Henge: Intent-driven Multi-Tenant Stream Processing

TL;DR

Henge is a system that enables multi-tenant stream processing with intent-based resource management, dynamically adapting to meet individual job SLOs in shared clusters.

Contribution

Henge introduces intent-driven multi-tenancy support with real-time resource allocation to meet diverse SLOs in stream processing systems.

Findings

Successfully integrated with Apache Storm

Achieves SLO satisfaction under dynamic workloads

Maximizes overall system utility

Abstract

We present Henge, a system to support intent-based multi-tenancy in modern stream processing applications. Henge supports multi-tenancy as a first-class citizen: everyone inside an organization can now submit their stream processing jobs to a single, shared, consolidated cluster. Additionally, Henge allows each tenant (job) to specify its own intents (i.e., requirements) as a Service Level Objective (SLO) that captures latency and/or throughput. In a multi-tenant cluster, the Henge scheduler adapts continually to meet jobs' SLOs in spite of limited cluster resources, and under dynamic input workloads. SLOs are soft and are based on utility functions. Henge continually tracks SLO satisfaction, and when jobs miss their SLOs, it wisely navigates the state space to perform resource allocations in real time, maximizing total system utility achieved by all jobs in the system. Henge is integrated in Apache Storm and we present experimental results using both production topologies and real datasets.