Google Cluster Trace Dataset Overview

• Google Cluster Trace Dataset is a comprehensive collection of anonymized logs capturing workloads and resource usage from Google’s Borg-managed clusters.
• It features detailed schema tables including machine events, job and task events, and resource usage metrics, facilitating high-fidelity simulation and analysis.
• The dataset underpins advanced research in workload classification, scheduling policies, and capacity planning by revealing heterogeneous and over-provisioned resource utilization patterns.

The Google Cluster Trace Dataset is a canonical benchmark collection of production workload and resource utilization logs from multiple Google data center clusters, providing the foundational empirical basis for academic and industrial research in large-scale cloud scheduling, workload modeling, and infrastructure management. The dataset captures exhaustive event-based traces for tens of thousands of heterogeneous machines and millions of jobs and tasks, spanning multiple periods and cluster generations. Its design enables robust statistical analysis and simulation of cloud-scale workload behavior under actual Google Borg scheduling.

1. Dataset Origin, Scope, and Logistics

The Google Cluster Trace originates from anonymized logs collected from Google's Borg-managed production clusters, beginning with the release of clusterdata-2011-1 in May 2011. Early versions comprise traces from a single “cell,” defined as a pool of approximately 11 000–12 500 machines under one cluster manager, recorded over a continuous 29–31 day period. Later releases (e.g., 2014, 2019, 2020) expand coverage to eight geographically distributed clusters with total data volumes exceeding 2.4 TiB (Alam et al., 2015, Bappy et al., 2023, Loo et al., 2022).

Each dataset includes six principal CSV tables: machine events, machine attributes, job events, task events, task constraints, and task (instance) resource usage. Sampling frequencies are event-driven for lifecycle transitions (state changes per machine, job, or task) and periodic for resource usage metrics (fixed intervals, typically 5 minutes). Ingestion pipelines leverage scalable storage solutions such as HDFS, BigQuery, and custom simulators (e.g., AGOCS in Scala/Akka) to accommodate dataset sizes ranging from 40 to 191 GB compressed (Balliu et al., 2014, Loo et al., 2022, Sliwko et al., 30 Sep 2025).

2. Schema, Field Definitions, and Metrics

The core tables follow a consistent schema across versions, preserving the ability to reconstruct job/task lifecycles, resource requirements, and real-time machine state:

Table	Key Fields	Sample Frequency
machine_events	timestamp, machine_id, event_type, cpu_capacity, ram_capacity	event-driven
machine_attributes	machine_id, attribute_name, attribute_value	initial registration
job_events	timestamp, job_id, event_type, scheduling_class, job_name_hash	job state changes
task_events	timestamp, job_id, task_index, event_type, priority, resources	task state changes
task_constraints	job_id, task_index, constraint_key, constraint_value	on task creation
(instance) usage	start_time, end_time, job_id/task_id, cpu_usage, ram_usage, disk_io	periodic (5 min)

Resource usage metrics include normalized CPU core utilization, memory usage (GB), requested vs. assigned memory, disk I/O time, cache memory, cycles-per-instruction, and memory-accesses-per-instruction. Sampling is aggregated over the reporting window, with raw measurements available at sub-minute resolutions in some versions (Balliu et al., 2015, Sliwko et al., 30 Sep 2025).

3. Statistical Characteristics and Distributional Patterns

Extensive profiling establishes that job size, resource appetite, and durations are highly heterogeneous and exhibit multimodal and heavy-tailed distributions:

• Job Cardinality and Duration: Most jobs are short-lived (≤ minutes, bulk of submission count), whereas long jobs, though rare, dominate total resource consumption. Approximately 85 % of jobs run <1 000 s; only ~0.4 % exceed one day (Loo et al., 2022).
• Resource Request and Usage: Tasks are generally over-provisioned: actual CPU usage is only 10–20 % of requested, RAM usage hovers around 30 % of requested. Skewness is pronounced: a small fraction of jobs/tasks consume most resources (Shakil et al., 2015, Sliwko et al., 30 Sep 2025).
• Job Size (Tasks per Job): Distribution is heavy-tailed; most jobs have <10 tasks, with outliers >2 000 (Balliu et al., 2014, Loo et al., 2022).
• Temporal Dynamics: Inter-arrival times follow a Weibull or Poisson model in aggregate, but arrive in bursts with sub-second to ~10 s intervals. Task runtime distributions are heavy-tailed, modeled by Pareto survival functions: $P(X > x) = (x_m / x)^\alpha$ (Shakil et al., 2015, Bappy et al., 2023).

4. Clustering and Classification of Workloads

Clustering analysis utilizing $\mathbf{k}$-means and variants (e.g. k-means++, silhouette selection) segments jobs into distinct types based on $(\text{CPU usage},\, \text{Memory usage})$ or request vectors:

• Tri-modal Clustering: Jobs segregate into three coarse types—Short, Medium, Long—each with characteristic resource profiles. Short jobs exhibit bursty, efficient resource usage; Medium jobs are well-packed in memory without waste; Long jobs can be resource-hungry and prone to over-allocation (Alam et al., 2015, Shakil et al., 2015).
• Refined Sub-clusters: $\mathbf{k}=5$ clustering elucidates fine-grained sub-classes (e.g., Approaching-Mid, Receding-Long). Centroids can be used to seed synthetic trace generators (Alam et al., 2015).
• Cluster Interpretations:

Cluster Type	CPU Request (mean)	Memory Request (mean, GB)	Scheduling Implication
Minor-usage	0.01	0.10	Pack tightly, low waste
Mediocre-usage	0.20	0.50	Steady, moderate resources
Major-usage	0.75	2.00	Require headroom, variable

Job classification by cluster type assists in designing multi-class schedulers and empirical workload models (Shakil et al., 2015).

5. Preprocessing and Analysis Frameworks

Early pipelines used raw CSV logs loaded into HDFS, Hive, Pig, and SQLite, with custom ETL for aggregation and filtering (Shakil et al., 2015, Balliu et al., 2015). Modern scalable infrastructure leverages BigQuery and Dataproc for interactive analytics, supporting SQL aggregation, PySpark distributed processing, and direct integration with simulation frameworks (e.g., AGOCS) (Loo et al., 2022, Sliwko et al., 30 Sep 2025).

Preprocessing steps typically include:

• Filtering missing/invalid entries (NA fields).
• Summarizing task-level time series into job-level profiles (aggregation by sum/mean over tasks).
• Dimensionality reduction by choice of feature vectors (e.g., normalized CPU/memory fractions).
• Exporting cleaned feature sets to numerical analysis packages (MATLAB, R).

Best practice mandates use of efficient I/O (NVMe/SSD), aggressive RAM provisioning (≥16 GB), and parallel parsing routines for large traces (Sliwko et al., 30 Sep 2025).

6. Use Cases: Simulation, Scheduling, Characterization

The dataset's extensive fidelity allows a range of practical and research-driven use cases:

• Simulation Models: AGOCS and BiDAl use trace-derived empirical distributions to drive discrete-event and cycle-accurate cluster simulations, enabling direct comparison of scheduling algorithms (e.g., greedy, evolutionary, annealing) with live job/task replay (Balliu et al., 2014, Sliwko et al., 30 Sep 2025).
• Scheduling Policies: Researchers derive headroom and over-commitment formulas from utilization change CDFs, pursuing tighter packing of batch jobs and improved reclamation (e.g., reserve $R_m(t_i) = U_m(t_i) + \alpha_p$, where $\alpha_p$ is the $p$-th quantile of utilization burst) (Zhu et al., 2015).
• Failure Prediction and Dynamic Rescheduling: Modern analyses characterize failure risk by logistic regression over normalized CPU usage, runtime percentiles, resubmission count, and other attributes; identified high-risk jobs can be rescheduled or allocated extra resources (Bappy et al., 2023).
• Resource Sizing and Capacity Planning: Empirical data reveals systematic over-provisioning and persistent headroom (up to 30–40 % of resources remain idle), informing autoscaling and cluster right-sizing strategies (Loo et al., 2022).
• Serverless/FaaS Integration: Observation of idle capacity and job composition supports opportunistic co-location of stateless functions, minimizing impact on production SLAs (Loo et al., 2022).

7. Key Findings, Implications, and Research Directions

The Google Cluster Trace Dataset underpins several foundational findings and open challenges:

• Workload Heterogeneity: Empirical distributions consistently show job/task cardinality, duration, and resource demand are both multimodal and heavily skewed. Scheduling must attend to both fairness and efficiency across classes (Alam et al., 2015, Loo et al., 2022).
• Temporal Stability and Moderation: Borg achieves smooth moderation in submission/completion rates; latency and execution durations exhibit high variance but no clear degradation under load (Zhu et al., 2015).
• Symmetry in Long Jobs: New insight from trace analytics confirms high symmetry among sibling tasks in long jobs, enabling improved predictability and headroom calculation (Alam et al., 2015).
• Dominance of Few Users: In modern traces, a handful of users and jobs account for the majority of events and failures, driving research into quota systems and dynamic control of "noisy neighbor" effects (Bappy et al., 2023).
• Resource Allocation Efficiency: Empirical headroom and over-commitment formulas allow for aggressive task co-packing while protecting performance-critical workloads (Zhu et al., 2015).
• Simulation Accuracy: Replaying actual workload traces (as in AGOCS) achieves high-fidelity modeling, outperforming synthetic or simplistic queue-based models (Sliwko et al., 30 Sep 2025).

Analysis of Google Cluster Trace datasets informs workload generators, benchmarking, simulation, scheduling policy design, and resource management for cloud and data-center computing. Continued facility for fine-grained event-log parsing and real-time resource analytics enables the evolution of efficient, scalable, and robust cloud infrastructure research.