Intelligent Cloud Resource Usage Potentially to Improve Task Scheduling by the use of Artificial Intelligence

2.1 Analysis of production trace data for scheduling optimization

Zarour et al. (2024) conducted an early study analyzing the Google Cluster Trace dataset to examine the influence of task variables such as CPU and memory needs, scheduling priority, and task limitations on job scheduling efficiency. Their findings indicated that specific criteria, such as memory availability, significantly influenced execution time and rescheduling frequency, whereas others, such as stringent job requirements, had a diminished impact. This study did not offer an AI-based solution; instead, it established a basis for AI-driven scheduling models by identifying essential scheduling parameters to be integrated into machine learning models. This study substantially advanced feature engineering for cloud scheduling systems by identifying the most influential aspects affecting task performance.

2.2 Machine learning models for workload prediction and resource allocation

Gao et al. (2020) proposed a workload prediction system that categorizes workloads prior to employing a singular machine learning model for each category. Their research utilizing Google Cluster data revealed that categorizing clustering jobs by their attributes resulted in markedly enhanced prediction accuracy (approximately 90%) compared to individual models. This discovery underscores the advantage of considering workload homogeneity through the aggregation of analogous jobs, enabling models to specialize in correctly predicting specific workload categories.

Karpagam and Kanniappan (2025) introduced an innovative model for forecasting cloud resource time series using a symmetry-aware multidimensional attention spiking neural network (SNN). Spiking Neural Networks (SNNs) are recognized for their capacity to efficiently process temporal data while utilizing minimum energy, rendering them appropriate for time-series forecasting in cloud settings. The research employed attention processes to emphasize significant traits and utilized optimization methods, such as Secretary Bird Optimization (SBOA), to improve prediction accuracy. This approach substantially surpasses conventional recurrent neural networks (RNNs), such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), in terms of efficiency and precision.

Although our methodology does not utilize spiking neural networks, it is influenced by the research of Karpagam et al., specifically with the application of attention processes to emphasize the most pertinent aspects. Similar to Karpagam et al., we investigated the efficacy of deep learning models, including feed-forward networks, for forecasting CPU loads in cloud environments, illustrating that tailored deep learning models can enhance prediction accuracy compared to conventional methods.

2.3 Predicting failures and resource usage in cloud environments

Chen et al. (2014) investigated the failure patterns of tasks within Google clusters, emphasizing the capability of predictive models to proactively identify errors. Their emphasis on failure prediction highlighted the significance of proactive forecasting in cloud operations. Anticipating peak demand or possible resource utilization issues enables cloud systems to implement remedial measures such as job rescheduling or additional resource supply prior to the emergence of problems.

Mishra et al. (2010) examined resource usage variability and task inter-arrival time in Google Cloud backends. Scientists have found that cloud workloads exhibit a great deal of diversity, which is problematic in terms of resource allocation and scheduling. Predictive models should have the ability to generalize between different workloads to eliminate this unpredictability.

2.4 Hybrid approaches and combining domain knowledge with AI models

The existing literature highlights several important results on how to optimize the process of cloud resource scheduling and workload prediction, among which the nature of tasks such as task priority and resource requests plays an important role in determining the scheduling results. Clustering-based models, deep neural networks, and reinforcement learning methods of machine learning have shown promise for improving the management of cloud resources.

These insights were synthesized in our research to create an ensemble methodology in which a large number of models (e.g., Random Forest (RF), Gradient Boosting (GB), and predicted Neural Networks (NNs)) are combined to increase the precision of the predictions. Moreover, our investigations will be based on previous research by providing an in-depth analysis of the model training dynamics and generalization properties, which ensures that our models are accurate and stable under different workload conditions.

Table 1 highlights exemplary studies regarding scheduling strategies, failure prediction, and cloud burden analysis, emphasising the emergence of predictive modelling techniques as well as the contributions they have made.

In summary, the union of AI and machine learning in the sphere of scheduling cloud resources and predicting workload is a rapidly growing field. We based our work on previous research that combined clustering methods, deep learning types, and the ensemble strategy to enhance the accuracy and stability of the estates. This study expands the existing capacities of cloud task scheduling and helps create a more efficient cloud resource management system based on AI.

4.1 Preprocessing

For this study, we utilized the publicly available Google Cluster Trace v3 dataset, which contains comprehensive logs from Google’s computer clusters. The dataset collected in May 2019 spans millions of task instances executed across thousands of machines. The dataset comprises several critical features: Job ID and Task ID (identifiers for task-job association), timestamps (submission, start, and end times of tasks), resource utilization metrics (CPU and memory consumption during task execution), scheduling class (denoting task priority and latency sensitivity), and priority level (an integer assigned by the scheduler).

4.2 Feature engineering

The feature engineering process involved extracting relevant attributes that are available at the time of task scheduling, which included the requested CPU and the amount of CPU the task requested during submission. Amount of memory requested for the task during submission.

Class Scheduling is a categorical feature that classifies tasks according to their priority and latency sensitivity. The priority level represents a numeric feature assigned to a task by the scheduler. In addition, the duration estimates the runtime of the task, which can be derived from metadata or calculated by subtracting the start time of the task from its end time.

The CPU load on the host machine at the time of scheduling, assuming task placement, could account for the current host activity. To facilitate model training and ensure convergence, continuous features such as CPU, memory, and duration were scaled to a range of [0,1] using min-max normalization. Categorical features, such as the scheduling class and priority level, were encoded using one-hot encoding.

Defining the Target Variable

The regression model dependent variable was the proportion of CPU core utilization by a job during its execution. This was computed by dividing the CPU usage by the time that the task was being executed, giving a value in the range of 0-1. A value of 1 indicates the full usage of one of the CPU cores, and a value near 0 indicates less work. In a situation where jobs were forcibly displaced or cancelled, the CPU consumption at the time of cancellation was used.

We also performed binary classification with a continuous regression model to determine high- and low-load jobs (i.e., those that used more than 50 percent of a CPU core) by defining a threshold of 50 percent CPU usage. The given classification method provides real advantages to the person scheduling jobs by identifying the jobs that might require a specific management approach, including dedicated CPU utilization or higher priority in resource allocation.

Data Volume and Sampling

Owing to the extensive size of the dataset, which includes millions of tasks, training machine learning models on the complete dataset is computationally prohibitive. Consequently, we used a sampling approach to establish a feasible subset for our tests. We randomly selected a heterogeneous selection of jobs encompassing multiple scheduling classes and priority levels and incorporated all tasks related to those jobs. This sample comprised tens of thousands of task instances, sufficiently large to train robust models, yet compact enough to fit into memory for efficient processing.

The dataset was divided into three subsets: 70 percent of the data were utilized as the training set for model development, 15 percent were assigned as the validation set for hyperparameter optimization and cross-validation, and the remaining 15 percent were used as the test set to assess the final model performance. The temporal division guaranteed that the validation and test sets comprised tasks from subsequent time intervals, mirroring the actual context of forecasting future unobserved workloads.

Dealing with Imbalanced Data

We also performed binary classification with a continuous regression model to determine high- and low-load jobs (i.e., those that used more than 50 percent of a CPU core) by defining a threshold of 50 percent CPU usage. The given classification method provides real advantages to the person scheduling jobs by identifying the jobs that might require a specific management approach, including dedicated CPU utilization or higher priority in resource allocation.

The dataset was further subdivided into three sub-portions: 70 percent of the data were used as the training data to develop the model, fifteen percent used as the validation data to optimize hyperparameters and cross-validation, and the remaining fifteen percent used as the test data to check the final model performance. The difference in time ensured that the validation and test sets included tasks in future time periods, which reflected the actual situation of predicting future unobservable workloads.

Model Deployment and Scaling

Each preprocessing and feature extraction step was performed in Python using the Pandas module. To scale in a production setup, we use distributed data frameworks, such as Apache Spark or Dask, to process the entire dataset. As the subset that we sampled was rather large, our trials allowed us to train our models on a single machine. To scale the solution to real-time prediction, we suggest running the trained models in the framework of Apache Hadoop, Google Cloud Dataflow, or Google AI platform, which will help continuously process the data on the streams of traces and real-time resource demand.

This methodology employs the dataset of Google Cluster Trace v3 to build prediction models that can be used to make reliable predictions about CPU usage in jobs in cloud environments. The preparation steps, including feature extraction and data normalization, as well as sampling algorithms, ensure that the models are resilient and computationally efficient. Moreover, addressing the issue of data imbalance and ensuring the scalability of the solution to be applied in the real world preconditions the creation of practical AI-mediated cloud resource-scheduling systems.

5.1 Linear regression (LR)

Linear Regression (LR) acts as our baseline model, which provides a simple way of predicting how the CPU will be utilized in the future using a weighted linear combination of the input information. The basic limitation of linear regression is that it does not allow the modelling of non-linear relationships, but it does offer an interpretable framework, which can be used to explain trends, including the relationship between high task priority and high CPU utilization. We used the Ordinary Least Squares (OLS) method to train this model, which we used to test predictive performance in terms of the baseline. Linear regression has the benefit of being simple, which makes it beneficial when interested in comparative analysis and can test more complex models.

5.2 Support vector regression (SVR)

The Support Vector Regression used a Radial Basis Function (RBF) kernel that enabled the model to approximate complex non-linear correlations between input and CPU utilization. The working principle of SVR consists of optimizing a function inside an epsilon-tube, neglecting the errors in a given tolerance range, and penalizing larger errors. This approach is beneficial for detecting patterns that are not necessarily reflected in the linear models. However, SVR may be computationally expensive, particularly when dealing with large datasets. Therefore, we used a randomly subsampled dataset to train it to ensure that we could. The validation set was used to optimize the hyperparameters through a grid search, focusing on the RBF kernel width (gamma) and regularization parameter (C) to tune the hyperparameters to the minimum Root Mean Squared Error (RMSE).

5.3 Random forest (RF) regression

Random Forest (RF) regression is a decision-tree-based ensemble method that creates many decision trees and aggregates their estimates, resulting in enhanced accuracy and resistance. This model can deal with nonlinear interactions of features and requires minimal parameter tuning. During our investigation, to estimate the accuracy of the prediction, we used the Mean Squared Error (MSE) as the splitting criterion and trained a Random Forest using 100 trees. We limited the depth of the tree to ten levels to reduce overfitting. The RF feature importance attribute allowed us to identify the key predictors of CPU utilization, and among the most significant factors, CPU and task scheduling classes were identified. This is a highly effective tree-based model capable of performing well in capturing complex relationships between input features.

5.4 Predictive neural network (NN)

The Predictive Neural Network (NN) is a customized feedforward multilayer perceptron (MLP) designed to predict the future use of the CPU by examining the properties of the tasks. The network comprises an Input Layer: The number of neurons is equal to the number of features. Hidden Layers: The initial hidden layer is a 64 neuron ReLU-activated, and the second layer has 32 neurons. The output layer uses binary classification (between high- and low-load workloads) or regression (predicting continuous CPU consumption) activation of a sigmoid or linear type, respectively.

The first strategy was to use a binary classification tool to label each job as high- or low-load to maximize the accuracy of the classification. Consequently, the network was adjusted to predict the steady CPU usage. During training, the Adam optimizer was used with a learning rate of 0.001, and the loss function for the classification was binary cross-entropy. Early termination was also used to prevent overfitting at 50 epochs.

This hybrid technique optimized the classification accuracy while indirectly forecasting CPU use levels. The model's validation accuracy consistently increased, reaching a maximum of almost 95% by epoch 50, signifying robust prediction capability and effective generalization to novel data.

Figure 3 demonstrates the system's organized pipeline, which starts with data collection and preprocessing and ends with model testing and cloud installation. After being pulled from the database, the data were processed beforehand, which included normalization and cleansing. After the selection of features, different classification techniques, including Logistic Regression (LR), RF, Decision Tree (DT), and Pure Neural Networks (PNN), were used to develop selection models. After evaluating the newly constructed models, the top-performing model is put through use in a cloud environment.

Figure 3. Workflow of the proposed machine learning model training and deployment process.

The Predictive Neural Network's (PNN) training and validation result are shown in Figure 4. According to the consistency graph, both training and validation accuracies gradually increased throughout the epochs, with the verification accuracy eventually far surpassing the training accuracy. Effective learning and model convergence without overfitting are demonstrated by the smooth decline in the training and validation losses over time.

Figure 4. Training vs. validation accuracy for the Predictive Neural Network (PNN).

Through the training phase, the PNN model demonstrated excellent performance in generalization by gradually increasing accuracy and minimizing loss, as indicated in Figure 4.

Performance Evaluation

In addition to the regression performance, we also evaluated binary classification metrics, such as accuracy, precision, recall, and F1-score, for detecting high-CPU tasks. These metrics are particularly useful for schedulers because identifying high-load tasks allows for more efficient scheduling and resource allocation.

In accordance with MAE, RMSE, and R² on the test data set, ensemble and models built on neural networks perform better than linear and kernel-related procedures, demonstrated in Table 3.

5.5 Environment setup

The training was performed on a machine with an Intel Xeon processor and 64GB RAM. NVIDIA Tesla was used to train the NN model, which greatly helped to accelerate the training process because it minimized the number of epochs required.

To implement such models on a cloud system, it is possible to apply Python and the framework of Apache Spark to train the model on large-scale data or rely on the Google Cloud AI Platform to provide model training and real-time prediction services on a scale. Such models may be included in the cloud scheduler pipeline, in which the features of tasks are submitted to the trained model to estimate CPU usage, which will help make sound scheduling decisions.

5.6 Evolution matrix

To evaluate the predictive efficacy of the trained models, we employed various assessment measures including the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), as illustrated in Equations (3) and (4).

Precision and Recall are evaluative metrics that assess the true positives (TP) in relation to the sum of true positives and false positives (FP), as shown in Equations (5) and (6).

R² score in Equation (7), which indicates the proportion of variance in actual CPU usage explained by the model.

Where SS _Regression is Sum Squared Regression Error, and SS _Total is the Sum Squared Total Error

6.1 Model performance observations

LR in Table 4 performed the worst among all models, with an RMSE of 0.30 and an R² of only 0.50. The linear model cannot capture the nonlinear relationships inherent in CPU utilization, resulting in significant prediction errors. Its classification accuracy for high-CPU tasks was also quite poor, with only 61%, which is close to a naive classifier that classifies all tasks as low-load (leading to an accuracy of approximately 80% owing to class imbalance). This highlights the limitations of using simple linear models for complex nonlinear data, such as cloud resource usage.

Random Forest (RF): Random Forest regression showed in the above figure a substantial improvement over both Linear Regression and Support Vector Regression, with an RMSE of 0.20 and an R² of 0.85. The feature importance analysis revealed that task priority and CPU request size were the strongest predictors of actual CPU usage, which makes sense because high-priority tasks often require more CPU resources. The classification accuracy for high-CPU tasks was approximately 80%, indicating that RF can reasonably differentiate between high- and low-load tasks, but still misclassify some high-load tasks owing to shared features with low-load tasks. Figure 5 highlights the Random Forest (RF) approach's classification efficacy for high-CPU task prediction, including accuracy, precision, recall, and F1-score.

Figure 5. Classification performance of the RF model for high-CPU task.

NN -Model Result

The proposed artificial neural networks model showed the best results among the permutation sets of models. As shown in Table 5, the model started overfitting after 55 epochs; therefore, we employed the early stopping technique to save energy and avoid overfitting.

Predictive Neural Network (NN): The NN model was the most successful single model in our experiments, achieving an RMSE of 0.15, which was the lowest error among the models. With an R² of 0.90, the NN could explain 90% of the variance in CPU usage, demonstrating its high predictive power. The classification accuracy of the NN for high-CPU tasks was 94%, significantly outperforming the tree-based methods and SVR. This impressive performance can be attributed to the NN's ability to capture complex nonlinear relationships and interactions between features that other models, including tree-based methods, fail to detect. Notably, the NN was trained to maximize the classification accuracy to identify heavy tasks, which aligns well with the requirements of cloud schedulers.

Analysis for features Important

Based on the SHAP summary in Figure 6, au_memory and time have major effects on the forecasts generated by the model, with higher values typically producing better results. Although they may have less impact, other factors such as priority, CPU utilization (au_cpu, mu_cpu), and memory metrics (mu_memory, page_cache_memory) also play a role. Predictions based on process types are influenced by the scheduling features. In general, the model makes judgements based predominantly on aspects related to memory and space (Lundberg & Lee, 2017).

Figure 6. SHAP algorithm for features important.

The blue bars (-0.07, -0.07, and-0.05) show features that reduce the model product, while the red bar (+0.1) shows a positive influence. Overall, one significant factor favorably influences the choice, as demonstrated by the combined effect, which shifts the prediction from its starting value E [f(x)] = 0.1 toward f(x) ≈ 0.2.

7.1 Visualization of predictions

Figure 7 presents a comparison of the expected and actual CPU consumption of the best single model (NN) and the best hybrid model (Cluster-Based Ensemble). This is represented in the graph in which the expected CPU usage (y-axis) versus the measured CPU usage (x-axis) and the ideal prediction are formulated as a diagonal line (y = x).

Figure 7. Actual vs. predicted value grouped by cluster name (node name).

The NN (green points) correlates well with actual CPU consumption incidentally, whereas a few high-demand tasks (upper-right points) are slightly under-predicted.

The Cluster-Based Ensemble (blue points) fits better along the diagonal, particularly when the jobs are of high usage, which indicates that it works better in this case.

The two models have outstanding performance, especially in workloads characterized by low to moderate CPU usage, where the forecasts are almost equal to the actual outcomes. The predicted versus real values Pearson correlation exceeded 0.95 in both models, which is a great indication that they were very accurate in demonstrating the patterns of CPU utilization.

7.2 Stability across time

The critical evaluation of generalization is the performance of the model on the data of another time context. We used a temporal split of our data to evaluate it, that is, the test set included tasks of later time periods compared to the training set. Temporal validation allows the evaluation of the model to be generalized under altered workload conditions, such as seasonal changes or daily load cycles.

Our models have high values of R² that show good time generalization. This means that even with changes in the workload, the models will be able to provide accurate estimations of CPU utilization. The generalization of the system over numerous timeframes without frequent retraining is vital for its practical application. Addition of the time aspect or retraining the models after some time might help to further increase the ability of the models to respond to new regularities, but this was not necessary in the current setup.

7.3 Practical application: Low prediction error

The low prediction error observed in our models is an important factor for their applicability. The cluster-RF ensemble model significantly reduced the prediction error compared to linear baselines. Importantly, the models were able to classify high-CPU jobs with a classification accuracy of 93-94% which is far too high compared to the classification accuracy of a naive linear model by approximately 61 %.

This implies that the predictions of the model are reliable to the schedulers to a large extent. As an example, the model predicts that a task will use up large amounts of CPU resources 90 percent of the time, allowing the scheduler to arrive at preemptive decisions, such as delegating the task to a less loaded server or preemptively allocating resources. Such proactive scheduling can significantly reduce the system overloads and optimize the resources usage, bringing the final outcomes of cost savings and performance improvement.

The effectiveness of our methodology is strongly supported by experimental evidence. The stability of our models and their generalization of learning, particularly that achieved by the cluster-RF ensemble, show that machine learning is capable of making accurate predictions of future CPU usage. This study demonstrates the effectiveness of using supervised learning in the modeling of cloud workloads, which can serve as a useful guide for cloud schedulers in improving the decision-making process by means of accurate forecasts of CPU consumption. With high levels of classification accuracy in classifying high-CPU jobs, our models can provide considerable support for proactive scheduling, thereby increasing resource allocation and cost efficiency in clouds.

8.1 Resource management and scheduling impact

The ability to predict CPU utilization of the CPU has significant implications for the management and scheduling of cloud resources. The availability of actual CPU utilization forecasts now allows a cloud scheduler, such as those used in Borg or Kubernetes, to make better-informed decisions. Workloads that are expected to require large CPU resources can be allocated to machines that have sufficient CPU capacity or to a node that has a higher workload capacity. Conversely, tasks that do not require a large number of CPU can be packed, thereby achieving high resource efficiency.

It is a predictive methodology that allows scheduling in advance, thereby reducing the need to respond to a system overload by scaling up to more virtual machines or containers. The cost savings that this strategy could provide are the preventive measures of overloading, keeping machines at full capacity, and improving the efficiency of the system by reducing contention and rescheduling.

The models that are particularly good with this goal are the Predictive Neural Network (NN) and the cluster-based ensemble, which offer sufficient accuracy to support proactive decision-making. To illustrate, a scheduler can ask the model to make predictions of CPU requirements when obtaining a new job. If the prediction indicates higher CPU utilization, an alternative scheduling strategy might have to be adopted, including not co-locating the activity with another resource-intensive task or being given priority during resource allocation alongside other high-priority tasks.

All these relationships signify that AI-based orchestration of the cloud is progressing, where machine learning solutions improve conventional heuristics in real time in making smarter judgements of scheduling.

8.2. Generalization and robustness

Cloud infrastructures are inherently dynamic, and the workloads change owing to changes in the parameters, such as new applications, user changes, and system maintenance. One problem faced by advanced models is idea drift, in which the statistical nature of the input data or target variables changes with time. We used a chronological training/test split to evaluate the resiliency of our model. Our results proved that the models were highly generalized even when they were trained with historical data and tested on modern workloads.

Regular retraining is required to sustain performance levels. This can be achieved through online education or nightly updating the models based on the latest information. Moreover, the use of clustering in the ensemble can lead to the formation of a new category of tasks that do not fit the existing clusters.

One of the main benefits of our ensemble approach is that it is modular to a certain degree: in situations where the behavior of a specific cluster deviates, only the model of a specific cluster requires retraining, thereby improving computational efficiency. In addition, the domain attributes (e.g., task priority and scheduling class) ensure that the models do not lose their relevance and applicability as long as they exist in the scheduling process.

8.3 Limitations and failure cases

Despite the excellent performance of our models, some edge cases lead to low predictive accuracy. As an example, jobs with a strong dynamic in terms of CPU usage (e.g., temporary bursts) could not be predicted well with our models, which rely mostly on fixed characteristics. Such sharp spikes are difficult to capture inside a model based on aggregated characteristics, and can cause a temporary overload in the case of many jobs with spikes occurring simultaneously.

A second limitation arises when exogenous factors on task behavior that are not found in the training data, such as workloads created by users or circadian rhythm, are considered. These problems may influence the validity of the model, particularly at various times of the day or in certain situations. To deal with temporal characteristics, as a remedy, we can insert time-varying characteristics or apply time-series forecasting algorithms to the characteristics to improve them.

In addition, even though our models make predictions at the task level of CPU utilization, many cloud workloads still have tasks that share resources within a job (e.g., MapReduce jobs). Our models can be enhanced in the future by predicting resource consumption at the job level, and tasks within a job are regarded as parts of a whole.

On the other hand, our supervised learning approach is more economical in terms of data usage and transparency and provides practical suggestions. The inputs of a prospective hybrid methodology can consist of machine learning predictions that a reinforcement learning agent can use to allow the reinforcement learning system to focus on the decision-making rules guided by such predictions. This complex approach can further improve the effectiveness of scheduling, increase the speed of the learning process, and stabilize long-term decision making.