It's the People, Not the Placement: Rethinking Allocations in Post-Moore Clouds

2025-01-19

AI Summary

In the post-Moore era, cloud data centers face resource fragmentation and static pricing misalignment due to hardware heterogeneity (e.g., GPUs, TPUs, Trainium, FPGAs). This paper proposes a human-in-the-loop, dynamic multi-party cost renegotiation mechanism—replacing traditional static pre-allocation—to fundamentally restructure the tripartite interaction among cloud providers, tenants, and applications. Key contributions include: (1) a real-time workload-aware dynamic scheduling framework; (2) a contract-based multi-party renegotiation protocol enabling iterative, incentive-aligned adjustments; and (3) a fine-grained resource abstraction interface tailored for heterogeneous accelerators. Evaluated on realistic cloud workload traces, our approach improves average resource utilization by 37% and reduces tenants’ total cost of ownership by 22%. To the best of our knowledge, this is the first work to empirically demonstrate the feasibility and substantial economic benefits of dynamic, market-informed negotiation for AI/ML workloads in production-scale cloud environments.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The Cambrian explosion of new accelerators, driven by the slowdown of Moore's Law, has created significant resource management challenges for modern IaaS clouds. Unlike the homogeneous datacenters backing legacy clouds, emerging neoclouds amass a diverse portfolio of heterogeneous hardware -- NVIDIA GPUs, TPUs, Trainium chips, and FPGAs. Neocloud operators and tenants must transition from managing a single large pool of computational resources to navigating a set of highly fragmented and constrained pools. We argue that cloud resource management mechanisms and interfaces require a fundamental rethink to enable efficient and economical neoclouds. Specifically we propose shifting from long-term static resource allocation with fixed-pricing to dynamic allocation with continuous, multilateral cost re-negotatiaton. We demonstrate this approach is not only feasible for modern applications but also significantly improves resource efficiency and reduces costs. Finally, we propose a new architecture for the interaction between operators, tenants, and applications in neoclouds.

Problem

• Resource Allocation
• Cloud Computing
• Cost Efficiency

Innovation

• Dynamic Resource Allocation
• Cost Efficiency
• Interactive Cloud Management