The rapid growth of connected devices and associated data has increased pressure on cloud-edge infrastructures, where cloud servers (CSs) saturate as devices scale. Most existing studies minimize execution time but overlook system scalability, while many simulation tools are unable to model how performance bottlenecks evolve as systems grow. In this work, we propose a server capacity-driven methodology using the VisualSim simulator that improves scalability by reducing the utilization of CSs and edge servers (ESs), enabling additional devices to be supported without infrastructure upgrades. The methodology progresses by saturating the CSs with computations to evaluate their maximum capacity, offloading computations to ESs to assess their limits, and distributing computations across CSs and ESs to achieve additional headroom for computations. Scalability is then measured by integrating new device data into the system for processing. It is evaluated using three heterogeneous systems: System-1, System-2, and System-3 with 20, 30, and 40 devices, and compared against Greedy Earliest Finish Time (EFT), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The experimental results show that our method enables System-1, System-2, and System-3 to process 66.67%, 62.50%, and 60% more device data, respectively, while reducing execution time by up to 48.40% and energy consumption by up to 12.50% for System-3. The proposed method consistently outperforms Greedy EFT, GA, and PSO, achieving up to 40% faster execution, 16% energy savings, and 7% lower CS utilization. This methodology offers a generalizable approach for analyzing the scalability of complex systems used in defense, healthcare, and other fields.
LawtonG.Machine-to-machine technology gears up for growth. Computer2004; 37: 12–15.
2.
AbdelkaderGElgazzarKKhamisA.Connected vehicles: technology review, state of the art, challenges and opportunities. Sensors (Basel)2021; 21: 7712.
3.
ZhongMYangYYaoH, et al. 5G and IoT: towards a new era of communications and measurements. IEEE Instrum Meas Mag2019; 22: 18–26.
4.
SettembreM.Towards a hyper-connected world. In: 2012 15th international telecommunications network strategy and planning symposium (NETWORKS), Rome, 15–18 October 2012, pp. 1–5. New York: IEEE.
5.
ShiWCaoJZhangQ, et al. Edge computing: vision and challenges. IEEE Internet Things J2016; 3: 637–646.
6.
JadejaYModiK.Cloud computing: concepts, architecture and challenges. In: 2012 international conference on computing, electronics and electrical technologies (ICCEET), Nagercoil, India, 21–22 March 2012, pp. 877–880. New York: IEEE.
7.
ZhangYLiuBGongY, et al. Application of machine learning optimization in cloud computing resource scheduling and management. In: 5th international conference on computer information and big data applications, Wuhan, China, 26–28 April 2024, pp. 171–175. New York: ACM.
8.
PereiraPPEliassonJKyusakovR, et al. Enabling cloud connectivity for mobile Internet of Things applications. In: 2013 IEEE seventh international symposium on service-oriented system engineering, San Francisco, CA, 25–28 March 2013, pp. 518–526. New York: IEEE.
9.
YangRYuFRSiP, et al. Integrated blockchain and edge computing systems: a survey, some research issues and challenges. IEEE Commun Surv Tutor2019; 21: 1508–1532.
10.
NingZKongXXiaF, et al. Green and sustainable cloud of things: enabling collaborative edge computing. IEEE Commun Mag2018; 57: 72–78.
11.
CalheirosRNRanjanRBeloglazovA, et al. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp2011; 41: 23–50.
12.
SonmezCOzgovdeAErsoyC.EdgeCloudSim: an environment for performance evaluation of edge computing systems. Trans Emerg Telecommun Technol2018; 29: e3493.
13.
BalaMIChishtiMA.Offloading in cloud and fog hybrid infrastructure using iFogSim. In: 2020 10th international conference on cloud computing, data science & engineering (confluence), Noida, India, 29–31 January 2020, pp. 421–426. New York: IEEE.
14.
GuptaHDastjerdiAVGhoshSK, et al. IFogSim: a toolkit for modeling and simulation of resource management techniques in the Internet of Things, edge and computing environments. Softw Pract Exp2017; 47: 1275–1296.
15.
MechalikhCTaktakHMoussaF. PureEdgeSim: a simulation toolkit for performance evaluation of cloud, fog, and pure edge computing environments. In: 2019 international conference on high performance computing & simulation (HPCS), Dublin, 15–19 July 2019, pp. 700–707. New York: IEEE.
16.
AmarasingheGde AssunçãoMDHarwoodA, et al. ECSNeT++: a simulator for distributed stream processing on edge and cloud environments. Future Gener Comput Syst2020; 111: 401–418.
17.
ZhaiXPengYGuoX.Edge-cloud collaboration for low latency, low-carbon, and cost-efficient operations. Comput Electr Eng2024; 120: 109758.
18.
QiJLiuCZhangX, et al. A survey on open-source edge computing simulators and emulators: the computing and networking convergence perspective. arXiv:2505.09995 2025, https://arxiv.org/abs/2505.09995
19.
DagliIMorshedlouARostamiJ, et al. H-EYE: holistic resource modeling and management for diversely scaled edge-cloud systems. arXiv:2402.04522 2024, https://arxiv.org/abs/2402.04522
20.
JiCLiYQiuW, et al. Big data processing in cloud computing environments. In: 2012 12th international symposium on pervasive systems, algorithms and networks, San Marcos, TX, 13–15 December 2012, pp. 17–23. New York: IEEE.
21.
CragoSDunnKEadsP, et al. Heterogeneous cloud computing. In: 2011 IEEE international conference on cluster computing, Austin, TX, 26–30 September 2011, pp. 378–385. New York: IEEE.
22.
BahgaAMadisettiVK.A cloud-based approach for interoperable electronic health records (EHRs). IEEE J Biomed Health Inform2013; 17: 894–906.
23.
ShahVKondaSR.Cloud computing in healthcare: opportunities, risks, and compliance introduction. Rev Esp Doc Cient2022; 16: 50–71.
24.
SonkambleRGPhansalkarSPPotdarVM, et al. Survey of interoperability in electronic health records management and proposed blockchain based framework: MyBlockEHR. IEEE Access2021; 9: 158367–158401.
25.
AgomuoOCJnrOWBMuzamalJH. Energy-aware AI-based optimal cloud infra allocation for provisioning of resources. In: 2024 IEEE/ACIS 27th international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD), Beijing, China, 5–7 July 2024, pp. 269–274. New York: IEEE.
26.
JainVKKumarS.Big data analytic using cloud computing. In: 2nd IEEE international conference on advances in computing & communication engineering (ICACCE-2015), Dehradun, India, 1–2 May 2015, pp. 667–672. New York: IEEE.
27.
TranTXHajisamiAPandeyP, et al. Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Commun Mag2017; 55: 54–61.
28.
TopcuogluHHaririSWuMY.Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst2002; 13: 260–274.
29.
BraunTDSiegelHJBeckN, et al. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J Parallel Distrib Comput2001; 61: 810–837.
30.
GaoTTangQLiJ, et al. A particle swarm optimization with Lévy flight for service caching and task offloading in edge-cloud computing. IEEE Access2022; 10: 76636–76647.
31.
SalamaMElkhatibYBlairG. IoTNetSim: a modelling and simulation platform for end-to-end IoT services and networking. In: Proceedings of the 12th IEEE/ACM international conference on utility and cloud computing (UCC’19), Auckland, New Zealand, 2–5 December 2019, pp. 251–261. New York: ACM.
32.
ZengXGargSKStrazdinsP, et al. IOTSim: a simulator for analysing IoT applications. J Syst Archit2017; 72: 93–107.
33.
NguyenTDHuhEN. ECSim++: an INET-based simulation tool for modeling and control in edge cloud computing. In: 2018 IEEE international conference on edge computing (EDGE), San Francisco, CA, 2–7 July 2018, pp. 80–86. New York: IEEE.
34.
LuQLiGYeH.EdgeSim++: a realistic, versatile, and easily customizable edge computing simulator. IEEE Internet Things J2024; 11: 35341–35360.
35.
JhaDNAlwaselKAlshoshanA, et al. IoTSim-Edge: a simulation framework for modeling the behaviour of IoT and edge computing environments. Softw Pract Exp2020; 50: 844–867.
36.
PastorLBosquwe OreroJL. An efficiency and scalability model for heterogeneous clusters. In: Proceedings 2001 IEEE international conference on cluster computing, Las Vegas, NV, 14–17 October 2001, pp. 427–434. New York: IEEE.
37.
SunXHChenYWuM. Scalability of heterogeneous computing. In: 2005 international conference on parallel processing (ICPP’05), Oslo, Norway, 14–17 June 2005, pp. 557–564. New York: IEEE.
38.
BanditwattanawongTUthayopasP.Improving cloud scalability, economy and responsiveness with client-side cloud cache. In: 2013 10th international conference on electrical engineering/electronics, computer, telecommunications and information technology, Krabi, Thailand, 15–17 May 2013, pp. 1–6. New York: IEEE.
39.
ChavanVKaveriPR. Clustered virtual machines for higher availability of resources with improved scalability in cloud computing. In: Proceedings of the 2014 first international conference on networks & soft computing (ICNSC2014), Guntur, India, 19–20 August 2014, pp. 221–225. New York: IEEE.
40.
TaherkordiAEliassenF. Scalable modeling of cloud-based IoT services for smart cities. In: 2016 IEEE international conference on pervasive computing and communication workshops (PerCom workshops), Sydney, NSW, Australia, 14–18 March 2016, pp. 1–6. New York: IEEE.
41.
CerritosELinFJde la BastidaD. High scalability for cloud-based IoT/M2M systems. In: 2016 IEEE international conference on communications (ICC), Kuala Lumpur, Malaysia, 22–27 May 2016, pp. 1–6. New York: IEEE.
42.
MaheshwariSRaychaudhuriDSeskarI, et al. Scalability and performance evaluation of edge cloud systems for latency constrained applications. In: 2018 IEEE/ACM symposium on edge computing (SEC), Seattle, WA, 25–27 October 2018, pp. 286–299. New York: IEEE.
43.
SunJZhangYWuZ, et al. An efficient and scalable framework for processing remotely sensed big data in cloud computing environments. IEEE Trans Geosci Remote Sens2019; 57: 4294–4308.
44.
JavedAMalhiAKinnunenT, et al. Scalable IoT platform for heterogeneous devices in smart environments. IEEE Access2020; 8: 211973–211985.
45.
BadshahADaudAKhanHU, et al. Optimizing the over and underutilization of network resources during peak and off-peak hours. IEEE Access2024; 12: 82549–82559.
46.
SriramulugariSKGorantlaVAKGudeV, et al. Exploring mobility and scalability of cloud computing servers using logical regression framework. In: 2024 2nd international conference on disruptive technologies (ICDT), Greater Noida, India, 15–16 March 2024, pp. 488–493. New York: IEEE.
47.
TangMWongVW.Deep reinforcement learning for task offloading in mobile edge computing systems. IEEE Trans Mob Comput2020; 21: 1985–1997.
48.
WangJZhaoLLiuJ, et al. Smart resource allocation for mobile edge computing: a deep reinforcement learning approach. IEEE Trans Emerg Top Comput2019; 9: 1529–1541.
49.
ChenJXingHXiaoZ, et al. A DRL agent for jointly optimizing computation offloading and resource allocation in MEC. IEEE Internet Things J2021; 8: 17508–11524.
50.
WuZJiaZPangX, et al. Deep reinforcement learning-based task offloading and load balancing for vehicular edge computing. Electronics2024; 13: 1511.
51.
ZhuXZhangTZhangJ, et al. Deep reinforcement learning-based edge computing offloading algorithm for software-defined IoT. Comput Netw2023; 235: 110006.
52.
KhanAR.Dynamic load balancing in cloud computing: optimized RL-based clustering with multi-objective optimized task scheduling. Processes2024; 12: 519.
53.
SaeikFAvgerisMSpatharakisD, et al. Task offloading in Edge and Cloud Computing: a survey on mathematical, artificial intelligence and control theory solutions. Comput Netw2021; 195: 108177.
54.
HortelanoDde MiguelIBarrosoRJ, et al. A comprehensive survey on reinforcement-learning-based computation offloading techniques in edge computing systems. J Netw Comput Appl2023; 216: 103669.
HuXWangLWongKK, et al. Edge and central cloud computing: a perfect pairing for high energy efficiency and low latency. IEEE Trans Wireless Commun2020; 19: 1070–1083.
57.
FaridMLatipRHussinM, et al. Scheduling scientific workflow using multi-objective algorithm with fuzzy resource utilization in multi-cloud environment. IEEE Access2020; 8: 24309–24322.
58.
VarmaR.Storage media for computers in radiology. Indian J Radiol Imaging2008; 18: xx–xx.
59.
ZhuangYZhangCHuaiJ, et al. Bluetooth localization technology: principles, applications, and future trends. IEEE Internet Things J2022; 9: 23506–23524.
60.
Al-SarawiSAnbarMAlieyanK, et al. Internet of Things (IoT) communication protocols. In: International conference on information technology (ICIT), Amman, Jordan, 17–18 May 2017, pp. 685–690. New York: IEEE.
61.
UddinMRAsaduzzamanA. Pairing computations at the edge and cloud servers to improve performance of heterogeneous systems. In: 2024 9th international conference on fog and mobile edge computing (FMEC), Malmö, Sweden, 2–5 September 2024, pp. 212–219. New York: IEEE.
