Network-accelerated Scheduling for Large Clusters
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Date
2020-05-04
Authors
Kettaneh, Ibrahim
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
We explore a novel design approach for accelerating schedulers for large scale clusters. Our approach
follows a centralized design and leverages the programmability of recent programmable switches to
accelerating scheduling operations. We demonstrate the feasibility and benefits of this approach by
building two schedulers: one for accelerating data analytics scheduling and one for accelerating
scheduling in key-value stores.
First, we present a scheduler designed for low-latency data analytics workloads. The proposed
scheduler receives job description, maintains a task queue in the switch memory, and schedules tasks
on the next available worker at line-rate. The core of this design is a novel pipeline-based scheduling
logic that can schedule tasks at line-rate. Our prototype evaluation on a cluster with a Barefoot Tofino
switch shows that the proposed approach can reduce scheduling overhead by an order of magnitude
compared to state-of-the-art schedulers.
Second, we present a network-accelerated scheduler for linearizable key-value stores. The proposed
design exploits programmable switches to keep track of write requests and responses, and to identify
where the latest version of each object is stored. Our prototype evaluation shows that the proposed
design achieves up to 42% higher throughput, and 35-97% lower latency than the current state-of-the art approaches.
Description
Keywords
schedulers, network, accelerated