A Fog-Computing Multi-Metric Hybrid Dynamic Programming Latency and Energy Optimization: Fog/Edge Computing

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

doi:10.25299/itjrd.2026.25088

Authors

Nengak Sitlong Department of Computer, Faculty of Science, Federal University of Education (FUEP), Pankshin, Plateau State, Nigeria
Abraham E. Evwiekpaefe Department of Computer, Faculty of Military Science and Interdisciplinary Studies, Nigerian Defence Academy (NDA), Kaduna, Nigeria
Martins E. Irhebhude Department of Computer, Faculty of Military Science and Interdisciplinary Studies, Nigerian Defence Academy (NDA), Kaduna, Nigeria

DOI:

https://doi.org/10.25299/itjrd.2026.25088

Keywords:

Fog Computing, Task Scheduling, Response Time, Energy Efficiency

Abstract

Efficient task scheduling in fog computing requires balancing multiple quality of service (QoS) parameters, including response time, Service Level Agreement (SLA) compliance, container reliability, migration overhead, and energy consumption. This study evaluated seven optimization algorithms; hybrid DP+LSTM, GOBI2, DRL, GOBI, POND, MILP, and GA based on their impact on average response time while considering migration cost, container destruction, SLA violations, Wait time and energy per container. Results reveal that DP+LSTM achieves the lowest average response time (82.91 ms), outperforming GOBI2 (395.13 ms) by 79.0%, DRL (512.41 ms) by 83.8%, GOBI (776.09 ms) by 89.3%, POND (2789.09 ms) by 97.0%, MILP (35,720.49 ms) by 99.8%, and Genetic Algorithm (145,465.53 ms) by 99.94%. Additionally, Hybrid DP+LSTM maintains zero SLA violations, only three destroyed containers, and the lowest migration overhead (0.018 ms), while also consuming the least energy per container (2,835,048 J). In contrast, MILP and GA incur extreme delays, high container destruction (351 and 449, respectively), hundreds of SLA violations (397 and 223, respectively), thousands milliseconds of wait time (11,835ms and 5,251ms, respectively) rendering them unsuitable for practical fog environments. Overall, hybrid DP+LSTM demonstrates superior scalability, energy efficiency, and QoS preservation, making it the most effective scheduling strategy for fog computing environments.

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