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A starvation free IMLFQ scheduling algorithm based on neural network
... Proposed algorithm has shown some improvements in terms of turnaround time, as the MLFQ scheduling performance depends on the number of queues and the length of time quantum is assigned to each queue. With this aim, Parvar and Safari have utilized the recurrent neural network to optimize the number of queues and the size of time quantum of each queue of MLFQ scheduler [12]. Hoganson has pointed the performance of MLFQ scheduler in terms of task starvation. ...
... Turnaround time can be computed by adding the waiting time and the burst time of the task. Average turnaround time can be calculated as given in Eq. (12). Here TT 1 , TT 2 , . . ...
Multilevel 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.
... These factors affect the response time directly. In [17] Parvar Mohammad et al., a new algorithm is presented to solve these problems and minimize the response time simultaneously. The proposed idea to solve the starvation problem while considering the processes priority too. ...
Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system.
... As a result, the lower priority queue is crowded with CPU-bounded process [6]. These processes will suffer from starvation for a long time [7]. The basic MLFQ model has several advantages in scheduling and the algorithm is simple and easy to understand. ...
The new design of multilevel feedback queue (MLFQ) will depend on usage new technique in computing the quantum to produce an Auto Detect Quantum (ADQ) which is relied on the burst of each process has enrolled to the system. By summating the burst time of each process has arrived and divided it by the number of available processes, we can obtain the dynamic quantum in each level of scheduling. The processes are scheduled and shifted down from queue to others according to their remaining bursts time that should be updated periodically. Every queue has a unique auto-detected quantum which is gradually increased or decreased from top-level to bottom level queues according to the case of arriving processes. Depending on the results of the graphical simulating algorithm on cases study, we can discover that a dynamic quantum is very suitable to accommodate low priority processes that still for a long duration to complete their requests, i.e. avoid the starvation of CPU- bounded processes. Although, it stills compatible with high priority processes (Input/Out-Bounded) to provide fair interactivity with them. In comparison to traditional MLFQ, the performance of the new scheduling technique is better and practical according to the applied results. Additionally, we developed suitable software to simulate the new design and test it in different cases to prove it.
... In Multilevel Feedback Queue [1], [3] processes are scheduled according to their remaining CPU burst and they are shifted down from queue to queue as they have some remaining CPU burst. Every queue has unique time slice that gradually increases from upper level queue to lower level queue. ...
Feedback scheduling is a kind of process scheduling mechanism where process doesn't come with any priority. According to the CPU burst needed by the process and the CPU burst remaining the processes are shifted between queues of the feedback scheduler to get completed. In multilevel feedback queue the total architecture is divided into multiple prioritised queues. In this paper, we give an approach for jobs which starve in the lower priority queue for long time to get CPU cycle. As a result response time of those starved processes decreases eight to ten percent and over all turn around time of the whole scheduling process decreases around eight to ten percents. In comparison to other types of MLFQs the performance of the proposed scheduling technique is better and practical according to the consequence.
Real-time embedded systems have tight time constraints on the system response to external events. They are being used in many mission critical applications like avionics, industrial control systems, etc. Both hardware and software components might have an impact on the real-time system’s overall performance. For the most part, hardware components are designed with large-scale production in mind. Memory management and key kernel components like the scheduler are only two examples of software that interacts with an operating system. The problem of real-time scheduling extends range of algorithms from simple uniprocessor to highly complex multiprocessor scheduling algorithms. Over the last decade, in order to meet the demands of ever-increasing performance from the commercial market and faced with the fundamental performance limits which could not be achieved on a single-core processor due to clock speed ceiling, semiconductor manufacturers transitioned to multicore processor architectures to achieve better performance needed for real-time embedded systems. Further online (dynamic) scheduling algorithms are more flexible than offline (static) algorithms. It has been suggested in this research to survey a variety of dynamic scheduling techniques for real-time embedded systems. The characteristics and limitations of real-time activities are the subject of our research. The choice of scheduling algorithm depends on its ability to fulfill task time restrictions demanded by-product requirements.KeywordsReal-time embedded systemMulticoreKernel component schedulerEarliest deadline firstMultilevel feedback queue
We are articulating the task states of New Multi Level Feedback Queue [NMLFQ] Scheduler in this research paper. The contingent of task transitions with triggers which leads to a change of state is depicted. In real time scenario, the literal time line of real time process, with time instants and intervals are elucidated.
In multilevel feedback queue scheduling algorithm the major concern is to improve the turnaround time by keeping the system responsive to the user. Presence of vagueness in a system can further affect these performance metrics. With this intent, we attempt to propose a fuzzy based multilevel feedback queue scheduler which deals with the vagueness of parameters associated with tasks as well as to improve the performance of system by reducing the waiting time, response time, turnaround time and normalized turnaround time. Performance analysis shows that our methodology performs better than the multilevel feedback scheduling approach.
The new design of multilevel feedback queue will depend on usage new technique in computing the quantum to produce (ADQ) Auto Detect Quantum which is relied on the burst of each process has enrolled to the system. By summating the burst time of each process has arrived and dividing it by the number of available processes, we can obtained the dynamic quantum in each level of scheduling. The processes are scheduled and shifted down from queue to other according to their remaining bursts time that should be updated periodically. Every queue has a unique auto detected quantum which is gradually increased or decreased from top level to bottom level queues according to the case of arriving processes. Depending on the results of graphical simulating algorithm on cases study, we can discover that a dynamic quantum is very suitable to accommodate low priority processes that still for a long duration to complete their requests, i.e. avoid the starvation of CPU- bounded processes. Although, it stills compatible with high priority processes (I/O-Bounded) to provide a fair interactivity with them. In comparison to traditional MLFQ the performance of the new scheduling technique is better and practical according to the applied results. Additional, we developed suitable software to simulate the new design and test it on different cases to prove it.
Multilevel feedback queue scheduling algorithm allows a process which is entering to the system to move between several queues. Here, the processes initially does not come with any priority but during scheduling the processes according to their CPU burst time may be shifted to the lower level queues. Here an effective dynamic time slice is used to schedule the processes. As a result we found reduction in turnaround time, average waiting time and better throughput as compared to the previous approaches and hence increase in the overall performance. A control flow diagram is used to describe the sequence of flow of control of the processes with different conditional statements, repetition of the flow structures and case conditions. The algorithm is proposed in such a way that it reduces the starvation of the long processes. An entering process is inserted into the top level queue. When selected, processes in the queue are allocated a relatively small time slice. Upon expiration of time slice the process is moved to lower level queue.
Response time is one of the characteristics of scheduler, happens to be a
prominent attribute of any CPU scheduling algorithm. The proposed New Multi
Level Feedback Queue [NMLFQ] Scheduler is compared with dynamic, real time,
Dependent Activity Scheduling Algorithm (DASA) and Lockes Best Effort
Scheduling Algorithm (LBESA). We abbreviated beneficial result of NMLFQ
scheduler in comparison with dynamic best effort schedulers with respect to
response time.
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