Workload Intelligence: Punching Holes Through the Cloud Abstraction
Lexiang Huang, Anjaly Parayil, Jue Zhang, Xiaoting Qin, Chetan Bansal,, Jovan Stojkovic, Pantea Zardoshti, Pulkit Misra, Eli Cortez, Raphael Ghelman,, \'I\~nigo Goiri, Saravan Rajmohan, Jim Kleewein, Rodrigo Fonseca, Timothy, Zhu, Ricardo Bianchini

TL;DR
This paper introduces Workload Intelligence (WI), a framework enabling dynamic, bidirectional communication between cloud workloads and platforms, leading to better optimization, simplified platform offerings, and significant cost reductions.
Contribution
The paper presents a novel framework for real-time, programmable interaction between workloads and cloud platforms, enhancing flexibility and efficiency.
Findings
Platform costs reduced by 48.8% on average
Workloads can adapt behaviors based on platform signals
Platforms can optimize resource allocation dynamically
Abstract
Today, cloud workloads are essentially opaque to the cloud platform. Typically, the only information the platform receives is the virtual machine (VM) type and possibly a decoration to the type (e.g., the VM is evictable). Similarly, workloads receive little to no information from the platform; generally, workloads might receive telemetry from their VMs or exceptional signals (e.g., shortly before a VM is evicted). The narrow interface between workloads and platforms has several drawbacks: (1) a surge in VM types and decorations in public cloud platforms complicates customer selection; (2) essential workload characteristics (e.g., low availability requirements, high latency tolerance) are often unspecified, hindering platform customization for optimized resource usage and cost savings; and (3) workloads may be unaware of potential optimizations or lack sufficient time to react to…
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Taxonomy
TopicsBig Data and Business Intelligence
