The Round Robin algorithm is considered as one of the most common scheduling algorithms due to its simplicity and fairness. It is applied in many fields to improve system performance. In this paper, we conducted a review on how the researchers applied the RR algorithm in the CPU scheduling as well as in the cloud computing environment. Many researchers proposed various techniques in order to improve the RR algorithm and selecting the optimal time quantum which plays a vital role to enhance this algorithm. This paper presents a review and new classification on these techniques. Studies have been classified into two categories: RR based on static quantum, and RR based on dynamic quantum. Furthermore, the second category was divided into dynamic quantum for each round and dynamic quantum for each process. Moreover, an analytical comparison has been provided. This review can be a strong starting point for researchers who seek to enhance or optimize RR algorithm or either apply it in other fields.

Recently, there has been significant growth in the popularity of cloud computing systems. One of the main issues in building cloud computing systems is task scheduling. It plays a critical role in achieving high-level performance and outstanding throughput by having the greatest benefit from the resources. Therefore, enhancing task scheduling algorithms will enhance the QoS, thus leading to more sustainability of cloud computing systems. This paper introduces a novel technique called the dynamic round-robin heuristic algorithm (DRRHA) by utilizing the round-robin algorithm and tuning its time quantum in a dynamic manner based on the mean of the time quantum. Moreover, we applied the remaining burst time of the task as a factor to decide the continuity of executing the task during the current round. The experimental results obtained using the CloudSim Plus tool showed that the DRRHA significantly outperformed the competition in terms of the average waiting time, turnaround time, and response time compared with several studied algorithms, including IRRVQ, dynamic time slice round-robin, improved RR, and SRDQ algorithms.

Fog computing has emerged as a promising paradigm to extend cloud services toward the edge of the network, enabling low-latency processing and real-time responsiveness for Internet of Things (IoT) applications. However, the distributed, heterogeneous, and resource-constrained nature of fog environments introduces significant challenges in balancing workloads efficiently. This study presents a systematic literature review (SLR) of 113 peer-reviewed articles published between 2020 and 2024, aiming to provide a comprehensive overview of load-balancing strategies in fog computing. This review categorizes fog computing architectures, load-balancing algorithms, scheduling and offloading techniques, fault-tolerance mechanisms, security models, and evaluation metrics. The analysis reveals that three-layer (IoT–Fog–Cloud) architectures remain predominant, with dynamic clustering and virtualization commonly employed to enhance adaptability. Heuristic and hybrid load-balancing approaches are most widely adopted due to their scalability and flexibility. Evaluation frequently centers on latency, energy consumption, and resource utilization, while simulation is primarily conducted using tools such as iFogSim and YAFS. Despite considerable progress, key challenges persist, including workload diversity, security enforcement, and real-time decision-making under dynamic conditions. Emerging trends highlight the growing use of artificial intelligence, software-defined networking, and blockchain to support intelligent, secure, and autonomous load balancing. This review synthesizes current research directions, identifies critical gaps, and offers recommendations for designing efficient and resilient fog-based load-balancing systems.

Recently, there has been a significant growth in the popularity of cloud computing systems as an on-demand computing service with a utility computing model with which the users pay as per the usage. One of the main goals of cloud computing is to provide efficient access to resources remotely while obtaining a maximum profit. Therefore, the principal issue in building cloud computing systems is the scheduling where it concentrates on assigning tasks to the available resources at a particular time. In this paper, we provided a comparative simulation study considering the most popular and common task scheduling algorithms in cloud computing, namely FCFS, STF, LTF, and RR algorithms. The evaluation was conducted by CloudSim considering both scheduling allocation policies which are time-shared and space-shared. The Simulation results showed that with space-shared allocation policy the STF algorithm outperformed the others in terms of the total completion time. On the other hand, time-shared allocation policy has a better performance in reducing the completion time of tasks compared to the space-shared policy.