# ERA: A Framework for Economic Resource Allocation for the Cloud

## TL;DR

ERA introduces a flexible, economics-based framework for cloud resource scheduling and pricing, aiming to optimize resource utilization by dynamically adjusting prices based on demand predictions, and is adaptable across various cloud infrastructures.

## Contribution

The paper presents ERA, a novel, modular framework that separates cloud infrastructure from economic algorithms, enabling improved resource allocation through dynamic pricing and scheduling.

## Key findings

- Effective resource utilization demonstrated on Azure Batch and Hadoop/YARN
- Framework supports plug-in algorithms for scheduling, pricing, and demand prediction
- Simulation platform facilitates collaboration between economics and system researchers

## Abstract

Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal allocation of the costly hardware resources. In this paper we present Economic Resource Allocation (ERA), a complete framework for scheduling and pricing cloud resources, aimed at increasing the efficiency of cloud resources usage by allocating resources according to economic principles. The ERA architecture carefully abstracts the underlying cloud infrastructure, enabling the development of scheduling and pricing algorithms independently of the concrete lower-level cloud infrastructure and independently of its concerns. Specifically, ERA is designed as a flexible layer that can sit on top of any cloud system and interfaces with both the cloud resource manager and with the users who reserve resources to run their jobs. The jobs are scheduled based on prices that are dynamically calculated according to the predicted demand. Additionally, ERA provides a key internal API to pluggable algorithmic modules that include scheduling, pricing and demand prediction. We provide a proof-of-concept software and demonstrate the effectiveness of the architecture by testing ERA over both public and private cloud systems -- Azure Batch of Microsoft and Hadoop/YARN. A broader intent of our work is to foster collaborations between economics and system communities. To that end, we have developed a simulation platform via which economics and system experts can test their algorithmic implementations.