# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 4 Background and Related Work
[52] that allocates tasks for execution on a four-tier architecture. The algorithm demonstrates its effectiveness in improving the performance of energy consumption during the execution and scheduling processes of tasks. [PERSON] et al. [53] also present an energy-efficient scheduling algorithm that minimizes the energy consumption for IoT workflows. [PERSON] et al. [54] propose a scheduling algorithm to minimize the energy consumption in the cloud computing environment. They present a QoS model with respect to response time and throughput of jobs, as well as present an energy model for physical machines of the cloud environment. However, proposed algorithms do not measure mutual performance impacts between the energy consumption of machines and the QoS requirements of jobs. In addition, the genetic algorithm, as a metaheuristic approach, has been extensively applied to mitigate the complexity of scheduling problems. [PERSON] et al. [55] tackle the scheduling process in cloud-fog computing systems by formulating a model that accounts for different performance constraints and applying metaheuristic approaches to solve a multi-objective optimization scheduling problem. Similarly, such approaches are applied by [PERSON] et al. [56] to propose a job scheduling method for cloud environments based on dynamic dispatch queues. [PERSON] et al. [57] analyze popular first-come first-served, shortest job first, round robin, Min-Min, Max-Min, genetic, and ant colony optimization scheduling algorithms by comparing them in terms of response time and makespan. Thus, the genetic approach has proven its effectiveness in achieving the best performance on the metric of response time, whereas the ant colony optimization algorithm outperforms other scheduling algorithms in terms of makespan. In addition, the genetic approach has been utilized in [PERSON] et al. [58] to solve the job-shop scheduling problem and produce a good-quality, energy-efficient scheduling solution in a reasonable time. Their approach adopts machine resources that are modeled to consume different energy rates for processed jobs. [PERSON] et al. [59] also minimize the energy consumption in a job-shop scheduling problem by utilizing a multi-objective, genetic-based approach. In contrast, [PERSON] et al. [60] propose a framework in which they employ modern artificial intelligence techniques to overcome limitations of traditional heuristic-based schedulingalgorithms and cope with dynamic changes in cloud environments. The framework utilizes the power of the deep learning approach to propose a model for scheduling jobs in cloud data centers. In addition, the framework adopts a deep Q-network model for resource allocation to deploy virtual machines to physical servers to execute jobs. Moreover, a scheduling scheme is proposed by [PERSON] et al. [61] to minimize average waiting times and makespans under different deadline constraints, in which a reinforcement-learning-based approach is utilized in a grid and in Infrastructure-as-a-Service (IaaS) cloud computing environments. Furthermore, [PERSON] et al. [62] propose a resource management framework for virtualized two-tier cloud data center environments, in which the framework demonstrates better performance in improving resource utilization and obtains an energy saving of 13.8%. The enhancement in energy consumption is also achieved in [PERSON] et al. [63] by a multi-objective scheduling algorithm, as well as in [PERSON] et al. [64], in which a commonly used approach in control theory called model predictive control is utilized to address the scheduling problem for deferrable jobs in a tiered architecture data center. Overall, the proposed scheduling methods in existing frameworks and models do not optimize the performance of energy efficiency based on the QoS obligations of fog jobs.