# Performance Isolation in Multi Tenant Cloud Data Centers

Ahmed Ben Ali, Yann Labit, Kokouvi Benoit Nougnanke

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## To cite this version:

Ahmed Ben Ali, Yann Labit, Kokouvi Benoit Nougnanke. Performance Isolation in Multi Tenant Cloud Data Centers. IEEE NOMS 2025, IEEE, May 2025, Honolulu, United States. 10.1109/NOMS57970.2025.11073739. [hal-04995752](https://hal HAL Id: hal-04995752)

Submitted on 18 Mar 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Performance Isolation in Multi Tenant Cloud Data Centers

Ahmed Ben Ali

Université de Toulouse
LAAS-CNRS

Toulouse, France

Email: abenali@laas.fr

Yann Labit

Université de Toulouse
LAAS-CNRS

Toulouse, France

Email: ylabit@laas.fr

Benoit Nougnanke

Télécom SudParis
Paris, France

Email: kokouvi-benoit.nougnanke@telecom-sudparis.eu

*Abstract*—Ensuring network performance isolation in multi-tenant cloud data centers is critical for maintaining Service Level Agreement (SLA) compliance while supporting diverse and dynamic workloads. Traditional approaches predominantly emphasize flow-level fairness, which, although effective in certain scenarios, often fails to address the broader needs of tenant-based fairness essential for SLA-driven environments. My research introduces a novel framework leveraging control theory and AI-enhanced decision-making to achieve adaptive and intelligent network resource management. By shifting the focus to tenant-based fairness, the proposed solution ensures that resources are allocated equitably among tenants while meeting SLA requirements under fluctuating conditions. Preliminary investigations demonstrate the potential of this approach to enhance reliability, scalability, and efficiency, addressing the limitations of fairness-centric methods focused solely on flows. This work lays the groundwork for intelligent, automated resource management in cloud data centers, advancing the capabilities of multi-tenant infrastructure to meet future demands.

*Index Terms*—Multi-tenant environment, Cloud Data Center, Control theory, AQM, Fairness, Performance isolation, Artificial Intelligence.

# I. INTRODUCTION

## A. Background and Motivation

In multi-tenant cloud data centers, multiple tenants share the same physical infrastructure, creating significant challenges in ensuring fair resource allocation and maintaining performance isolation [1]–[3]. Each tenant generates traffic with varying workloads, leading to dynamic and unpredictable network conditions. These challenges are further compounded by the growing adoption of containerized microservices, which amplify traffic fluctuations and risk degrading the quality of service if network resources are not managed effectively. In this complex and dynamic environment, traditional congestion control methods, such as those embedded in Transmission Control Protocol (TCP) [4], struggle to maintain consistent performance across tenants.

The way to achieve this is *Active Queue Management (AQM)* [5] techniques that have been widely adopted to mitigate congestion by managing queue lengths at network routers. Popular AQM methods, such as *Random Early Detection (RED)* [6] and *Controlled Delay (CoDel)* [7], attempt to preemptively address congestion by dropping packets based on queue size or delay. However, these approaches face significant limitations in multi-tenant cloud settings. For instance, RED's reliance on probabilistic packet dropping requires careful tuning to accommodate diverse traffic conditions, rendering it

unsuitable for environments with frequent workload changes. Similarly, CoDel focuses on reducing latency by monitoring queue delay but fails to explicitly consider fairness among tenants, often leading to uneven resource distribution and performance degradation.

Recent advancements in AQM, such as the Proportional Integral Controller Enhanced (PIE) [8], incorporate control-theoretic principles to adjust packet drop rates dynamically based on queue delay trends, improving stability under fluctuating traffic conditions. While these methods offer enhancements over traditional approaches, they are not designed for multi-tenant cloud environments and lack explicit mechanisms to ensure tenant-level fairness. Consequently, “noisy neighbors”—tenants with bursty or aggressive traffic patterns—can still degrade the quality of service for others. Similarly, advanced control strategies like Model-Free Control (MFC) [9], which adapt without relying on predefined mathematical models, enhance adaptability but fail to address tenant-specific fairness requirements, making them inadequate for multi-tenant scenarios.

To address the limitations of existing approaches in multi-tenant environments, this research proposes a novel framework that integrates control-theoretic principles with AI-enhanced decision-making to prioritize tenant-based fairness and performance isolation. By dynamically adjusting control parameters to accommodate tenant-specific needs expressed through Service Level Agreements (SLAs) and Service Level Objectives (SLOs), this framework ensures fair resource allocation and minimizes the impact of disruptive tenants. It promotes scalability and adaptability in cloud data centers, addressing both current challenges and future demands.

## B. Research Questions and Objectives

To address the challenges of performance isolation and tenant-based fairness in multi-tenant cloud data centers (MTCDCs), this study seeks to answer the following key research questions:

1) How can AI-assisted control theory be leveraged to ensure tenant-specific fairness while maintaining scalability and efficiency in MTCDCs?

2) What are the limitations posed by the lack of a comprehensive model, and how can an effective model be established to integrate AI-assisted control
**mechanisms and address the shortcomings of existing techniques in managing resources dynamically and equitably?**

3) **What methodologies can be developed to quantify and monitor tenant-specific fairness and performance isolation in real-time?**

The objectives of this research are structured to systematically address the identified research questions.

1) **Develop a first foundation of an AQM for Tenant-Based Fairness:** Design an AQM based on control theory and tailored to ensure equitable resource allocation and performance isolation among tenants in multi-tenant cloud data centers.

2) **Establish a Model and Control Mechanism:** Develop a robust and adaptable model to represent tenant behavior and dynamic network conditions, and use it to implement effective control mechanisms. The results from this objective will be validated through the identification results of the previous one.

3) **Add a Layer of AI:** Incorporate Artificial Intelligence (AI) to enhance decision-making, adaptability, and efficiency in achieving tenant fairness and performance isolation.

By addressing these objectives, this research aims to establish a strong foundation for intelligent and automated resource management in multi-tenant cloud data centers.

## II. STATE OF THE ART

AQM techniques play a crucial role in managing congestion and maintaining performance in multi-tenant cloud data centers (MTCDCs). In this context, a tenant refers to an individual user, application, or organization that shares the cloud infrastructure and utilizes its network resources, often generating multiple network flows. These environments present unique challenges due to the need for fair resource allocation and performance isolation among tenants. Traditional AQM approaches, control-theoretic methods, and Quality of Service (QoS) mechanisms each address aspects of congestion control but often fail to deliver tenant-based fairness and adaptive traffic management effectively.

### A. Traditional AQM Approaches

*Proportional Integral Controller Enhanced (PIE)* [8] is an AQM method that dynamically adjusts the packet drop rate based on queue delay, providing improved adaptability over older approaches like Random Early Detection (RED) [6] and Controlled Delay (CoDel) [7]. However, PIE lacks explicit mechanisms to manage fairness among tenants.

*Low Latency, Low Loss, Scalable Throughput (L4S)* [10] focuses on achieving low latency and loss while maintaining scalability. Despite its promise in reducing congestion, it does not address fairness requirements specific to multi-tenant cloud environments.

### B. Control-Theoretic Approaches for Resource Allocation

Control-theoretic methods have been widely applied in congestion management to improve queue stability and responsiveness. For instance, *Hollot et al.* [11] introduced PI controllers for AQM that regulate queue length effectively, though the focus remains on flow-level rather than tenant-level fairness.

*Ariba et al.* [12] developed state-feedback control mechanisms to address heterogeneous delays in TCP/AQM networks, enhancing performance in diverse traffic conditions. However, these methods do not account for fairness among tenants.

Recent work on *Model-Free Control (MFC)* [9] employs adaptive mechanisms without relying on detailed mathematical models, enhancing robustness and adaptability. While promising, it still does not offer explicit solutions for tenant-specific fairness.

### C. Quality of Service (QoS) Mechanisms and AI-Enhanced Techniques

QoS mechanisms, such as *Differentiated Services (Diff-Serv)*, utilize policies like *Weighted Random Early Detection (WRED)* and *Class-Based Weighted Fair Queueing (CB-WFQ)* [13] to manage congestion and prioritize traffic. These techniques focus on traffic classes but fail to ensure equitable resource allocation for tenants.

*Machine Learning (ML)-Based Techniques*, including deep reinforcement learning [14], have been explored to optimize AQM settings and predict traffic patterns dynamically. These approaches hold potential for fairness but require integration with control-theoretic principles to address the complexities of resource allocation.

### D. Our Approach: Leveraging Control Theory and AI for Tenant-Based Fairness

To address the limitations of existing approaches in multi-tenant environments, our research proposes a novel framework that integrates control-theoretic principles with AI-enhanced decision-making to prioritize tenant-based fairness and performance isolation. By dynamically adjusting control parameters to accommodate tenant-specific needs expressed through Service Level Agreements (SLAs) and Service Level Objectives (SLOs), this framework ensures fair resource allocation and minimizes the impact of disruptive tenants. The integration of AI allows for predictive analytics and adaptive learning, enabling the system to respond proactively to changing network conditions and tenant demands.

Our approach distinguishes itself by:

* **Tenant-Level Fairness:** Explicitly focusing on equitable resource distribution among tenants rather than individual flows or traffic classes.

* **Adaptive Control:** Utilizing control theory to dynamically adjust network parameters in real time, ensuring stability and responsiveness.

* **AI-Enhanced Decision Making:** Incorporating AI techniques to predict traffic patterns and optimize resource allocation proactively.
* **Performance Isolation:** Enhancing the ability to isolate the performance of each tenant, preventing "noisy neighbors" from impacting others.

By combining these elements, our framework aims to overcome the shortcomings of traditional AQMs, control-theoretic methods, and QoS mechanisms in the context of MTCDCs. It promotes scalability and adaptability in cloud data centers, addressing both current challenges and future demands.

## E. *Comparative Summary*

The following table(Table I) provides a comparison of traditional AQMs, control-theoretic approaches, QoS mechanisms, and our proposed approach, focusing on how they address fairness at the tenant or flow level.

# III. PROPOSED ARCHITECTURE

## A. *Overview of the Architecture*

To effectively ensure performance isolation in shared network environments while maintaining fair resource allocation and meeting service level objectives (SLOs), we propose a framework inspired by the MAPE-K [17] model. This framework consists of several key components working together to monitor, evaluate, and adapt system behavior dynamically you can see the model in Figure 1.

The Measuring Module gathers essential metrics, such as latency and bandwidth, from applications. This data is then processed by an Estimation Module, which employs machine learning or statistical methods to analyze and predict factors influencing system performance. A state classifier categorizes the current state of the system based on these estimations, allowing adjustments to the level of resource allocation as necessary.

A Policy Maker formulates dynamic strategies, modifying resource distribution and triggering actions like scaling resources based on the system's needs. A continuous Feedback Loop ensures that the system adapts to evolving conditions in real-time, creating a flexible and effective architecture for maintaining performance standards.

The implementation of this framework involves several key phases. First, a comprehensive understanding of the target system is required, including its components, operational dynamics, and specific challenges. This phase establishes the foundation for developing effective control and resource management strategies. Second, a robust model representing the system and its behaviors is created and validated to ensure accurate predictions and control capabilities under varying conditions. Next, the framework's core mechanisms are designed to ensure adaptability to a wide range of scenarios and the ability to address system-specific requirements. The testing process will be conducted in two phases: initially using NS3 [18] simulations to evaluate the framework's performance in a controlled virtual environment followed by testing on a Kubernetes cluster testbed. This structured approach enables iterative refinement and optimization, ensuring the framework meets performance objectives and remains robust under diverse operating conditions.

# IV. PROGRESS TOWARD OBJECTIVES

Some progress has been made in addressing the research objectives. For **Objective 1**, the development of an AQM tailored to ensure equitable resource allocation and performance isolation among tenants has been successfully completed. This system leverages control-theoretic principles to dynamically adjust queue management parameters, effectively responding to tenant-specific behaviors and resource contention. By incorporating fairness metrics and adaptive adjustments, the AQM system mitigates the impact of disruptive tenants and ensures stable performance across diverse workloads. Its effectiveness has been validated through extensive simulations, demonstrating its ability to maintain fairness and scalability under varying conditions. This work is detailed in our proposition at *NOMS 2025. Work on Objective 2* is currently underway, focusing on developing a robust and adaptable model to represent tenant behaviors, traffic variability, and dynamic network conditions. This model aims to simulate realistic multi-tenant scenarios and serve as a foundation for designing advanced control mechanisms. Preliminary results are promising, and ongoing validation efforts are refining its accuracy and adaptability to diverse environments. The results will be confronted with previously found results in **Objective 1**. For **Objective 3**, efforts will begin after the completion of Objective 2, with plans to integrate Artificial Intelligence (AI) for enhanced decision-making and adaptability. The AI layer will employ predictive analytics and Machine Learning techniques to anticipate changes in network conditions and tenant demands, ensuring proactive and intelligent resource management.

# V. CONCLUSION

This research emphasizes addressing the pressing challenges of fairness and performance isolation in multi-tenant cloud data centers (MTCDCs). Through a novel framework combining AI-assisted control theory with real-time monitoring and adaptive mechanisms, the study aims to bridge gaps left by traditional approaches such as AQM techniques, control-theoretic methods, and QoS mechanisms.

The proposed approach leverages a robust, tenant-aware model to dynamically manage resources, ensuring tenant-specific fairness while maintaining scalability and efficiency. By addressing limitations like the lack of an adaptable traffic model, unpredictable tenant behavior, and synchronization issues in variable network conditions, this work lays the groundwork for future advancements in intelligent, automated resource management.

Ultimately, the study positions itself to significantly advance the capabilities of MTCDCs by providing scalable, adaptive, and fair resource allocation mechanisms, enabling these infrastructures to meet diverse and dynamic tenant requirements. Future work will focus on validating the proposed framework through simulations and real-world implementation, ensuring its applicability in complex cloud environments.
<table><caption>TABLE I
COMPARISON OF AQM APPROACHES</caption><thead><tr><td>Name</td><td>Approach</td><td>Multi-Tenant Support</td><td>Fairness Level</td><td>Control Strategy</td><td>Resource Focused</td><td>Performance Under Heavy Load</td></tr></thead><tbody><tr><td>RED [6]</td><td>Probability-based early drops</td><td>No explicit support</td><td>Non-explicit</td><td>Requires tuning of drop probabilities</td><td>Bandwidth</td><td>Good, but tuning-sensitive</td></tr><tr><td>CoDel [7]</td><td>Delay-based packet dropping</td><td>No explicit support</td><td>Non-explicit</td><td>Self-configuring, delay-based</td><td>Latency and delay</td><td>Good for low-latency environments</td></tr><tr><td>PIE [8]</td><td>Proportional-Integral controller</td><td>No explicit support</td><td>Non-explicit</td><td>Self-configuring, delay-based</td><td>Latency and queue length</td><td>Good, adapts to dynamic traffic</td></tr><tr><td>L4S [10]</td><td>Scalable low-latency congestion control</td><td>Supports multiple flows</td><td>Non-explicit</td><td>Low-latency feedback</td><td>Latency</td><td>Scalable but lacks tenant fairness</td></tr><tr><td>MFC [9]</td><td>Model-Free Control</td><td>Real-time adjustment</td><td>Non-explicit</td><td>MFC-based, adaptable control</td><td>Queue length, stability, and adaptation</td><td>Robust under varying network conditions</td></tr><tr><td>Sonnet [15]</td><td>Feedback-based resource allocation</td><td>Dynamic resource allocation, not tenant-specific</td><td>Non-explicit</td><td>Control-theory-based, system-wide feedback</td><td>CPU and memory</td><td>Optimized for dynamic workloads</td></tr><tr><td>Skynet [16]</td><td>Centralized control-theoretic feedback control</td><td>Supports multiple terminals</td><td>Non-explicit</td><td>Centralized control, system-wide feedback</td><td>Bandwidth and resource allocation</td><td>Scales well with large-scale environments</td></tr><tr><td>Our Approach</td><td>Control theory + AI-enhanced decision-making</td><td>Direct support for multi-tenant environments</td><td>Tenant-level</td><td>Adaptive control with AI predictions</td><td>Bandwidth and flow allocation</td><td>Maintains fairness across tenants</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>Fig. 1. General Overview of the Architecture</figcaption></figure>

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