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Taming Energy Consumption Variations In Systems Benchmarking

https://doi.org/10.1145/3358960.3379142
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-04519-1_1openalex-snapshot
Adding Virtualization Capabilities to the Grid’5000 Testbed
inferredcites →https://doi.org/10.1007/978-3-319-13326-3_23openalex-snapshot
Resources Description, Selection, Reservation and Verification on a Large-Scale Testbed
inferredcites →https://doi.org/10.1007/978-3-642-03547-0_54openalex-snapshot
Energy Efficiency of Thermal-Aware Job Scheduling Algorithms under Various Cooling Models
inferredcites →https://doi.org/10.1007/s11276-018-1882-1openalex-snapshot
Potential effects on server power metering and modeling
inferredcites →https://doi.org/10.1016/j.jss.2018.07.001openalex-snapshot
The next 700 CPU power models
inferredcites →https://doi.org/10.1016/j.jss.2018.07.077openalex-snapshot
How does docker affect energy consumption? Evaluating workloads in and out of Docker containers
inferredcites →https://doi.org/10.1080/01621459.2000.10474333openalex-snapshot
The Bootstrap and Modern Statistics
inferredcites →https://doi.org/10.1109/hpca.2010.5463056openalex-snapshot
Worth their watts? - an empirical study of datacenter servers
inferredcites →https://doi.org/10.1109/igcc.2013.6604506openalex-snapshot
Your cluster is not power homogeneous: Take care when designing green schedulers!
inferredcites →https://doi.org/10.1109/ithings/greencom/cpscom/smartdata.2019.00133openalex-snapshot
Experimental Characterization of Variation in Power Consumption for Processors of Different Generations
inferredcites →https://doi.org/10.1109/jssc.2002.803949openalex-snapshot
Adaptive body bias for reducing impacts of die-to-die and within-die parameter variations on microprocessor frequency and leakage
inferredcites →https://doi.org/10.1109/mm.2005.110openalex-snapshot
Designing Reliable Systems from Unreliable Components: The Challenges of Transistor Variability and Degradation
inferredcites →https://doi.org/10.1109/sp.2019.00002openalex-snapshot
Spectre Attacks: Exploiting Speculative Execution
inferredcites →https://doi.org/10.1136/bmj.e4483openalex-snapshot
Pearson's correlation coefficient
inferredcites →https://doi.org/10.1145/125826.125925openalex-snapshot
The NAS parallel benchmarks---summary and preliminary results
inferredcites →https://doi.org/10.1145/2742351openalex-snapshot
Noise-Tolerant Explicit Stencil Computations for Nonuniform Process Execution Rates
inferredcites →https://doi.org/10.1145/2807591.2807638openalex-snapshot
Analyzing and mitigating the impact of manufacturing variability in power-constrained supercomputing
inferredcites →https://doi.org/10.1145/2851553.2851567openalex-snapshot
Variations in CPU Power Consumption
inferredcites →https://doi.org/10.1145/2925426.2926279openalex-snapshot
Runtime-Guided Mitigation of Manufacturing Variability in Power-Constrained Multi-Socket NUMA Nodes
inferredcites →https://doi.org/10.1145/2925426.2926289openalex-snapshot
Variation Among Processors Under Turbo Boost in HPC Systems
inferredcites →https://doi.org/10.1145/3149412.3149421openalex-snapshot
An empirical survey of performance and energy efficiency variation on Intel processors
inferredcites →https://doi.org/10.1145/3177754openalex-snapshot
RAPL in Action
inferredcites →https://doi.org/10.2172/1163229openalex-snapshot
Comparing Server Energy Use and Efficiency Using Small Sample Sizes
inferredcites →https://doi.org/10.48550/arxiv.1801.02381openalex-snapshot
Benchmarking Crimes: An Emerging Threat in Systems Security
inferredcites →https://doi.org/10.48550/arxiv.1801.04329openalex-snapshot
Effect of Meltdown and Spectre Patches on the Performance of HPC Applications

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Energy Performance Analysis of Advanced Transactional Models
inferred← citeshttps://doi.org/10.1007/978-3-031-11513-4_5openalex-snapshot
Tales from the Code #2: A Detailed Assessment of Code Refactoring’s Impact on Energy Consumption
inferred← citeshttps://doi.org/10.1007/s10664-023-10429-1openalex-snapshot
Can we spot energy regressions using developers tests?
inferred← citeshttps://doi.org/10.1109/ccgrid64434.2025.00024openalex-snapshot
CINERGY: Reasoning Over the Worst Case Power Consumption of Cloud Virtual Machines
inferred← citeshttps://doi.org/10.1109/cloud62652.2024.00058openalex-snapshot
Process-Based Efficient Power Level Exporter
inferred← citeshttps://doi.org/10.1109/ic2e59103.2023.00019openalex-snapshot
Studying the Energy Consumption of Stream Processing Engines in the Cloud
inferred← citeshttps://doi.org/10.1109/icsme52107.2021.00007openalex-snapshot
Evaluating The Energy Consumption of Java I/O APIs
inferred← citeshttps://doi.org/10.1109/tgcn.2026.3660816openalex-snapshot
Investigating the Potential of Kepler Toward Power Observability for Sustainable Cloud Computing
inferred← citeshttps://doi.org/10.1145/3427921.3450234openalex-snapshot
Creating a Virtuous Cycle in Performance Testing at MongoDB
inferred← citeshttps://doi.org/10.1145/3475716.3475774openalex-snapshot
Evaluating the Impact of Java Virtual Machines on Energy Consumption
inferred← citeshttps://doi.org/10.1145/3698038.3698531openalex-snapshot
Accountable Carbon Footprints and Energy Profiling For Serverless Functions
inferred← citeshttps://doi.org/10.1145/3744915.3748463openalex-snapshot
On the Effect of Feature Reduction on Energy Consumption: An Exploratory Study
inferred← citeshttps://doi.org/10.14778/3785297.3785302openalex-snapshot
DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning
inferred← citeshttps://doi.org/10.3390/electronics13234578openalex-snapshot
Impact of Real-World Energy Consumption Variance on Internet of Things Node Lifetime Predictions
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