Online Fragmentation-Aware GPU Scheduler Improves Multi-Tenant MIG Cloud Resource Allocation by Minimizing 10% Waste – Quantum Zeitgeist

Report on a Novel Scheduling Framework for Sustainable GPU Cloud Computing

Introduction: The Challenge to Sustainable Digital Infrastructure

The escalating demand for artificial intelligence (AI) applications imposes considerable strain on global computing infrastructure, challenging sustainability objectives. The inefficient use of Graphics Processing Units (GPUs) in cloud environments, specifically due to resource fragmentation, directly contravenes the principles of sustainable resource management. This report details a new scheduling framework designed to address this inefficiency, aligning technological advancement with key Sustainable Development Goals (SDGs).

• Resource Underutilisation: The fixed partitioning of Multi-Instance GPU (MIG) technology often leads to GPU fragmentation, leaving valuable, energy-intensive hardware idle despite available capacity.
• Hindrance to Innovation: Inefficient resource allocation limits the number of AI workloads that can be accommodated, creating a bottleneck for research and development that could otherwise contribute to solving global challenges.
• Unsustainable Growth: GPU fragmentation can necessitate premature hardware expansion, increasing electronic waste and the carbon footprint associated with manufacturing and data center operations, undermining SDG 12 (Responsible Consumption and Production).

A Framework for Enhanced Resource Efficiency and Sustainability

In response to these challenges, researchers at Fondazione Bruno Kessler have developed a novel scheduling framework to mitigate GPU fragmentation in MIG-based cloud environments. The solution is engineered to maximize the utility of existing hardware, thereby promoting a more sustainable operational model for cloud providers.

1. Development of a Fragmentation Metric: A new metric was created to analytically quantify the severity of GPU fragmentation. This tool enables an informed, data-driven approach to resource allocation, which is fundamental for efficient and sustainable management of digital infrastructure (SDG 9).
2. Implementation of a Fragmentation-Aware Scheduling Algorithm: An online, greedy scheduling algorithm was designed to prioritise the minimisation of fragmentation growth. With each new workload, the algorithm selects GPU resources in a manner that preserves the capacity to accommodate future requests, maximising overall system throughput.

Empirical results demonstrate that this framework consistently schedules approximately 10% more workloads under heavy load conditions, representing a significant improvement in resource utilisation and operational efficiency.

Direct Contributions to Sustainable Development Goals (SDGs)

The implementation of this scheduling framework provides tangible contributions to several United Nations Sustainable Development Goals.

• SDG 9: Industry, Innovation, and Infrastructure: By optimising the use of existing hardware, this innovation fosters a more resilient, efficient, and sustainable digital infrastructure. It enhances the technological capability of cloud platforms, supporting further innovation across all industries.
• SDG 12: Responsible Consumption and Production: The framework’s primary function is to reduce resource waste. By maximising the number of workloads on a single physical GPU, it promotes the sustainable and efficient use of resources, reduces the need for new hardware production, and helps minimise the generation of electronic waste.
• SDG 7: Affordable and Clean Energy: Increasing workload density improves the energy efficiency per computational task. This leads to a reduction in the overall energy consumption of data centers, contributing directly to global efforts to ensure sustainable energy for all.
• SDG 13: Climate Action: Reduced energy consumption directly translates to lower greenhouse gas emissions from data center operations, supporting urgent action to combat climate change and its impacts.

Conclusion and Future Outlook

The research delivers a practical and impactful solution for improving the sustainability of AI-driven cloud services. By addressing the critical issue of GPU fragmentation, the developed metric and scheduling algorithm enable a significant increase in resource efficiency. This advancement allows cloud providers to accommodate greater demand without a proportional increase in hardware or energy consumption. While future work may incorporate workload predictions for further optimisation, the current framework represents a substantial step toward aligning the rapid growth of AI with global sustainability targets, proving that technological innovation can and should advance responsible consumption and environmental stewardship.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 9: Industry, Innovation, and Infrastructure

  The article directly relates to this goal by focusing on technological innovation to improve digital infrastructure. The research presents a new scheduling framework for cloud environments, which is a critical component of modern industrial and technological infrastructure. The development of a novel algorithm to enhance the efficiency of Multi-Instance GPU (MIG) technology is a clear example of advancing technological capabilities.

• SDG 12: Responsible Consumption and Production

  This goal is addressed through the theme of resource efficiency. The article’s central problem is “GPU fragmentation,” which leads to underutilized and wasted computing resources. By developing a method to schedule “approximately 10% more workloads under heavy load,” the research promotes a more sustainable and efficient use of existing hardware, reducing the need for additional physical resources and the associated energy consumption and electronic waste.

• SDG 8: Decent Work and Economic Growth

  The article connects to this goal by highlighting how technological innovation can drive economic productivity. The improved efficiency in GPU utilization “potentially increasing revenue for cloud providers by accommodating a greater number of applications.” This enhancement of resource productivity within the rapidly growing AI sector contributes to sustainable economic growth.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. Under SDG 9: Industry, Innovation, and Infrastructure

   • Target 9.4: “By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes…” The article’s focus on a new scheduling algorithm to achieve “a substantial improvement in resource utilisation” and “maximise resource efficiency” directly aligns with this target by making cloud infrastructure more sustainable through increased efficiency.
   • Target 9.5: “Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries… encouraging innovation…” The research itself, conducted by scientists from Fondazione Bruno Kessler, which resulted in a “novel scheduling framework” and a “new fragmentation metric,” is a direct contribution to enhancing scientific research and upgrading the technological capabilities of the cloud computing industry.

2. Under SDG 12: Responsible Consumption and Production

   • Target 12.2: “By 2030, achieve the sustainable management and efficient use of natural resources.” While GPUs are manufactured resources, their production and operation consume significant natural resources (minerals, energy). By maximizing the utilization of existing GPUs, the technology described helps reduce the demand for new hardware, thus promoting more efficient use of the underlying natural resources. The article states the goal is to overcome “underutilized resources” and improve “GPU utilization.”

3. Under SDG 8: Decent Work and Economic Growth

   • Target 8.2: “Achieve higher levels of economic productivity through diversification, technological upgrading and innovation…” The development of a new scheduling algorithm is a technological innovation that directly increases the productivity of cloud infrastructure. The ability to handle “10% more workloads” on the same hardware is a clear measure of increased economic productivity for cloud service providers.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article mentions several specific, quantifiable indicators that can be used to measure progress:

• Workload Acceptance Rate: This is a primary indicator of efficiency. The article explicitly states that the new method “consistently schedules approximately 10% more workloads under heavy load,” providing a direct metric for improved resource utilization (relevant to Targets 9.4 and 8.2).
• GPU Fragmentation Level: The research developed a “new metric to analytically measure the severity of GPU fragmentation.” This metric itself serves as a direct indicator to track and manage resource waste. The article notes that the new algorithm leads to “reduced fragmentation levels,” which can be measured to show progress towards Target 12.2.
• GPU Usage: The article mentions that the improvements are achieved while “maintaining comparable GPU usage to benchmark methods.” This indicates that efficiency is gained not by simply running the hardware hotter or longer, but by smarter allocation, which is a key aspect of sustainable management.

4. Summary Table of SDGs, Targets, and Indicators

Source: quantumzeitgeist.com