Kubestorage: A Cloud Native Storage Engine for Massive Small Files Cloud Native, the emerging computing infrastructure has become a new trend for cloud computing, especially after the development of containerization technology such as docker and LXD, and the orchestration system for them like Kubernetes and Swarm. With the growing popularity of Cloud Native, the following problems have been raised: (i) most Cloud Native applications were designed for making full use of the cloud platform, but their file storage has not been completely optimized for adapting it. (ii) the traditional file system is designed as a utility for storing and retrieving files, usually built into the kernel of the operating systems. But when placing it to a largescale condition, like a network storage server shared by thousands of computing instances, and stores millions of files, it will be slow and even unstable. (iii) most storage solutions use metadata for faster tracking of files, but the metadata itself will take up a lot of space, and the capacity of it is usually limited. If the file system store metadata directly into hard disk without caching, the tracking of massive small files will be a lot slower. (iv) The traditional object storage solution canu0027t provide enough features to make itself more practical on the cloud such as caching and auto replication. This paper proposes a new storage engine based on the wellknown Haystack storage engine, optimized in terms of service discovery and Automated fault tolerance, make it more suitable for Cloud Native infrastructure, deployment and applications. We use the object storage model to solve the large and highfrequency file storage needs, offering a simple and unified set of APIs for application to access. We also take advantage of Kubernetesu0027 sophisticated and automated toolchains to make cloud storage easier to deploy, more flexible to scale, and more stable to run.

On the TradeOff Between Performance and Storage Efficiency of ReplicationBased Object Storage The object storage systems are used to store and manage unstructured data. Most object storage systems provide the replication policy (REP) or erasure code policy (EC) to ensure the reliability and availability of data. In this paper, we study the tradeoff between performance and storage efficiency of these policies with respect to different data sizes of user requests. To this end, we present a hybrid policy management system that takes advantage of both policies by automatically changing policy based on data size. We have implemented the hybrid system in OpenStack Swift. Our evaluation results show the throughput of GET request increases up to 36% while improving storage efficiency by up to 53% compared to that using the REP policy.