F2: Designing a Key-Value Store for Large Skewed Workloads

Today's key-value stores are either disk-optimized, focusing on large data and saturating device IOPS, or memory-optimized, focusing on high throughput with linear thread scaling assuming plenty of main memory. However, many practical workloads demand high performance for read and write working sets that are much larger than main memory, over a total data size that is even larger. They require judicious use of memory and disk, and today's systems do not handle such workloads well. We present F2, a new key-value store design based on compartmentalization -- it consists of five key components that work together in well-defined ways to achieve high throughput -- saturating disk and memory bandwidths -- while incurring low disk read and write amplification. A key design characteristic of F2 is that it separates the management of hot and cold data, across the read and write domains, and adapts the use of memory to optimize each case. Through a sequence of new latch-free system constructs, F2 solves the key challenge of maintaining high throughput with linear thread scalability in such a compartmentalized design. Detailed experiments on benchmark data validate our design's superiority, in terms of throughput, over state-of-the-art key-value stores, when the available memory resources are scarce

Blox: A Modular Toolkit for Deep Learning Schedulers

Deep Learning (DL) workloads have rapidly increased in popularity in enterprise clusters and several new cluster schedulers have been proposed in recent years to support these workloads. With rapidly evolving DL workloads, it is challenging to quickly prototype and compare scheduling policies across workloads. Further, as prior systems target different aspects of scheduling (resource allocation, placement, elasticity etc.), it is also challenging to combine these techniques and understand the overall benefits. To address these challenges we propose Blox, a modular toolkit which allows developers to compose individual components and realize diverse scheduling frameworks. We identify a set of core abstractions for DL scheduling, implement several existing schedulers using these abstractions, and verify the fidelity of these implementations by reproducing results from prior research. We also highlight how we can evaluate and compare existing schedulers in new settings: different workload traces, higher cluster load, change in DNN workloads and deployment characteristics. Finally, we showcase Blox's extensibility by composing policies from different schedulers, and implementing novel policies with minimal code changes. Blox is available at \url{https://github.com/msr-fiddle/blox}.Comment: To be presented at Eurosys'2

Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo

Resource provisioning in multi-tenant stream processing systems faces the dual challenges of keeping resource utilization high (without over-provisioning), and ensuring performance isolation. In our common production use cases, where streaming workloads have to meet latency targets and avoid breaching service-level agreements, existing solutions are incapable of handling the wide variability of user needs. Our framework called Cameo uses fine-grained stream processing (inspired by actor computation models), and is able to provide high resource utilization while meeting latency targets. Cameo dynamically calculates and propagates priorities of events based on user latency targets and query semantics. Experiments on Microsoft Azure show that compared to state-of-the-art, the Cameo framework: i) reduces query latency by 2.7X in single tenant settings, ii) reduces query latency by 4.6X in multi-tenant scenarios, and iii) weathers transient spikes of workload