Workload Intelligence: Workload-Aware IaaS Abstraction for Cloud Efficiency
- Lexiang Huang ,
- A. Parayil ,
- Jue Zhang ,
- Xiaoting Qin ,
- Chetan Bansal ,
- Jovan Stojkovic ,
- Pantea Zardoshti ,
- Pulkit Misra ,
- Eli Cortez ,
- Raphael Ghelman ,
- Íñigo Goiri ,
- Saravan Rajmohan ,
- Jim Kleewein ,
- Rodrigo Fonseca ,
- Timothy Zhu ,
- Ricardo Bianchini
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) |
Today, cloud workloads are largely 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 minimal information from the platform; generally, only telemetry from their VMs or occasional signals (e.g., just 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) key workload characteristics (e.g., low availability requirements) 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 platform events. To resolve these issues and improve cloud efficiency, we propose Workload Sage, a framework for enabling dynamic bi-directional communication between cloud workloads and cloud platform.