Batch Auction Design For Cloud Container Services

TL;DR

This paper introduces a novel market mechanism for cloud container services that optimizes social welfare while ensuring incentive compatibility and computational efficiency, leveraging batch online optimization and delay tolerance.

Contribution

It is the first to design efficient auction mechanisms specifically for container-based cloud jobs, integrating advanced optimization and pricing techniques.

Findings

The proposed auction mechanism achieves high economic efficiency.

The algorithms are computationally efficient and incentive compatible.

Empirical results validate the effectiveness of the designed market mechanisms.

Abstract

Cloud containers represent a new, light-weight alternative to virtual machines in cloud computing. A user job may be described by a container graph that specifies the resource profile of each container and container dependence relations. This work is the first in the cloud computing literature that designs efficient market mechanisms for container based cloud jobs. Our design targets simultaneously incentive compatibility, computational efficiency, and economic efficiency. It further adapts the idea of batch online optimization into the paradigm of mechanism design, leveraging agile creation of cloud containers and exploiting delay tolerance of elastic cloud jobs. The new and classic techniques we employ include: (i) compact exponential optimization for expressing and handling non-traditional constraints that arise from container dependence and job deadlines; (ii) the primal-dual schema for designing efficient approximation algorithms for social welfare maximization; and (iii) posted price mechanisms for batch decision making and truthful payment design. Theoretical analysis and trace-driven empirical evaluation verify the efficacy of our container auction algorithms.