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8:30-9:35 AM
HYPR-201A-1: Flash and PM in Hyperscale (Hyperscale Applications Track)
Paper Title: Using an In-Memory Data Accelerator to Improve Cloud Analytics

Paper Abstract: The need to serve ever-increasing amounts of data driven by AI and business intelligence workloads makes moving the required analytics to the cloud more attractive, as the cloud offers rapid provisioning, excellent scalability, easy management, and pay as your grow flexibility. However, common cloud storage does not natively act like a file system as it lacks critical features such as support for transactional renames. More importantly, operations on it may run quite slowly. A solution is to add an in-memory data accelerator, a new high performance layer leveraging state of the art technologies such as persistent memory and RDMA to speed up ephemeral data access. Promising results on prototypes illustrate how the accelerator increases throughput for hybrid transactional analytical processing (HTAP) workloads in the cloud.

Paper Author: Jian Zhang, Software Development Engineer, Intel

Author Bio: Jian Zhang is a senior software engineering manager at Intel, where he and his team primarily focus on Open Source Storage development and optimizations on Intel platforms, and build reference solutions for customers. He has over 10 years of experience in performance analysis and optimization for many open source projects such as Xen, KVM, Swift, Ceph, Spark, and Hadoop and benchmarking workloads such as those from SPEC or TPC. He earned a master's degree in Computer Science and Engineering at Shanghai Jiaotong University. He has five publications and has also presented at the OpenStack Summit, Vault 2016, Strata Data conference, Cephalocon, OFA workshop, and last year’s Flash Memory Summit.