Round Robin Scheduling Algorithm Research Articles

An enhanced round robin using dynamic time quantum for real-time asymmetric burst length processes in cloud computing environment.

Cloud computing is a popular, flexible, scalable, and cost-effective technology in the modern world that provides on-demand services dynamically. The dynamic execution of user requests and resource-sharing facilities require proper task scheduling among the available virtual machines, which is a significant issue and plays a crucial role in developing an optimal cloud computing environment. Round Robin is a prevalent scheduling algorithm for fair distribution of resources with a balanced contribution in minimized response time and turnaround time. This paper introduced a new enhanced round-robin approach for task scheduling in cloud computing systems. The proposed algorithm generates and keeps updating a dynamic quantum time for process execution, considering the available number of process in the system and their burst length. Since our method dynamically runs processes, it is appropriate for a real-time environment like cloud computing. The notable part of this approach is the capability of scheduling tasks with asymmetric distribution of burst time, avoiding the convoy effect. The experimental result indicates that the proposed algorithm has outperformed the existing improved round-robin task scheduling approaches in terms of minimized average waiting time, average turnaround time, and number of context switches. Comparing the method against five other enhanced round robin approaches, it reduced average waiting times by 15.77% and context switching by 20.68% on average. After executing the experiment and comparative study, it can be concluded that the proposed enhanced round-robin scheduling algorithm is optimal, acceptable, and relatively better suited for cloud computing environments.

Various Variants of Round Robin Scheduling Algorithm

Various Variants of Round Robin Scheduling Algorithm

Application Layer Load Balancing in Software Defined Networking Using Priority Based Round Robin Scheduling Algorithm

Application Layer Load Balancing in Software Defined Networking Using Priority Based Round Robin Scheduling Algorithm

Dynamic Time Quantum Computation for Improved Round Robin Scheduling Algorithm Using Quartiles and Randomization (IRRQR)

Scheduling is a decision-making process through which large numbers of tasks compete for various system resources. The availability of limited resources makes scheduling a challenge. Among resources, the processor is the most important resource for in-time completion of tasks therefore; developing an efficient processor scheduler is still a topic of interest. In this paper, we have proposed a modified Round Robin scheduling algorithm, in which the dynamic time quantum is computed based on Quartiles and randomness. We have considered average waiting time, average turnaround time, and the number of context switches as performance metrics and compared the proposed scheduling algorithm with existing approaches. The results show that the average waiting time, average turnaround time, and the number of context switches using the proposed algorithm are significantly reduced as compared other algorithms.

Research and design of simulation and verification system of intelligent expressway based on ETC big data

Electronic toll collection (ETC) system records a large number of travel trajectories of vehicles on expressways, and it has a great potential application value. However, the current simulation system mainly focuses on simulating the characteristics of traffic flow while ignoring the real-time flow conditions of the road is difficult to calculate and display quantitatively, and the overall optimization cost is also notably substantial. Currently, there is a lack of a simulation system tailored for the ETC environment, which addresses the challenge of real-time traffic flow computation and holistic optimization, fulfilling the requisites of pertinent research. According to the topological structure inherent to an actual provincial road network on expressways, this paper devises a framework for a simulation system that conforms to the current ETC environment. We solved the critical problem of generating simulation data in the simulation system by establishing a Feature Extraction Algorithm for spatio-temporal features derived from ETC transaction data (Edata). Then we put forward Traffic Control Strategy Algorithm in ETC simulation system, which can provide decision indicators for simulating the control of traffic flow of the expressway. At the same time, we optimized the improved Multi-Task Scheduling Algorithm (ETC_MTS) based on the application scenario of real-time parallelism of multi-task on expressways, which provides better execution performance compared with the current mainstream algorithms such as Shortest Job First Scheduling Algorithm (SJFS), Priority Scheduling Algorithm (Priority), First Come First Serve Scheduling Algorithm (FCFS) and Round Robin Scheduling Algorithm (RR).

Performance Analysis of Scheduling Algorithms on Fog Computing using YAFS

A smart device that has seen more development is the Internet of Things (IoT). An IoT system implementation requires a device that can accept and handle various sorts of data. Fog Computing is a solution to the issue since the IoT demands a device that can provide Real-Time. Certainly, load balancing involves scheduling the IoT devices and data used. Because Cloud and Fog Computing models enable data growth management and deployment planning, which necessitate a quicker response from platforms and applications, processing power scheduling is essential. The purpose of this study is to evaluate the performance of effective scheduling algorithms that adhere to these computing models platform requirements. The scheduling algorithm that can produce the lowest Processing Time and the resulting Time Efficiency is more efficient can be called the best scheduling algorithm. In this research, the author analyzes the performance of scheduling algorithms in the form of Round Robin and Priority Scheduling on Fog Computing. In this research, testing was carried out by creating a scenario of the effect of increasing the number of Fog Nodes and Devices used. The average result of scenario testing obtained for processing time for Round Robin is lower, and the highest Time Efficiency for Round Robin over Priority Scheduling is 11%. With these test results, the Round Robin scheduling algorithm has a simpler level of complexity. So, it can be concluded that Round Robin belongs to the category of the best scheduling algorithm in this case.

Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling

Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts the time slice allocated to each task based on execution time and priority, resulting in more efficient resource utilization. We also prioritize higher-priority tasks and execute them as soon as they arrive in the ready queue, ensuring the timely completion of critical tasks. We evaluate the performance of our algorithm using a set of real-world tasks and compare it with traditional Round Robin scheduling. The results show that our proposed approach significantly improves task execution time and resource utilization compared to conventional Round Robin scheduling. Our approach offers a promising solution for optimizing task execution in real-time systems. The combination of dynamic time quantum and priorities adds a unique element to the existing literature in this field.

Optimized Round Robin Scheduling Algorithm Using Dynamic Time Quantum Approach in Cloud Computing Environment

Cloud computing refers to a sophisticated technology that deals with the manipulation of data in internet-based servers dynamically and efficiently. The utilization of the cloud computing has been rapidly increased because of its scalability, accessibility, and incredible flexibility. Dynamic usage and process sharing facilities require task scheduling which is a prominent issue and plays a significant role in developing an optimal cloud computing environment. Round robin is generally an efficient task scheduling algorithm that has a powerful impact on the performance of the cloud computing environment. This paper introduces a new approach for round robin based task scheduling algorithm which is suitable for cloud computing environment. The proposed algorithm determines time quantum dynamically based on the differences among three maximum burst time of tasks in the ready queue for each round. The concerning part of the proposed method is utilizing additive manner among the differences, and the burst times of the processes during determining the time quantum. The experimental results showed that the proposed approach has enhanced the performance of the round robin task scheduling algorithm in reducing average turn-around time, diminishing average waiting time, and minimizing number of contexts switching. Moreover, a comparative study has been conducted which showed that the proposed approach outperforms some of the similar existing round robin approaches. Finally, it can be concluded based on the experiment and comparative study that the proposed dynamic round robin scheduling algorithm is comparatively better, acceptable and optimal for cloud environment.

Simulation of Priority Round Robin Scheduling Algorithm

In this journal, simulation of priority round robin scheduling algorithm is presented. To imitate the processes of operating system operation, simulation can be used. By simulation, model is used, namely models that represent the characteristics or behaviour of systems. Process scheduling is one important operation in operating system. OS-SIM can be used to model and simulate the operations of process scheduling. Some scheduling algorithms are available in modern operating systems, like First come First Serve (FCFS), Shortest Job First (SJF), Round Robin (RR), Priority Scheduling or combination of these algorithms. One important scheduling algorithm for real-time or embedded system is priority round robin scheduling algorithm. Priority round robin scheduling algorithm is a preemptive algorithm. Each process is given time quantum. Each process has a priority. Here time quantum 3 is given. The higher the time quantum, the more the context switching. By the use of OS-SIM, simulation can be understood easily and thoroughly. The statistics, will be calculated automatically by the system by the simulator, like the number of context switching, average waiting time, average turn around time, and average responds time. With one example, by using quantum=3. The average turn around time is 18.25 ms. The Average Waiting Time is 12 ms. The Average Responds time is 2.75 ms. The total burst time is 25 ms.

Analysis of rank-based latency aware fog scheduling using validating internet of things at large scales

With the increase in internet of things (IoT) applications' range and scale, it is essential to test the applications before deploying them in the real world. Most common approaches utilize simulations and testbeds; however, these methods lack real-time failure scenarios and the capability to scale, respectively. A virtual environment is a suitable approach that overcomes these drawbacks further, IoT applications using cloud computing have evolved to shift some computing and storage capabilities to the edge networks for ensuring adherence to strict latency constraints for real-time applications. This led to the emergence of fog computing which provides lower latency and better security, among other advantages. As for any processing tasks, scheduling becomes a critical concern for matching the tasks with the devices having appropriate resources. This paper analyzes a hybridized fog scheduling algorithm based on a ranking approach considering latency as the main parameter. It builds a software layer for scheduling on top of the validating internet of things at large scales (VIoLET) infrastructure. The results are compared with the round-robin scheduling algorithm, and it is found that the hybridized algorithm provides closer actual latency values to the expected.

A new median-average round Robin scheduling algorithm: An optimal approach for reducing turnaround and waiting time

A variety of algorithms handles processes on the CPU. The round-robin algorithm is an efficient CPU scheduling mechanism for a time-sharing operating system. The system processes the methods based on the time slice; however, determining the time slice has proven highly challenging for the researchers. As a result, a variety of dynamic time quantum scheduling techniques are presented by various academics to address this challenge. This study aims to determine how to best schedule resources to maximize efficiency. It is important to note that this scheduling mechanism rotates between the processes after the static quantum time is complete. However, the quantum decision affects how effectively and efficiently the procedures may be scheduled. Additionally, the quantum decision has an impact on the scheduling of processes. The average waiting time, turnaround time, and context switch times of the Round Robin scheduling algorithm are high enough to influence the system's performance. To get over the round-drawbacks, robin's the authors in this study suggest using the improved algorithm Median-Average Round Robin (MARR). Using the median and average of the burst time of each process, the author proposes a dynamic time quantum for the system. The authors compared the proposed model with four other scheduling algorithms. The results vividly depict that the proposed algorithms successfully give effective results with reduced average turnaround time and waiting time. In the future, cost and RAM utilization will be considered to enhance the algorithm.

VORR: A New Round Robin Scheduling Algorithm

VORR: A New Round Robin Scheduling Algorithm

Analysis and Optimization of Minimum Hydraulic Brake-by-Wire System for Wheeled Vehicles Based on Queueing Theory

Based on Queueing Theory, this paper conducts the analysis and optimization of a novel hydraulic brake-by-wire system configuration involving electric-mechanical brake actuators + hydraulic control unit (eActuators + HCU). The functional requirement and topological analysis of frictional brakes are stated. The queueing model is built, providing a theoretical basis to the development of the minimum hydraulic brake-by-wire system involving one eActuator + s valves. Based on the minimum hydraulic brake-by-wire system, the equivalent queueing model is proposed and the evaluation index is defined as average waiting time of wheel cylinder pressure (AWT of WCP). The queueing disciplines are designed and optimized. Integrated-electro-hydraulic brake system (IEHB), a scheme on behalf of the minimum hydraulic brake-by-wire system, is developed and modeled. In accordance to the IEHB physical system, the comparison verifications for the proposed queueing disciplines are conducted via simulations and experiments. The correlation analysis and sensitivity analysis are carried out respectively. Several typical pressure-tracking experiments have been carried out to demonstrate the IEHB control system. This work has two original contributions: 1) The topological and conjoint analysis of the novel hydraulic brake-by-wire system configuration (eActuators+valves) is conducted via Queueing Theory. 2) The performance of the minimum hydraulic brake-by-wire system is improved by using the balanced algorithm, compared with the round-robin scheduling algorithm and rotary control algorithm proposed in the previous study.

Optimal Cloud Orchestration Model of Containerized Task Scheduling Strategy Using Integer Linear Programming: Case Studies of IoTcloudServe@TEIN Project

As a playground for cloud computing and IoT networking environment, IoTcloudServe@TEIN has been established in the Trans-Eurasia Information Network (TEIN). In the IoTcloudServe@TEIN platform, a cloud orchestration for conducting the flow of IoT task demands is imperative for effectively improving performance. In this paper, we propose the model of optimal containerized task scheduling in cloud orchestration that maximizes the average payoff from completing tasks within the whole cloud system with different levels of cloud hierarchies. Based on integer linear programming, the model can take into account demand requirement and resource availability in terms of storage, computation, network, and splittable task granularity. To show the insights obtainable from the proposed model, the edge-core cluster of IoTcloudServe@TEIN and its peer-to-peer federated cloud scenario with OF@TEIN+ are numerically experimented and herein reported. To evaluate the model’s performance, payoff level and task completion time are considered by comparing with a well-known round-robin scheduling algorithm. The proposed ILP model can be a guideline for the cloud orchestration in IoTcloudserve@TEIN because of the lower task completion time and the higher payoff level especially upon the large demand growth, which is the major operation range of concerns in practice. Moreover, the proposed model illustrates mathematically the significance of implementing cloud architecture with refined splittable task granularity via the light-weighted container technology that has been used as the basis for IoTcloudServe@TEIN clustering design.

A Multi-Tier Architecture for the Management of Supply Chain of Cloud Resources in a Virtualized Cloud Environment

As cloud resource demand grows, supply chain management (SCM), which is the core function of cloud computing, faces serious challenges. Quite a number of techniques have been proposed by many researchers for such a challenge. As such, numerous proposed strategies are still under reckoning and modification so as to enhance its potential. An optimized dynamic scheme that combined several algorithms' characteristics was proposed to map out such a challenge. The hybridized proposed scheme involved the meta-heuristic swarm mechanism of ant colony optimization (ACO) and deterministic spanning tree (SPT) algorithm as it obtained faster convergence chain, ensured resource utilization in least time and cost. Extensive experiments conducted in cloudsim simulator provided an efficient result in terms of minimized makespan time and throughput as compared to SPT, round robin (RR), and pre-emptive fair scheduling algorithm (PFSA) as it significantly improves performance.

Intelligent Computation Offloading for IoT Applications in Scalable Edge Computing Using Artificial Bee Colony Optimization

Most of the IoT-based smart systems require low latency and crisp response time for their applications. Achieving the demand of this high Quality of Service (QoS) becomes quite challenging when computationally intensive tasks are offloaded to the cloud for execution. Edge computing therein plays an important role by introducing low network latency, quick response, and high bandwidth. However, offloading computations at a large scale overwhelms the edge server with many requests and the scalability issue originates. To address the above issues, an efficient resource management technique is required to maintain the workload over the edge and ensure the reduction of response time for IoT applications. Therefore, in this paper, we introduce a metaheuristic and nature-inspired Artificial Bee Colony (ABC) optimization technique that effectively manages the workload over the edge server under the strict constraints of low network latency and quick response time. The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Round-Robin (RR) Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. Furthermore, the proposed technique scales the edge server to meet the demand of high QoS for IoT applications.

Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks

The resource management in wireless networks with massive Internet of Things (IoT) users is one of the most crucial issues for the advancement of fifth-generation networks. The main objective of this study is to optimize the usage of resources for IoT networks. Firstly, the unmanned aerial vehicle is considered to be a base station for air-to-ground communications. Secondly, according to the distribution and fluctuation of signals; the IoT devices are categorized into urban and suburban clusters. This clustering helps to manage the environment easily. Thirdly, real data collection and preprocessing tasks are carried out. Fourthly, the deep reinforcement learning approach is proposed as a main system development scheme for resource management. Fifthly, K-means and round-robin scheduling algorithms are applied for clustering and managing the users’ resource requests, respectively. Then, the TensorFlow (python) programming tool is used to test the overall capability of the proposed method. Finally, this paper evaluates the proposed approach with related works based on different scenarios. According to the experimental findings, our proposed scheme shows promising outcomes. Moreover, on the evaluation tasks, the outcomes show rapid convergence, suitable for heterogeneous IoT networks, and low complexity.

Blockchain-based mobile edge computing system

With the development of the Internet of Things (IoT), the number of mobile terminal devices is increasing rapidly. Due to high transmission delay and bandwidth limitation, computing power requirements for IoT devices are getting higher and higher. Recently, edge computing is an effective way to reduce system delay, and blockchain solves the security problem of edge computing. In this paper, a three-layer network model, named blockchain-based mobile edge computing system (BMEC), is proposed for clone block identification. Specifically, a neural network based clone block identification (NCBI) method is proposed to prevent clone block attacks. After that, the Prim algorithm is applied to BMEC to generate a weighted undirected graph minimum spanning tree that is composed of edge blocks. This can divide a main chain into several side chains to improve the transaction speed of blockchain. Finally, the blockchain is constructed based on the time slicing round-robin scheduling algorithm to control resources from edge servers and regulate edge devices’ activities based on the predefined rules of priority, application type, and past behavior. Experimental results show that our clone block identification method can achieve block validation effectively in BMEC, and our construction of blockchain delay is lower than conventional edge computing methods.

Modified Optical Burst Switching (OBS) Based Edge Node Architecture Using Real-Time Scheduling Techniques

Contention is the main problem in optical burst switching (OBS) systems that lead to loss more burst by dropping the contended burst. To deal with this problem, we propose a new proactive method which is based on the integration of Real-Time Scheduling Techniques: Earliest-deadline-first (EDF) and round-robin (RR). Based on this consideration, this paper proposes a series of novel coherent interdependence methods to propose a novel OBS edge node Architecture: (i) An adaptive priority system to obtain QoS guarantees, (ii) Admission control and bandwidth distribution to maximize the link throughput. The proposed technique classifies the incoming data packets into two categories: granted service (GS) for high priority and best effort (BE) for low priority. The RR technique is used for scheduling different BE and GS packets when they have the same deadlines while the EDF is used to manage GS packets with different deadlines. In addition, the admission and bandwidth assignment policies vary in function of traffic characteristics. For GS traffic, only the bandwidth used is needed, while the remaining bandwidth is assigned to best effort traffic. With the EDF and RR scheduling algorithms, the proposed edge node design obtains a brand new architecture. The proposed OBS architecture has been tested for dynamic traffic in which both BE and GS traffic arrives according to a Poisson distribution for different scenarios based on traffic distribution and switch resources reservation. In scenario#1, 90% GS traffic and 10% BE traffic and reserve 7% of the switch resources for BE traffic are considered. Therefore, the proposed architecture provides a higher grade of service to the end-users with the guaranteed service level of agreements than the existing architecture that does not implement resource reservation protocols. In addition, the modified OBS-edge node is designed and implemented on FPGA Virtex-11C2V40. The hardware performance analysis shows that the network dimension is a key factor in latency measurement. A 2×2 network dimension can serve approximately 230 Mbps throughput and approximately 400 Mbps in the case when the number of flits (segment of burst) is twice.

Improved Version of Round Robin Scheduling Algorithm Based on Analytic Model

Scheduling is one of the most important issues in the operating system, such as the tasks must be affected to the appropriate virtual machines, considering different factors at the same time to ensure better use of resources. A lot of research has been carried out to propose more efficient task scheduling algorithms. Round robin is one of the most powerful algorithms in this field but its main challenge is the choice of time quantum. The effectiveness of Round Robin depends on the choice of this parameter. In this paper, we aimed to overcome these challenge by proposing an improved Round Robin scheduling algorithm using a variable time quantum based on an analytic model. Our analytical model takes into consideration different parameters to determine the order of tasks execution. The use of burst time as parameters in our model ensures a more suitable time quantum. This algorithm can be applied in any operating system and therefore in the cloud computing environment. In order to assess the performance and effectiveness of the proposed approach, five different scenarios have been implemented; the comparisons made with existing works have shown that the proposed approach improves the average waiting time and the average turnaround time, which ensures better scheduling of tasks and better use of resources.