{"id":"73721cea-a8d8-4f23-bb68-be2d235a3bd3","arxiv_id":"2605.25292","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":3.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"DECICE delivers an Integrated AI Scheduler with RNN prediction and a Digital Twin for energy-aware workload scheduling in Kubernetes and Slurm environments as part of a European project.","lead":"The DECICE project built an open-source framework using AI and digital twins to schedule workloads across cloud, HPC, and edge systems. A smart generalist might read it to see how such tools could help manage energy use and resources in large distributed computing setups.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"The paper's central claim that IAIS (RNN prediction + formal modeling) and Digital Twin deliver practically superior scheduling rests on unshown quantitative results whose methodology, baselines, and effect sizes are not detailed enough to evaluate.","rationale":"The reader's weakest_assumption directly identifies the missing empirical grounding. Because the manuscript is framed as a project overview that nevertheless asserts quantitative results, the load-bearing gap is precisely the lack of reproducible evidence for the claimed improvements; confirming this gap leaves the UNVERDICTED verdict unchanged.","tokens_in":1681,"tokens_out":342,"duration_ms":14793,"concrete_test":"Locate the sections or tables that report the quantitative evaluation results for IAIS and Digital Twin; extract the exact baselines used, the performance deltas with confidence intervals, and any ablation that isolates the RNN component. If those numbers or the experimental protocol are absent or only high-level, the superiority claim is unsupported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract states that quantitative evaluation results are presented, yet the description of IAIS reduces to 'RNN-based prediction and formal workflow modeling for constraint-aware workload mapping' with no mention of RNN architecture, training corpus, prediction horizon, how formal models encode constraints, or any comparison against Kubernetes default scheduler, Slurm, or prior AI schedulers. The Digital Twin is similarly described only at the level of 'aggregating real-time metrics with carbon intensity and anomaly prediction.' Without these specifics or reported metrics (accuracy, makespan reduction, energy savings, statistical tests), the claim that the combination produces better outcomes than existing methods cannot be assessed and remains an assertion rather than a demonstrated result.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper describes the DECICE project (Horizon Europe Grant 101092582), an open-source framework for AI-driven workload scheduling across the cloud-HPC-edge continuum. It outlines an Integrated AI Scheduler (IAIS) that combines RNN-based prediction with formal workflow modeling for constraint-aware mapping, a Digital Twin that aggregates real-time metrics, carbon intensity, and anomaly prediction for energy-aware decisions, Kubernetes-native operation with Slurm integration, unified workflow ingestion, and contributions from six work packages. The manuscript covers project vision, architecture, work-package results, quantitative evaluation, and open-source release.","tokens_in":1836,"tokens_out":374,"duration_ms":19264,"significance":"Scheduling across heterogeneous compute continua with energy and constraint awareness is a relevant problem in distributed systems. A working implementation that demonstrably improves on baselines in makespan, energy, or constraint satisfaction could be useful for practitioners. The manuscript, however, functions primarily as a project overview rather than a self-contained technical contribution with novel algorithms or detailed empirical validation.","major_comments":[{"comment":"Abstract and sections describing IAIS and the Digital Twin: the central claim that IAIS (RNN prediction + formal modeling) and the Digital Twin produce practically superior scheduling outcomes is unsupported because no RNN architecture, training corpus, prediction horizon, constraint-encoding method, anomaly-prediction technique, baselines (Kubernetes, Slurm, prior AI schedulers), or quantitative metrics (accuracy, makespan reduction, energy savings, statistical tests) are supplied.","section":"Abstract and evaluation sections"}],"minor_comments":[{"comment":"The manuscript would benefit from an explicit section that separates project-level description from the specific technical contributions and their evaluation.","section":null}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive review and the recommendation for major revision. The manuscript is intended as a high-level overview of the DECICE project, its architecture, and work-package contributions rather than a self-contained algorithmic paper. We address the single major comment below.","responses":[{"response":"We agree that the current manuscript does not supply the requested low-level specifications (RNN architecture details, training corpus, prediction horizon, constraint-encoding method, anomaly-prediction technique, explicit baselines, or statistical quantitative metrics). The paper frames itself as a project overview that describes the IAIS and Digital Twin at the architectural level and states that quantitative evaluation results exist from the work packages; it does not advance a central claim of practical superiority with concrete numbers. Because the requested details are absent, the evaluation sections are underspecified for a reader seeking reproducible technical validation. We will revise the manuscript to either (a) incorporate additional technical descriptions and high-level metrics drawn from the project deliverables where they can be released without violating consortium agreements, or (b) explicitly qualify the evaluation claims as high-level outcomes and point to companion technical reports or future publications for the missing details.","revision_made":"yes","referee_comment":"[Abstract and evaluation sections] Abstract and sections describing IAIS and the Digital Twin: the central claim that IAIS (RNN prediction + formal modeling) and the Digital Twin produce practically superior scheduling outcomes is unsupported because no RNN architecture, training corpus, prediction horizon, constraint-encoding method, anomaly-prediction technique, baselines (Kubernetes, Slurm, prior AI schedulers), or quantitative metrics (accuracy, makespan reduction, energy savings, statistical tests) are supplied."}],"tokens_in":1303,"tokens_out":359,"duration_ms":21358,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The one thing to know is that this is a project report on the DECICE framework for scheduling across cloud, HPC, and edge using AI and digital twins, and it does not introduce a standalone new scientific result.\n\nThe new element is the specific combination of an Integrated AI Scheduler that uses recurrent neural networks for workload prediction alongside formal workflow models to handle constraints, paired with a Digital Twin that pulls in real-time data, carbon intensity, and anomaly detection to guide energy-aware decisions. The framework runs on Kubernetes, accepts workflows in various formats, and adds a layer to work with Slurm for HPC parts. The project organized this across six work packages with partners from six countries, and they plan to release the code.\n\nThis setup does a reasonable job of addressing the practical challenges of managing workloads across very different compute environments while trying to account for energy use. The mention of use case validation suggests they tested it in real scenarios.\n\nThe soft spot is the lack of detail around the results. The text says quantitative evaluation results are presented, but the high-level description does not include the RNN architecture, training data, prediction performance, how the formal models encode constraints, the comparison methods against standard schedulers, or any reported numbers on improvements. This makes it difficult to judge whether the approach delivers better outcomes than existing tools.\n\nThis paper is aimed at researchers and practitioners working on compute continuum orchestration, digital twins for systems, or energy-efficient scheduling. Someone already familiar with Kubernetes and Slurm might find the integration points useful as a reference.\n\nIt deserves a serious referee because the topic is timely and the open-source component adds value that others can use, even if the current write-up needs more on the evaluation to stand on its own.\n\nI would recommend sending it out for peer review with the expectation that the authors will need to expand the results section substantially.","headline":"DECICE describes a Kubernetes-based framework combining RNN scheduling with a digital twin for cloud-HPC-edge workloads but presents the performance claims without enough methodological or numerical detail to assess them.","tokens_in":2349,"tokens_out":458,"would_cite":false,"duration_ms":25970,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"DECICE combines an RNN-based AI scheduler with a digital twin to map workloads across cloud, HPC, and edge systems while respecting constraints and tracking carbon intensity.","keywords":["AI scheduling","digital twin","cloud-HPC-edge continuum","Kubernetes","energy-aware scheduling","RNN prediction","workload mapping","Slurm integration"],"falsifier":"A controlled multi-site deployment that measures task completion time, energy consumption, and constraint violations when using DECICE versus standard Kubernetes or Slurm schedulers on the same workload mix.","tokens_in":2607,"feed_emoji":"","tokens_out":636,"duration_ms":22361,"temperature":0.7,"pith_summary":"The paper presents the DECICE project, which built an open-source framework for scheduling workloads across the heterogeneous cloud-HPC-edge compute continuum. Its Integrated AI Scheduler uses recurrent neural network predictions together with formal workflow models to place tasks under given constraints. A Digital Twin layer gathers live metrics, carbon-intensity signals, and anomaly forecasts to steer energy-aware decisions. The implementation runs inside Kubernetes and adds a Slurm bridge for HPC resources, with workflow ingestion from multiple formats. A sympathetic reader would care because coordinated scheduling in mixed environments can improve resource use and lower overall energy demand.","feed_headline":"RNN scheduler plus digital twin coordinates cloud-HPC-edge workloads","feed_subtitle":"DECICE framework uses real-time metrics and carbon intensity to map tasks under constraints inside Kubernetes and Slurm environments.","key_machinery":"The Integrated AI Scheduler (IAIS) with RNN prediction plus formal workflow modeling, together with the Digital Twin that folds in carbon intensity and anomaly data for energy-aware decisions.","core_discovery":"The DECICE framework supplies an Integrated AI Scheduler (IAIS) that employs RNN-based prediction and formal workflow modeling for constraint-aware workload mapping, paired with a Digital Twin that aggregates real-time metrics with carbon intensity and anomaly prediction to support energy-aware scheduling; the system operates in Kubernetes environments, accepts unified workflow input from several formats, and bridges cloud-native and HPC orchestration through a Slurm integration layer.","pith_inferences":["Live carbon data inside the twin could let operators set explicit emission budgets rather than simple performance goals.","Anomaly prediction may trigger proactive migration before performance degrades across the continuum.","Open release of the code allows third parties to test the same scheduler logic on additional hardware mixes or prediction models."],"forward_implications":["Workloads are placed with explicit respect for constraints through the combination of RNN forecasts and formal models.","Scheduling decisions incorporate carbon intensity to favor lower-emission placements.","A single Kubernetes-based system ingests workflows from multiple formats and routes them across cloud, HPC, and edge resources.","HPC clusters become reachable from cloud orchestration via the Slurm integration layer."],"fun_headline_variants":["DECICE IAIS uses RNN for constraint-aware cloud-HPC-edge scheduling","Digital twin and RNN scheduler optimize energy in DECICE Kubernetes setup","DECICE integrates AI scheduler with carbon-predicting digital twin","RNN-based IAIS and digital twin bridge HPC cloud edge in DECICE"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"RNN-based prediction together with the digital twin will produce practically better scheduling outcomes than existing methods in real deployments across the compute continuum.","fun_headline_variants_meta":{"raw":{"variants":["DECICE IAIS uses RNN for constraint-aware cloud-HPC-edge scheduling","Digital twin and RNN scheduler optimize energy in DECICE Kubernetes setup","DECICE integrates AI scheduler with carbon-predicting digital twin","RNN-based IAIS and digital twin bridge HPC cloud edge in DECICE"]},"model":"grok-4.3","cost_usd":0.005139,"raw_usage":{"total_tokens":2482,"prompt_tokens":638,"num_sources_used":0,"completion_tokens":73,"cost_in_usd_ticks":51387000,"prompt_tokens_details":{"text_tokens":638,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1771,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":638,"tokens_out":73,"duration_ms":13207,"temperature":1.0,"reasoning_tokens":1771,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T23:18:37.583246+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A controlled multi-site deployment that measures task completion time, energy consumption, and constraint violations when using DECICE versus standard Kubernetes or Slurm schedulers on the same workload mix.","supporting_citations":[],"review_version":1}