In the evolving landscape of fog-cloud hybrid computing, efficient task scheduling plays a vital role in meeting real-time requirements while optimizing resource utilization and energy consumption. This work proposes balanced minimum response time (MRT) and balanced minimum energy consumption (MEC) heuristics to address the challenges of task allocation on hybrid computing platforms in a balanced manner. These heuristics are employed to assess the efficacy of complementing cloud or fog networks, supported by dynamic voltage and frequency scaling (DVFS), leveraging deadline laxities without compromising user satisfaction. Three hybrid computing sub-scenarios are introduced: cloud-oriented, fog-oriented, and balanced-hybrid, based on the relative compute capacity of fog and cloud networks. The results reveal that a cloud network complementing a fog network is more beneficial for achieving lower makespan, while for lower energy consumption, it is preferable for fog network to complement the cloud network.  The research emphasizes the advantages of balanced heuristics under different computing scenarios to optimize makespan, energy consumption, and workload allocation, leading to overall computing cost reduction using realistic workloads. Further, judiciously applying DVFS to low-capacity fog nodes is more fruitful than powerful cloud nodes due to extravagant idle energy consumption in the generated scheduling holes.