💻 computer science

Using Budgets to Reduce Application Emissions

This paper proposes using time-bound emissions budgets within a MAPE-K feedback loop to dynamically manage application resource allocation, demonstrating through simulation that this approach significantly improves task fulfillment in variable carbon intensity grids compared to traditional fixed-rate methods while maintaining performance in stable environments.

Original authors: Leo Wilhelm Lierse, Mahyar Tourchi Moghaddam, Sebastian Werner

Published 2026-04-15

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Leo Wilhelm Lierse, Mahyar Tourchi Moghaddam, Sebastian Werner

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Problem: The "Carbon Tax" is Coming

Imagine you run a factory. For a long time, the electricity you used was free (or cheap), and nobody cared how much pollution your machines made. But now, the government is introducing a strict rule: You have to pay for every puff of smoke you produce.

In the real world, this is happening with the EU's carbon pricing. As the price of carbon goes up, software companies (who run massive data centers) are going to face huge bills if their apps use too much dirty energy.

The Old Solution: The "Speed Limit"

Previously, engineers tried to solve this by setting a fixed speed limit on their software.

The Analogy: Imagine you are driving a car with a strict rule: "You can never go faster than 50 miles per hour, no matter what."
The Problem: This works okay on a straight, empty highway (a stable power grid). But what if the road is full of hills and valleys?
- If you hit a steep hill (high pollution energy), you have to slow down to 50 mph, even if you have plenty of fuel. You waste time.
- If you hit a flat, downhill stretch (clean energy), you are still forced to drive at 50 mph, even though you could safely go 80 mph without breaking the law. You miss out on speed.
The Result: You arrive late, or you waste your potential.

The New Idea: The "Carbon Budget"

The authors of this paper propose a smarter way: The Emissions Budget.
Instead of a speed limit, imagine you are given a weekly allowance of gas (or money) for your trip.

The Analogy: You have a tank of gas that lasts exactly one week.
- On the downhill (clean energy days): You can floor it! You drive at 100 mph, burning gas fast, because you know you have plenty left in the tank.
- On the uphill (dirty energy days): You cruise slowly at 20 mph to save gas.
- The Goal: As long as you don't run out of gas by Sunday night, you are fine.

This allows the software to be flexible. It can work hard when the energy is clean and cheap, and slow down when the energy is dirty and expensive, all while staying within the total "budget" for the week.

How It Works (The "Smart Butler")

The paper describes a system called MAPE-K, which acts like a super-smart butler for your software:

Monitor: The butler checks the weather (how clean the energy is right now) and how much gas is left in the tank (the budget).
Analyze: "Oh, the sun is shining in Germany right now! The energy is super clean. Let's go fast!" Or, "Oh no, it's cloudy and we're using coal. Let's slow down."
Plan: The butler decides: "I will use the extra gas I saved yesterday to power a big task today."
Execute: The software scales up (gets more powerful) or moves to a different server to finish the work quickly.

What They Found (The Results)

The researchers tested this idea using real data from three European countries: France, Poland, and Germany.

France (The Stable Grid): The energy is always very clean (mostly nuclear).
- Result: The "Budget" and the "Speed Limit" worked exactly the same. No big difference.
Poland (The Dirty Grid): The energy is always very dirty (mostly coal).
- Result: Both methods had to slow down a lot to stay within limits. Again, no big difference.
Germany (The Wild Grid): The energy swings wildly between clean (solar/wind) and dirty (coal) throughout the day.
- Result: The Budget won big!
- The "Budget" approach completed 36% more tasks than the "Speed Limit" approach.
- Why? Because the Budget system could "bank" clean energy during the day and use it to power heavy workloads later, whereas the Speed Limit system was stuck driving at a slow, constant pace the whole time.

The Catch (The Trade-off)

There is one downside. Even with the smart Budget system, the software still had to slow down significantly compared to a system with no rules at all.

The Analogy: Even if you have a full tank of gas, you still have to drive slowly on the steep hills. You can't just ignore the hills.
The Reality: To keep costs predictable and stay within the carbon limit, the software has to accept that it will sometimes be slower or do less work than if it just ignored the rules. But, it guarantees you won't get a massive bill at the end of the month.

The Bottom Line

As carbon prices rise, software companies need a way to manage their "carbon wallet."

Fixed Limits (Speed Limits) are too rigid for a world with lots of wind and solar power.
Emissions Budgets are like a flexible allowance. They let software "save up" clean energy for rainy days, allowing them to get more work done without breaking the bank (or the carbon laws).

It's a practical tool for the future: Don't just drive at a fixed speed; drive smart based on how much fuel you have left.

1. Problem Statement

As carbon pricing mechanisms (e.g., the EU Emissions Trading System) increase the cost of energy consumption, software architects face pressure to design applications that operate within strict, financially predictable emission constraints.

Limitations of Current Approaches: Existing methods typically enforce fixed per-interval emission rates (e.g., a constant $gCO_2/s$ $g C O_{2} / s$ limit). While effective in grids with stable carbon intensity (e.g., France or Poland), these rigid rates fail in grids with high renewable adoption (e.g., Germany), where carbon intensity fluctuates dynamically.
- If the rate is set too high, the emission limit is underutilized.
- If set too low, the application suffers excessive throttling or suspension during high-intensity periods, leading to poor performance.
The Challenge: There is a need for a mechanism that allows applications to dynamically manage emissions to balance environmental constraints, operational costs, and service quality without sacrificing performance in variable grid conditions.

2. Methodology

The authors propose a MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) feedback loop architecture for self-adaptive, emissions-aware applications.

A. Core Concept: Emissions Budgets

Instead of a fixed instantaneous rate, the system uses an emissions budget ( $B$ ) allocated over a defined planning horizon ( $T$ ).

Flexibility: Applications can "save" unused allowances during low-carbon-intensity periods and "spend" them during high-intensity or high-workload periods.
Hard Constraint: The cumulative emissions over the lifetime must not exceed the budget ( $\sum e_t \leq B$ ). If the budget is exhausted before the period ends, the application is suspended.
Strategy: This prevents the "waste" inherent in fixed rates where unused capacity cannot be carried over.

B. Architecture Components

Monitor: Tracks application power consumption and real-time grid carbon intensity.
Analyze: Assesses the remaining budget, current usage rate, and identifies viable migration targets (nodes with better energy efficiency).
Plan: A policy engine selects adaptation actions (scale up/down, migrate, or no action).
- Greedy Policy (Baseline): Spends surplus budget immediately to maximize responsiveness. It forbids "front-loading" (borrowing against future allocations) to prevent premature exhaustion.
Execute: Enacts decisions via vertical scaling or migration.

C. Experimental Setup

Simulation: A discrete-event simulation using Python and SimPy, based on the LEAF simulator.
Workload: Synthetic task traces with diurnal patterns (two peaks per day).
Data: Six weeks of real-world hourly carbon intensity data from Germany (high variability), France (low intensity, low variability), and Poland (high intensity, low variability).
Policies Compared:
1. Unlimited: No constraints (baseline for efficiency).
2. Fixed Rate: Constant $16.6 mg/s$ limit.
3. Greedy Budget: Total weekly budget equivalent to the fixed rate average, managed dynamically.

3. Key Contributions

Architectural Framework: A general MAPE-K architecture for self-adaptive applications that treats emissions as a cumulative budget rather than a rate.
Comparative Analysis: A rigorous comparison of budget-based vs. fixed-rate policies across diverse grid conditions (stable vs. dynamic).
Financial Impact Assessment: Evaluation of policies not just on emissions, but on the financial costs associated with carbon pricing (modeled at €700/ton $CO_2$ ).
Performance-Efficiency Trade-off: Demonstration that budgets can significantly outperform fixed rates in variable grids while maintaining hard emission caps.

4. Results

The simulation results highlight distinct outcomes based on grid variability:

Variable Grids (Germany - DE1/DE2):
- Task Fulfillment: The Greedy Budget policy improved task completion by 36% (DE1) and 16% (DE2) compared to the Fixed Rate policy.
- Emissions: Emissions increased slightly (11% in DE1, 4% in DE2) compared to the fixed rate, but remained within the hard budget limit.
- Mechanism: The budget allowed the system to utilize high-performance nodes during low-carbon periods to "bank" capacity, compensating for high-carbon spikes later.
Stable Grids (France & Poland):
- Parity: In France (low intensity) and Poland (high intensity but stable), the Budget and Fixed Rate policies achieved parity in both performance and emissions.
- Significance: This proves budgets are a "safe replacement" for fixed rates, offering no performance degradation in stable environments.
Comparison with Unlimited:
- While budgets improve performance over fixed rates, they still incur a performance penalty compared to an Unlimited system (34% to 94% fewer tasks).
- Reason: The "idle power overhead" of nodes. Even when doing little work, nodes consume static power (e.g., 50W), which consumes a large portion of the emission budget at low utilization.
- Cost: Despite the performance drop, budgets successfully capped costs. The Unlimited policy resulted in costs up to €28.66 (in Poland), whereas budgeted policies kept costs below €7.10.

5. Significance and Conclusion

Practical Mechanism: Emissions budgets provide a practical way to balance environmental compliance with service quality. They allow applications to adapt to the dynamic nature of renewable energy grids without violating financial or regulatory caps.
Cost Predictability: By enforcing hard limits, organizations can avoid volatile carbon costs, which is critical as carbon prices rise (projected to exceed €700/ton by 2050).
Trade-off Acknowledgement: The paper acknowledges a fundamental trade-off: Hard emission limits reduce overall energy efficiency due to the necessity of maintaining resources (and their idle power) to meet service demands during high-carbon periods. However, in a world of carbon pricing, this performance loss is often the necessary cost of financial predictability and sustainability.
Future Work: The authors plan to implement this in production environments (e.g., Kubernetes) to test real-world constraints like migration latency and multi-tenant interference.

In summary, the paper argues that emissions budgets are superior to fixed rates for managing applications in modern, renewable-heavy electrical grids, offering up to 36% better task fulfillment while strictly adhering to emission caps and financial constraints.