| Journal of Collective Sciences and Sustainability

Received: 18 July 2026; Revised: 05 September 2026; Accepted: 16 September 2026; Published Online: 25 September 2026.

J. Collect. Sci. Sustain., 2026, 2(3), 26409 | Volume 2 Issue 3 (September 2026) | DOI: https://doi.org/10.64189/css.26409

This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)

RescueTails: A MERN Stack-based Web Platform for

Centralized Stray Animal Rescue Coordination

Zehra Shaikh,

∗

Nabiha Shaikh,

Hamsa Naik,

Maryam Lokhandwala and Chaitali Mahajan

Department of Computer Science & Engineering (AIML), M.H. Saboo Siddik College of Engineering, Mumbai, Maharashtra, 400008,

India

*Email: zehra.231757.cs@mhssce.ac.in (Zehra Shaikh)

Abstract

Stray animals in urban environments frequently suffer from injuries, abandonment, disease, and a lack of timely

medical intervention. Despite growing public willingness to assist, the absence of a centralized digital system to

connect concerned citizens with nearby nongovernmental organizations (NGOs), rescue teams, veterinary

services, and foster homes results in fragmented and delayed rescue operations. In this paper, RescueTails, a

full-stack web application built on the MERN (MongoDB, Express.js, React.js, Node.js) technology stack, is

designed to serve as a centralized platform for stray animal rescue coordination. The system enables rapid

online reporting of animals in distress, structured communication between stakeholders, streamlined adoption

workflows, and structured resource allocation. The platform implements role-based access control, a RESTful

API architecture, and a responsive user interface to ensure multidevice accessibility. Evaluation through

functional verification and JMeter-based benchmark load testing provides evidence of stable API performance

under the evaluated load conditions, subsecond API latency, and operational feasibility for centralized animal

rescue coordination. RescueTails contributes to social-impact technology by providing a solution designed to

support scalable animal rescue coordination by addressing an identified gap in urban animal welfare software

infrastructure.

Keywords: Stray Animal Rescue; MERN Stack; Web Application; Animal Welfare; Rescue Coordination; Adoption

Platform; RESTful API; Social Impact Technology.

1. Introduction

The global stray animal population constitutes a notable public health, ecological, and humanitarian challenge.

World Health Organization (WHO) reports estimate hundreds of millions of stray dogs globally, with tens of

millions present in urban environments across developing nations. These animals face frequent traffic injuries,

infectious diseases, malnutrition, abuse, and environmental hazards. Despite increasing public awareness, the

lack of structured, technology-driven coordination mechanisms often hampers the efficiency of rescue and

rehabilitation efforts.

[1,2]

Current animal rescue operations in many urban regions rely heavily on informal

communication channels such as social media posts, phone calls to local shelters, and word-of-mouth referrals

creating challenges for structured reporting and coordination.

[1,2]

This ad-hoc approach introduces operational

inefficiencies: delayed response times, duplication of effort, loss of case context, inability to track rescue status,

and poor coordination between citizens, NGOs, veterinary professionals, and foster caregivers. Delayed access

to veterinary intervention can increase the risk of complications and adverse outcomes for injured animals.

[3]

The proliferation of modern web frameworks presents an opportunity to bridge these systemic communication

gaps.

[4]

Modern full-stack development frameworks, particularly the MERN stack (MongoDB, Express.js, React.js,

Node.js), offer the technical foundation to build responsive, feature-rich web applications that serve as

centralized platforms for social coordination.

[5]

The MERN stack provides distinct architectural advantages,

including a unified JavaScript ecosystem across the development pipeline, non-blocking asynchronous I/O

operations for handling concurrent client requests, flexible document-based data storage, and component-

driven frontend architecture.

[6]

1.1 Related work

Research at the intersection of information technology and animal welfare encompasses isolated reporting tools,

adoption portals, and environmental monitoring systems. Table 1 presents a comparative synthesis of related

works and identified research gaps.

Table 1: Comparative Analysis of Related Literature and Identified Research Gaps

Study

Title

Technology Used

Identified Gaps / Limitations

[1]

PAWrfect Match: A Web-Based Animal

Adoption and Rescue System that uses

Content-Based Filtering Algorithm for

Recommending Potential Adoptees

Web-based platform,

Content-Based Filtering

Primarily focuses on adoption and adopter–animal

matching; does not provide a complete rescue-to-

adoption workflow with clinical and foster

management.

[2]

Pet Adoption System Using Web Technology

Web Technology

Focuses mainly on pet adoption; lacks

comprehensive emergency reporting, rescue

dispatch, and case tracking.

[3]

Pet Adoption System

Web Application

Concentrates on adoption management; limited

support for real-time rescue coordination and

veterinary case management.

[4]

PetHub: A Platform for Pet Adoption

Web Platform

Primarily adoption-oriented; does not integrate

emergency rescue reporting, rescue dispatch, and

complete animal case histories.

[5]

Pet Adoption and Rescue Center

Web-based System

Combines adoption and rescue functions but does

not comprehensively integrate clinical tracking,

foster management, and coordinated incident

workflows.

[6]

FurEver: Pet Adoption Platform

Web Platform

Mainly addresses pet adoption; lacks structured

emergency reporting, rescue prioritization, and

end-to-end case tracking.

[7]

Design of an Automated Animal Rescue and

Adoption Management System

Automated Web-Based

Management System

Addresses rescue and adoption management but

has limited integration of community reporting,

clinical records, foster coordination, and unified

case tracking.

[8]

Web Application for Stray Animal Adoption

and Care Website

Web Application

Supports stray animal adoption and care but lacks

a comprehensive workflow connecting incident

reporting, rescue dispatch, treatment, foster care,

and adoption.

[9]

Design and Development of Android Based

Animal Healthcare Application

Android Application, Mobile

Healthcare

Focuses on animal healthcare; does not cover

community rescue reporting, rescue coordination,

foster management, or adoption workflows.

[10]

A Literature Survey on Smart Emergency

Management Systems for Stray Animals Using

Community Reporting and Rescue

Prioritization

Community Reporting,

Rescue Prioritization, Smart

Emergency Management

Focuses on emergency reporting and

prioritization; does not itself provide an integrated

operational platform covering treatment, foster

care, and adoption.

[11]

Streamlining Rescue Efforts: A Study on the

Impact of Organizational Coordination in

Animal Rescue Operations

Organizational Coordination,

Rescue Operations

Emphasizes coordination among rescue

organizations; does not provide a unified software

workflow for reporting, clinical tracking, foster

management, and adoption.

1.1.1 Synthesis and research gap

Existing animal rescue systems mainly focus on adoption, animal management, and healthcare.

[1-9]

Recent

studies have also explored community-based emergency reporting, rescue prioritization, and organizational

coordination.

[10,11]

1.1.2 Animal rescue and management systems

Existing platforms such as PAWrfect Match, Pet Adoption System, PetHub, FurEver, and other adoption and

rescue systems primarily support animal listing, adoption, rescue management, or care.

[1-8]

These systems

demonstrate the usefulness of digital platforms for animal welfare but generally address specific stages rather

than maintaining a unified case from initial reporting through rescue, treatment, foster care, and adoption. The

animal healthcare application proposed by Shelke et al. further demonstrates the role of digital tools in

supporting animal health management.

[9]

1.1.3 Emergency reporting and coordination

Recent research has expanded beyond adoption toward emergency rescue coordination. Ramani et al.

examined smart emergency management using community reporting and rescue prioritization, highlighting the

importance of structured incident information.

[10]

Islam et al. studied organizational coordination in animal

rescue operations, emphasizing coordination among stakeholders.

[11]

These works support the need for

structured rescue workflows, while RescueTails extends this approach by connecting reporting, rescue

coordination, case tracking, clinical management, and adoption within one platform.

1.1.4 MERN stack applications in social-impact domains

The literature reviewed demonstrates the use of web-based and mobile technologies for animal adoption, rescue

management, healthcare, community reporting, and organizational coordination.

[1-11]

Web-based systems have

been used to support adoption and rescue activities, while mobile applications have been explored for animal

healthcare.

[1-9]

Recent studies have also highlighted community reporting, rescue prioritization, and organizational

coordination as important components of animal rescue operations.

[10,11]

These findings support the need for an

integrated web-based platform that can connect multiple stakeholders and manage the different stages of the

animal rescue process. RescueTails addresses this need through a unified web application integrating incident

reporting, rescue coordination, clinical tracking, foster management, and adoption workflows.

1.1.5 Research gap

Existing literature demonstrates a clear gap in software systems that support the complete stray animal welfare

lifecycle. Available platforms typically handle isolated segments—such as reporting, directory listings, or

adoption cataloging—resulting in fragmented data and ad-ministrative overhead. RescueTails bridges this gap

by providing an integrated web system spanning incident reporting, rescue dispatch, clinical tracking, foster

management, and adoption placement within a unified platform.

1.2 Problem statement

Stray animals in urban environments face recurring risks from vehicle accidents, untreated diseases, and severe

physical distress. The operational rescue landscape is currently hindered by the following structural

challenges:

Fragmented reporting channels: Citizens lack a single, standardized platform to submit incident details,

leading to unstructured posts across social media or delayed phone calls.

[10,11]

Information silos between stakeholders: Key stakeholders—reporters, rescue teams, veterinarians,

foster caregivers, and adopters—operate in isolation without shared dynamic status updates.

[11]

Lack of case tracking and audit trails: Informal rescue requests lack systematic tracking, making it difficult

to verify whether an animal has received care, assigned responsibility, or completed treatment.

[12]

Disjointed adoption management: Transitioning rescued animals to permanent homes involves

screening and health verifications that are often handled through manual, paper-based, or uncoordinated

methods.

[1-8,13-15]

1.3 Objectives and contributions

To address these challenges, RescueTails provides a centralized web system for stray animal rescue

management. The primary contributions of this paper are as follows:

•

Development of a centralized MERN-based web platform connecting citizens, NGOs, rescue personnel,

veterinarians, and foster caregivers via dynamic role-based dashboards.

•

Implementation of a structured rescue workflow featuring location-assisted incident submission, status

tracking, and verifiable case histories.

•

Design of an integrated adoption module bridging medical clearance, applicant screening, and adoption

application tracking.

•

Quantitative evaluation of system performance through functional verification and benchmark load testing

using Apache JMeter to establish latency, throughput, and operational stability under evaluated test loads.

2. Methods/Operational architecture

2.1 Proposed rescue coordination framework

The proposed RescueTails framework formalizes the stray animal welfare lifecycle into a multistage workflow

designed to maintain transparency and case context across operational transitions. As illustrated in Fig. 1, the

lifecycle encompasses seven key phases:

Incident reporting: Citizens submit situational report details, including geographical coordinates, media

uploads, and initial urgency levels.

Radius screening & ngo matching: The backend queries active registered NGOs within an adaptive

geographical radius (MaxRadius).

Dispatch & queue assignment: Nearby rescue personnel view and accept incoming incidents via an

interactive dispatch queue.

On-site rescue & triage: Rescuers update case status to Rescue and record preliminary triage findings.

Veterinary & medical treatment: Medical personnel record diagnostic logs, vaccination details, and medical

updates.

Foster Placement: Animals cleared for temporary care are assigned to verified foster caregivers.

Adoption Facilitation & Closure: Formally cleared animals enter the public adoption gallery, concluding

upon applicant vetting and case archival.

Fig. 1: End-to-end stray animal rescue coordination lifecycle and state transitions within the RescueTails system.

2.2 System overview

RescueTails is structured as a three-tier web application following the Model‒View‒Controller (MVC)

architectural pattern, built using the MERN technology stack. The system comprises four primary operational

components: User Management, Rescue Request Management, Adoption Management, and the NGO/Shelter

Dashboard. The high-level system architecture is shown in Fig. 2.

Fig. 2: High-level system architecture of RescueTails showing the three-tier MERN implementation.

Stakeholders

Citizens

NGOs

Rescue

Teams

Foster

Homes

HTTP/HTTPS

Presentation Layer — React.js

HTML5/CSS3

JavaScript (ES6+)

Rescue Form; Dashboard; Animal Gallery;

Adoption

REST API

Application Layer —

Node.js/Express.js

Dev

Tools

RESTful API

JWT Authentication

Middleware

Git/GitHu

Postman

Auth API; Rescue API; Animal API;

Adoption API

Mongoose ODM

Data Layer — MongoDB/Mongoose

Document Store

Mongoose ODM

Users; Rescue Requests; Animals; Adoptions;

NGOs

2.3 Technology stack justification

The MERN stack was selected on the basis of architectural and performance considerations outlined in Table 2.

Table 2: Technology stack components and architectural rationale

Layer

Technology

Justification

Frontend

React.js

Component-based architecture enables modular UI development; dynamic state

management ensures efficient updates.

Backend

Node.js, Ex-press.js

Nonblocking I/O event loop handles concurrent API requests efficiently; unified JavaScript

syntax across client and server.

Database

MongoDB, Mongoose

Document-oriented structure easily adapts to dynamic rescue data; Mongoose ODM

enforces schema constraints and relationships.

API

RESTful API

Stateless HTTP interface supports modular deployment, simplified routing, and potential

external client integration.

2.4 Data model design

The MongoDB database contains five primary document collections designed to support incident lifecycles, entity

tracking, and organizational oversight:

•

User Collection: Stores user credentials, contact parameters, profile metadata, and security role assignments

(Citizen, NGO, Rescue Team, Foster Home, Admin).

•

Rescue Requests Collection: Store incident entries, including coordinates, urgency ratings, attached image

URLs, reporter references, assigned responders, and status history logs.

•

Animal collection: Records for rescued animals, including species, medical history, vaccination records,

behavioral notes, and current location, were maintained.

•

Adoptions Collection: Record adoption applications, applicant background information, approval statuses,

and follow-up notes.

•

NGOs/Shelters Collection: Store organizational profiles, operating service areas, housing capacity

parameters, and contact references.

Fig. 3 displays the entity–relationship (ER) schema, which illustrates the core collections and references. While

the full database schema is provisioned to support end-to-end state transitions across the rescue lifecycle,

current prototype frontend interactions focus on incident submission, spatial queue retrieval, and adoptable

animal cataloging, with administrative and clinical fields populated via backend API controllers.

Fig. 3: Entity–relationship schema displaying core collections and relationships within MongoDB.

2.5 API design and execution workflow

The backend API handles application functionality via structured HTTP request endpoints:

•

/api/auth – Handles user registration, login, token generation, and credential verification.

•

/api/rescue

– Manage incident submission, status modification, geographic querying, and queue

retrieval.

•

/api/animals – Supports reading and updating animal records, clinical updates, and adoptable catalog

listings.

•

/api/adoptions – Processes adoption application submissions, review status transitions, and approval

workflows.

1:1

1:N

RESCUE REQUESTS

assignedNGO (Ref: NGOs)

1:N

1:1

1:N

USERS

ANIMALS

ADOPTIONS

animalID (Ref: Animals)

NGOS/SHELTERS

•

/api/ngos – Handles NGO organizational profile updates, service radius settings, and shelter capacity

tracking.

The step-by-step API processing pipeline for client requests is shown in Fig. 4.

Fig. 4. RESTful API request–response processing pipeline in RescueTails.

Algorithm 1. Proposed Stray Animal Rescue Incident Dispatch & NGO Coordination Workflow

Require: Incident Report 𝑅 = {loc, urgency, media, reporterID}, Set of Registered NGOs 𝑁

Ensure: Incident Assignment 𝐴 and Status State 𝑆

1: Initialize: 𝑆 ← “reported”

2: 𝑅.timestamp ← GetCurrentTimeStamp()

3: Save 𝑅 to MongoDB with initial status 𝑆

4: 𝑁

active

← {𝑛

∈

𝑁

𝑛.status = “active” ∧ Distance(𝑛.loc, 𝑅.loc) ≤ MaxRadius}

5: if 𝑁

active

∅

then

6: Expand search radius: MaxRadius ← MaxRadius × 1.5

7: 𝑁

active

← {𝑛

∈

𝑁

Distance(𝑛.loc, 𝑅.loc) ≤ MaxRadius}

8: end if

9: for each 𝑛𝑔𝑜 ∈ 𝑁

active

10: TriggerNotification(𝑛𝑔𝑜.id, 𝑅.id, 𝑅.urgency)

11: end for

12: while 𝑆 = “reported” and ElapsedTime() < 𝑇

threshold

13: if 𝑛𝑔𝑜

𝑗

accepts 𝑅.id then

14: 𝐴 ← {incidentID: 𝑅.id, assignedNGO: 𝑛𝑔𝑜

𝑗

.id}

15: 𝑆 ← “acknowledged”

16: UpdateDatabase(𝑅.id, 𝑆, 𝑛𝑔𝑜

𝑗

.id)

17: end if

18: end while

19: if 𝑆 = “reported” then

20: EscalateToSystemAdmin(𝑅.id)

21: end if

22: return 𝐴, 𝑆

*Implementation Note: The workflow described in Section 2.1 and Algorithm 1 define the intended operational

architecture. The current software prototype implements client incident reporting, spatial coordinate filtering, and

interactive dispatch queue visualization. Automated push-notification dispatch, dynamic search radius expansion (×1.5),

and administrative escalation represent the proposed deployment-stage extensions.

2.6 Proposed Layerwise GUI Architecture and Stakeholder Workflows

The front-end presentation layer uses a modular layout built with React.js components. Interface access and

control options are tailored dynamically on the basis of the verified role claims of logged-in users:

1. Citizen Interface (Implemented): Interface options allow citizens to submit incident reports, attach

geolocation details, track case status transitions, and submit adoption inquiries.

2. NGO/Rescue Team Dashboard (Implemented): Provides interactive regional incident queue

6. Mongoose ODM MongoDB Database Operation

5. Controller Logic (Business Rules & Radius Filtering)

3. JWT Middleware (Authentication & Role Check)

1. Client Request (React.js UI/Axios)

7. Formatted JSON HTTP Response

(Status Codes 200/201/400/500)

visualization, manually initiated status modification, case filtering, and incident management.

3. Veterinary Panel (Proposed): Focuses on medical record maintenance, enabling verified clinicians to log

diagnostic reports, update vaccination flags, and record medical clearances.

4. Foster Caregiver Panel (Proposed): Designed to allow caregivers to log hosting capacity, monitor assigned

animals, and provide status updates during temporary care.

5. System Administrator Interface (Proposed): Intended for platform-wide administrative oversight, user

account verification, global role management, and system auditing.

2.7 Security Mechanisms

The application architecture includes standard web security controls:

•

Authentication & Authorization: Implements JSON Web Tokens (JWT) for stateless session verification

across restricted API endpoints.

•

Password Hashing: User passwords are hashed using bcrypt prior to persistence in the database.

•

Input Validation:

Request validation middleware (express-validator) sanitizes input

parameters to

mitigate common injection risks and invalid payload entries.

•

HTTP Security Headers & Cross-Origin Policies: Use Helmet.js middleware to count security-related HTTP

response headers and apply CORS middleware to regulate cross-origin API requests.

3. Results and Evaluation

3.1 Experimental Setup and Metric Definitions

Evaluation was conducted within a controlled benchmarking environment hosted on an iso-lated cloud instance

(8 vCPU, 16 GB RAM, Ubuntu 22.04 LTS, Node.js v18.16.0, MongoDB 6.0). Load testing was executed using

Apache JMeter 5.5 to evaluate API latency and system throughput under simulated concurrent user requests

across the active prototype endpoints (POST

/api/auth/login,

POST

/api/rescue/report,

GET

/api/rescue/nearby,

and GET

/api/animals

adoptable catalog) over a 10-minute sampling window

with a 30-second ramp-up.

The performance evaluation focused on four standard metrics:

•

Average API latency: mean duration (ms) from request departure to full-client HTTP response receipt.

•

System Throughput: Total successfully processed HTTP transactions per second (requests per second,

RPS).

•

Database Query Latency: Average execution duration (ms) recorded for Mon-goose/MongoDB query

processing.

•

Error Rate: Percentage of HTTP requests yielding 4xx or 5xx status responses relative to total generated

requests.

3.2 Quantitative Benchmark Evaluation

Table 3 summarizes benchmark performance measurements across escalating levels of simulated concurrent user

traffic.

Table 3: Apache JMeter Load Test Results across Concurrent User Load Levels

Concurrent Users

Avg API Latency (ms)

Throughput (RPS)

DB Query Latency (ms)

Error Rate (%)

310

0.00%

100

560

0.00%

250

135

870

0.01%

500

265

1110

0.12%

1000

495

1220

1.05%

3.3 Comparison of Functional Capabilities

Table 4 compares the operational capabilities supported by RescueTails with those of traditional informal

channels and directory-based pet portals.

Table 4: Functional Capability Matrix: Traditional Channels vs. RescueTail Platform

Functional Dimension

Informal/Directory Platforms

RescueTails Platform

Incident Reporting

Unstructured (Social Media/Calls)

Structured Web Form with Geo-Coordinates

Location-Based NGO Search

Manual Search/Directory Lookup

Spatial Coordinates Filtering

Case Lifecycle Tracking

Absent

End-to-End Status Tracking

Medical History Logging

Disjointed/Paper-Based

Integrated Medical Database

Foster Care Management

Manual Verification

Role-Based Foster Dashboards

Adoption Application Flow

External Inquiries

Built-in Application Screening

Role-Based Access Control

Minimal/None

Granular Role Claims (5 Roles)

System Audit Trail

Absent

Database Case History Logs

3.4 Discussion and statistical interpretation

As the simulated user concurrency increased from 50 to 1000 virtual users, the average API response latency

expanded from 42 ms to 495 ms, whereas the overall throughput scaled from 310 RPS to 1220 RPS. The database

query execution latency correspondingly increased from 12 ms to 76 ms. The progressive increase in latency

under higher user loads reflects the expected database connection pool queuing and Node.js event-loop

processing overhead under concurrent request bursts. Continued throughput growth up to 1000 virtual users

indicates that the server architecture efficiently handled substantial traffic volumes, with a minor 1.05% error

rate observed at peak concurrency, signaling the initial onset of system resource contention. As highlighted in

Table 4, consolidating incident reporting, status tracking, and adoption pipelines within a single platform

addresses data fragmentation and improves operational visibility across animal welfare stakeholders.

4. Testing, quality assurance and future scope

4.1 Testing and quality assurance

To ensure platform stability, data integrity, and interface responsiveness across devices, a multitiered quality

assurance protocol was applied during prototype development:

•

Unit and Component Verification: Core JavaScript utility functions and React component rendering states

were verified using the Jest and React Testing Library to prevent regression bugs during interface updates.

•

API Endpoint and Integration Testing:

Backend controller routes

(/api/auth,

/api/rescue,/api/animals)

were systematically tested using automated Postman test suites to validate

payload structures, HTTP status codes, and JWT access control authorship.

•

Cross-Device Usability Verification: Responsive layouts were validated across multiple screen viewports

(mobile, tablet, desktop) using Chrome DevTools and physical device testing to ensure seamless accessibility

for field rescuers and citizens.

•

Input Sanitization and Security Auditing: The request payload boundaries were verified using express-

validator rules to mitigate invalid entries, malformed coordinates, and unauthenticated state modifications.

4.2 Future Scope

Future enhancements to the RescueTails platform will focus on scaling field operations and automation:

•

Offline-First Progressive Web App (PWA): Incorporating service workers and IndexedDB client-side

caching to support offline incident reporting in low-connectivity urban zones.

•

Automated Push Dispatch Engine: Integrating Firebase Cloud Messaging (FCM) and Web Push APIs to

execute automated, radius-based emergency notification alerts.

•

AI-Assisted Severity Triage: Deploying lightweight Convolutional Neural Network (CNN) vision models to

automatically analyze uploaded incident photos and prioritize critical injury cases.

5. Conclusion

In this paper, RescueTails, a full-stack MERN platform designed for centralized stray animal rescue

coordination, case tracking, and adoption management, is presented. By integrating citizen reporting, location-

assisted NGO coordination, medical tracking, and adoption management into a single web application,

RescueTails addresses operational fragmentation in urban animal rescue workflows. Benchmark evaluation

provides evidence of subsecond API response times and low error rates across the evaluated prototype

endpoints under simulated multiuser test loads. Future development will focus on incorporating progressive

web application (PWA) offline capabilities, real-time push notification services, and automated computer vision

models to assist in incident severity classification.

Acknowledgement

None

CRediT Author Contribution Statement

Zehra Shaikh: Conceptualization, Investigation, Methodology, Software, Writing – Original draft. Nabiha

Shaikh: Data Curation, Software, Validation, Visualization. Hamsa Naik: Formal Analysis, Methodology,

Software, Writing – Review & editing. Maryam Lokhandwala: Investigation, Project Administration,

Resources, Writing – Review & editing. Chaitali Mahajan: Methodology, Resources, Supervision, Validation,

Writing – Review & editing. All authors have read and approved the final version of the manuscript for publication

and agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity

of any part of the work are appropriately investigated and resolved.

Funding Declaration

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-

profit sectors.

Data Availability Statement

The datasets generated during the current study, along with the JMeter benchmark configurations, are available

from the corresponding author upon reasonable request.

Conflict of Interest

There is no conflict of interest.

Artificial Intelligence (AI) Use Disclosure

The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,

grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical

content, experimental implementation, results, and interpretations were independently developed and verified

by the authors.

Supporting Information

Not applicable.

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