Hybrid Dynamic Programming Healthcare Cloud-Based Quality of Service Optimization

Nengak I. Sitlong; Abraham E.  Evwiekpaefe; Martins E.  Irhebhude

doi:10.62411/jcta.14455

Authors

Nengak I. Sitlong Federal University of Education
Abraham E. Evwiekpaefe Nigerian Defence Academy
Martins E. Irhebhude Nigerian Defence Academy

DOI:

https://doi.org/10.62411/jcta.14455

Keywords:

Cloud, Dynamic Programming, Energy Optimization, Fog/Edge Computing, Healthcare, Spatiotemporal, Unsupervised LSTM, Task Scheduling

Abstract

The integration of Internet of Things (IoT) with cloud computing has revolutionized healthcare systems, offering scalable and real-time patient monitoring. However, optimizing response times and energy consumption remains crucial for efficient healthcare delivery. This research evaluates various algorithmic approaches for workload migration and resource management within IoT cloud-based healthcare systems. The performance of the implemented algorithm in this research, Hybrid Dynamic Programming and Long Short-Term Memory (Hybrid DP+LSTM), was analyzed against other six key algorithms, namely Gradient Optimization with Back Propagation to Input (GOBI), Deep Reinforcement Learning (DRL), improved GOBI (GOBI2), Predictive Offloading for Network Devices (POND), Mixed Integer Linear Programming (MILP), and Genetic Algorithm (GA) based on their average response time and energy consumption. Hybrid DP+LSTM achieves the lowest response time (82.91ms) with an energy consumption of 2,835,048 joules per container. The outcome of the analysis showed that Hybrid DP+LSTM have significant response times improvement, with percentage increases of 89.3%, 79.0%, 83.8%, 97.0%, 99.8%, and 99.94% against GOBI, GOBI2, DRL, POND, MILP, and GA, respectively. In terms of energy consumption, Hybrid DP+LSTM outperforms other approaches, with GOBI2 (3,664,337 joules) consuming 29.3% more energy, DRL (2,973,238 joules) consuming 4.9% more, GOBI (4,463,010 joules) consuming 57.4% more, POND (3,310,966 joules) consuming 16.8% more, MILP (3,005,498 joules) consuming 6.0% more, and the GA (3,959,935 joules) consuming 39.7% more. The result of ablation of the Hybrid DP+LSTM model achieves a 47.05% improvement over DP-only (156.57ms) and a 70.64% improvement over LSTM-only (282.41ms) in response time. On the energy efficiency side, Hybrid DP+LSTM shows 22.80% improvement over LSTM-only (3,671,51 joules), but 7.34% underperformance compared to DP-only (2,640,93). These research findings indicate that the Hybrid DP+LSTM technique provides the best trade-off between response time and energy efficiency. Future research should further explore hybrid approaches to optimize these metrics in IoT cloud-based healthcare systems.

Author Biographies

Nengak I. Sitlong, Federal University of Education

Faculty of Sciences, Computer Science Department, Federal University of Education (FUEP), Pankshin, Plateau State 930001, Nigeria

Abraham E. Evwiekpaefe, Nigerian Defence Academy

Faculty of Military Science and Interdisciplinary Studies, Computer Science Department, Nigerian Defence Academy (NDA), Kaduna 700001, Nigeria

Martins E. Irhebhude, Nigerian Defence Academy

Faculty of Military Science and Interdisciplinary Studies, Computer Science Department, Nigerian Defence Academy (NDA), Kaduna 700001, Nigeria

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