily on the host hypervisor to expose hardware virtualization support to the guest hypervisor, not only complicating cloud management but also raising concerns about an increased attack surface at the host hypervisor. This paper presents the design and implementation of PVM, a high-performance guest hypervisor for KVM that is transparent to the host hypervisor and assumes no hardware virtualization support. PVM leverages two key designs: 1) a minimal shared memory region between the guest and guest hypervisor to facilitate state transition between different privilege levels and 2) an efficient shadow page table design to reduce the cost of memory virtualization. PVM has been adopted by a major IaaS cloud provider for hosting tens of thousands of secure containers on a daily basis. Our experiments demonstrate that PVM significantly outperforms current nested virtualization in KVM for memory virtualization, particularly for concurrent workloads, while maintaining comparable performance in CPU and I/O virtualization. 

Container networking, which provides connectivity among containers on multiple hosts, is crucial to building and scaling container-based microservices. While overlay networks are widely adopted in production systems, they cause significant performance degradation in both throughput and latency compared to physical networks. This paper seeks to understand the bottlenecks of in-kernel networking when running container overlay networks. Through profiling and code analysis, we find that a prolonged data path, due to packet transformation in overlay networks, is the culprit of performance loss. Furthermore, existing scaling techniques in the Linux network stack are ineffective for parallelizing the prolonged data path of a single network flow. We propose FALCON, a fast and balanced container networking approach to scale the packet processing pipeline in overlay networks. FALCON pipelines software interrupts associated with different network devices of a single flow on multiple cores, thereby preventing execution serialization of excessive software interrupts from overloading a single core. FALCON further supports multiple network flows by effectively multiplexing and balancing software interrupts of different flows among available cores. We have developed a prototype of FALCON in Linux. Our evaluation with both micro-benchmarks and real-world applications demonstrates the effectiveness of FALCON, with significantly improved performance (by 300% for web serving) and reduced tail latency (by 53% for data caching).