Carbon-Efficient Deployment

• Carbon-efficient deployment is the strategic design and scheduling of computing tasks to minimize both operational and embodied carbon emissions using real-time grid and forecast data.
• It leverages methodologies such as MILP, constraint programming, and machine learning with empirical evidence showing up to 29% emission reductions in edge-cloud and LLM inference scenarios.
• Applications span hyperscale data centers, edge infrastructures, and IoT microservices, integrating carbon telemetry and adaptive scheduling for optimized environmental and performance outcomes.

Carbon-efficient deployment encompasses the design, scheduling, and operation of computational tasks, systems, and applications to minimize associated greenhouse gas emissions across both the operational and embodied carbon domains. The field integrates real-time and forecasted carbon-intensity data, device and data-center heterogeneity, user experience constraints, and energy systems variability. Emerging as a critical concern for edge-cloud infrastructures, hyperscale data centers, LLM inference platforms, and IoT-augmented microservices, carbon-efficient deployment introduces a new optimization axis that is orthogonal to traditional metrics such as latency, throughput, and energy efficiency.

1. Carbon Modeling: Operational and Embodied Emissions

Advanced carbon-efficient deployment frameworks formalize both operational and embodied carbon at fine granularity. The total carbon cost of a computational task or application invocation is determined by:

• Operational carbon: Computed by integrating the instantaneous power draw $P_j(t)$ of the platform over its execution interval, multiplied by the location- and time-dependent grid carbon intensity $CI(t, L_j)$. For tasks assigned to device or server $j$:

$$C_\text{op}(i,j) = \int_{t_i^s}^{t_i^e} P_j(t) \cdot CI(t, L_j)\,dt$$

The $CI(t, L_j)$ is typically resolved from real-time or forecasted grid generator mix and emission factors using external datasets (e.g., WattTime, electricityMap).

• Embodied carbon: The “upfront” emissions incurred through manufacturing, transport, and construction, denoted as $ECF_j$ for platform $j$. Embodied carbon is amortized over the expected useful lifetime $LT_j$ and further apportioned across simultaneous users $N_j$ for shared infrastructure:

$$C_\text{emb}(i,j) = ECF_j \cdot\frac{\Delta_{i,j}}{LT_j}\cdot\frac{1}{N_j}$$

The total per-invocation carbon cost is then $$C_\text{total}(i,j) = C_\text{op}(i,j) + C_\text{emb}(i,j)$$ (Kim et al., 2023).

This dual accounting is increasingly standardized across multi-tier systems to support workload placement and migration decisions. In hyperscale data centers, server heterogeneity, repair/replace cycles, and utilization-driven aging are explicitly accommodated in MILP-based optimization formulations (Zhang et al., 1 Apr 2025). For accelerators and XR platforms, system-level carbon models include both process-energy-based manufacturing terms and operational energy metrics, yielding comprehensive life-cycle carbon accounting (Elgamal et al., 2023).

2. Formulation of the Carbon-Aware Scheduling Problem

The core carbon-efficient deployment scheduling problem is characterized by discrete application request assignments over a set of heterogeneous resources, subject to performance constraints (e.g., latency or throughput) and infrastructure limits. The formal optimization is typically:

$$\min_{x_{i,j}} \quad \sum_{i \in A}\sum_{j \in J} x_{i,j} C_\text{total}(i,j)$$

subject to:

• Task assignment: $\sum_{j \in J} x_{i,j} = 1, \; \forall i$
• Deadline/QoS: $\Delta_{i,j} x_{i,j} \leq D_i, \; \forall i,j$
• Resource capacity: $\sum_{i} x_{i,j} \leq \text{cap}_j,\ \forall j$

Variants include incorporating backup/replication (for reliability), joint optimization of batch and interactive workloads, budget/hard constraints on carbon emissions, and additional constraints reflecting user utility, cost, or reliability (Kim et al., 2023, Zhang et al., 1 Apr 2025, Souza et al., 2022). The objective functional is convex when evaluating each $C_\text{total}(i,j)$ as above, but the dispatch/allocation problem is integer (MILP/MIP).

Notably, in grid-interfaced scenarios, accurate nodal attribution of carbon emissions is necessary. Average and marginal emission rates per consumption node can be obtained via power-flow-based proportional sharing analysis, enabling spatially granular demand-shifting and DER dispatch (Chen et al., 2023).

3. Algorithmic and Policy Solutions

Carbon-efficient placement and scheduling strategies encompass both offline and online approaches:

• Exhaustive grid search: For combinatorial setups of moderate scale (e.g., edge-cloud offloading across ~200k parameter points), exhaustive enumeration of all parameter combinations yields a full carbon-optimal map, supporting subsequent online predictors (Kim et al., 2023).
• Machine-learning-based schedulers: Linear regression, SVMs, Bayesian optimization, and reinforcement learning (RL) policies have demonstrated efficacy for real-time, adaptive scheduling. RL agents, operating with the state space $(CI(t), \text{workload\_features}, ...)$ and action space of possible execution targets, can achieve near-optimal carbon savings (<2.4% added latency) (Kim et al., 2023). Similarly, RL can be employed to adaptively adjust placement weights or migration triggers in federated, multi-cloud deployments (Ruilova et al., 24 Jun 2025).
• Constraint programming and MILP: For cross-stack resource provisioning and robust multi-site data-center scenarios, constraint programming (CP) and MILP models support multi-objective (operational + embodied carbon + reliability) resource allocation and workload migration, with linearization strategies (e.g., SOS2, McCormick envelopes) ensuring tractable solves (Zhang et al., 1 Apr 2025).
• Monte Carlo Tree Search (MCTS): Large-scale, long-horizon planning of geographically shiftable resources (e.g., data centers, batch workloads, EVs) uses MCTS over an iterative priority tree (IPT) representation, enabling nearly optimal deployment plans in multidecade, multibillion-variable instances where direct MIP is infeasible (He et al., 2023).

Declarative methodologies (e.g., Prolog-based service placement) encode per-service and infrastructural characteristics with backtracking assignment search and direct optimization of the carbon objective, enabling exhaustive Pareto-front enumeration for small- to medium-scale edge-cloud applications (Forti et al., 2021).

4. Empirical Validation and Comparative Results

Evaluation across canonical hardware platforms, workload types, and operational scenarios consistently demonstrates that carbon-optimal deployments differ from both latency-optimal and energy-optimal solutions. Empirical highlights include:

• Edge-cloud application scheduling (GreenScale): Carbon-aware schedulers cut emissions by up to 29.1% versus energy-optimal policies, with even larger gains in scenarios exhibiting high CI temporal variance. For battery-powered devices, deferring charging to low-CI windows reduced emissions by up to 61.2% (Kim et al., 2023).
• LLM Inference (Vidur–Vessim): Scenario-based co-simulation reveals that carbon-aware scheduling and alignment with renewable generation can offset up to 69.2% of LLM inference emissions, with little performance penalty if throughput and batch sizes are tuned accordingly (Özcan et al., 15 Jul 2025).
• Cloud workload scaling (CarbonScaler): Greedy marginal allocation achieves up to 51% carbon savings versus baseline, 37% over suspend-resume, and >8% over the best static scaling policy, with only modest (<12%) cost and timing overheads (Hanafy et al., 2023).
• Containerized application regulation (Carbon Containers): Empirical evaluation confirms 2–5× better work/g CO₂e than suspend/resume-only regulators and up to 80 percentage points lower throttling, maintaining performance while meeting carbon ceilings (Thiede et al., 2023).
• Integrated hardware–software codesign (DSE for AI/XR): Pareto-optimized accelerators achieve 10× tCDP improvement, and right-sized provisioning on commercial platforms yields 12–21% life-cycle carbon savings (Elgamal et al., 2023).
• Geo-migratory scheduling (DCs): Spatio-temporally optimized migration and backup placement yield up to 21% total CO₂ reduction, with absolute reliability maintained through chance-constrained backup allocation (Zhang et al., 1 Apr 2025).

Notably, for microservice-based applications deployed over the cloud-edge continuum, multi-criteria adaption (e.g., as implemented in FREEDA) reduces emissions by ≈35% while eliminating downtime under dynamic failures and resource exhaustion (Ponce et al., 7 Jan 2026).

5. Design Guidelines and Best Practices

Distilled from system-level and workload-specific studies, actionable guidelines for carbon-efficient deployment include:

1. First-class use of carbon-intensity telemetry: Always treat $CI(t, l)$ as a first-class input; integrate real-time/future grid CI data into offloading, scheduling, and autoscaling decisions (Kim et al., 2023, Özcan et al., 15 Jul 2025).
2. Amortize embodied carbon across user cohorts: Leverage large user cohorts and batch assignments to minimize per-user amortization of manufacturing emissions; explicitly recognize embodied carbon in SLAs and resource-leasing contracts (Kim et al., 2023, Zhang et al., 1 Apr 2025).
3. Exploit geographical and temporal diversity: Migrate workloads, data, or services to nodes with lowest $CI$ given network/SLA constraints; shift delay-tolerant loads to low-CI periods; site new resources in regions where marginal emissions and average CI are lowest (Ruilova et al., 24 Jun 2025, Chen et al., 2023).
4. Adaptive workload partitioning and co-design: Tailor model depth, kernel, and execution granularity for per-device/region carbon efficiency. For LLM inference, balance batch size, parallelism, and QPS for optimal per-token CO₂e (Özcan et al., 15 Jul 2025, Cai et al., 2019).
5. Hybrid, dynamic enforcement mechanisms: Use multi-level enforcement (vertical resource scaling, migration, suspension) to smoothly regulate application-level carbon rates while optimizing energy efficiency or performance, depending on workload need (Thiede et al., 2023).
6. Integrate battery/storage and smart charging: Defer mobile/batch job runs to periods of abundant renewables or low CI, using storage or demand response where possible (Souza et al., 2022).
7. Cross-stack instrumentation and automation: Routinely profile and monitor resource power/capacity, build embodied-carbon catalogs, and implement online adaptation loops via cluster managers/Kubernetes, augmented with automatic forecast-driven scheduling (Li et al., 7 Feb 2025, Hanafy et al., 2023).

For system architects, holistic metrics such as tCDP (total CO₂e × delay) enable direct multi-objective trade-offs. Design flows should be extended to consider both embodied and operational carbon as first-class concerns alongside cost, energy, and area (Elgamal et al., 2023, Lee et al., 2024).

6. Limitations, Open Challenges, and Future Research

Key limitations across the literature include:

• Forecast quality and control timescales: Reactive control is bounded by the granularity and reliability of $CI(t)$ forecasts; abrupt grid/carbon market changes may outpace controller adaptation, leading to transient overshoots (Thiede et al., 2023, Souza et al., 2022).
• Embodied-carbon uncertainty: LCA-based variability, and disparities between ACT and alternative LCA tool choices, can induce up to 28% error in ECF_j, fundamentally affecting deployment recommendations (Kim et al., 2023).
• Scalability in constraint optimization: MILP and CP solvers may exhibit exponential scaling; decomposition (e.g., Benders, rolling horizon) and algorithmic accelerators (e.g., MCTS for siting) are required for geographic-scale systems (He et al., 2023, Zhang et al., 1 Apr 2025).
• Network overhead and SLA tension: Aggressive migration or batch alignment, especially in multi-cloud/edge federations, can increase network load, data egress, and potentially impact SLA compliance (Ruilova et al., 24 Jun 2025).
• Robustness to model drift: IA/ML schedulers and predictors require continuous retraining and input telemetry to avoid carbon savings regression under shifting workloads or hardware generations (Li et al., 7 Feb 2025, Cai et al., 2019).

Open research areas include the integration of carbon market signals with real-time control, intelligent learning-based adaptation in highly volatile carbon regimes, the co-optimization of embodied/operational carbon, and robust MSO-based approaches for dynamic, large-scale federated clouds.

7. Policy, Economic, and Ecosystem Integration

Broader transitions toward carbon-efficient deployment will require:

• Market-driven signals: Adoption of renewable energy credits (RECs), demand-response, and carbon pricing into real-time deployment platforms to directly monetize operational carbon (Lee et al., 2024).
• Lifecycle-oriented procurement: Modular hardware with independent refresh cycles and secondary market pathways for extending hardware lifespans and amortizing embodied carbon (Lee et al., 2024).
• Standardized reporting: Harmonized frameworks for carbon accounting, transparent publication of LCA/embodied-carbon data, and cross-industry adoption of standardized APIs for grid/carbon data (Lee et al., 2024).
• Policy alignment: Regulatory support for 24/7 carbon-free mandates, dynamic carbon-aware scheduling, and fleet operations integrated with power-grid decarbonization (Zhang et al., 1 Apr 2025, Khan et al., 2022).

Society-scale decarbonization of computation will be realized through the deployment of carbon-adaptive, telemetered, and policy-coupled computing infrastructures—embedding carbon as both a first-class design objective and an operational control signal throughout the system stack.