Archipelago: A Scalable Low-Latency Serverless Platform
Arjun Singhvi, Kevin Houck, Arjun Balasubramanian, Mohammed Danish, Shaikh, Shivaram Venkataraman, Aditya Akella

TL;DR
Archipelago is a scalable serverless platform that reduces latency and tail latency for multi-tenant FaaS workloads by partitioning clusters, using latency-aware scheduling, and proactive sandboxing.
Contribution
It introduces a novel architecture with semi-global schedulers and load balancing to meet latency deadlines in serverless environments.
Findings
Achieves over 99% latency deadline adherence
Reduces tail latencies by up to 36 times
Effective in handling short-lived, unpredictable functions
Abstract
The increased use of micro-services to build web applications has spurred the rapid growth of Function-as-a-Service (FaaS) or serverless computing platforms. While FaaS simplifies provisioning and scaling for application developers, it introduces new challenges in resource management that need to be handled by the cloud provider. Our analysis of popular serverless workloads indicates that schedulers need to handle functions that are very short-lived, have unpredictable arrival patterns, and require expensive setup of sandboxes. The challenge of running a large number of such functions in a multi-tenant cluster makes existing scheduling frameworks unsuitable. We present Archipelago, a platform that enables low latency request execution in a multi-tenant serverless setting. Archipelago views each application as a DAG of functions, and every DAG in associated with a latency deadline.…
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Taxonomy
TopicsCloud Computing and Resource Management · Distributed and Parallel Computing Systems · Parallel Computing and Optimization Techniques
