分析了基于处理器硬件虚拟化技术实现的KVM子系统的架构。针对KVM跟踪独立事件信息的局限性,提出一种新的KVM事件跟踪机制(kvmtrace)来达到性能调节的目的,并使用relayfs接口进行了设计与实现。同时探讨了Linux kernel Markers实现机制及其在kvmtrace的实际应用。

Kernel-based virtual machine事件跟踪机制的设计与实现

Containers offer an efficient way to run workloads as independent microservices that can be developed, tested and deployed in an agile manner. To facilitate this process, container frameworks offer a registry service that enables users to publish and version container images and share them with others. The registry service plays a critical role in the startup time of containers since many container starts entail the retrieval of container images from a registry. To support research efforts on optimizing the registry service, large-scale and realistic traces are required. In this paper, we perform a comprehensive characterization of a large-scale registry workload based on traces that we collected over the course of 75 days from five IBM data centers hosting production-level registries. We present a trace replayer to perform our analysis and infer a number of crucial insights about container workloads, such as request type distribution, access patterns, and response times. Based on these insights, we derive design implications for the registry and demonstrate their ability to improve performance. Both the traces and the replayer are open-sourced to facilitate further research.

/pdf/improving-docker-registry-design-based-on-production-4djazw3pzq.pdf

Improving Docker Registry Design Based on Production Workload Analysis

Containerization is a lightweight virtualization technology enabling the deployment and execution of distributed applications on cloud, edge/fog, and Internet-of-Things platforms. Container technol ...

The state‐of‐the‐art in container technologies: Application, orchestration and security

Non-volatile memory (NVM) technologies are considered as promising candidates of the next-generation main memory. However, the non-volatility of NVMs leads to new security vulnerabilities. For example, it is not difficult to access sensitive data stored on stolen NVMs. Memory encryption can be employed to mitigate the security vulnerabilities, but it increases the number of bits written to NVMs due to the diffusion property and thereby aggravates the NVM wear-out induced by writes. To address these security and endurance challenges, this paper proposes DeWrite, a secure and deduplication-aware scheme to enhance the performance and endurance of encrypted NVMs based on a new in-line deduplication technique and the synergistic integrations of deduplication and memory encryption. Specifically, it performs low-latency in-line deduplication to exploit the abundant cache-line-level duplications leveraging the intrinsic read/write asymmetry of NVMs and light-weight hashing. It also opportunistically parallelizes the operations of deduplication and encryption and allows them to co-locate the metadata for high time and space efficiency. DeWrite was implemented on the gem5 with NVMain and evaluated using 20 applications from SPEC CPU2006 and PARSEC. Extensive experimental results demonstrate that DeWrite reduces on average 54% writes to encrypted NVMs, and speeds up memory writes and reads of encrypted NVMs by 4.2 × and 3.1 ×, respectively. Meanwhile, DeWrite improves the system IPC by 82% and reduces 40% of energy consumption on average.

Improving the performance and endurance of encrypted non-volatile main memory through deduplicating writes

Non-volatile memory (NVM) technologies can manipulate persistent data directly in memory. Ensuring crash consistency of persistent data enforces that data updates reach all the way to NVM, which puts these write requests on the critical path. Recent literature sought to reduce this performance impact. However, prior works have not fully accounted for all the backend memory operations (BMOs) performed at the memory controller that are necessary to maintain persistent data in NVM. These BMOs include support for encryption, integrity protection, compression, deduplication, etc., necessary to provide security, endurance, and lifetime guarantees. These BMOs significantly increase the NVM write latency and exacerbate the performance degradation caused by the critical write requests. The goal of this work is to minimize the BMO overhead of write requests in an NVM system. The central challenge is to figure out how to optimize these seemingly dependent and monolithic BMOs. Our key insight is to decompose each BMO into a series of sub-operations and then reduce their overall latency through two mechanisms: (i) parallelize sub-operations across BMOs and (ii) pre-execute sub-operations off the critical path as soon as their inputs are ready. We expose a generic software interface that can be used to issue pre-execution requests compatible with common crash-consistency programming models and various BMOs. Based on these ideas, we propose Janus11Janus ( /'dʒeinɘs/) is a god in Roman mythology. He has two opposite faces: one looks to the future and the other to the past. - a hardware-software co-design that parallelizes and pre-executes BMOs in an NVM system. We evaluate Janus in an NVM system that integrates encryption, integrity verification, and deduplication and issues pre-execution requests through the proposed software interface, either manually or using an automated compiler pass. Compared to a system that performs these operations serially, Janus achieves 2.35x and 2.00x speedup using manual and automated instrumentation, respectively.

Janus: optimizing memory and storage support for non-volatile memory systems

Flash caching has emerged as a promising solution to the scalability problems of storage systems by using fast flash memory devices as the cache for slower primary storage. But its adoption faces serious obstacles due to the limited capacity and endurance of flash devices. This paper presents CacheDedup, a solution that addresses these limitations using in-line deduplication. First, it proposes a novel architecture that integrates the caching of data and deduplication metadata (source addresses and fingerprints of the data) and efficiently manages these two components. Second, it proposes duplication-aware cache replacement algorithms (D-LRU, DARC) to optimize both cache performance and endurance. The paper presents a rigorous analysis of the algorithms to prove that they do not waste valuable cache space and that they are efficient in time and space usage. The paper also includes an experimental evaluation using real-world traces, which confirms that CacheDedup substantially improves I/O performance (up to 20% reduction in miss ratio and 51% in latency) and flash endurance (up to 89% reduction in writes sent to the cache device) compared to traditional cache management. It also shows that the proposed architecture and algorithms can be extended to support the combination of compression and deduplication for flash caching and improve its performance and endurance.

/pdf/cachededup-in-line-deduplication-for-flash-caching-29z6eodvh3.pdf

CacheDedup: in-line deduplication for flash caching

Containers are a widely successful technology today popularized by Docker. Containers improve system utilization by increasing workload density. Docker containers enable seamless deployment of workloads across development, test, and production environments. Docker's unique approach to data management, which involves frequent snapshot creation and removal, presents a new set of exciting challenges for storage systems. At the same time, storage management for Docker containers has remained largely unexplored with a dizzying array of solution choices and configuration options. In this paper we unravel the multi-faceted nature of Docker storage and demonstrate its impact on system and workload performance. As we uncover new properties of the popular Docker storage drivers, this is a sobering reminder that widespread use of new technologies can often precede their careful evaluation.

In Search of the Ideal Storage Configuration for Docker Containers

Containers are a widely successful technology today popularized by Docker. They improve system utilization by increasing workload density and enable seamless deployment of workloads across development, test, and production environments. Docker’s unique approach to data management, which involves frequent snapshot creation and removal, presents a new set of exciting challenges for storage systems. At the same time, storage management for Docker containers has remained largely unexplored with a dizzying array of solution choices and configuration options. In this paper we unravel the multi-faceted nature of Docker storage and demonstrate its impact on system and workload performance. As we uncover new properties of the popular Docker storage drivers, this is a sobering reminder that widespread use of new technologies can often precede their careful evaluation.

Wenji Li

Papers

CacheDedup: in-line deduplication for flash caching

In Search of the Ideal Storage Configuration for Docker Containers

Evaluating Docker storage performance: from workloads to graph drivers