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Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities

Junjie Chu, Prashant Singh, Salman Toor

TL;DR

The paper tackles inefficient resource pricing and utilization in distributed cloud infrastructures by introducing a 1-to-1 auto-negotiation framework between providers and tenants based on fuzzy logic. It combines a transparent fuzzy negotiation system with a neural-network surrogate to accelerate negotiations while preserving interpretability, and it systematically compares membership functions, rule sets, and negotiation scenarios. Key contributions include (i) a comprehensive fuzzy negotiation workflow for VCPU, RAM, and storage scheduling, (ii) surrogate neural models that replicate FNS outputs with dramatic speedups, and (iii) extensive experiments demonstrating improved resource utilization and actionable trade-offs, alongside an open-source codebase. The work has practical impact by enabling faster, interpretable, and cost-effective cloud resource scheduling in real-world multi-tenant environments.

Abstract

In the past few decades, the rapid development of information and internet technologies has spawned massive amounts of data and information. The information explosion drives many enterprises or individuals to seek to rent cloud computing infrastructure to put their applications in the cloud. However, the agreements reached between cloud computing providers and clients are often not efficient. Many factors affect the efficiency, such as the idleness of the providers' cloud computing infrastructure, and the additional cost to the clients. One possible solution is to introduce a comprehensive, bargaining game (a type of negotiation), and schedule resources according to the negotiation results. We propose an agent-based auto-negotiation system for resource scheduling based on fuzzy logic. The proposed method can complete a one-to-one auto-negotiation process and generate optimal offers for the provider and client. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenario cases on the offers to optimize the system. It can be concluded that our proposed method can utilize resources more efficiently and is interpretable, highly flexible, and customizable. We successfully train machine learning models to replace the fuzzy negotiation system to improve processing speed. The article also highlights possible future improvements to the proposed system and machine learning models. All the codes and data are available in the open-source repository.

Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities

TL;DR

The paper tackles inefficient resource pricing and utilization in distributed cloud infrastructures by introducing a 1-to-1 auto-negotiation framework between providers and tenants based on fuzzy logic. It combines a transparent fuzzy negotiation system with a neural-network surrogate to accelerate negotiations while preserving interpretability, and it systematically compares membership functions, rule sets, and negotiation scenarios. Key contributions include (i) a comprehensive fuzzy negotiation workflow for VCPU, RAM, and storage scheduling, (ii) surrogate neural models that replicate FNS outputs with dramatic speedups, and (iii) extensive experiments demonstrating improved resource utilization and actionable trade-offs, alongside an open-source codebase. The work has practical impact by enabling faster, interpretable, and cost-effective cloud resource scheduling in real-world multi-tenant environments.

Abstract

In the past few decades, the rapid development of information and internet technologies has spawned massive amounts of data and information. The information explosion drives many enterprises or individuals to seek to rent cloud computing infrastructure to put their applications in the cloud. However, the agreements reached between cloud computing providers and clients are often not efficient. Many factors affect the efficiency, such as the idleness of the providers' cloud computing infrastructure, and the additional cost to the clients. One possible solution is to introduce a comprehensive, bargaining game (a type of negotiation), and schedule resources according to the negotiation results. We propose an agent-based auto-negotiation system for resource scheduling based on fuzzy logic. The proposed method can complete a one-to-one auto-negotiation process and generate optimal offers for the provider and client. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenario cases on the offers to optimize the system. It can be concluded that our proposed method can utilize resources more efficiently and is interpretable, highly flexible, and customizable. We successfully train machine learning models to replace the fuzzy negotiation system to improve processing speed. The article also highlights possible future improvements to the proposed system and machine learning models. All the codes and data are available in the open-source repository.
Paper Structure (29 sections, 6 equations, 5 figures, 14 tables, 5 algorithms)

This paper contains 29 sections, 6 equations, 5 figures, 14 tables, 5 algorithms.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Scenario
  4. Core components
  5. System architecture
  6. Overall design
  7. Fuzzy negotiation system
  8. System workflow
  9. Fuzzy decision-making system
  10. Algorithms
  11. Machine learning process
  12. Workflow
  13. Model architecture
  14. Results and discussion
  15. Settings
  16. ...and 14 more sections

Figures (5)

  • Figure 1: Overview of different methods
  • Figure 2: Flow charts in different cases. Algorithm 3 has multiple versions according to different cases.
  • Figure 3: Workflow of training machine learning models
  • Figure 4: Comparison of the ratios (VCPU)
  • Figure 5: Plots of ratios in different experiments. Horizontal axes represent the index of the input requirement. The vertical axes represent ratios. The baseline is 1.0, which means values in final offers are the same as those in the original requirements.