2nd Workshop on Scheduling Variable Capacity
Resources for Sustainability

Anne Benoit, Andrew A Chien, Yves Robert

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2nd Workshop on Scheduling Variable Capacity
Resources for Sustainability

Organizers:

Anne Benoit (ENS Lyon and CNRS/LIP)

Andrew A. Chien (University of Chicago and Argonne National Laboratory)

Yves Robert (ENS Lyon and CNRS/LIP)

10 – 12 September, 2025

The University of Chicago Center in Paris, France
# Executive Summary

This is the report for the second workshop on *Scheduling Variable Capacity Resources for Sustainability*. The first workshop in 2023 nucleated a research community focused on compute scheduling in the new age of renewable power generation – where variation in weather and solar radiation drives variation in compute capacity (opportunity). The goal was to mobilize a combination of the scheduling community, cloud resource management community, and new leaders creating adaptive datacenters. These adaptive grid loads could then accelerate the use of clean renewable energy and thereby power grid decarbonization. This first workshop posited 1) variable capacity, flexible compute platforms; 2) dynamic grids and datacenter-grid decoupling; 3) adaptive scheduling approaches to accommodate these varying platforms and respond to grid demands; and 4) a growing social and government demand for carbon progress in datacenter systems. In September 2025, all of these technical changes come to reality, at large scale because of the AI fueled explosion of datacenter power demand and grid stress.

However, in September 2025, the world of variable capacity scheduling is also shaped by two radical changes. First, a geo-political shock. While the dangers of climate change continue to grow, the turn of the US government's policies away from renewables, even promoting fossil fuels, has created a global turmoil. Second, a workload shock. Over the past two years, the AI revolution dramatically accelerated the growth of electric power consumption by computing – in North America, Europe, Asia (notably China), and the Middle East. In short, the trends that motivated the workshop have only increased in importance and urgency.

At the second workshop, we assessed progress made in 2.5 years on the key problems of variable capacity scheduling, and also trends, challenges, and opportunities.

1. Platforms are rapidly increasing in dynamics, with growing adoption of dynamic power management and adaptive scheduling to meet demand-response and other grid needs. There is a growing two-way relationship between datacenters and grids; opportunities abound.

2. Workloads become more malleable, delay flexible, or even acceptably approximate. AI training workloads are a major example, with hyper-parameter optimization and checkpoints enabling significant flexibility.

3. The scheduling algorithms and metrics are evolving rapidly, reflecting the interplay between flexible workloads and dynamically varying platforms. New metrics are emerging to capture both performance and non-performance attributes (e.g. carbon emissions), and new notions of progress (model improvement).

4. Societal concerns about the sustainability of computing have exploded into the public eye with growing resistance and protest against datacenters.
Criticism of perpetual growth gives rise to questions about new models of sufficiency, as well as growing awareness, responsibilities, and action.

The talks and position papers of the participants are available at https://people.cs.uchicago.edu/~aachien/workshops/varsched25/.

As a follow-up of both workshops, a monograph entitled *Scheduling Variable Capacity Resources for Sustainability* edited by the three workshop organizers will be published by Chapman/CRC Press in Spring 2026.
# Contents

<table><tr><td>1</td><td>Objectives</td><td>5</td></tr><tr><td>2</td><td>Organization</td><td>5</td></tr><tr><td>2.1</td><td>Workshop Agenda</td><td>6</td></tr><tr><td>2.2</td><td>Workshop Attendees and Affiliation</td><td>8</td></tr><tr><td>3</td><td>Platforms and Resources</td><td>9</td></tr><tr><td>3.1</td><td>Summary of Last Workshop Report</td><td>9</td></tr><tr><td>3.2</td><td>What has changed since 2023?</td><td>9</td></tr><tr><td>3.3</td><td>Research Areas and Questions</td><td>10</td></tr><tr><td>3.4</td><td>Summary</td><td>12</td></tr><tr><td>3.5</td><td>Contributors / Participants</td><td>13</td></tr><tr><td>4</td><td>Workloads</td><td>14</td></tr><tr><td>4.1</td><td>Summary of Last Workshop Report</td><td>14</td></tr><tr><td>4.2</td><td>What has changed since 2023?</td><td>14</td></tr><tr><td>4.3</td><td>Research Areas and Questions</td><td>15</td></tr><tr><td>4.4</td><td>Contributors / Participants</td><td>17</td></tr><tr><td>5</td><td>Scheduling</td><td>18</td></tr><tr><td>5.1</td><td>Summary of Last Workshop Report</td><td>18</td></tr><tr><td>5.2</td><td>What has changed since 2023?</td><td>19</td></tr><tr><td>5.3</td><td>Research Areas and Questions</td><td>20</td></tr><tr><td>5.4</td><td>Contributors / Participants</td><td>20</td></tr><tr><td>6</td><td>Policy and Societal Factors</td><td>21</td></tr><tr><td>6.1</td><td>What is the same?</td><td>21</td></tr><tr><td>6.2</td><td>What has changed since 2023?</td><td>21</td></tr><tr><td>6.3</td><td>Updated Recommendations</td><td>22</td></tr><tr><td>6.4</td><td>Conclusion</td><td>22</td></tr><tr><td>6.5</td><td>Contributors / Participants</td><td>23</td></tr></table>
# 1 Objectives

The objective of the workshop was to assess the progress since the 1st Work-
shop on Scheduling Variable Capacity Resources for Sustainability in 2023. That
workshop nucleated a research community focused on compute scheduling in the
new age of renewable power generation – where variation in weather and solar
radiation drives variation in compute capacity (opportunity). In this age, effec-
tive utilization of that opportunity is the key to sustainable computing. The
workshop was by invitation only and gathered European and US research par-
ticipants together with key industrial practice leaders. The goal was to bridge
a traditional scheduling community, a cloud resource management community,
and new leaders creating adaptive datacenters. These flexible grid loads can
accelerate the use of clean renewable energy and thereby power grid decar-
bonization.

# 2 Organization

The 2nd Workshop on “Scheduling Variable Capacity Resources for Sustain-
ability” was held at the Paris Center of the University of Chicago from 11th to
13th of September 2025, with the support of the University of Chicago under
the “France And Chicago Collaborating in the Sciences” (FACCTS) program<fnref n="1" />.

The workshop was by invitation only and gathered European, US, and Asian
researchers from universities, national laboratories, and industry. Specifically,
attendees traveled from France, Switzerland, Germany, the US, and Japan, and
had a diverse range of expertise in areas of sustainability, carbon emissions
analysis, cloud and HPC resource management, scheduling, and power man-
agement. They contributed towards the continued discussion and advancement
of the variable capacity research domain. To facilitate the interaction between
different communities, the workshop began with presentations by attendees, in-
terleaved with discussions and panels. The workshop concluded with working
groups on the identified topics of interest: platforms and resources, scheduling,
workloads, policy and societal factors. We extend our sincere gratitude to the
FACCTS program at the University of Chicago, whose generous support made
this workshop possible, and to the staff of the UChicago Paris Center for their
hospitality and assistance.

<footnote n="1">¹For more information on the program, see https://fcc.uchicago.edu/faccts/</footnote>
## 2.1 Workshop Agenda

<table><thead><tr><th colspan="2">Day 0 (Tuesday, 9th September)</th></tr></thead><tbody><tr><td>19h30-21h</td><td>Reception</td></tr><tr><th colspan="2">Day 1 (Wednesday, 10th September)</th></tr><tr><td>9h30-10h</td><td>Meet and Greet, and Charge (Andrew A Chien, Yves Robert, Anne Benoit)</td></tr><tr><td rowspan="4">10h-11h</td><td>Morning Session</td></tr><tr><td>Reconciling Variable Computing with Electricity System Dynamics, Pierre Segonne, Electricity Maps (Abstract)</td></tr><tr><td>Explosive AI/ML Growth, AI Leadership in Geopolitics, and the Future of Datacenters, Andrew A Chien, University of Chicago and Argonne National Laboratory (Abstract)</td></tr><tr><td>New generation of data centers, Jean-Marc Nicod, U Besancon, France and Denis Trystram, U. Grenoble, France (Abstract)</td></tr><tr><td>11h-11h30</td><td>Break</td></tr><tr><td>11h30-12h30</td><td>Panel — Platforms, Resources and Society (Led by: Andrew A Chien, Panelists: Pierre Segonne, Denis Trystram, Laurent Lefevre, Guillaume Pallez and Fumiyoshi Shoji)</td></tr><tr><td>12h30-14h</td><td>Lunch</td></tr><tr><td rowspan="4">14h-15h</td><td>Afternoon Session 1 — Workflows</td></tr><tr><td>Reducing HPC Waste with Adaptive Job Time Limits, Florina Ciorba, U. Basel, Switzerland (Abstract)</td></tr><tr><td>Carbon impact of HPC and AI, Fanny Dufossé, INRIA and U Grenoble, France (Abstract)</td></tr><tr><td>Scheduling under a power cap and its validation using realistic simulation, Georges Da Costa, U Toulouse, France (Abstract)</td></tr><tr><td>15h-15h30</td><td>Break</td></tr><tr><td rowspan="3">15h30-16h30</td><td>Afternoon Session 2 — Platforms</td></tr><tr><td>Data/HPC center construction and operation for Sustainability, Fumiyoshi Shoji, Riken, Japan (Abstract)</td></tr><tr><td>Carbon footprint allocation models in distributed systems, Anne-Cécile Orgerie, CNRS & U. Rennes, France (Abstract)</td></tr><tr><td>16h40-18h</td><td>Discussion breakout</td></tr><tr><th colspan="2">Day 2 (Thursday, 11th September)</th></tr><tr><td colspan="2">Morning Session — Scheduling</td></tr></tbody></table>

9h30-10h30
<table><tbody><tr><td></td><td>Carbon-Aware Workflow Scheduling with Fixed Mapping and Deadline Constraint, Henning Meyerhenke, Karlsruhe Institute of Technology, Germany (Abstract)</td></tr><tr><td></td><td>Green Scheduling on the Edge, Frédéric Vivien, INRIA &amp; ENS Lyon (Abstract)</td></tr><tr><td></td><td>Navigating the Energy Doldrums: Can We Exploit Volatile Energy Prices To Lower the Cost of Computation?, Felix Wolf and Peter Arzt, U. Darmstadt, Germany (Abstract, Preprint)</td></tr><tr><td>10h30-11h</td><td>Break</td></tr><tr><td>11h-12h30</td><td>Panel — Workflows and Scheduling (Led by: Anne Benoit, Panelists: Florina Ciorba, Georges Da Costa, Yves Robert, Anne-Cécile Orgerie, Ayse Coskun and Henning Meyerhenke)</td></tr><tr><td>12h30-14h</td><td>Lunch</td></tr><tr><td rowspan="4">14h-15h20</td><td>Afternoon Session</td></tr><tr><td>Flexible Computing in Data Centers for Sustainable AI Growth, Ayse Coskun, Boston University (Abstract)</td></tr><tr><td>Sufficient computing through Variable capacity, Guillaume Pallez, Inria &amp; U. Bordeaux, France (Abstract)</td></tr><tr><td>Revisiting virtual machine consolidation to save resources and energy in heterogeneous production cloud infrastructures, Laurent Lefevre, INRIA &amp; ENS Lyon, France (Abstract)</td></tr><tr><td></td><td>Exploring the memory / execution time tradeoff in dataflow graphs, Alain Girault, U Grenoble, France (Abstract)</td></tr><tr><td>15h20-15h50</td><td>Break</td></tr><tr><td>15h50-17h30</td><td>Discussion breakout</td></tr><tr><td>19h</td><td>Dinner at Anco Paris</td></tr><tr><td colspan="2"><strong>Day 3 (Friday, 12th September)</strong></td></tr><tr><td>9h30-10h30</td><td>Breakout</td></tr><tr><td>10h30-11h</td><td>Break</td></tr><tr><td>11h-12h</td><td>Report out by all Discussion Groups</td></tr><tr><td>12h-13h</td><td>Lunch</td></tr><tr><td>13h-14h30</td><td>Writing Session</td></tr></tbody></table>
## 2.2 Workshop Attendees and Affiliation

### Participants/Speakers

* Anne Benoit, ENS Lyon & CNRS/LIP, France

* Andrew A Chien, University of Chicago and Argonne National Laboratory, USA

* Florina Ciorba, U. Basel, Switzerland

* Ayse Coskun, Boston University, USA

* Georges Da Costa, U. Toulouse, France

* Fanny Dufossé, INRIA & U. Grenoble, France

* Alain Girault, U. Grenoble, France

* Laurent Lefevre, INRIA & ENS Lyon, France

* Henning Meyerhenke, Karlsruhe Institute of Technology, Germany

* Jean-Marc Nicod, U. Besancon, France

* Anne-Cécile Orgerie, CNRS & U. Rennes, France

* Guillaume Pallez, Inria & U. Bordeaux, France

* Yves Robert, ENS Lyon & CNRS/LIP, France

* Pierre Segonne, Electricity Maps

* Fumiyoshi Shoji, Riken, Japan

* Denis Trystram, U. Grenoble, France

* Frédéric Vivien, INRIA & ENS Lyon, France

* Felix Wolf, U. Darmstadt, Germany

### Student Recorders

* Peter Arzt, U. Darmstadt, Germany

* Joachim Cendrier, ENS Lyon & CNRS/LIP, France

* Max Hawkins, GaTech, US

* Reto Krummenacher, U. Basel, Switzerland

* Rajini Wijayawardana, U. Chicago, US
# 3 Platforms and Resources

## 3.1 Summary of Last Workshop Report

The previous workshop emphasized that computing platforms are entering a new phase where resource capacity depends on external constraints, such as carbon intensity, power prices, and power availability. It identified several key research directions for future platforms: the need to describe resource capacity as a function of time; negotiating capacity between compute management and external factors; understanding how variable capacity affects external utility such as carbon emissions and grid support; reporting non-performance attributes like carbon and water use; and addressing increasing heterogeneity across cloud, edge, mobile, and IoT environments.

## 3.2 What has changed since 2023?

Since the 2023 workshop, datacenters have experienced a significant growth in demand, driven by the rapid growth of AI. Consequently, the thinking around platforms has shifted towards greater flexibility and integration with the power grid. Below, we highlight notable changes.

* *Dynamic platforms*; Datacenter flexibility is increasingly becoming a requirement for grid connection [1, 2], especially with the growing power demand of AI datacenters. Hyperscalers are adopting flexible operations [3], and the *DCFlex* initiative [4] pushes this effort forward.

* *Dynamic management*; AI datacenters are moving away from “design first, adjust later” approaches toward designing with platform flexibility in mind. Systems like *Emerald AI* [5] supports this by providing grid demand response in AI datacenters, while ensuring performance guarantees for users.

* *Grid-informed management* has become more practical with platforms like *Electricity Maps* [6], which provide real-time data and short-term predictions that can be used for intelligent datacenter management and scheduling.

* *Incentive structures for flexibility*; *Fugaku* deploys power modes and a point-based priority scheduling system [7], encouraging users to make energy-efficient choices. Users earn points for lower energy use, which can be redeemed for higher job priority—a practical step toward flexible, energy-aware HPC operation that has proven successful in reducing the platform's overall energy consumption.

* *Power microgrids* (batteries and on-site generation) [8] and *thermal microgrids* (such as cold UTES) are being integrated into datacenters to support dynamic operation.
Many parts of the 2023 “dynamic platforms” vision have now become reality. Yet, even more complexity and dynamism are still emerging.

## 3.3 Research Areas and Questions

We organize emerging research areas and questions around the information required across system layers to make effective scheduling decisions in dynamic datacenters.

**Information about Platform and Resources (for Scheduler and Jobs)**

Future platforms should be “malleable”. Below we outline resource-related information that should be made visible to the scheduler and jobs, allowing them to better characterize the platform.

* System variability properties: Systems can be characterized by their variation properties such as the dynamic range, frequency of change, shape of change (change of capacity over time, change of carbon over time), carbon per compute ratio, hardware control knobs (e.g., power modes, power capping, network power, memory bandwidth). These properties should be understood and exposed by the platform, for the benefit of schedulers and jobs.

* In-advance properties: System variation can be characterized in advance, using forecast capacity, forecast load, and forecast grid information (carbon intensity, curtailed power). Such information is increasingly available through platforms such as “Electricity Maps” and other advanced power-grid information services.

* Metrics beyond system utilization: Dynamic platforms should move beyond utilization as the sole objective and instead consider energy consumption, carbon emissions, and other new metrics that capture the indirect consequences of variability.

* State of dynamic platform elements: As datacenters increasingly integrate dynamic local energy and cooling resources, information about their energy and thermal storage becomes an important consideration for scheduling decisions.

* Controls: Which components of the system could be controlled by schedulers and jobs? For example, the flexibly to turn subsystems down/off (e.g., network switches, GPUs, etc.), and control of interfaces for “mid-dlebox” structures.

* Modular Supercomputing: Datacenters should be designed with the ability to disaggregate compute and memory, allowing their pairing to suit the workload, and exploiting lower power for maximum capability.
* Transparency with instrumentation and dashboards: This would allow jobs to understand their achieved performance (since the platform is only partially responsible) against their resource usage. This information is also valuable to the platform, in understanding how platform-level changes affect jobs and platform metrics.

* Exploiting new platform capabilities: Turbo mode allows the system to decrease “efficiency” to increase capacity for the same capex, requiring higher cooling energy.

### Information from the Grid (for Datacenter Management and Scheduling)
Datacenter capacity variation is increasingly becoming a requirement for grid connection, operation, and in the future, for economics. As power systems integrate more renewables and face tighter operational constraints, datacenters must adapt to grid signals and actively support grid stability. The power grid could expose variation to datacenters in several ways.

* Grids could inflict on the scheduler a shape or uncertainty of capacity. Examples such as *Emerald AI* show dynamic control of datacenter capacity – dictated by the power grid. This will become increasingly common.

* Variation is an opportunity for increased power use (e.g., to run Turbo). Schedulers should be designed to anticipate and exploit increased resource capacity during such time periods.

* Grids could inflict a carbon restriction (e.g., reduce power at high carbon times), requiring increased awareness from schedulers.

* Datacenters could exploit grid economic opportunities (e.g., demand response payments, support regulation by replacing lack of inertia) through intelligent scheduling.

* Grids could enforce ramping and oscillation constraints, limiting instantaneous capacity changes.

* Power grid constraints and requirements span multiple time scales: short term (milliseconds to seconds), middle term (hours to days), and long term (months to years).

Since datacenters are large loads in the power grid, they must behave responsibly, supporting grid operation and stability.

### Information from Jobs and Schedulers (for the Platform)

Schedulers have access to a wide range of information that can inform platform-level decisions, including:

* Actual power requirements of jobs (not just the last-run estimate), with models of computational power use ranging from perfect to approximate, with varying look-ahead horizons and hardware granularity.
* Aggregated workload properties (e.g., diurnal patterns, holiday effects); statistical characterization of power use with varying accuracy and horizon, covariance across jobs, and hardware scopes.

* New workload types are emerging beyond batch compute, including HPC + inference, HPC + storage, interactive workloads, and HPC + network-intensive streaming workloads.

Job requirements and characteristics are often unspecified or poorly understood. However, this information is essential for understanding platform capabilities and for informing shaping decisions.

* Information on job-specific needs includes their performance requirements and how they behave under different power modes.

* Signals could be provided at job submission time (e.g., expected runtime, resource profile, priority).

* Incentive mechanisms should be used to get accurate job information from users.

## Managing Dynamic Platform Elements (Middlebox and Local Energy/Cooling Resources)

* It is becoming increasingly essential to coordinate between datacenter workloads/schedulers and the grid to determine shapes for variation. Only with three parties involved can suitable tradeoffs be achieved.

* What are the right optimization objectives? For the grid there are hard constraints arising. Schedulers and workloads will need to adapt. For schedulers and workloads, they can give input, but they must be flexible to external changing constraints.

* What information can we exploit? (from schedulers, workloads, jobs, and the platform). Grid information is increasingly available, but compute workload opacity remains a difficult problem. Providers are either unable or resisting transparency.

* How can these tensions be resolved while maintaining performance as a functional requirement? Technical solutions are more accessible than practical solutions, as business structure increasingly impedes information sharing.

## 3.4 Summary

Future computing platforms will require tighter coupling with the power grid, becoming increasingly dynamic and complex. Schedulers must not only manage capacity variation, but also coordinate with the grid and workloads across both
instantaneous and longer timescales. Variation must be handled while main-
taining key performance metrics such as energy and carbon efficiency, hardware
utilization, and quality of service.

3.5 Contributors / Participants

* Andrew A. Chien, University of Chicago and Argonne National Labora-
tory

• Fanny Dufosse, INRIA and University of Grenoble Alpes, France

* Guillaume Pallez, Inria and University of Bordeaux, France

• Pierre Segonne, Electricity Maps

* Fumiyoshi Shoji, Riken, Japan

• Rajini Wijayawardana, University of Chicago
# 4 Workloads

## 4.1 Summary of Last Workshop Report

As computing environments evolve toward greater heterogeneity and fluctuat-
ing resource availability, workloads must be designed to adapt dynamically to
the platform's capacity. In the last workshop, focus was on flexibility in both
scheduling and resource allocation which can enhance efficiency by allowing jobs
to execute whenever sufficient resources are available, rather than adhering to
rigid start times or fixed configurations. One could consider incentive mecha-
nisms to encourage users to design applications capable of operating under such
conditions.

Flexibility is further discussed by weighing up computational accuracy and
environmental sustainability. The notion of approximate computing is intro-
duced as a means of trading precision for performance, enabling faster execution
when minor deviations in accuracy are acceptable. Additionally, the need to con-
sider the ecological implications of computing activities requires that workload
management should incorporate cost functions that account for both energy
consumption and carbon emissions. This perspective aligns with the broader
objective of reducing the environmental footprint of large-scale computing sys-
tems.

Other dimensions of flexibility, which are raised in the last workshop report,
include the adoption of service-level agreements (SLAs), and the importance
of data locality, given the cost and governance challenges associated with data
movement.

Overall, it envisions a transition from rigid and performance-centric toward
adaptive, resource-aware, and environmentally responsible workloads.

## 4.2 What has changed since 2023?

**AI** Since the previous workshop, workloads have evolved, mostly caused by the growing influence of AI workloads. AI training and inference jobs exhibit distinct resource usage pattern and flexibility requirements when compared to more traditional HPC jobs. However, AI workloads are still difficult to characterize since they occur in a wide variety of shapes.

**Scheduling** The discussions, especially during the joint sessions with the Scheduling group, highlighted progress and open questions in dynamic and data-aware scheduling. Modern schedulers should be able to leverage real-time electricity mix predictions and power grid signals, as well as consider heterogeneous architectures when making scheduling decisions.

Work by Anne-Cécile Orgerie [9] has demonstrated that cooperation and mi-
gration across data centers is feasible, enabling energy-aware scheduling strate-
gies that align with carbon reduction goals. New algorithms also address schedul-
ing within a single data center, incorporating constraints like deadlines, power
caps, or carbon limits.
**Geo-shifting** There was also growing interest in geo-shifting of workloads, i.e., moving computations across locations depending on local power conditions, and node sharing, as potential levers for improving energy proportionality. However, concerns remain about data locality, privacy, and uncertainty in task duration and energy cost estimation.

**Flexibility and Incentives** A recurring theme was the need to promote flexibility in HPC applications. Although technical capabilities exist, there remains a lack of user incentives and education. Users are not yet systematically motivated to optimize for energy or carbon impact. Suggested measures include:

* Introducing reward systems for energy-efficient or flexible job submissions.

* Providing feedback and analysis tools that help users understand the impact of their workloads (e.g., energy, carbon, efficiency metrics).

**Workload model** Participants reported that real workloads are still often modeled under the assumption of constant capacity resources, which no longer reflects current realities. The community recognized the need to build and maintain a comprehensive database of diverse workloads, including new AI-based and hybrid applications. Feedback from the plenary session underlined that workload research should also consider:

* The feasibility of removing or delaying jobs for sustainability.

* The unclear interaction between application-level and scheduler-level sustainability efforts.

* The importance of maintaining an annual workload database to track trends and evolution.

## 4.3 Research Areas and Questions

Deeper interaction between workload descriptions and scheduler decisions (to have more information about workloads and what is acceptable for the user, e.g.: degraded techniques). The group discussion came up with a range of open questions that should be investigated in future research:

### Workload Characterization and Flexibility

* How can we classify and model AI workloads within HPC, distinguishing their computational, data, and flexibility properties?

* What kinds of malleability or adaptability (spatial, temporal, frequency-based) are most relevant for future HPC workloads?

* To what extent can workloads tolerate degraded modes of execution (e.g., reduced precision or partial completion) while maintaining scientific validity?
## User Incentives and Social Policies

* What incentive mechanisms can encourage users to submit flexible or energy-aware jobs?

* How can policy and administrative frameworks support the transition toward energy-efficient and sustainable workload practices?

* Could faster scheduling or priority access serve as rewards for flexible workloads?

## Scheduling and Systems Integration

* How should future schedulers leverage detailed workload descriptions to make more informed decisions about energy, power, and carbon trade-offs?

* How can external data sources (e.g., electricity mix forecasts, carbon programs, power grid data) be integrated into scheduling frameworks?

* What are the implications of increasing heterogeneity in HPC system architectures for workload management and scheduling strategies?

## Data Locality and Geo-Flexibility

* Can geo-shifting of loads be implemented in a way that balances performance, cost, and environmental objectives?

* How can schedulers reconcile data locality constraints with the desire to move workloads for carbon efficiency?

## Measurement, Evaluation, and Tooling

* How can we establish standardized methodologies for evaluating the carbon footprint of workloads and systems?

* What tools are needed to predict short-term energy or carbon impacts and provide actionable feedback to users?

* How can workload databases be maintained and expanded to include AI-based, hybrid, and flexible workloads for benchmarking and research?

## Long-Term Outlook

* How will AI-powered scientific applications (e.g., in molecular dynamics or climate modeling) reshape workload profiles and scheduling needs?

* How can HPC systems evolve to support interactive, dynamic, and sustainability-aware job management without compromising scientific productivity?
## 4.4 Contributors / Participants

* Peter Arzt, U. Darmstadt, Germany

* Anne Benoit, ENS Lyon & CNRS/LIP, France

* Joachim Cendrier, ENS Lyon & CNRS/LIP, France

* Ayse Coskun, Boston University, USA

* Fanny Dufossé, INRIA & U. Grenoble, France

* Reto Krummenacher, U. Basel, Switzerland

* Henning Meyerhenke, Karlsruhe Institute of Technology, Germany

* Yves Robert, ENS Lyon & CNRS/LIP, France

* Frédéric Vivien, INRIA & ENS Lyon, France

* Felix Wolf, U. Darmstadt, Germany
# 5 Scheduling

## 5.1 Summary of Last Workshop Report

Modern parallel and distributed computing systems require scheduling models that account for variable and heterogeneous resources, as well as their dynamic availability.

a) New models are needed to accurately depict resource variability in homogeneous and heterogeneous systems [10, Section 5.1a]. These models must account for fluctuating machine availability due to energy constraints, user demand, or geographic distribution (e.g., "follow the sun"). The concept of system malleability describes the system's ability to dynamically adjust allocations and a job's ability to scale its resource needs during runtime. Accurate modeling of this behavior is essential for aligning job scheduling with real-world availability patterns and constraints.

b) Similarly, models for workload characterization must evolve to include job flexibility and timing [10, Section 5.1b]. This includes supporting non-instant computing, in which jobs can be delayed or approximated to allow for trade-offs between result accuracy and completion time. Defining mandatory and optional job components enables approximate computing and iterative refinement. Models must also account for data locality and volume. These considerations are essential for scheduling complex, flexible workloads in variable environments.

c) Traditional performance metrics are no longer sufficient. New metrics must account for attributes beyond performance, such as power and energy consumption, CO₂ emissions, data center water use, and network congestion [10, Section 5.1c]. There is a growing emphasis on "green" computing and transitioning from maximizing speed to minimizing energy and environmental impact.

The traditional assumption of stable, homogeneous resources and rigid jobs is insufficient. New job types—moldable, malleable, and evolvable—allow for the dynamic adaptation of resource allocation at submission time, during execution, or based on application-level decisions. Scheduling strategies must incorporate risk awareness and support job suspendability and checkpointing to reduce communication overhead and energy cost while maintaining locality. The growing use of techniques such as oversubscription, dynamic voltage and frequency scaling (DVFS), and power-aware node selection illustrates the shift toward adaptive, resource-aware scheduling.

Concepts such as goodput and energy-aware cost-to-solution are becoming more relevant. However, caution is needed to avoid rebound effects, in which efficiency gains unintentionally increase overall consumption. Additionally, the explainability of scheduling decisions, especially those made by machine learning algorithms, must be preserved to ensure transparency and user trust.
Thus, effective scheduling depends on transparent communication between
jobs and schedulers, which can be supported by incentives, user models, and
service-level objectives. Handling uncertainty in workloads and platforms is
crucial to avoiding misallocation and ensuring system robustness. Adaptive job
reshaping and real-time platform awareness are necessary for managing fluc-
tuating capacities. In summary, multi-objective optimization strategies that
balance performance, sustainability, and flexibility are necessary for distributed
and heterogeneous environments.

## 5.2 What has changed since 2023?

In relation to the need for new models that address resource variability, as outlined in point 3.3.1a, the discussion revealed that many of the identified challenges remain unresolved. While new metrics, such as network congestion, have been acknowledged as important, the core problem of accurately predicting resource variability, especially in terms of scheduling intervals and energy availability, persists. While short-term predictions (on the order of minutes) are feasible, accuracy declines significantly for longer forecasting horizons. The increasing availability of data center traces has enabled the development of more data-driven models; however, these models are still highly localized and reflect only the behavior and usage patterns of individual data centers. Furthermore, a critical missing component is the ability to anticipate future load dynamics globally and across heterogeneous resources. Overall, progress has been made in data collection and the conceptual framework for modeling. However, robust, predictive models that support effective scheduling under dynamic conditions are lacking.

Regarding job classification and workload flexibility (see Section 3.3.1b), ex-
isting batch systems like SLURM technically support features such as delayed
job execution. However, these capabilities are rarely used in practice. There is
a growing recognition that the current focus on carbon optimization may not
be appropriate for all systems, particularly those that already operate on green
energy, such as LUMI. The broader goal is to give schedulers more flexibility by
encouraging users to accurately declare job characteristics, especially execution
time estimates and job start time flexibility. However, inaccurate estimates are
still common, often due to reused and poorly adapted job scripts. Introduc-
ing incentives, such as pricing mechanisms tied to time limits or job flexibility,
could encourage users to improve their submissions. This would require reliable
checkpointing mechanisms and accounting models that consider the quality of
user estimates, particularly for workloads such as AI training, where require-
ments and precision can vary over time. While the conceptual understanding
of job flexibility has deepened, its effective integration into scheduling practice
remains challenging.

Regarding new metrics (see Section 3.3.1c), replacing traditional time-to-
solution metrics with cost- or energy-aware alternatives has gained traction.
One proposed alternative is the energy-delay product, which considers the trade-
offs between execution time and energy cost. Dynamic pricing models based on
energy availability, such as differentiating cost levels for running jobs during periods of cheap, moderate, or expensive energy, are increasingly considered viable. One potential reward structure could base user charges on the amount of time and resources consumed and further differentiate them by job type (e.g., CPU vs. GPU), job flexibility, and the environmental impact of the energy used. However, it was emphasized that such cost formulas must be simple and transparent to encourage user acceptance. While this area has shown clear conceptual development since the last report, the implementation of practical, user-friendly models remains an open task.

## 5.3 Research Areas and Questions

The research questions on scheduling models and metrics were jointly addressed together with those on workloads. The combined discussion is presented in Section 4. The evolution since the previous workshop also highlighted the close link between the two topics, as many of the proposed solutions—such as moldable, malleable, and evolvable jobs—directly connect workload flexibility with new scheduling approaches.

## 5.4 Contributors / Participants

* Peter Arzt, U. Darmstadt, Germany

* Anne Benoit, ENS Lyon & CNRS/LIP, France

* Joachim Cendrier, ENS Lyon & CNRS/LIP, France

* Florina Ciorba, U. Basel, Switzerland

* Ayse Coskun, Boston University, USA

* Fanny Dufossé, INRIA & U. Grenoble, France

* Reto Krummenacher, U. Basel, Switzerland

* Henning Meyerhenke, Karlsruhe Institute of Technology, Germany

* Yves Robert, ENS Lyon & CNRS/LIP, France

* Frédéric Vivien, INRIA & ENS Lyon, France

* Felix Wolf, U. Darmstadt, Germany
# 6 Policy and Societal Factors

Overall, the challenges and recommendations of the previous report are still relevant today. The continued growth of computing demand has increased the severity of existing impacts and brought about new considerations. Achieving meaningful progress towards sustainable computing practices is possible but will require consistent effort.

## 6.1 What is the same?

Some trends from the previous report are still present two years later. The digitization of our society is increasing, AI continues to grow, and the effects of digital services largely remain hidden to end users. These realities are not unexpected, but are important to highlight as their combination makes addressing the impacts of computing increasingly important.

## 6.2 What has changed since 2023?

The past two years have seen massive changes in computing and its effects on society. Although AI growth was expected two years ago, the speed and scale we've seen are unprecedented. Datacenter build-out is so widespread and at such a scale that energy availability is often a limiting factor.

The scale of computing has reached a point where there is growing public awareness and interest in computing. Even just a few ago, the public's awareness of computing infrastructure was much less. Now, the local and global effects of datacenters are relevant in current political and general discourse. Major news outlets now regularly feature articles on the announcement or build-out of datacenters, and computing infrastructure and regulation are now pressing topics in political. These two trends combine to raise public awareness and interest in computing and its effects; a much-needed first step of education and action.

However, public interest in this space is not always met with clear or transparent data. The quality of public data on the resource requirements of individual digital services (e.g. How much energy and water does a single AI chatbot query consume?) is lacking. Reports from for-profit-companies often lack clear methodology when discussing resource consumption which makes scientific evaluation and public contextualization difficult. Furthermore the shift of government policy in the USA against combatting climate change has disrupted the consensus on environmental protection. Policy and business tenor is now divided. The public's genuine interest in the effects of their personal digital actions must be met with accurate and scientifically rigorous data.

These effects are also more complicated due to their sheer scale. Computing now results in massive impacts on the energy grid, water management, and local environmental aspects (e.g. noise pollution, property devaluation, and tax benefits). Transient computing power demand (e.g. during large AI model training or rapid datacenter shutdown/disconnect) is also a growing concern.
## 6.3 Updated Recommendations

1. *Define and Disseminate the Ideas of Computing Sufficiency and Sobriety*
We must properly define these ideas and spread them widely. Increases in computing efficiency alone do not guarantee a sustainable future in isolation. Jevon's paradox and the rebound effect have a demonstrated history in computing, and there is no reason to believe this will end now. More efficient computing only translates to decreased planetary impact if the efficiency doesn't induce additional demand.

2. *Work to Make the Effects of Digital Services Concrete*
Computer scientists must help make the impacts concrete. Frequently, the use of digital services is a seamless and completely opaque process for the user (often a goal of for-profit services). However, these services have real-world impact. Computing is a physical process with real-world resource demands. As part of this effort, we should promote open, scientifically validated platform usage datasets.

3. *Improve Sustainability Education in All Areas*
Sustainability education should be added or improved to all relevant parties: the general public, scientists, HPC users, computer/computational science education, etc.

4. *Conduct Research on Incentives for Sustainable Computing*
Both users and researchers respond to incentives. We must investigate how to incentivize sustainable computing practices (like RIKEN's Fugaku 'points' system).

5. *Create Sustainability Metrics*
How is it possible to quantify our contribution to sustainability? Can we measure the effects of the recommendations themselves?

6. *Consolidate, clarify, and Contextualize Relevant Information*
The lack of clear, public data on the impacts of individual computing actions (like per-query AI energy consumption) make it challenging to educate general computing users on their personal impact. We must transparently and publicly share this impact data and ensure it has clear methodology and limitations. This can prevent misinformation and manipulation of critical data, which could potentially delay or deter meaningful action.

## 6.4 Conclusion

The challenges computing presents will not be solved by technology alone. The changes must be societally prepared (e.g. via public and democratic discussions) with the challenges, goals, and tools to achieve them clearly identified. There must be transparency in these actions otherwise imposing necessary changes on an unaccepting society will not be actionable nor adopted. Such changes will be long-term and require collective effort.

The lack of large differences between this and the previous report highlights the need for action. Without it, the status quo becomes the future. A crucial next step is to foster an informed, democratic discussion about the goals,
tools, and constraints required. Only by making the impacts transparent can we ensure that necessary changes are not simply imposed but are understood, accepted, and collectively adopted.

## 6.5 Contributors / Participants

* Jean-Marc Nicod, U. Besancon, France (Lead)

* Alain Girault, U. Grenoble, France

* Anne-Cécile Orgerie, CNRS & U. Rennes, France

* Denis Trystram, U. Grenoble, France

* Fanny Dufossé, INRIA & U. Grenoble, France

* Georges Da Costa, U. Toulouse, France

* Guillaume Pallez, Inria & U. Bordeaux, France

* Laurent Lefevre, INRIA & ENS Lyon, France

* Max Hawkins, Georgia Tech, USA
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