Design and Performance Evaluation of Smart Job First Dynamic Round Robin (SJFDRR) Scheduling Algorithm with Individual Time Quantum for Each Process

Round Robin scheduling is mostly used CPU scheduling algorithm; it gives better result in comparison to other scheduling algorithm. But this algorithm may lead many problems directly related to time quantum. If selected time quantum is large, then the response time of the processes may turn in too high. On the other hand, if time quantum is short, it increases the number of context switch which may lead overhead of the CPU. In this paper, researcher proposed a new algorithm, called Smart Job First Dynamic Round Robin (SJFDRR) with individual time quantum for each process. The proposed algorithm calculates smart priority factor (SPF) and individual time quantum for each process. The factor SPF is calculated on the basis of user priority and system priority. The individual time quantum is calculated on the basis of burst time of the process. Based on the analysis, researcher has shown that the new proposed algorithm (SJFDRR) with individual time quantum solves the fixed time quantum problem and enhanced the performance of Round Robin

Divide & Quantum

When the operating system was introduced to the world, many functions of it were also introduced that included process management, file management, memory management, networking. As the operating system tends to interact with different operations at the runtime so keeping that in mind we can say that the process management is most essential in an operating system because it allows the operating system to interact with different operations more efficiently and it also improves the timing of the operating systems to interact between the processes. In order to manage between processes many algorithms were introduced by different people and different minds and today we find this as an opportunity develop something new by keeping the previous algorithms in mind and compare them and find a best possible result

Cloud service analysis using round-robin algorithm for quality-of-service aware task placement for internet of things services

Round-robin (RR) is a process approach to sharing resources that requires each user to get a turn using them in an agreed order in cloud computing. It is suited for time-sharing systems since it automatically reduces the problem of priority inversion, which are low-priority tasks delayed. The time quantum is limited, and only a one-time quantum process is allowed in round-robin scheduling. The objective of this research is to improve the functionality of the current RR method for scheduling actions in the cloud by lowering the average waiting, turnaround, and response time. CloudAnalyst tool was used to enhance the RR technique by changing the parameter value in optimizing the high accuracy and low cost. The result presents the achieved overall min and max response times are 36.69 and 650.30 ms for running 300 min RR. The cost for the virtual machines (VMs) is identified from $0.5 to$3. The longer the time used, the higher the cost of the data transfer. This research is significant in improving communication and the quality of relationships within groups

Improved time quantum length estimation for round robin scheduling algorithm using neural network

In most cases, the quantum time length is taken to be fix in all applications that use Round Robin (RR) scheduling algorithm. Many attempts aim to determination of the optimal length of the quantum that results in a small average turnaround time, but the unknown nature of the tasks in the ready queue make the problem more complicated: Considering a large quantum length makes the RR algorithm behave like a First Come First Served (FIFO) scheduling algorithm, and a small quantum length cause high number of contexts switching. In this paper we propose a RR scheduling algorithm based on Neural Network Models for predicting the optimal quantum length which lead to a minimum average turnaround time. The quantum length depends on tasks burst times available in the ready queue. Rather than conventional traditional methods using fixed quantum length, this one giving better results by minimizing the average turnaround time for almost any set of jobs in the ready queue

A hybrid approach for scheduling applications in cloud computing environment

Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list

Cloud Computing CPU Allocation and Scheduling Algorithms using CloudSim Simulator

In this paper, we describe the Cloud Computing basic compute resources scheduling and allocation algorithms, in addition to the working mechanism. This paper also presents a number of experiments conducted based on CloudSim simulation toolkit in order to assess and evaluate the performance of these scheduling algorithms on Cloud Computing like infrastructure. Furthermore, we introduced and explained the CloudSim simulator design, architecture and proposed two new scheduling algorithms to enhance the existent ones and highlight the weaknesses and/or effectiveness of these algorithms

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date:September 30th 2022