Affinity-Aware Resource Provisioning for Long-Running Applications in Shared Clusters

TL;DR

This paper introduces an affinity-aware resource provisioning method for long-running applications in shared clusters, aiming to minimize compute nodes while respecting application constraints, thus reducing operational costs and carbon footprint.

Contribution

It proposes a novel affinity-aware scheduling approach tailored for LRAs with multiple constraints, and evaluates various algorithms against real-world data for improved efficiency.

Findings

Algorithms achieve competitive effectiveness

Reduced number of compute nodes used

Efficient scheduling in large-scale scenarios

Abstract

Resource provisioning plays a pivotal role in determining the right amount of infrastructure resource to run applications and target the global decarbonization goal. A significant portion of production clusters is now dedicated to long-running applications (LRAs), which are typically in the form of microservices and executed in the order of hours or even months. It is therefore practically important to plan ahead the placement of LRAs in a shared cluster so that the number of compute nodes required by them can be minimized to reduce carbon footprint and lower operational costs. Existing works on LRA scheduling are often application-agnostic, without particularly addressing the constraining requirements imposed by LRAs, such as co-location affinity constraints and time-varying resource requirements. In this paper, we present an affinity-aware resource provisioning approach for deploying large-scale LRAs in a shared cluster subject to multiple constraints, with the objective of minimizing the number of compute nodes in use. We investigate a broad range of solution algorithms which fall into three main categories: Application-Centric, Node-Centric, and Multi-Node approaches, and tune them for typical large-scale real-world scenarios. Experimental studies driven by the Alibaba Tianchi dataset show that our algorithms can achieve competitive scheduling effectiveness and running time, as compared with the heuristics used by the latest work including Medea and LraSched.