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Lauritz Thamsen

Publications and source records attributed to Lauritz Thamsen.

At least 19 recordsLinked to original sources
arXiv2026

Spatio-Temporal Shifting to Reduce Carbon, Water, and Land-Use Footprints of Cloud Workloads

In this paper, we investigate the potential of spatial and temporal cloud workload shifting to reduce carbon, water, and land use footprints. Specifically, we perform a simulation study leveraging publicly available data on the cloud infrastructure of major providers (AWS and Azure) as well as real-world workload traces (big data analytics and FaaS) and grid mix data to consider two different scenarios. Our simulation results indicate that spatial shifting can substantially lower carbon, water, and land use footprints. In the FaaS applications, shifting the spatiotemporal workload achieves carbon savings of up to 85%, water savings of around 50%, and reductions in land use of up to 45%, all while optimizing for the respective factors. Mixed optimization yields results comparable to those of land use alone. For big data workloads, spatiotemporal shifting delivers reductions of up to 45% in carbon emissions, 40% in water consumption, and nearly 40% in land use when optimized for the respective factors. Temporal shifting also decreases the footprint, though to a lesser extent. When applied together, the two strategies yield the greatest overall reduction, driven mainly by spatial shifting with temporal adjustments providing an additional, incremental benefit. Sensitivity analysis demonstrates that such shifting is robust to prediction errors in grid mix data and to variations across different seasons.

cs.DC↗
arXiv2026

Scoring and Gamification to Encourage Sustainable Use of Compute Clusters

The environmental cost of computing continues to grow, yet behaviour change remains limited. We present a composite sustainability score integrating average carbon intensity, resource utilisation, and embodied emissions into a single 0-100 metric designed for gamified feedback. Each component rewards a different dimension of sustainable behaviour: carbon-aware workload shifting, high resource utilisation, and selecting hardware that is commonly underutilised. This scoring system is built into an existing cluster management interface and underpins three dashboard conditions: raw metrics, composite score, and a gamified tree visualisation, which we are planning to evaluate in a 12-week within-subjects study with approximately 35 researchers. Furthermore, we open the discussion on the challenge of defining computational work `goodness' in the context of sustainability scores.

cs.HC↗
arXiv2026

Could Model Partitioning Make Federated Learning More Sustainable?

As federated learning (FL) extends from distributed machine learning between low-power devices to cross-silo scenarios involving edge servers and data centres, its carbon footprint has become a growing concern. Addressing this, methods for sustainable FL align training with low-carbon energy availability or low grid demand and reduce the energy consumption of clients powered by high-carbon sources by decreasing the size of their models. We propose applying model partitioning, which can shift energy consumption by offloading parts of a model to another participant, in response to carbon- or grid-aware signals. Our preliminary findings show that for some partition points, model partitioning can reduce a participant's energy consumption by up to 76% without any significant time or energy consumption overhead compared to non-partitioned training.

cs.DC↗
arXiv2026

How Accurately Can the Energy Use of Spark Applications Be Estimated Based on Resource Utilisation?

Distributed batch data processing applications are widely executed on cloud-based resources where restricted user access to node-level hardware energy counters hinders transparent sustainability accounting. Energy and carbon attribution methodologies therefore depend on power models and available resource utilisation traces, yet the accuracy of these estimates has to be validated while direct counters are available. In this work, we use Apache Spark running on Kubernetes as a case-study dataflow runtime and cluster resource manager to compare model-based energy estimates to Intel RAPL package and DRAM energy on an AWS bare-metal cloud and an on-premises cluster, comparing different CPU usage signals and memory coefficients. We show that external monitoring improves signed package-energy error relative to Spark task traces, reducing underestimation from -29.58% to -24.41% on AWS and from -24.00% to -16.22% on-premises.

cs.DC↗
arXiv2026

Ichnos+: Estimating the Carbon Footprint of Scientific Workflows Using Fitted Power Models

As data-intensive scientific workflows scale to facilitate the automation of analysis of increasing amounts of data, their resource-intensive and long-running execution incurs significant energy consumption and carbon emissions. Given the already significant and rising emissions from the ICT sector, it is crucial to quantify and understand the carbon footprint of scientific workflows. However, existing tooling is commonly not usable in shared, virtualized environments or resorts to power models that are based on only one or two generic data points. To address this gap, this paper presents Ichnos+, a novel system to quantify the environmental footprint of Nextflow scientific workflows. Ichnos+ enables post-hoc footprint estimation based on existing workflow traces, node-specific power models for the computational resources utilized, and carbon intensity data aligned with the execution time. We evaluate Ichnos+ against hardware-level energy measurements obtained using Intel RAPL, and the nf-core co2footprint plugin, which implements the Green Algorithms methodology. We find that Ichnos+ is capable of estimating workflow energy consumption with an estimation error of 10.8% across three compute clusters, significantly outperforming the nf-core plugin. We further show that Ichnos+ extends beyond operational carbon to estimate embodied emissions as well as water and land use. Finally, we demonstrate how Ichnos+ can be extended for another workflow system, Apache Airflow, maintaining a similarly high degree of estimation accuracy.

cs.DC↗
arXiv2026

Predicting Lakehouse Performance in Clouds: An Empirical Exploration of Query Runtime Variance

Data analytics increasingly runs on distributed lakehouse systems, where platform operators must optimise monetary, resource, and environmental costs. Query Performance Prediction (QPP) helps to balance these costs and supports workload management techniques, such as adaptive resource scaling and low-carbon scheduling. However, runtimes in lakehouses can vary substantially, and the impact of runtime variance on QPP accuracy and workload orchestration has not previously been systematically studied for lakehouse systems. This paper addresses this gap by investigating the runtime variance observed for distributed lakehouse analytical queries and its impact on QPP. First, we quantify the run-to-run variance using Kubernetes deployments across three public clouds and one private cloud, spanning multiple database scales and three analytical benchmarks. Our results demonstrate that repeated executions of the same query can vary in runtime by nearly twofold. Second, we conduct a factor analysis study assessing key sources of this runtime variance such as data locality, co-tenant load, and caching effects. Third, we examine how variance influences state-of-the-art QPP models, revealing that addressing key sources of variance can reduce prediction error up to 80%. Finally, we demonstrate the downstream implications for low-carbon scheduling as an example of a workload management technique that relies on performance prediction, showing that accounting for runtime variance can lead to a significant reduction in carbon costs.

cs.DC↗
arXiv2026

Augur: Pre-Execution Energy Prediction for Workflow Tasks in Heterogeneous Clusters

Scientific workflows are widely used to process large quantities of data, leading to significant energy consumption and carbon emissions. To reduce this environmental impact, energy and carbon-aware scheduling approaches could be employed. However, such methods require runtime and energy predictions, which are typically only available for workflows that have been executed previously. Meanwhile, scientists may execute new or modified workflows, use workflows with different input data, or run them on alternative infrastructure. To address this critical gap, we propose Augur, a novel method to predict the energy consumption of scientific workflow tasks prior to execution. By efficiently profiling both the available cluster infrastructure and the workflow at hand, Augur is capable of predicting the overall energy consumption of the workflow with a median prediction error of $16.3\pm15.3\%$ compared to Ichnos, an energy estimation method that uses fitted power models, and $18.2\pm14.7\%$ compared to Intel RAPL, as observed in our experimental evaluation on public and private cloud infrastructure. Relying on only minimal historical execution data, Augur outperforms two state-of-the-art methods in predicting both task runtime and total workflow energy, providing a robust foundation for energy-efficient and carbon-aware scientific data analysis.

cs.DC↗
arXiv2026

Nf-PEAK: Process-Based Energy Attribution for Nextflow Workflows on Kubernetes Clusters

Scientific workflows are pipelines of interdependent tasks. They are increasingly executed on shared Kubernetes clusters via workflow engines such as Nextflow. Their energy consumption matters for both cost and sustainability. It is necessary to examine and optimize workflow tasks individually, because they can be very heterogeneous. However, estimating task-level energy on clusters is difficult: Intel RAPL counters report only node-level energy, access to counters and host process information is typically restricted, and concurrent workloads introduce resource contention and measurement noise. We present Nf-PEAK, a containerized method to attribute CPU-package and DRAM energy to individual processes and Nextflow tasks. Nf-PEAK (i) identifies workflow pods, (ii) maps pods to host processes via cgroup metadata, (iii) samples RAPL and per-process performance counters, and (iv) applies a non-linear energy-credit model before aggregating results at task level. On a Kubernetes cluster, we evaluate three nf-core workflows under controlled co-located CPU load. Nf-PEAK reaches an average Mean Absolute Percentage Error of 6.6% in isolated runs and 10.9% when an unrelated workload saturates 8 of 32 hardware threads per node, and remains stable across 2, 3, 4, and 8 nodes. Compared to the state-of-the-art Kubernetes tool Kepler, Nf-PEAK yields lower error on average, particularly under co-located load.

cs.DC↗
arXiv2026

A Systematic Evaluation of the Potential of Carbon-Aware Execution for Scientific Workflows

Scientific workflows are critical to scientific data analysis and often involve computationally intensive processing of large datasets on compute clusters. As such, their execution tends to be long-running and resource-intensive, resulting in significant energy consumption and carbon emissions. While carbon-aware computing methods have received considerable attention in general cloud contexts, their application to scientific data analysis workflows remains a critical research gap. Our study addresses this oversight by showing how the delay tolerance, interruptibility, and scalability of scientific workflows can be leveraged for a significantly more sustainable execution model. In this study, we first quantify the problem of carbon emissions associated with running scientific workflows, and then demonstrate the transformative potential for carbon-aware workflow execution. We estimate the carbon footprint of seven real-world Nextflow workflows executed on diverse dedicated cluster and public cloud resources using high-resolution average and marginal grid carbon intensity data from open and commercial data providers. Furthermore, we conduct a systematic evaluation of the impact of carbon-aware temporal shifting, and the dynamic pausing and resuming of the workflow. Moreover, we investigate the impact of resource scaling at both workflow and workflow task levels. Finally, we report substantial potential reductions in overall carbon emissions, with temporal shifting capable of decreasing emissions by over 80%, and resource scaling by 67%.

cs.DC↗
arXiv2025

BENNS: A Surrogate Model for Hybrid Online-Offline Evolution of SFC Embedding

Service Function Chains (SFCs) enable programmatic control of the functions and services in a computer network. By leveraging Software Defined Networking to control the links between virtualised network functions, SFCs provide a scalable approach to dealing with the increased pressures on network operation and management. However, embedding SFCs onto the underlying physical network and compute infrastructure is an NP-hard problem. Genetic Algorithms (GAs) have been used to address this issue, but they require significant time to evaluate solution quality (fitness) online, with most existing approaches instead adopting offline simulations or analytical evaluations. To enable online use of GAs in solving the SFC embedding problem, we introduce a hybrid online-offline approach to efficiently evaluate the fitness of generated solutions. At the core of this is BENNS: a surrogate model that approximates fitness and is agnostic to topology, traffic, and SFC-embedding. We evaluate our approach in a static environment across five experiments, varying available resources and traffic loads, and in a dynamic network environment. Our results demonstrate that our approach is capable of exploring thousands of potential configurations and generating deployable solutions in 19.1 minutes on average, compared to online-only approaches, which take 17.8 hours on average to explore ten solutions in our experiments and do not converge on an optimal solution.

cs.NI↗
arXiv2025

Simultaneous Genetic Evolution of Neural Networks for Optimal SFC Embedding

The reliance of organisations on computer networks is enabled by network programmability, which is typically achieved through Service Function Chaining. These chains virtualise network functions, link them, and programmatically embed them on networking infrastructure. Optimal embedding of Service Function Chains is an NP-hard problem, with three sub-problems, chain composition, virtual network function embedding, and link embedding, that have to be optimised simultaneously, rather than sequentially, for optimal results. Genetic Algorithms have been employed for this, but existing approaches either do not optimise all three sub-problems or do not optimise all three sub-problems simultaneously. We propose a Genetic Algorithm-based approach called GENESIS, which evolves three sine-function-activated Neural Networks, and funnels their output to a Gaussian distribution and an A* algorithm to optimise all three sub-problems simultaneously. We evaluate GENESIS on an emulator across 48 different data centre scenarios and compare its performance to two state-of-the-art Genetic Algorithms and one greedy algorithm. GENESIS produces an optimal solution for 100% of the scenarios, whereas the second-best method optimises only 71% of the scenarios. Moreover, GENESIS is the fastest among all Genetic Algorithms, averaging 15.84 minutes, compared to an average of 38.62 minutes for the second-best Genetic Algorithm.

cs.NE↗
arXiv2025

HyProv: Hybrid Provenance Management for Scientific Workflows

Provenance plays a crucial role in scientific workflow execution, for instance by providing data for failure analysis, real-time monitoring, or statistics on resource utilization for right-sizing allocations. The workflows themselves, however, become increasingly complex in terms of involved components. Furthermore, they are executed on distributed cluster infrastructures, which makes the real-time collection, integration, and analysis of provenance data challenging. Existing provenance systems struggle to balance scalability, real-time processing, online provenance analytics, and integration across different components and compute resources. Moreover, most provenance solutions are not workflow-aware; by focusing on arbitrary workloads, they miss opportunities for workflow systems where optimization and analysis can exploit the availability of a workflow specification that dictates, to some degree, task execution orders and provides abstractions for physical tasks at a logical level. In this paper, we present HyProv, a hybrid provenance management system that combines centralized and federated paradigms to offer scalable, online, and workflow-aware queries over workflow provenance traces. HyProv uses a centralized component for efficient management of the small and stable workflow-specification-specific provenance, and complements this with federated querying over different scalable monitoring and provenance databases for the large-scale execution logs. This enables low-latency access to current execution data. Furthermore, the design supports complex provenance queries, which we exemplify for the workflow system Airflow in combination with the resource manager Kubernetes. Our experiments indicate that HyProv scales to large workflows, answers provenance queries with sub-second latencies, and adds only modest CPU and memory overhead to the cluster.

cs.DC↗
arXiv2025

Ichnos: A Carbon Footprint Estimator for Scientific Workflows

Scientific workflows facilitate the automation of data analysis, and are used to process increasing amounts of data. Therefore, they tend to be resource-intensive and long-running, leading to significant energy consumption and carbon emissions. With ever-increasing emissions from the ICT sector, it is crucial to quantify and understand the carbon footprint of scientific workflows. However, existing tooling requires significant effort from users - such as setting up power monitoring before executing workloads, or translating monitored metrics into the carbon footprints post-execution. In this paper, we introduce a system to estimate the carbon footprint of Nextflow scientific workflows that enables post-hoc estimation based on existing workflow traces, power models for computational resources utilised, and carbon intensity data aligned with the execution time. We discuss our automated power modelling approach, and compare it with commonly used estimation methodologies. Furthermore, we exemplify several potential use cases and evaluate our energy consumption estimation approach, finding its estimation error to be between 3.9-10.3%, outperforming both baseline methodologies.

cs.DC↗
arXiv2025

Energy-Aware Workflow Execution: An Overview of Techniques for Saving Energy and Emissions in Scientific Compute Clusters

Scientific research in many fields routinely requires the analysis of large datasets, and scientists often employ workflow systems to leverage clusters of computers for their data analysis. However, due to their size and scale, these workflow applications can have a considerable environmental footprint in terms of compute resource use, energy consumption, and carbon emissions. Mitigating this is critical in light of climate change and the urgent need to reduce carbon emissions. In this chapter, we exemplify the problem by estimating the carbon footprint of three real-world scientific workflows from different scientific domains. We then describe techniques for reducing the energy consumption and, thereby, carbon footprint of individual workflow tasks and entire workflow applications, such as using energy-efficient heterogeneous architectures, generating optimised code, scaling processor voltages and frequencies, consolidating workloads on shared cluster nodes, and scheduling workloads for optimised energy efficiency.

cs.DC↗
arXiv2025

Predicting the Performance of Scientific Workflow Tasks for Cluster Resource Management: An Overview of the State of the Art

Scientific workflow management systems support large-scale data analysis on cluster infrastructures. For this, they interact with resource managers which schedule workflow tasks onto cluster nodes. In addition to workflow task descriptions, resource managers rely on task performance estimates such as main memory consumption and runtime to efficiently manage cluster resources. Such performance estimates should be automated, as user-based task performance estimates are error-prone. In this book chapter, we describe key characteristics of methods for workflow task runtime and memory prediction, provide an overview and a detailed comparison of state-of-the-art methods from the literature, and discuss how workflow task performance prediction is useful for scheduling, energy-efficient and carbon-aware computing, and cost prediction.

cs.DC↗
arXiv2025

WOW: Workflow-Aware Data Movement and Task Scheduling for Dynamic Scientific Workflows

Scientific workflows process extensive data sets over clusters of independent nodes, which requires a complex stack of infrastructure components, especially a resource manager (RM) for task-to-node assignment, a distributed file system (DFS) for data exchange between tasks, and a workflow engine to control task dependencies. To enable a decoupled development and installation of these components, current architectures place intermediate data files during workflow execution independently of the future workload. In data-intensive applications, this separation results in suboptimal schedules, as tasks are often assigned to nodes lacking input data, causing network traffic and bottlenecks. This paper presents WOW, a new scheduling approach for dynamic scientific workflow systems that steers both data movement and task scheduling to reduce network congestion and overall runtime. For this, WOW creates speculative copies of intermediate files to prepare the execution of subsequently scheduled tasks. WOW supports modern workflow systems that gain flexibility through the dynamic construction of execution plans. We prototypically implemented WOW for the popular workflow engine Nextflow using Kubernetes as a resource manager. In experiments with 16 synthetic and real workflows, WOW reduced makespan in all cases, with improvement of up to 94.5% for workflow patterns and up to 53.2% for real workflows, at a moderate increase of temporary storage space. It also has favorable effects on CPU allocation and scales well with increasing cluster size.

cs.DC↗
arXiv2025

Exploring the Potential of Carbon-Aware Execution for Scientific Workflows

Scientific workflows are widely used to automate scientific data analysis and often involve processing large quantities of data on compute clusters. As such, their execution tends to be long-running and resource intensive, leading to significant energy consumption and carbon emissions. Meanwhile, a wealth of carbon-aware computing methods have been proposed, yet little work has focused specifically on scientific workflows, even though they present a substantial opportunity for carbon-aware computing because they are inherently delay tolerant, efficiently interruptible, and highly scalable. In this study, we demonstrate the potential for carbon-aware workflow execution. For this, we estimate the carbon footprint of two real-world Nextflow workflows executed on cluster infrastructure. We use a linear power model for energy consumption estimates and real-world average and marginal CI data for two regions. We evaluate the impact of carbon-aware temporal shifting, pausing and resuming, and resource scaling. Our findings highlight significant potential for reducing emissions of workflows and workflow tasks.

cs.DC↗