Edge computing is an emerging computing paradigm where data is generated and processed in the field using distributed computing devices. Many applications such as real-time video processing, augmented/virtual reality gaming, environment sensing, benefit from such decentralized, close-to-user deployments where low-latency, real-time results are expected. As with any distributed application, one of the key challenges in the development of collaborative applications is how to efficiently share data and state among multiple edge clients. The dynamic and heterogeneous environment together with diverse application’s requirements make data sharing at the edge a challenging problem. In comparison to the cloud, where there are more than half of dozen services available for data sharing, how does such a service should look like at the edge?
The emerging next generation of cloud services like Granular and Serverless computing are pushing the boundaries of the current cloud infrastructure. In order to meet the performance objectives, researchers are now leveraging low-level hardware microarchitectural resources in clouds. At the same time these resources are also a major source of security problems that can compromise the confidentiality and integrity of sensitive data in multi-tenant shared cloud infrastructures. The core of the problem is the lack of isolation due to the unsupervised sharing of microarchitectural resources across different performance and security boundaries. In this work, we propose to build Stratus clouds that treat the isolation on microarchitectural elements as the key design principle when allocating cloud resources. This isolation improves both performance and security, but at the cost of reducing resource utilization. Stratus captures this trade-off using a novel abstraction that we call isolation credit, and show how it can help both providers and tenants when allocating microarchitectural resources using Stratus’s declarative interface. There are multiple implementation, mechanisms, policies challenges associated with building Stratus that we explore in this work.
How can modern NVM storage and high-performance networking technologies can help with building efficient machine learning pipelines?
Currently, I am investigating what would it take to process relational SQL data at the rate of 100 Gbps - the rate at which modern networking and storage devices can serve data today. As a first step, I have investgiated the reading performance of file formats (JSON, Avro, Parquet, ORC, and Arrow). The finding are summaried in my blog post and our USENIX ATC 2018 paper.
Crail is an open source user-level I/O architecture for the Apache data processing ecosystem designed from ground up for high-performance storage and networking hardware (40-100Gbps RDMA, flash, GPUs, etc). It involves groud-up design and implementation of multiple components and tools for Apache data processing ecosystem. More details can be found at https://crail.incubator.apache.org/.
Data center RPC or DaRPC is a Java library that provides ultra-low latency RPC for RDMA capable network interfaces. DaRPC is built on DiSNI. DaRPC source code is available https://github.com/zrlio/darpc.
DiSNI is a Java library for direct storage and networking access from userpace. It currently provides an RDMA interface. Support for other userpace storage and networking interfaces, such as DPDK or SPDK, are in planning. DiSNI source code is available at https://github.com/zrlio/disni.
SoftiWARP (siw) is a software iWARP kernel driver and user library for Linux. It implements the iWARP protocol suite (MPA/DDP/RDMAP, IETF-RFC 5044/5041/5040) completely in software, without requiring any dedicated RDMA hardware. SoftiWARP source code is available at https://github.com/zrlio/softiwarp.
Distributed DRAM stores have become an attractive option for providing fast data accesses to analytics applications. To accelerate the performance of these stores, researchers have proposed using RDMA technology. RDMA offers high bandwidth and low latency data access by carefully separating resource setup from IO operations, and making IO operations fast by using rich network semantics and offloading. Despite recent interest, leveraging the full potential of RDMA in a distributed environment remains a challenging task.
RStore is a DRAM-based data store that delivers high performance by extending RDMA’s separation philosophy to a distributed setting. RStore achieves high aggregate bandwidth (705 Gb/s) and close-to-hardware latency on our 12-machine testbed. We developed a distributed graph processing framework and a Key-Value sorter using RStore’s unique memory-like API. The graph processing framework, which relies on RStore for low-latency graph access, outperforms state-of-the-art systems by margins of 2.6-4.2x when calculating Page Rank. The Key-Value sorter can sort 256 GB of data in 31.7 sec, which is 8x better than Hadoop TeraSort in a similar setting.
During the past decade, network and storage devices have undergone rapid performance improvements, delivering ultra-low latency and several Gbps of bandwidth. Nevertheless, current network and storage stacks fail to deliver this hardware performance to the applications, often due to the loss of IO efficiency from stalled CPU performance. While many efforts attempt to address this issue solely on either the network or the storage stack, achieving high-performance for networked-storage applications requires a holistic approach that considers both.
FlashNet is a software I/O stack that unifies high-performance network properties with flash storage access and management. FlashNet builds on RDMA principles and abstractions to provide a direct, asynchronous, end-to-end data path between a client and remote flash storage. The key insight behind FlashNet is to co-design the stack’s components (an RDMA controller, a flash controller, and a file system) to enable cross-stack optimizations and maximize IO efficiency. In micro-benchmarks, FlashNet improves 4kB network IOPS by 38.6% to 1.22M, decreases access latency by 43.5% to 50.4 µsecs, and prolongs the flash lifetime by 1.6-5.9x for writes. We illustrate the capabilities of FlashNet by building a Key-Value store, and porting a distributed data store that uses RDMA on it. The use of FlashNet’s RDMA API improves the performance of KV store by 2x, and requires minimum changes for the ported data store to access remote flash devices.