Designing of Vague Logic Based 2-Layered Framework for CPU Scheduler

Fuzzy based CPU scheduler has become of great interest by operating system because of its ability to handle imprecise information associated with task. This paper introduces an extension to the fuzzy based round robin scheduler to a Vague Logic Based Round Robin (VBRR) scheduler. VBRR scheduler works on 2-layered framework. At the first layer, scheduler has a vague inference system which has the ability to handle the impreciseness of task using vague logic. At the second layer, Vague Logic Based Round Robin (VBRR) scheduling algorithm works to schedule the tasks. VBRR scheduler has the learning capability based on which scheduler adapts intelligently an optimum length for time quantum. An optimum time quantum reduces the overhead on scheduler by reducing the unnecessary context switches which lead to improve the overall performance of system. The work is simulated using MATLAB and compared with the conventional round robin scheduler and the other two fuzzy based approaches to CPU scheduler. Given simulation analysis and results prove the effectiveness and efficiency of VBRR scheduler

Dynamic Time Slice Calculation for Round Robin Process Scheduling Using NOC

Process scheduling means allocating a certain amount of CPU time to each of the user processes.  One of the popular scheduling algorithms is the “Round Robin” algorithm, which allows each and every process to utilize the CPU for short time duration.  Processes which finish executing during the time slice are removed from the ready queue.  Processes which do not complete execution during the specified time slice are removed from the front of the queue, and placed at the rear end of the queue. This paper presents an improvisation to the traditional round robin scheduling algorithm, by proposing a new method. The new method represents the time slice as a function of the burst time of the waiting process in the ready queue. Fixing the time slice for a process is a crucial factor, because it subsequently influences many performance parameters like turnaround time, waiting time, response time and the frequency of context switches.  Though the time slot is fixed for each process, this paper explores the fine-tuning of the time slice for processes which do not complete in the stipulated time allotted to them

Revitalising the Single Batch Environment: A 'Quest' to Achieve Fairness and Efficiency

In the realm of computer systems, efficient utilisation of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimise process execution on the CPU, leading to the emergence of CPU scheduling as a field of study. This research proposes a novel algorithm for batch processing that operates on a preemptive model, dynamically assigning priorities based on a robust ratio, employing a dynamic time slice, and utilising periodic sorting technique to achieve fairness. By engineering this responsive and fair model, the proposed algorithm strikes a delicate balance between efficiency and fairness, providing an optimised solution for batch scheduling while ensuring system responsiveness

Designing of vague logic based multilevel feedback queue scheduler

AbstractMultilevel feedback queue scheduler suffers from major issues of scheduling such as starvation for long tasks, fixed number of queues, and static length of time quantum in each queue. These factors directly affect the performance of the scheduler. At many times impreciseness exists in attributes of tasks which make the performance even worse. In this paper, our intent is to improve the performance by providing a solution to these issues. We design a multilevel feedback queue scheduler using a vague set which we call as VMLFQ scheduler. VMLFQ scheduler intelligently handles the impreciseness and defines the optimum number of queues as well as the optimal size of time quantum for each queue. It also resolves the problem of starvation. This paper simulates and analyzes the performance of VMLFQ scheduler with the other multilevel feedback queue techniques using MatLab

Resource Management Techniques in Cloud-Fog for IoT and Mobile Crowdsensing Environments

The unpredictable and huge data generation nowadays by smart devices from IoT and mobile Crowd Sensing applications like (Sensors, smartphones, Wi-Fi routers) need processing power and storage. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important of these limitations are Resource Allocation and Task Scheduling. The resource allocation process is a mechanism that ensures allocation virtual machine when there are multiple applications that require various resources such as CPU and I/O memory. Whereas scheduling is the process of determining the sequence in which these tasks come and depart the resources in order to maximize efficiency. In this paper we tried to highlight the most relevant difficulties that cloud computing is now facing. We presented a comprehensive review of resource allocation and scheduling techniques to overcome these limitations. Finally, the previous techniques and strategies for allocation and scheduling have been compared in a table with their drawbacks

Resource Management Techniques in Cloud-Fog for IoT and Mobile Crowdsensing Environments

The unpredictable and huge data generation nowadays by smart devices from IoT and mobile Crowd Sensing applications like (Sensors, smartphones, Wi-Fi routers) need processing power and storage. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important of these limitations are Resource Allocation and Task Scheduling. The resource allocation process is a mechanism that ensures allocation virtual machine when there are multiple applications that require various resources such as CPU and I/O memory. Whereas scheduling is the process of determining the sequence in which these tasks come and depart the resources in order to maximize efficiency. In this paper we tried to highlight the most relevant difficulties that cloud computing is now facing. We presented a comprehensive review of resource allocation and scheduling techniques to overcome these limitations. Finally, the previous techniques and strategies for allocation and scheduling have been compared in a table with their drawbacks

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