INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026
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CoreWatch AI-Driven CPU/GPU Performance Analyzer
Karan N, Nimay N, Jeevanandan, Puneeth MS, Divyaprabha KN
Department of Computer Science and Engineering PES University Bangalore, Karnataka, India
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.150100088
Received: 30 January 2026; Accepted: 04 February 2026; Published: 14 February 2026
ABSTRACT
CoreWatch is a lightweight, cross-platform monitoring system designed to provide real-time insights into CPU,
GPU, memory, disk, and network performance. It uses Flask with Socket IO for low-latency metric streaming
and integrates psutil and NVIDIA-SMI for accurate data collection. The dashboard employs Chart.js for
smooth, interactive visualizations. An LSTM-based prediction module enhances monitoring by forecasting
short-term CPU and GPU trends. CoreWatch maintains under 3% system overhead, ensuring efficient
performance without additional load. Testing confirms stable responsiveness across Windows, Linux, and
macOS. The system focuses on accessibility, minimal setup, and clear visual analysis. Future extensions include
historical logging, improved AI models, and remote monitoring capabilities.
Keywords: System monitoring, CPU–GPU performance analysis, LSTM prediction, anomaly detection,
machine learning, real-time telemetry, resource optimization.
INTRODUCTION
Modern computing environments increasingly depend on effective resource monitoring, as system workloads
grow more demanding across personal systems, professional applications, and cloud-based infrastructures.
Gaining real-time visibility into CPU, GPU, memory, disk, and network performance has become crucial for
maintaining system stability. Prior research highlights the importance of continuous monitoring and predictive
analysis across heterogeneous networks [1], cloud systems [2], and real-time data applications [3], reinforcing
the need for lightweight solutions that can adapt to changing workloads.
Conventional monitoring tools such as Task Manager, Prometheus, or Grafana provide strong capabilities but
often require heavy configuration, external databases, or persistent background services. These tools generally
emphasize current readings rather than offering predictive insight. With recent progress in AI-driven
optimization including CPU resource management [4], GPU behavior analysis [5], and time-series forecasting
models [6] there is growing interest in platforms that combine intuitive visualization with intelligent trend
prediction.
CoreWatch is designed to address these needs by offering a cross-platform, browser-based dashboard that
streams real-time system metrics along with short-term workload forecasts. The backend captures accurate
CPU and GPU data using psutil and NVIDIA-SMI, while event-driven communication through Flask-SocketIO
ensures smooth and continuous metric updates. The frontend, powered by Chart.js, presents these trends
clearly, enabling both technical and non-technical users to interpret system behavior with ease. Additionally,
CoreWatch integrates an LSTM-based prediction module that estimates upcoming CPU and GPU utilization,
supporting proactive performance awareness similar to modern workload-forecasting methods used in
heterogeneous computing environments to minimize performance failures and resource imbalance [7][10][11].
To further improve usability, CoreWatch includes an integrated prediction layer that highlights short-term
variations in CPU and GPU behavior. This capability provides early visibility into rising workloads and aligns
with current developments in intelligent system management and edge-based resource forecasting. By
combining responsive visualization with predictive analytics, the system delivers an intuitive pathway for users
to understand and manage their device performance more effectively.
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In summary, CoreWatch brings together modern concepts in system monitoring by providing a lightweight,
cross-platform dashboard capable of real-time CPU, GPU, memory, disk, and network analysis. Unlike
traditional tools that require complex setup or focus only on current statistics, CoreWatch leverages
WebSocket-based updates and accurate telemetry collection through psutil and NVIDIA-SMI. With its clean
visualization layer and integrated LSTM prediction model, the system offers a practical, accessible, and
intelligent solution for tracking system performance and anticipating workload spikes across diverse computing
environments.
LITERATURE SURVEY
System monitoring technologies have undergone significant evolution, shifting from basic resource reporting
tools to more intelligent, adaptive, and analytics-driven frameworks. Early monitoring utilities including
Windows Task Manager and Linux top/htop provided essential visibility into CPU and memory usage but
lacked cross-platform consistency, extensibility, and predictive intelligence. They remain limited to real-time
snapshots without offering trend analysis or forecasting capabilities, which are increasingly required in modern,
heterogeneous computing environments.
Recent research emphasizes the need for scalable, real-time monitoring solutions capable of handling diverse
architectures and dynamic workloads. Aldea et al. [1] proposed an integrated monitoring architecture for
heterogeneous networks, highlighting the importance of unified frameworks but also revealing the complexity
and overhead associated with such systems. A broad systematic review by da Costa et al. [3] further confirmed
that most real-time monitoring solutions rely heavily on centralized data pipelines, making them unsuitable for
lightweight or personal systems. These findings underline the demand for monitoring tools that offer both
flexibility and low resource footprints.
In parallel, the rise of cloud and HPC workloads has accelerated research on predictive analytics for resource
utilization. Yildirim [2] and Patel & Bedi [7] demonstrated that machine learning models—including GRU,
LSTM, and attention-based architectures—significantly enhance workload forecasting accuracy in cloud
environments. Similarly, Nashold and Krishnan [6] explored LSTM and SARIMA models for CPU prediction
in cluster systems, reinforcing the value of time-series modeling for proactive performance management. These
studies collectively support the integration of ML-based forecasting in modern monitoring tools—an approach
CoreWatch adopts for CPU prediction.
GPU-aware monitoring has also expanded, especially with the rise of AI and edge computing. Woo [5]
investigated GPU sharing mechanisms for smart city applications, revealing the need for fine-grained GPU
telemetry in distributed systems. Weakley et al. [8] provided insights into GPU characterization across HPC
workloads, showing how GPU metrics correlate strongly with performance bottlenecks. These observations
justify CoreWatch’s GPU monitoring and visualization features via NVIDIA-SMI.
Researchers have also explored visualization and interaction methods for performance analytics. Shilpika et al.
[9] introduced a visual-analytics approach for hardware monitoring using streaming functional data,
emphasizing clarity and interpretability. Additionally, Huang et al. [10] showed how edge-based monitoring
can improve responsiveness and local decision-making, reinforcing the need for lightweight, on-device
telemetry systems similar to CoreWatch’s architecture. Wang and Xing [4] further highlighted the role of AI-
driven CPU resource management in modern operating systems, validating CoreWatch’s choice to include
prediction capabilities.
Despite these advancements, the literature reveals clear gaps in accessibility, low-overhead design, and user-
centered interaction. Most existing tools require complex setup pipelines, high processing overhead, or lack
predictive features. Few provide conversational interfaces or web-based, cross-platform dashboards that remain
lightweight while supporting intelligent analytics. CoreWatch addresses these shortcomings by integrating real-
time telemetry, AI-driven predictions, and a chatbot-driven interaction layer into a unified, resource-efficient
system.
Overall, the surveyed literature collectively underscores the need for a monitoring framework that combines
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026
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real-time data acquisition, predictive modeling, intuitive visualization, and broad platform compatibility.
CoreWatch emerges directly from these research insights, delivering an accessible, modular, and intelligent
monitoring solution that bridges the gap between heavy enterprise frameworks and simple system utilities.
Proposed Approach
This section presents the design and operational methodology of the CoreWatch AI-Powered Monitoring
Dashboard. The system architecture emphasizes intelligent, lightweight monitoring through modular
integration of data acquisition, real-time communication, predictive analytics, and user interaction. First, the
metric collection model is described. Second, the real-time communication and data transmission model are
detailed. Third, the visualization, alerting, and AI-based prediction modules are presented. Finally, the
optimization, chatbot integration, and overall workflow are discussed.
Metric Collection Model
The CoreWatch framework begins by acquiring live system metrics from the host machine using psutil and
NVIDIA System Management Interface (nvidia-smi) libraries. Each monitored parameter—CPU, GPU,
memory, disk, network, temperature, and battery, is represented as an independent metric
within the global
set.
Each metric
is sampled periodically at an interval (default = 2 seconds), ensuring real-time responsiveness
while maintaining minimal computational overhead. The data collection engine executes asynchronously using
Python threads, allowing smooth performance even under heavy system load.
GPU utilization, temperature, and memory usage are extracted via nvidia-smi in structured JSON format, while
CPU and system-level data are obtained using psutil.
All collected metrics are normalized into a unified JSON schema for consistent communication with the
backend streaming layer.
Real-Time Communication and Data Transmission Model
CoreWatch uses Flask as the central backend engine, enhanced with Flask-SocketIO to enable real-time,
bidirectional communication between the server and the browser-based dashboard. Unlike traditional REST
polling where the client repeatedly requests fresh data CoreWatch adopts a continuous, event-driven
WebSocket pipeline. This significantly reduces redundant traffic, lowers latency, and ensures smoother metric
streaming under varying workloads.
Let
denote the real-time reading of metric
at time . The complete data stream is represented as:
This stream is continuously broadcast to all connected dashboard clients through SocketIO channels. The
communication follows a publisher–subscriber architecture: the backend acts as the publisher, while the
frontend clients subscribe to updates. This approach significantly reduces redundant HTTP requests, ensuring
near-instant updates of all visual and analytical components while maintaining low network overhead.
Visualization and Alert Generation Model
The CoreWatch frontend is built using HTML, CSS, and Chart.js, offering an interactive and visually refined
dashboard with separate pages for CPU, GPU, memory, network, and disk metrics. Chart.js dynamically
redraws graphs upon receiving new SocketIO events, ensuring uninterrupted visualization without page
refreshes.
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The alerting mechanism continuously compares current readings
with predefined threshold values
. An
alert event
is triggered as:
if
otherwise
When triggered, alerts are logged and displayed with visual cues (color indicators, timestamps, and severity
levels) on the dashboard.
The prediction model evaluates short sequences of recent CPU and GPU readings (default sequence length L
= 10) and estimates the next expected utilization values
and
. If the trained LSTM
model or its corresponding scaler is unavailable, CoreWatch automatically falls back to a lightweight linear
estimation method to ensure uninterrupted predictive output. These forecasts enable early detection of potential
performance spikes, allowing users to take proactive actions before resource thresholds are exceeded.
Optimization and Performance Considerations
CoreWatch is designed to remain efficient across a wide range of systems, from low-power laptops to high-
performance workstations. Its architecture avoids heavy databases or logging services, instead using
lightweight, in-memory buffers that refresh periodically. This reduces disk I/O and prevents unnecessary
storage overhead.
Furthermore, by using Flask-SocketIO with asynchronous background threads, the system maintains real-time
responsiveness with an average CPU overhead consistently below 3%. The sampling interval is adaptive,
balancing responsiveness with energy efficiency. CoreWatch also ensures that predictive inference is executed
only when sufficient historical data is available, reducing computational load associated with machine learning
processes.
These design optimizations collectively enable sustained performance even during long-duration monitoring
sessions, making CoreWatch suitable for continuous use in personal, academic, and professional environments.
Operational Workflow
The complete operational workflow of the CoreWatch AI-Powered Monitoring Dashboard integrates metric
acquisition, real-time communication, visualization, and prediction into a unified loop. The system begins with
the initialization of the Flask backend and WebSocket communication channels, followed by continuous data
acquisition and live metric broadcasting. Each cycle of operation ensures seamless updates and proactive
performance awareness across all components using the following steps.
1. Start
2. Initialize Flask backend and establish WebSocket channels using Flask-SocketIO.
3. Collect real-time system metrics
through psutil and NVIDIA-SMI APIs.
4. Normalize and format the collected data into a unified JSON schema.
5. Stream metric data to connected dashboard clients via SocketIO.
6. Visualize CPU, GPU, and other system parameters dynamically using Chart.js components.
7. Compare each metric
with its corresponding threshold
; if
, trigger alert event
.
8. Execute prediction module to estimate future CPU and GPU utilization using the LSTM model or fallback
predictor.
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9. Log alert events, predictions, and system performance summaries for analysis.
10. Repeat monitoring and update cycle at defined interval .
11. Stop
This integrated workflow ensures low-latency, high-efficiency monitoring suitable for personal, educational,
and professional environments.
RESULTS AND DISCUSSION
This section presents the implementation details and performance analysis of the CoreWatch AI-Powered
Monitoring Dashboard, comparing it with traditional system monitoring tools. The framework was
implemented using Python Flask, Flask-SocketIO, and psutil for backend data acquisition, with Chart.js for
frontend visualization. The system was tested across Windows 11, Ubuntu 22.04, and macOS Ventura to
validate cross-platform compatibility.
Performance metrics considered for evaluation include CPU responsiveness, GPU monitoring accuracy, system
overhead, and real-time predictive efficiency. The figures in this section represent actual dashboard snapshots
and measured outcomes under varying system loads.
CPU Utilization and Responsiveness
CoreWatch efficiently captures and visualizes CPU utilization metrics in real time using the psutil library. Per-
core utilization, average system load, and process counts are collected asynchronously every 2 seconds and
transmitted via SocketIO to the frontend dashboard.
The asynchronous data collection process runs in independent background threads, ensuring that even during
heavy system activity, the monitoring engine remains non-blocking and stable. Each collected data point is
instantly normalized into a structured JSON format and transmitted to the frontend dashboard via Flask-
SocketIO channels, eliminating the need for frequent HTTP polling requests. This event-driven communication
design enables smooth, uninterrupted data flow between backend and frontend components.
Figure 1 illustrates the CPU Dashboard, showing real-time CPU usage graphs, average utilization, and process
count updates. The chart dynamically refreshes without manual reload, maintaining a latency of less than 150
milliseconds between backend sampling and frontend visualization.
Figure 1. Real-time CPU Utilization Dashboard.
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Experimental testing demonstrated that Corewatch’s CPU monitoring module operates with less than 3%
CPU overhead, significantly lower than traditional monitoring tools like Windows Task Manager or Netdata,
which can consume between 5–8% CPU resources. This lightweight performance highlights the framework’s
suitability for continuous monitoring.
In addition to real-time display, CoreWatch’s CPU module also maintains short-term historical data buffers
that allow users to visually analyze workload fluctuations and temporal patterns over time. This feature
provides valuable insight into system behavior without requiring persistent database storage. Overall, the
combination of low overhead, high responsiveness, and accurate visualization establishes CoreWatch as a
highly effective solution for continuous system performance monitoring.
GPU Monitoring and Temperature Tracking
GPU monitoring in CoreWatch is implemented using the NVIDIA System Management Interface (nvidia-
smi), which provides direct access to GPU hardware statistics through a structured JSON output. The
monitoring module retrieves key parameters such as GPU utilization percentage, core temperature, and power
consumption, ensuring comprehensive visibility into graphics hardware performance.
Figure 2 presents the GPU Monitoring Dashboard, where the real-time GPU usage and thermal readings are
plotted using Chart.js. The readings are refreshed at the same interval (2 seconds) to ensure synchronization
with CPU data.
Real-time GPU Utilization and Temperature Dashboard.
The GPU monitoring module introduces minimal additional load, with average execution latency below 0.5
seconds per sampling cycle. Comparative analysis shows that Corewatch’s data accuracy aligns closely with
NVIDIA’s official desktop monitoring tools, confirming reliability. Additionally, the GPU module supports
predictive workload estimation using short-term historical sequences processed by the integrated LSTM-based
model, enabling CoreWatch to forecast upcoming spikes in GPU utilization during graphically intensive tasks
such as gaming, rendering, or AI model inference. This predictive layer enhances the user’s ability to manage
workloads proactively, preventing overheating and maintaining stable performance.
Memory/Disk, and Network Performance
In addition to CPU and GPU tracking, CoreWatch provides dedicated dashboards for monitoring memory, disk,
and network performance, ensuring a complete overview of system health in real time.
The memory module continuously measures and displays parameters such as total memory, used memory, and
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available memory, allowing users to easily identify potential memory bottlenecks or abnormal consumption
patterns. Similarly, the disk activity dashboard provides live insights into overall disk utilization and
performance trends, helping users access system load during intensive operations such as application launches
or background data processing.
Figure 3 illustrates the integrated Memory and Disk Utilization Dashboard, where each metric is represented
through interactive and visually distinct charts designed for both clarity and accessibility.
Figure 3. Memory and Disk Utilization Dashboard.
The network monitoring module in CoreWatch provides a clear visualization of real-time incoming and
outgoing bandwidth, enabling users to detect abnormal data transfer rates, excessive background activity, or
potential network bottlenecks with ease. By continuously updating every two seconds, the module ensures that
network usage patterns are accurately reflected, helping users maintain optimal connectivity and system
performance.
Figure 4 illustrates the Real-Time Network Activity Dashboard, showcasing dynamic bandwidth graphs that
simplify the interpretation of network behavior for both technical and non-technical users.
Figure 4. Real-time Network Activity Dashboard.
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Alert Mechanism and Intelligent Chatbot Interface
The CoreWatch dashboard integrates a threshold-based alert system and an AI-powered chatbot interface,
creating a unified, interactive, and user-centric monitoring experience. The alert module continuously tracks
system parameters against configurable thresholds, automatically generating color-coded warning cards when
limits are exceeded (e.g., CPU > 90%, GPU > 95%, temperature > 80 °C). These alerts appear in real time with
severity indicators and timestamps, enabling users to respond promptly to performance issues. Complementing
this, the AI chatbot allows users to query system performance through simple natural-language commands such
as “Show GPU usage” or “What is my CPU temperature?”, interpreting user intent and providing real-time
metrics with contextual recommendations. Together, the alert system and chatbot enhance accessibility and
situational awareness for both technical and non-technical users, bridging the gap between traditional
dashboards and conversational system management.
Figures 5 and 6 illustrate the alert notification system and AI-based chatbot interface, respectively,
demonstrating how CoreWatch simplifies system insights through an intelligent, visually intuitive design.
Figure 5. Threshold-Based Alert Notification System
Figure 6. AI-powered chatbot
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Together, the alert mechanism and chatbot interface transform CoreWatch from a conventional monitoring tool
into an interactive, intelligent assistant. This integrated design ensures rapid response, improved usability, and
a more intuitive monitoring experience overall.
Comparative System Overhead Analysis
To quantify efficiency, Corewatch’s performance was benchmarked against Grafana, Task Manager, and
Netdata using identical test conditions on a 12th Gen Intel i5 system. Results show that Corewatch achieved
up to 60% lower resource overhead while maintaining comparable data update frequency and visualization
quality.
Table 1. Comparative Performance Analysis of System Monitoring Tools
This comparative evaluation confirms that CoreWatch achieves an optimal balance between performance,
accessibility, and system efficiency. Its low overhead, cross-platform adaptability, and minimal setup
requirements make it an ideal solution for personal, academic, and lightweight production environments.
CONCLUSION
The study identified that many existing system monitoring solutions, despite being feature-rich, are resource-
heavy, difficult to configure, and often restricted to specific platforms making them unsuitable for everyday
users, developers, and small-scale deployments. To overcome these limitations, this paper introduced
CoreWatch, an AI-enabled, lightweight, and cross-platform monitoring framework designed to deliver real-
time performance insights through a modular and efficient architecture. Leveraging Flask and Flask-SocketIO
for low-latency data streaming, psutil and NVIDIA-SMI for accurate metric acquisition, and Chart.js for
dynamic visualization, CoreWatch provides continuous monitoring with minimal resource consumption.
Experimental testing across Windows, macOS, and Linux confirmed that CoreWatch sustains an average CPU
overhead below 3%, outperforming tools such as Grafana and Netdata in terms of responsiveness and efficiency
while delivering comparable visualization quality. The system reliably monitors CPU, GPU, memory, disk,
and network performance, and incorporates intelligent features such as threshold-based alerting and a natural-
language chatbot interface to enhance accessibility for both technical and non-technical users.
In conclusion, CoreWatch emerges as a practical, scalable, and intelligent alternative to conventional
monitoring platforms by combining professional-grade insight with lightweight deployment and simplicity of
use. Future work aims to extend the framework with advanced machine learning models for deeper predictive
analytics, cross-device remote monitoring capabilities, and database integration to support long-term historical
trend analysis. These enhancements will help transform CoreWatch into a fully adaptive performance
management ecosystem capable of proactive decision-making and smarter system optimization.
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