{"id":"f4e8f605-e7b7-4ee8-b9b3-2ea95d2c98c5","arxiv_id":"2506.19972","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":3,"one_line_summary":"A carbon-aware ranking framework for private clouds reports 85.68% CO2 reduction versus an even-distribution baseline in a three-region simulation, but the result is an artifact of the baseline and the experiments do not exercise the proposed ranking algorithm.","lead":"MAIZX is a carbon-aware cloud scheduling framework that ranks computing nodes by carbon intensity, PUE, and energy use to shift workloads to cleaner data centers. In simulations with 2022 grid data from three European countries, the authors report an 85.68% CO2 reduction versus a baseline that ignores carbon intensity entirely.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"85.68% result is not a validated property of the MAIZX ranking algorithm; it is the arithmetic gap between an equal-load carbon-blind baseline and a greedy min-carbon-intensity policy.","rationale":"The reader's REJECT verdict is directionally correct, but my most load-bearing concern is slightly narrower. Even if the baseline were made more realistic, the paper still would not have demonstrated the MAIZX-specific claim: the experiments do not use MAIZ_RANKING. Section 3 gives only a symbolic equation with unspecified weights, and the scenarios are stated as min-carbon-intensity policies. Because CF is linear in CI (Eq. 2), the reported percentage reduction against an equal-load baseline is fully determined by the gap between average and minimum hourly carbon intensity in the three chosen regions. The Section 5 text also contradicts Section 4 on what Scenario B does, undermining confidence in the reported scenario definitions. Therefore the central claim needs either a reproduction of the 85.68% number from the raw data plus baseline formula, or an experiment that actually varies the four weights and shows the ranking changes outcomes. If the reproduction matches, the number is a data artifact; if a ranked experiment changes outcomes materially, the current paper has simply omitted that evidence. Either way the current \"MAIZX achieved 85.68%\" claim is unsupported. A single reproduction check is enough to settle the baseline-artifact hypothesis, and the fixed-minimum baseline check would settle the practical significance.","tokens_in":6822,"tokens_out":4566,"duration_ms":47538,"concrete_test":"Using the 2022 hourly Electricity Maps CI data for Spain, the Netherlands, and Germany referenced as [10], compute R = 1 - sum_t min(CI_ES, CI_NL, CI_DE) / sum_t mean(CI_ES, CI_NL, CI_DE). If R equals 85.68% at the reported precision while no MAIZ_RANKING weights appear in the computation, the headline is fully explained by baseline choice and regional CI data. Then re-run with a baseline that always places on the fixed lowest-carbon node; if the marginal reduction over that baseline is below roughly 10%, the framework-specific claim fails.","verdict_should_be":"REJECT","load_bearing_attack":"The central claim requires that the 85.68% reduction is produced by the MAIZX framework's ranking algorithm. Section 3 defines MAIZ_RANKING (Eq. 1) as a weighted sum of CFP, FCFP, CP_RATIO, and SCHEDULE_WEIGHT, but the paper never instantiates the weights or shows a score-based placement decision. Scenarios A and C are fixed heuristics: \"directs all computing power to the node with the lowest carbon intensity\" and \"actively shifts workloads to the nodes with the lowest carbon intensity.\" With CF = EC × PUE × CI (Eq. 2), Scenario C's savings against an equal-load baseline is determined by the difference between the average and the minimum hourly carbon intensity across Spain, the Netherlands, and Germany. The 85.68% figure is therefore an arithmetic consequence of the baseline and the regional CI spread, not evidence that Eq. 1 adds predictive value. The Baseline Scenario is also a strawman: even distribution with no carbon awareness is not a typical hypervisor schedule and is not the comparison implied by \"baseline hypervisor operations.\" A carbon-aware or fixed-minimum-CI baseline would erase most of the gap. The text compounds this by mixing up Scenarios B and C in Section 5, saying Scenario B \"evenly distributes workloads without considering carbon intensity\" even though Section 4 defines Scenario B as concentrating tasks on a single node while powering off others. Thus the load-bearing condition—that the experiments exercise MAIZX against a fair baseline—is not met.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript evaluates the MAIZX framework for carbon-aware cloud workload allocation in private and multi-cloud environments. It defines a ranking function, MAIZ_RANKING, as a weighted sum of carbon footprint, forecasted carbon footprint, computing power ratio, and scheduling weight (Eq. 1), and computes node carbon footprint as CF = EC × PUE × CI (Eq. 2). Using 2022 hourly carbon-intensity data for Spain, the Netherlands, and Germany, the paper compares four scenarios: a carbon-unaware even-load baseline, a greedy lowest-carbon-intensity placement, a single-node concentration strategy, and an active load-shifting strategy. The central claim is that Scenario C reduces CO2 emissions by 85.68% relative to the baseline, and the paper scales this to 27,686,054 units to project 19.754 Mt CO2eq savings over 10 years.","tokens_in":7171,"tokens_out":4417,"duration_ms":42536,"significance":"If substantiated, an 85.68% emission reduction for private-cloud hypervisor scheduling would be practically significant for carbon-aware cloud operations. The paper deserves credit for using real 2022 carbon-intensity data (Electricity Maps), for stating an explicit footprint formula (Eq. 2), and for defining separate scenarios with different scheduling policies. However, the headline result is not actually produced by the MAIZ_RANKING algorithm as described. The experiments reduce to a greedy lowest-carbon-intensity policy compared against an equal-load carbon-blind baseline, so the 85.68% figure is an arithmetic consequence of the regional carbon-intensity spread and the baseline choice rather than a validation of Eq. (1). As presented, the contribution is largely a restatement of the known benefit of geographic carbon-aware scheduling, without the algorithmic or experimental evidence needed to support the framework-specific claims.","major_comments":[{"comment":"The central claim that the MAIZX framework achieves an 85.68% reduction is not supported by the experiments because Eq. (1) is never instantiated. Scenario C is defined in Section 4 as \"actively shifts workloads to the nodes with the lowest carbon intensity,\" which is a pure minimum-carbon-intensity policy. With CF = EC × PUE × CI, the reported savings are determined by the difference between the average carbon intensity of the three regions and the hourly-minimum intensity. The weights w1–w4 and the MAIZ_RANKING scores never appear in the results, so the experiments cannot distinguish MAIZX's ranking algorithm from a trivial greedy scheduler.","section":"§3, §4, §5, Eq. (1), Eq. (2)"},{"comment":"The baseline is a strawman. The manuscript's baseline \"evenly distributes loads without any consideration of carbon intensity or footprint data\" is not shown to be representative of typical private-cloud hypervisor scheduling. The 85.68% reduction is essentially the ratio (average CI − minimum CI)/average CI under equal load; against any carbon-aware baseline, such as a fixed placement on the cleanest node, the same scenario would show a much smaller or zero reduction. Since the abstract and Section 5 claim a reduction \"compared to baseline hypervisor operations,\" this baseline choice is load-bearing and unsupported.","section":"§4, §5"},{"comment":"There is an internal contradiction in the scenario definitions. Section 4 defines Scenario B as \"concentrates tasks on a single node while powering off others,\" but Section 5 says \"Scenario B evenly distributes workloads without considering carbon intensity.\" This makes it impossible to determine which scenario produced the 85.68% result and confounds the comparison between Scenarios B and C.","section":"§4–§5"},{"comment":"The scaling calculation contains an arithmetic inconsistency. The paper states that over a 10-year period, with 27,686,054 units each reducing 713.5 kg CO2 per year, the total reduction target is 19.754 Mt CO2eq. Multiplying 27,686,054 units by 713.5 kg/year gives approximately 19.754 Mt CO2 per year, so the 10-year reduction would be 197.54 Mt, not 19.754 Mt. Alternatively, to reach 19.754 Mt over 10 years would require about 2.77 million units, not 27.69 million. The scaling also assumes that trans-national workload migration is free and that all units achieve the same 85.68% reduction, neither of which is justified.","section":"§5"}],"minor_comments":[{"comment":"Eq. (1) defines CFP, FCFP, CP_RATIO, and SCHEDULE_WEIGHT only verbally; please provide concrete measurable definitions, units, and a procedure for setting the weights w1–w4, or state that they are user-configured.","section":"§3"},{"comment":"The data-collection description says power consumption is measured every 20 seconds, but no details are given on the number of nodes, hardware configuration, measurement duration, or workload characteristics; these details are needed for reproducibility.","section":"§4"},{"comment":"Figure 2 is captioned \"MAIZX Framework Architecture\" but appears to display results or a chart; the caption should be updated to match the content.","section":"Figure 2"},{"comment":"There are several typographical and editorial errors: \"life cicle analys\" in Section 4, \"hybryd\" in Section 2, \"Sweeden\" in reference [29], and malformed publisher/location strings in references [17] and [36]; these should be corrected.","section":"Throughout"}],"recommendation":"reject","confidential_remarks":"The paper relies heavily on the first author's dissertation [29] for the framework definition and the preliminary 85.68% result, and the present manuscript does not provide new experimental evidence that exercises the ranking algorithm itself. The reviewers should also scrutinize the novelty and reproducibility claims, since the empirical section appears to use only external carbon-intensity data and a simple heuristic."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The headline result, 85.68% CO2 reduction, is a consequence of comparing an equal-load carbon-blind baseline against a policy that always picks the lowest-carbon node in a region mix with big intensity differences (Spain/Netherlands/Germany). That is not a validation of the MAIZX ranking algorithm, because the scenarios never actually use Eq. (1) — they implement simple heuristics. The paper itself is inconsistent: Section 4 says Scenario B concentrates tasks on one node and powers off others, but Section 5 says Scenario B evenly distributes workloads without considering carbon intensity. That matters, because the claim that Scenario C improves on B is built on the wrong description of B. Also, the scaling projection to 19.7 Mt CO2eq is a naive linear extrapolation from one 60-server unit to 27.7 million units, with no accounting for saturation or grid mix changes.\n\nGive credit where it is due: the survey of carbon-aware scheduling is competent, the CF = EC × PUE × CI formula is standard, and using real hourly carbon intensity data from Electricity Maps is a good practice. The idea of targeting private, hybrid, and multi-cloud deployments is worthwhile, since most prior work focuses on public clouds. The paper is clearly an extension of the first author's 2024 dissertation, which is cited, and the authors are transparent that this is a re-evaluation rather than a new algorithm.\n\nThe fundamental problem is that the central quantitative claim is not supported by the experiments as described. A fair baseline, such as fixed lowest-carbon placement or a simple carbon-aware heuristic, would shrink the reported gap dramatically. Without instantiating the weights in Eq. (1) and showing score-based placement decisions, the paper does not demonstrate that MAIZX adds predictive value. The missing parameters (PUE values, node energy consumption, weather or load variability) make the results non-reproducible.\n\nWho is this for? A reader interested in the landscape of carbon-aware scheduling for private clouds might get a useful survey and a concrete example of how regional intensity differences dominate scheduling outcomes. But as an evaluation of MAIZX itself, it falls short. It deserves a serious referee because the topic is important and the flaws are fixable, but the current version should not be accepted without major revision — rerun with a realistic baseline, actually exercise the ranking algorithm, fix the scenario definitions, and cut the speculative scaling to zero. I would cite it only as a cautionary example of baseline sensitivity.","headline":"The 85.68% number is real arithmetic but it belongs to the baseline choice, not to the MAIZX ranking algorithm; the paper is a rough workshop note with a fixable design flaw.","tokens_in":7678,"tokens_out":1242,"would_cite":false,"duration_ms":15516,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A carbon-aware cloud scheduler reports an 85.68% cut in CO2 emissions compared with a baseline that ignores carbon intensity.","keywords":["carbon-aware computing","cloud scheduling","emissions reduction","carbon intensity","private cloud","hypervisor integration","MAIZX framework","workload shifting"],"falsifier":"Using the same 2022 hourly carbon-intensity data, compute Scenario C's emissions against a baseline that always sends work to the single lowest-carbon node; if the reduction relative to that baseline is small, the claim that the ranking algorithm drives the saving is falsified.","tokens_in":6606,"feed_emoji":"🌱","tokens_out":5729,"duration_ms":61276,"temperature":0.7,"pith_summary":"The paper argues that a scheduling framework called MAIZX can reduce the carbon footprint of private and multi-cloud computing by ranking available nodes according to real-time and forecasted carbon intensity, power usage, and efficiency. Its central evidence is a year-long simulation across data centers in Spain, the Netherlands, and Germany: dynamically shifting workloads to the cleanest node cuts CO2 emissions by 85.68% relative to a baseline that splits load evenly without carbon data. If accurate, the same arithmetic implies each 60-server unit saves 713.5 kg of CO2 per year, and scaling to about 27.7 million units could avoid roughly 19.754 Mt CO2eq over ten years. This matters because private clouds, used by a large share of organizations, currently lack the carbon transparency that public providers offer.","feed_headline":"Carbon-aware cloud scheduler cuts CO2 by 85.68%","feed_subtitle":"Shifting cloud workloads to the cleanest regional grid nearly eliminates emissions in a three-country test.","key_machinery":"The central object is the MAIZ_RANKING score, a weighted sum $$\\mathit{MAIZ\\_RANKING} = w_1\\,\\mathrm{CFP} + w_2\\,\\mathrm{FCFP} + w_3\\,\\mathrm{CP\\_RATIO} + w_4\\,\\mathrm{SCHEDULE\\_WEIGHT},$$ where CFP is the node's measured carbon footprint, FCFP is the forecasted footprint from historical data, CP_RATIO is the node's energy efficiency, and SCHEDULE_WEIGHT encodes workload priorities and deadlines. The adjustable weights let the scheduler balance environmental impact against performance needs, while distributed agents feed real-time power and carbon-intensity readings into a centralized controller that coordinates with the hypervisor. This score carries the argument: the reported emissions reduction comes from routing work to nodes with the lowest combined score.","core_discovery":"On the paper's terms, the discovery is that a hypervisor-integrated ranking algorithm can make cloud scheduling carbon-aware without abandoning operational control. MAIZX computes a per-node score from four terms—carbon footprint, forecasted carbon footprint, computing-power ratio, and scheduling weight—and assigns workloads to the best-ranked node. In a three-region simulation using 2022 hourly carbon-intensity data, the active load-shifting scenario achieved an 85.68% reduction in CO2 emissions compared with the even-distribution baseline, and the authors scale this to a 19.754 Mt CO2eq saving over ten years across 27,686,054 units. The framework is positioned as extending carbon-aware scheduling to private, hybrid, and multi-cloud environments by interfacing directly with the hypervisor.","pith_inferences":["This is an editorial inference, not a paper claim: the 85.68% reduction is largely the arithmetic difference between the average carbon intensity of the three regions and the cleanest region, so a baseline that always used the single cleanest node would likely shrink the headline gain dramatically.","A testable extension would be to rerun Scenario C against several baselines—static lowest-carbon placement, a conventional carbon-agnostic load balancer, and a price-aware scheduler—to isolate how much of the saving comes from the ranking algorithm itself.","The linear scaling from one 60-server unit to 27.7 million units assumes identical per-unit savings and no grid or infrastructure saturation; real deployments with shared cooling and power systems may show nonlinear effects."],"forward_implications":["If the 85.68% result holds, carbon-aware ranking can be built into existing hypervisor schedulers, giving private clouds the dynamic load-shifting capability that public providers advertise.","A year-long active-shifting policy beats an even distribution in regions with heterogeneous grids; the same policy would be expected to produce smaller gains where regional carbon intensities are similar.","Scaling the measured per-unit saving of 713.5 kg CO2 per year across the EU Taxonomy data-related target implies 27,686,054 shifted units avoid roughly 19.754 Mt CO2eq over a decade.","The weighted score gives operators a single knob to favor emission cuts over performance, or vice versa, without replacing the underlying hypervisor."],"supporting_citations":[{"why":"Prior MAIZX dissertation; supplies the architecture, ranking algorithm, and the preliminary 85.68% result this paper evaluates.","marker":"[29]"},{"why":"Electricity Maps; supplies the 2022 hourly carbon-intensity time series for Spain, the Netherlands, and Germany used in all scenarios.","marker":"[10]"},{"why":"Cloud carbon accounting method; supplies the CF = EC × PUE × CI footprint calculation used across scenarios.","marker":"[8]"},{"why":"ICT-sector GHG guidance; provides the standard methodology behind the footprint formula.","marker":"[12]"},{"why":"OpenNebula scheduler documentation; supplies the hypervisor interface the framework integrates with.","marker":"[25]"},{"why":"EU Taxonomy Navigator; supplies the 19.754 Mt CO2eq target figure used for the ten-year scaling calculation.","marker":"[6]"},{"why":"Impact Forecast tool; supplies the climate-performance-potential model behind the scaled savings and cost estimates.","marker":"[15]"}],"fun_headline_variants":["MAIZX framework slashes cloud carbon by 85.68%","Cloud scheduler hits 85.68% CO2 cut","Carbon-aware MAIZX cuts emissions by 85.68%","MAIZX: 85.68% less CO2 from cloud workloads","Cut cloud carbon 85.68% with MAIZX ranking"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The headline number rests on a baseline that divides loads evenly across Spain, the Netherlands, and Germany with no carbon awareness; if a realistic scheduler already favors the cleanest region, the claimed 85.68% reduction falls apart.","fun_headline_variants_meta":{"raw":{"variants":["MAIZX framework slashes cloud carbon by 85.68%","Cloud scheduler hits 85.68% CO2 cut","Carbon-aware MAIZX cuts emissions by 85.68%","MAIZX: 85.68% less CO2 from cloud workloads","Cut cloud carbon 85.68% with MAIZX ranking"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00065,"raw_usage":{"total_tokens":2982,"prompt_tokens":942,"completion_tokens":2040,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":558,"completion_tokens_details":{"reasoning_tokens":1948}},"tokens_in":558,"tokens_out":2040,"duration_ms":15916,"temperature":1.0,"reasoning_tokens":1948,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T22:58:59.347010+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Using the same 2022 hourly carbon-intensity data, compute Scenario C's emissions against a baseline that always sends work to the single lowest-carbon node; if the reduction relative to that baseline is small, the claim that the ranking algorithm drives the saving is falsified.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Prior MAIZX dissertation; supplies the architecture, ranking algorithm, and the preliminary 85.68% result this paper evaluates."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Electricity Maps; supplies the 2022 hourly carbon-intensity time series for Spain, the Netherlands, and Germany used in all scenarios."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Cloud carbon accounting method; supplies the CF = EC × PUE × CI footprint calculation used across scenarios."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"ICT-sector GHG guidance; provides the standard methodology behind the footprint formula."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"OpenNebula scheduler documentation; supplies the hypervisor interface the framework integrates with."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"EU Taxonomy Navigator; supplies the 19.754 Mt CO2eq target figure used for the ten-year scaling calculation."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Impact Forecast tool; supplies the climate-performance-potential model behind the scaled savings and cost estimates."}],"review_version":1}