Average-Case and Smoothed Competitive Analysis of the Multilevel Feedback Algorithm

Becchetti, Luca; Leonardi, Stefano; Marchetti-Spaccamela, Alberto; Schfer, Guido; Vredeveld, Tjark

doi:10.1287/moor.1050.0170

Cited by 32 publications

(16 citation statements)

References 26 publications

(20 reference statements)

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“…[41]. Modular neural networks [36] Predication [37] Pattern recognition [38] Classification [39] -- Table 4.…”

Section: Resultsmentioning

confidence: 99%

Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Scheduling

Hicham¹,

Chaker²,

Elaachak³

2017

IJECE

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Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.

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“…[41]. Modular neural networks [36] Predication [37] Pattern recognition [38] Classification [39] -- Table 4.…”

Section: Resultsmentioning

confidence: 99%

Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Scheduling

Hicham¹,

Chaker²,

Elaachak³

2017

IJECE

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“…Conversely from time to time one needs to do somewhat dissimilar, such as to minimize the maximum response time [2][23] [6] [7]. Every so often it is most attractive to decrease the variation of criteria than the real charge [8][9] [10][11].…”

Section: Scheduling Criteriamentioning

confidence: 99%

An Improved MLFQ Scheduler with Dynamic Time Slice Using Neural Approach

Rao¹,

Srinivas²

2017

IJARCSSE

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Computers in the current times are indispensable to mankind. They perform many tasks in much less time, when compared to humans, opening up new paths of exploration. One important aspect of computers is multi-tasking, which enables a system to execute multiple tasks simultaneously. The scheduler causes the processor to perform context switching among the various processes. It primarily divides the CPU time to various processes, based on a specific policy related to response time, throughput, and user interactivity. Some of the criteria which are considered while selecting a good scheduling algorithm include CPU utilization time, Load Average, Response time, Turnaround time, and throughput. Ideally, one should view this as an optimization problem. Throughput and CPU utilization should be maximized; where as other factors should be minimized. Occasionally, it requires to put the variance of any aspect, to a minimum value, rather than minimizing the aspect itself. Consistency of the aspect is preferred over inconsistency.

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“…In paper [7], Recurrent Neural Network has been used to optimize the number of queues and quantum of each queue of MLFQ scheduler to decrease response time of processes and increase the performance of scheduling. In this paper the proposed neural network takes inputs of the quantum of queues and average response time.…”

Section: Related Workmentioning

confidence: 99%

A 2LFQ Scheduling with Dynamic Time Quantum using Mean Average

Lenka¹,

Ranjan²

2012

IJCA

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The efficiency and performance of multitasking operating systems essentially depends on the nature of CPU scheduling algorithm. There are many algorithms available for CPU scheduling. Each having its own deficiency and limitations. One of the most well-known approaches for scheduling is the Multi-level Feedback Queue (MLFQ). The MLFQ tries to work in a two-fold manner. First, it tries to optimize turnaround time as it is done by running shorter jobs first. Unfortunately, the OS doesn't generally have the knowledge that how long a job will run for, exactly the knowledge that algorithms like SJF (or SRTF) require. Second, MLFQ attempts to make a system feel responsive to interactive users (i.e. users sitting and staring at the screen, waiting for a process to finish), and thus minimize response time. Wellknown algorithms like Round Robin also reduce response time but are less suitable for turnaround time. In this paper, we proposed a new approach for feedback scheduling algorithm which helps to improve the efficiency of CPU. The paper presents an approach called dynamic-time-quantum 2LFQ (Two-level Feedback Queue) scheduling. The idea is to make the operating systems adjusts the time quantum according to the burst time of set of waiting processes in the ready queue.

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Average-Case and Smoothed Competitive Analysis of the Multilevel Feedback Algorithm

Abstract: In this paper we introduce the notion of smoothed competitive analysis of online algorithms. Smoothed analysis has been proposed by Spielman and Teng [20] We also prove an ª´¾ Ã µ lower bound

Cited by 32 publications

References 26 publications

Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Scheduling

Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Scheduling

An Improved MLFQ Scheduler with Dynamic Time Slice Using Neural Approach

A 2LFQ Scheduling with Dynamic Time Quantum using Mean Average

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