This paper introduce a new approach for scheduling algorithms which aim to improve real time operating system CPU performance. This new approach of CPU Scheduling algorithm is based on the combination of round-robin (RR) and Priority based (PB) scheduling algorithms. This solution maintains the advantage of simple round robin scheduling algorithm, which is reducing starvation and integrates the advantage of priority scheduling. The proposed algorithm implements the concept of time quantum and assigning as well priority index to the processes. Existing round robin CPU scheduling algorithm cannot be dedicated to real time operating system due to their large waiting time, large response time, large turnaround time and less throughput. This new algorithm improves all the drawbacks of round robin CPU scheduling algorithm. In addition, this paper presents analysis comparing proposed algorithm with existing round robin scheduling algorithm focusing on average waiting time and average turnaround time.
This work proposes an optimization algorithm to control speed of a permanent magnet synchronous motor (PMSM) during starting and speed reversal of motor, as well as during load disturbance conditions. The objective is to minimize the integral absolute control error of the PMSM shaft speed to achieve fast and accurate speed response under load disturbance and speed reversal conditions. The maximum overshoot, peak time, settling time and rise time of the motor is also minimized to obtain efficient transient speed response. Optimum speed control of PMSM is obtained with the aid of a PID speed controller. Modified Particle Swarm Optimization (MPSO) and Ant Colony Optimization (ACO) techniques has been employed for tuning of the PID speed controller, to determine its gain coefficients (proportional, integral and derivative). Simulation results demonstrate that with use of MPSO and ACO techniques improved control performance of PMSM can be achieved in comparison to the classical Ziegler-Nichols (Z-N) method of PID tuning.
This work proposes an optimization algorithm to control speed of a permanent magnet synchronous motor (PMSM) during starting and speed reversal of motor, as well as during load disturbance conditions. The objective is to minimize the integral absolute control error of the PMSM shaft speed to achieve fast and accurate speed response under load disturbance and speed reversal conditions. The maximum overshoot, peak time, settling time and rise time of the motor is also minimized to obtain efficient transient speed response. Optimum speed control of PMSM is obtained with the aid of a PID speed controller. Modified Particle Swarm Optimization (MPSO) and Ant Colony Optimization (ACO) techniques has been employed for tuning of the PID speed controller, to determine its gain coefficients (proportional, integral and derivative). Simulation results demonstrate that with use of MPSO and ACO techniques improved control performance of PMSM can be achieved in comparison to the classical Ziegler-Nichols (Z-N) method of PID tuning.
Operating Systems Process Scheduling Algorithmssathish sak
CPU scheduling big area of research in early ‘70s
Many implicit assumptions for CPU scheduling:
One program per user
One thread per program
Programs are independent
These are unrealistic but simplify the problem
Does “fair” mean fairness among users or programs?
If I run one compilation job and you run five, do you get five times as much CPU?
Often times, yes!
Goal: dole out CPU time to optimize some desired parameters of the system.
The note is compiled with reference from many sites and According to the syllabus of Real Time System (6th semester CSIT). Drive deep to the never ending knowledge.
SWARM INTELLIGENCE SCHEDULING OF SOFT REAL-TIME TASKS IN HETEROGENEOUS MULTIP...ecij
In this paper, a hybrid swarm intelligence algorithm (named VNABCSA) is presented for the scheduling of non-preemptive soft real-time tasks in heterogeneous multiprocessor platforms. The method is based on a combination of artificial bee colony and simulated annealing algorithms. The multi-objective function of the VNABCSA algorithm is defined to minimize the total tardiness of all tasks, total number of utilized processors, total completion time, total waiting time for all tasks, and total waiting time for all processors. We introduce a hybrid variable neighborhood search strategy to improve the convergence speed of the algorithm. Simulation results demonstrate the efficiency of the proposed methodology as compared with the
existing scheduling algorithms.
Optimal Round Robin CPU Scheduling Algorithm using Manhattan Distance IJECEIAES
In Round Robin Scheduling the time quantum is fixed and then processes are scheduled such that no process get CPU time more than one time quantum in one go. The performance of Round robin CPU scheduling algorithm is entirely dependent on the time quantum selected. If time quantum is too large, the response time of the processes is too much which may not be tolerated in interactive environment. If time quantum is too small, it causes unnecessarily frequent context switch leading to more overheads resulting in less throughput. In this paper a method using Manhattan distance has been proposed that decides a quantum value. The computation of the time quantum value is done by the distance or difference between the highest burst time and lowest burst time. The experimental analysis also shows that this algorithm performs better than RR algorithm and by reducing number of context switches, reducing average waiting time and also the average turna round time.
Task scheduling methodologies for high speed computing systemsijesajournal
High Speed computing meets ever increasing real-time computational demands through the leveraging of
flexibility and parallelism. The flexibility is achieved when computing platform designed with
heterogeneous resources to support multifarious tasks of an application where as task scheduling brings
parallel processing. The efficient task scheduling is critical to obtain optimized performance in
heterogeneous computing Systems (HCS). In this paper, we brought a review of various application
scheduling models which provide parallelism for homogeneous and heterogeneous computing systems. In
this paper, we made a review of various scheduling methodologies targeted to high speed computing
systems and also prepared summary chart. The comparative study of scheduling methodologies for high
speed computing systems has been carried out based on the attributes of platform & application as well.
The attributes are execution time, nature of task, task handling capability, type of host & computing
platform. Finally a summary chart has been prepared and it demonstrates that the need of developing
scheduling methodologies for Heterogeneous Reconfigurable Computing Systems (HRCS) which is an
emerging high speed computing platform for real time applications.
Process management is one of the important tasks performed by the operating system. The performance of
the system depends on the CPU scheduling algorithms. The main aim of the CPU scheduling algorithms is
to minimize waiting time, turnaround time, response time and context switching and maximizing CPU
utilization. First-Come-First-Served (FCFS) Round Robin (RR), Shortest Job First (SJF) and, Priority
Scheduling are some popular CPU scheduling algorithms. In time shared systems, Round Robin CPU
scheduling is the preferred choice. In Round Robin CPU scheduling, performance of the system depends on
the choice of the optimal time quantum. This paper presents an improved Round Robin CPU scheduling
algorithm coined enhancing CPU performance using the features of Shortest Job First and Round Robin
scheduling with varying time quantum. The proposed algorithm is experimentally proven better than
conventional RR. The simulation results show that the waiting time and turnaround time have been reduced
in the proposed algorithm compared to traditional RR.
An Improved Round Robin CPU Scheduling Algorithm with Varying Time QuantumIJCSEA Journal
Process management is one of the important tasks performed by the operating system. The performance of the system depends on the CPU scheduling algorithms. The main aim of the CPU scheduling algorithms is to minimize waiting time, turnaround time, response time and context switching and maximizing CPU utilization. First-Come-First-Served (FCFS) Round Robin (RR), Shortest Job First (SJF) and, Priority Scheduling are some popular CPU scheduling algorithms. In time shared systems, Round Robin CPU scheduling is the preferred choice. In Round Robin CPU scheduling, performance of the system depends on the choice of the optimal time quantum. This paper presents an improved Round Robin CPU scheduling algorithm coined enhancing CPU performance using the features of Shortest Job First and Round Robin scheduling with varying time quantum. The proposed algorithm is experimentally proven better than conventional RR. The simulation results show that the waiting time and turnaround time have been reduced in the proposed algorithm compared to traditional RR.
AN IMPROVED ROUND ROBIN CPU SCHEDULING ALGORITHM WITH VARYING TIME QUANTUMIJCSEA Journal
Process management is one of the important tasks performed by the operating system. The performance of
the system depends on the CPU scheduling algorithms. The main aim of the CPU scheduling algorithms is
to minimize waiting time, turnaround time, response time and context switching and maximizing CPU
utilization. First-Come-First-Served (FCFS) Round Robin (RR), Shortest Job First (SJF) and, Priority
Scheduling are some popular CPU scheduling algorithms. In time shared systems, Round Robin CPU
scheduling is the preferred choice. In Round Robin CPU scheduling, performance of the system depends on
the choice of the optimal time quantum. This paper presents an improved Round Robin CPU scheduling
algorithm coined enhancing CPU performance using the features of Shortest Job First and Round Robin
scheduling with varying time quantum. The proposed algorithm is experimentally proven better than
conventional RR. The simulation results show that the waiting time and turnaround time have been reduced
in the proposed algorithm compared to traditional RR.
AN OPTIMIZED ROUND ROBIN CPU SCHEDULING ALGORITHM WITH DYNAMIC TIME QUANTUMijcseit
CPU scheduling is one of the most crucial operations performed by operating system. Different algorithms
are available for CPU scheduling amongst them RR (Round Robin) is considered as optimal in time shared
environment. The effectiveness of Round Robin completely depends on the choice of time quantum. In this
paper a new CPU scheduling algorithm has been proposed, named as DABRR (Dynamic Average Burst
Round Robin). That uses dynamic time quantum instead of static time quantum used in RR. The
performance of the proposed algorithm is experimentally compared with traditional RR and some existing
variants of RR. The results of our approach presented in this paper demonstrate improved performance in
terms of average waiting time, average turnaround time, and context switching.
Operating Systems Process Scheduling Algorithmssathish sak
CPU scheduling big area of research in early ‘70s
Many implicit assumptions for CPU scheduling:
One program per user
One thread per program
Programs are independent
These are unrealistic but simplify the problem
Does “fair” mean fairness among users or programs?
If I run one compilation job and you run five, do you get five times as much CPU?
Often times, yes!
Goal: dole out CPU time to optimize some desired parameters of the system.
The note is compiled with reference from many sites and According to the syllabus of Real Time System (6th semester CSIT). Drive deep to the never ending knowledge.
SWARM INTELLIGENCE SCHEDULING OF SOFT REAL-TIME TASKS IN HETEROGENEOUS MULTIP...ecij
In this paper, a hybrid swarm intelligence algorithm (named VNABCSA) is presented for the scheduling of non-preemptive soft real-time tasks in heterogeneous multiprocessor platforms. The method is based on a combination of artificial bee colony and simulated annealing algorithms. The multi-objective function of the VNABCSA algorithm is defined to minimize the total tardiness of all tasks, total number of utilized processors, total completion time, total waiting time for all tasks, and total waiting time for all processors. We introduce a hybrid variable neighborhood search strategy to improve the convergence speed of the algorithm. Simulation results demonstrate the efficiency of the proposed methodology as compared with the
existing scheduling algorithms.
Optimal Round Robin CPU Scheduling Algorithm using Manhattan Distance IJECEIAES
In Round Robin Scheduling the time quantum is fixed and then processes are scheduled such that no process get CPU time more than one time quantum in one go. The performance of Round robin CPU scheduling algorithm is entirely dependent on the time quantum selected. If time quantum is too large, the response time of the processes is too much which may not be tolerated in interactive environment. If time quantum is too small, it causes unnecessarily frequent context switch leading to more overheads resulting in less throughput. In this paper a method using Manhattan distance has been proposed that decides a quantum value. The computation of the time quantum value is done by the distance or difference between the highest burst time and lowest burst time. The experimental analysis also shows that this algorithm performs better than RR algorithm and by reducing number of context switches, reducing average waiting time and also the average turna round time.
Task scheduling methodologies for high speed computing systemsijesajournal
High Speed computing meets ever increasing real-time computational demands through the leveraging of
flexibility and parallelism. The flexibility is achieved when computing platform designed with
heterogeneous resources to support multifarious tasks of an application where as task scheduling brings
parallel processing. The efficient task scheduling is critical to obtain optimized performance in
heterogeneous computing Systems (HCS). In this paper, we brought a review of various application
scheduling models which provide parallelism for homogeneous and heterogeneous computing systems. In
this paper, we made a review of various scheduling methodologies targeted to high speed computing
systems and also prepared summary chart. The comparative study of scheduling methodologies for high
speed computing systems has been carried out based on the attributes of platform & application as well.
The attributes are execution time, nature of task, task handling capability, type of host & computing
platform. Finally a summary chart has been prepared and it demonstrates that the need of developing
scheduling methodologies for Heterogeneous Reconfigurable Computing Systems (HRCS) which is an
emerging high speed computing platform for real time applications.
Process management is one of the important tasks performed by the operating system. The performance of
the system depends on the CPU scheduling algorithms. The main aim of the CPU scheduling algorithms is
to minimize waiting time, turnaround time, response time and context switching and maximizing CPU
utilization. First-Come-First-Served (FCFS) Round Robin (RR), Shortest Job First (SJF) and, Priority
Scheduling are some popular CPU scheduling algorithms. In time shared systems, Round Robin CPU
scheduling is the preferred choice. In Round Robin CPU scheduling, performance of the system depends on
the choice of the optimal time quantum. This paper presents an improved Round Robin CPU scheduling
algorithm coined enhancing CPU performance using the features of Shortest Job First and Round Robin
scheduling with varying time quantum. The proposed algorithm is experimentally proven better than
conventional RR. The simulation results show that the waiting time and turnaround time have been reduced
in the proposed algorithm compared to traditional RR.
An Improved Round Robin CPU Scheduling Algorithm with Varying Time QuantumIJCSEA Journal
Process management is one of the important tasks performed by the operating system. The performance of the system depends on the CPU scheduling algorithms. The main aim of the CPU scheduling algorithms is to minimize waiting time, turnaround time, response time and context switching and maximizing CPU utilization. First-Come-First-Served (FCFS) Round Robin (RR), Shortest Job First (SJF) and, Priority Scheduling are some popular CPU scheduling algorithms. In time shared systems, Round Robin CPU scheduling is the preferred choice. In Round Robin CPU scheduling, performance of the system depends on the choice of the optimal time quantum. This paper presents an improved Round Robin CPU scheduling algorithm coined enhancing CPU performance using the features of Shortest Job First and Round Robin scheduling with varying time quantum. The proposed algorithm is experimentally proven better than conventional RR. The simulation results show that the waiting time and turnaround time have been reduced in the proposed algorithm compared to traditional RR.
AN IMPROVED ROUND ROBIN CPU SCHEDULING ALGORITHM WITH VARYING TIME QUANTUMIJCSEA Journal
Process management is one of the important tasks performed by the operating system. The performance of
the system depends on the CPU scheduling algorithms. The main aim of the CPU scheduling algorithms is
to minimize waiting time, turnaround time, response time and context switching and maximizing CPU
utilization. First-Come-First-Served (FCFS) Round Robin (RR), Shortest Job First (SJF) and, Priority
Scheduling are some popular CPU scheduling algorithms. In time shared systems, Round Robin CPU
scheduling is the preferred choice. In Round Robin CPU scheduling, performance of the system depends on
the choice of the optimal time quantum. This paper presents an improved Round Robin CPU scheduling
algorithm coined enhancing CPU performance using the features of Shortest Job First and Round Robin
scheduling with varying time quantum. The proposed algorithm is experimentally proven better than
conventional RR. The simulation results show that the waiting time and turnaround time have been reduced
in the proposed algorithm compared to traditional RR.
AN OPTIMIZED ROUND ROBIN CPU SCHEDULING ALGORITHM WITH DYNAMIC TIME QUANTUMijcseit
CPU scheduling is one of the most crucial operations performed by operating system. Different algorithms
are available for CPU scheduling amongst them RR (Round Robin) is considered as optimal in time shared
environment. The effectiveness of Round Robin completely depends on the choice of time quantum. In this
paper a new CPU scheduling algorithm has been proposed, named as DABRR (Dynamic Average Burst
Round Robin). That uses dynamic time quantum instead of static time quantum used in RR. The
performance of the proposed algorithm is experimentally compared with traditional RR and some existing
variants of RR. The results of our approach presented in this paper demonstrate improved performance in
terms of average waiting time, average turnaround time, and context switching.
CPU scheduling is one of the most crucial operations performed by operating system. Different algorithms
are available for CPU scheduling amongst them RR (Round Robin) is considered as optimal in time shared
environment. The effectiveness of Round Robin completely depends on the choice of time quantum. In this
paper a new CPU scheduling algorithm has been proposed, named as DABRR (Dynamic Average Burst
Round Robin). That uses dynamic time quantum instead of static time quantum used in RR. The
performance of the proposed algorithm is experimentally compared with traditional RR and some existing
variants of RR. The results of our approach presented in this paper demonstrate improved performance in
terms of average waiting time, average turnaround time, and context switching.
AN OPTIMIZED ROUND ROBIN CPU SCHEDULING ALGORITHM WITH DYNAMIC TIME QUANTUMijcseit
CPU scheduling is one of the most crucial operations performed by operating system. Different algorithms
are available for CPU scheduling amongst them RR (Round Robin) is considered as optimal in time shared
environment. The effectiveness of Round Robin completely depends on the choice of time quantum. In this
paper a new CPU scheduling algorithm has been proposed, named as DABRR (Dynamic Average Burst
Round Robin). That uses dynamic time quantum instead of static time quantum used in RR. The
performance of the proposed algorithm is experimentally compared with traditional RR and some existing
variants of RR. The results of our approach presented in this paper demonstrate improved performance in
terms of average waiting time, average turnaround time, and context switching.
AN OPTIMIZED ROUND ROBIN CPU SCHEDULING ALGORITHM WITH DYNAMIC TIME QUANTUMijcseit
CPU scheduling is one of the most crucial operations performed by operating system. Different algorithms are available for CPU scheduling amongst them RR (Round Robin) is considered as optimal in time shared environment. The effectiveness of Round Robin completely depends on the choice of time quantum. In this paper a new CPU scheduling algorithm has been proposed, named as DABRR (Dynamic Average Burst Round Robin). That uses dynamic time quantum instead of static time quantum used in RR. The performance of the proposed algorithm is experimentally compared with traditional RR and some existing variants of RR. The results of our approach presented in this paper demonstrate improved performance in terms of average waiting time, average turnaround time, and context switching.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Comparision of different Round Robin Scheduling Algorithm using Dynamic Time ...Editor IJMTER
Performance of computer system is heavily depends on the scheduling of the
processes. The concept of scheduling helps in selection of the process for execution. There
are many scheduling techniques available like First-Come-First-Serve (FCFS), Shortest Job
First (SJF), Priority, Round Robin (RR) etc. Output of these scheduling technique is depends
on mainly three parameter, one is average waiting time, average turnaround time and number
of context switch. It also depends on the context switching of the processes. In this paper, we
focus on the RR scheduling techniques. There are two types of RR scheduling technique, one
is RR with static quantum and other is RR with dynamic quantum. In this paper we compare
the result of different RR algorithm techniques those having dynamic quantum and we show
that even-odd RR scheduling is the best scheduling technique compare to simple RR, average-max RR and average mid-max RR.
In a multiprogramming system, multiple processes exist concurrently in main memory. Each process alternates between using a processor and waiting for some event to occur, such as the completion of an I O operation. The processor or processors are kept busy by executing one process while the others wait. The key to multiprogramming is scheduling. CPU scheduling deals with the problem of deciding which of the processes in the ready queue is to be allocated the CPU. By switching the CPU among processor the operating system can make the computer more productive. Scheduling affectes the performance of the system because it determines which processes will wait and which will progress. In this paper, simulation of various scheduling algorithm First Come First Served FCFS , Round Robin RR , Shortest Process Next SPN and Shortest Remaining Time SRT is done over C Daw Khin Po ""Simulation of Process Scheduling Algorithms"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25124.pdf
Paper URL: https://www.ijtsrd.com/computer-science/operating-system/25124/simulation-of-process-scheduling-algorithms/daw-khin-po
Characteristic Specific Prioritized Dynamic Average Burst Round Robin Schedul...IJCSEA Journal
CPU scheduling is one of the most crucial operations performed by operating systems. Different conventional algorithms like FCFS, SJF, Priority, and RR (Round Robin) are available for CPU Scheduling. The effectiveness of Priority and Round Robin scheduling algorithm completely depends on selection of priority features of processes and on the choice of time quantum. In this paper a new CPU scheduling algorithm has been proposed, named as CSPDABRR (Characteristic specific Prioritized Dynamic Average Burst Round Robin), that uses seven priority features for calculating priority of processes and uses dynamic time quantum instead of static time quantum used in RR. The performance of the proposed algorithm is experimentally compared with traditional RR and Priority scheduling algorithm in both uni-processor and multi-processor environment. The results of our approach presented in this paper demonstrate improved performance in terms of average waiting time, average turnaround time, and optimal priority feature.
CHARACTERISTIC SPECIFIC PRIORITIZED DYNAMIC AVERAGE BURST ROUND ROBIN SCHEDUL...IJCSEA Journal
CPU scheduling is one of the most crucial operations performed by operating systems. Different conventional algorithms like FCFS, SJF, Priority, and RR (Round Robin) are available for CPU
Scheduling. The effectiveness of Priority and Round Robin scheduling algorithm completely depends on selection of priority features of processes and on the choice of time quantum. In this paper a new CPU scheduling algorithm has been proposed, named as CSPDABRR (Characteristic specific Prioritized Dynamic Average Burst Round Robin), that uses seven priority features for calculating priority of
processes and uses dynamic time quantum instead of static time quantum used in RR. The performance of
the proposed algorithm is experimentally compared with traditional RR and Priority scheduling algorithm
in both uni-processor and multi-processor environment. The results of our approach presented in this
paper demonstrate improved performance in terms of average waiting time, average turnaround time, and optimal priority feature.
STATISTICAL APPROACH TO DETERMINE MOST EFFICIENT VALUE FOR TIME QUANTUM IN RO...ijcsit
Scheduling various processes is one of the most fundamental functions of the operating system. In that
context one of the most common scheduling algorithms used in most operating systems is the Round Robin
method in which, the ready processes waiting in the ready queue, take control of the processor for a short
period of time known as the time quantum (or time slice) circularly. Here we discuss the use of statistics
and develop a mathematical model to determine the most efficient value for time quantum. This is strictly
theoretical as we do not know the values of times for the various processes beforehand. However the
proposed approach is compared with the recent developed algorithms to this regard to determine the
efficiency of the proposed algorithm.
Comparative analysis of the essential CPU scheduling algorithmsjournalBEEI
CPU scheduling algorithms have a significant function in multiprogramming operating systems. When the CPU scheduling is effective a high rate of computation could be done correctly and also the system will maintain in a stable state. As well as, CPU scheduling algorithms are the main service in the operating systems that fulfill the maximum utilization of the CPU. This paper aims to compare the characteristics of the CPU scheduling algorithms towards which one is the best algorithm for gaining a higher CPU utilization. The comparison has been done between ten scheduling algorithms with presenting different parameters, such as performance, algorithm’s complexity, algorithm’s problem, average waiting times, algorithm’s advantages-disadvantages, allocation way, etc. The main purpose of the article is to analyze the CPU scheduler in such a way that suits the scheduling goals. However, knowing the algorithm type which is most suitable for a particular situation by showing its full properties.
An improved approach to minimize context switching in round robin scheduling ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Reinforcement learning based multi core scheduling (RLBMCS) for real time sys...IJECEIAES
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
Similar to Priority based round robin (PBRR) CPU scheduling algorithm (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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Figure 1. Various types of schedulers
For long-term scheduler, it loads processes in memory for execution after selecting them from the
job pool. Concerning the short-term scheduler, it allocates the CPU to one process from those ready to be
executed. For medium-term scheduler, it takes off processes from main memory and place them on
secondary memory (such as a disk drive) or vice versa. This is commonly referred to as “Swapping” of
processes in memory [2].
The efficiency of the Round Robin Scheduling Algorithm depends on the time quantum size. Firstly,
if the quantum of time is extremely large, it decreases the response time and it behaves similar to FCFS
algorithm. In the other hand, if the quantum of time is extremely small this causes many context switches,
which decreases the CPU efficiency.
2. RELATED WORKS
In the recent years, a number of CPU scheduling mechanisms have been developed for predictable
allocation of processor. Extracting advantages of each algorithm and try to mix them to lead to the perfect
algorithm according to a specific situation.
In Mishra, an improved Round Robin scheduler is developed named Improved Round Robin (IRR)
CPU scheduling algorithm. It works similar to Round Robin (RR) with a small improvement [6]. IRR picks
the first process from the ready queue and allocate the CPU to it for a time interval of up to one QT. Every
time a process accomplish its QT, it checks the remaining CPU burst time of the currently running process.
If the remaining CPU burst time of the currently running process is less than one QT, the CPU again
allocated to the currently running process for remaining CPU burst time.
To evaluate the performance, let's consider the following ready queue as shown in Table 1. For
simple Round Robin algorithm, the Gantt chart is shown in Figure 2 for QT=10 ms. Concerning Improved
Round Robin algorithm (IRR), the Gantt chart will be as follow as shown in Figure 3. Performances of the
two algorithms are resumed in Table 2 regarding to Average WT and Average TT.
Table 1. Process's Id and Burst Time
Process ID Burst Time (ms)
P1 5
P2 12
P3 23
P4 26
P5 34
Table 2. Comparison of RR and IRR
Algorithm Average WT (ms) Average TT (ms)
RR 38.4 57.8
IRR 30.4 49.8
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Figure 2. Gantt chart for simple RR Figure 3. Gantt chart for IRR
The proposed IRR in CPU scheduling algorithm is giving better performances than RR. After
improvement in RR, it has been found that the WT and TT have been reduced drastically [6]. A modified
round robin algorithm, proposed by [7], consists of a mixture between shortest job first and round robin
algorithms. In this new algorithm, the QT takes the burst time of mid process when the number of processes
is odd else, it takes the average time of burst time of all processes.
To evaluate the performance, let's consider the following ready queue as shown in Table 3.
For simple Round Robin algorithm, the Gantt chart is shown in Figure 4 with QT=25 ms. Concerning
Modified Round Robin algorithm (MRR), the Gantt chart will be as follow as shown in Figure 5.
Performances of the two algorithms are resumed in Table 4. regarding to average WT and average TT.
Table 3. Process's Id and Burst Time
Process ID Burst Time (ms)
P1 14
P2 45
P3 36
P4 25
P5 77
Table 4. Comparison of RR and MRR
Algorithm Average WT (ms) Average TT (ms)
RR 70.2 109.6
MRR 56.8 96.2
Figure 4. Gantt chart for simple RR
P1 P2 P3 P4 P5 P2 P5 P5
0 14 50 86 111 147 156 192 197
Figure 5. Gantt chart for MRR
It is concluded from the above experiments that the proposed algorithm MRR performs better than
simple RR in terms of performance metrics such as average WT and average TT. In the work of [8], the
proposed algorithm is based on the integration of Round Robin and priority scheduling algorithm and also
implements the concept of aging by attributing new process priorities. First, the CPU is allocated to every
process in round robin fashion with a given priority and quantum. Then, processes are sorted in increasing
order according to their remaining CPU burst time in the ready queue. Consequently, new priorities are
assigned; the process with shortest remaining CPU burst is assigned with highest priority. Each process gets
the control of the CPU until they finished their execution. This new approach improves the performance of
CPU in real time operating system.
To evaluate the performance, let's consider the following ready queue as shown in Table 5.
For simple Round Robin algorithm with QT=5ms, we obtain the following Gantt chart as shown in Figure 6.
Concerning Priority based Round Robin algorithm (PRR), tasks are executed according to their priority, and
the Gantt chart will be as follow as shown in Figure 7. Performances of the two algorithms are resumed in
Table 6. regarding to average WT and Average TT.
Table 5. Process's Id and Burst Time
Process ID Burst Time (ms) Priority
P1 22 4
P2 18 2
P3 9 1
P4 10 3
P5 4 5
Table 6. Comparison of RR and PRR
Algorithm Average WT (ms) Average TT (ms)
RR 33.2 45.8
PRR 26.2 38.8
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Figure 6. Gantt chart for simple RR Figure 7. Gantt chart for PRR
The proposed algorithm improves all the drawbacks of round robin CPU scheduling algorithm.
It retains the advantage of round robin in reducing starvation and also integrates the advantage of priority
scheduling. In Abdulrahim et al. a New Improved Round Robin (NIRR) was developed [9]. This algorithm is
an improved version of Improved Round Robin (IRR) algorithm mentioned above in [6]. This algorithm
holds processes according to their arrival times in a queue named ARRIVE queue. In the other hand, other
processes are in a queue called REQUEST queue waiting their turn to occupy the CPU. The time quantum is
considered as the average of burst times of the processes in the REQUEST queue.
To evaluate the performance, let's consider the following ready queue as shown in Table 7.
For simple Round Robin algorithm with QT=50ms, we obtain the following Gantt chart as shown in
Figure 8. Concerning New Improved Round Robin algorithm (NIRR), the Gantt chart is as follows as shown
in Figure 9. Performances of the two algorithms are resumed in Table 8 regarding to the average WT and the
average TT.
Table 7. Process's with Its Id and Burst Time
Process ID Burst Time (ms)
P1 23
P2 75
P3 93
P4 48
P5 2
Table 8. Comparison of RR and NIRR
Algorithm Average WT (ms) Average TT (ms)
RR 113 161.2
NIRR 53.8 102
Figure 8. Gantt chart for simple RR Figure 9. Gantt chart for NIRR
In Emilio an algorithm called “Modulo Based Round Robin Algorithm” is proposed. This algorithm
is based on Round Robin fashion an intelligent time quantum and then assigns priority to the processes [10].
This algorithm start by calculating the average of CPU burst of all the processes (P) and then compute for
each process the burst time modulo P which is named (M). After that processes are sorted according to the
value of M and then the time quantum (QT) is considered equal to P.
To evaluate the performance, let's consider the following ready queue as shown in Table 9
(All processes arrive at t=0 and QT=10 ms). For simple Round Robin algorithm, we obtain the following
Gantt chart as shown in Figure 10. For “Modulo Based Round Robin Algorithm”, the Gantt chart is as
follows as shown in Figure 11. Performances of the two algorithms are resumed in Table 10 regarding to the
average WT and the average TT.
Table 9. Process's Id and Burst Time
Process ID Burst Time (ms)
P1 10
P2 20
P3 30
P4 40
P5 50
Table 10. Comparison of RR and “Modulo Based
Round Robin Algorithm”
Algorithm Average WT (ms) Average TT (ms)
RR 60 90
NIRR 52 82
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Figure 10. Gantt chart for simple RR Figure 11. Gantt chart for “Modulo Based
Round Robin Algorithm”
3. REAL TIME OPERATING SYSTEM (RTOS)
Real Time Operating System (RTOS) is a reactive OS that must respond continuously to stimuli
from a process that seeks to control. A real-time system is a reactive system that must meet time
constraints [11]. A real-time system must be able to process information from the process within a period
that does not affect the process control. React too late can lead to catastrophic consequences for the system
itself or the process. Compliance with time constraints is the main constraint to satisfy. The validity of a real
time system depends not only on the results of the treatment carried out but also the temporal aspect (a fair
calculation but out of time is an invalid calculation).
3.1. Real-time systems classification
The critical time constraints led to classify the real-time systems in the following three
categories [12]:
a. Real time strict system: a system that is subject to strict time constraints, that is to say for which the
slightest mistake time can have devastating human and economic consequences. Most applications in the
avionics field, automobile, etc., are strict real time;
b. Real-time flexible system: a system that is subject to flexible time constraints, a number of timing errors
can be tolerated.
c. Real-time mixed system: a system that is subject to strict and flexible time constraints.
3.2. Real-time task
A real-time task consists of a set of instructions that can be run in sequence on one or more
processors and meet time constraints. In the following, we will assume that a task will not run in parallel.
It can be repeated any number of times, possibly infinite. Each of these performances is called instance or
work ("job"). A real-time system consists of a set of real-time tasks subject to real-time constraints (Real time
constraints).
A real-time task can be:
a. Periodic: its instances (versions) are repeated indefinitely and there is a constant time between two
successive activations of instances referred period.
b. Sporadic: its instances (versions) are repeated indefinitely and there is a minimum time between two
successive instances.
c. Aperiodic: there is no correlation between successive instances.
3.3 Periodic tasks
The classic model of periodic tasks called Liu and Layland models, the most used in modeling real-
time systems [13]. This model allows defining multiple settings for a job. These parameters are of two types:
static parameters for the task itself and the dynamic parameters on each instance of the task as shown in
Figure 12. The basic static parameters of a periodic task τiis:
τi = (Ri, Ci, Di, Ti) (1)
Ri: (release time) date of first activation of task i, when τi can start its first performance.
Ci: (computing time): execution time of τi. This parameter is considered in several works on real-time
scheduling as the worst case execution time (WCET for worst-case execution time) on the processor on
which it will run.
Di: relative maturity or critical time frame for each activation of τi.
Ti: implementation period τi.
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Figure 12. Classic model in periodic tasks
Other static parameters are derived from the basic ones:
Ui = , CPU utilization factor for τi,Ui≤1.
CHi = , the density of the task τi, CHi≤1.
Concrete/ no concrete tasks
If all the first activation dates of all jobs are known, it is said that the tasks are concrete. On the
contrary, if we do not know the dates of first activation, it is said that these tasks are not concrete.
a. Synchronous/ asynchronous tasks
If all tasks are practical and have the same date of first activation, it is said that the tasks are
synchronized activations. Otherwise, they are asynchronous
b. Real-time constraints
In real-time scheduling, tasks can be subject to several constraints such as the constraints of deadlines,
strict periodicity, dependencies and precedence, etc.
c. Deadlines
The constraints of deadlines allow expressing a condition of the end date of implementation at latest of
a given task. Consider a system of periodic tasks, according to the relationship between the period Ti
and the deadline for each task i Di, we distinguish three types of deadlines:
Di = Ti: each activation of the task τi must be executed before the next activation. We talk about
deadlines on tasks activations, implicit deadlines or deadlines on requests.
Di≤Ti: each activation of the task τi must be executed on or before a date less than or equal to the
date of its next activation. We talk about stress at work deadlines.
Di≠Ti: there is no correlation between at latest end date of execution of τi upon activation and its
next activation of τi. One can have (Di≤Ti) or (Di>Ti), we speak here about arbitrary tasks
deadlines.
d. Strict periodicity
Consider a periodic task τi in a real time task system, strict periodicity constraint requires that the time
elapsed between two consecutive beginnings of execution dates 𝑠 and 𝑠 corresponds exactly to the
period of the task τi. The advantage of this constraint is that knowledge of the actual start date of the
first instance 𝑠 implies knowledge of the effectivestart date of all subsequent instances of the same task
[14], this is expressed by the relationship:
𝑠 =𝑠 +kTi (k≥ 1) (2)
e. Dependencies between tasks
A dependency between two tasks i and j can be of two types: A precedence dependency and/or data
dependency. A precedence dependency between (i, j) requires that the task j started running after the
task i have completely finished running [14, 15, 16, 17]. The precedence of constraints is indirectly real-
time constraints and we said that the task i is a predecessor of the task j and j is a successor of i. If task i
runs exactly once before a run of task j, we have then a simple precedence constraint if not it’s a wide
precedence [17, 2, 18]. A data dependency means the task i produces a result that is consumed by
j [14, 15], this dependence inevitably leads to precedence between tasks. Tasks are called independent if
they are defined only by their temporal parameters.
f. Latency
Latency is defined for dependent tasks by transitivity in the case of a path tasks. Let’s suppose τi and τj
two tasks, the latency between i and j denoted L (τi, τj) is the time between the start of execution of τi
and the end of execution of τj.
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4. SCHEDULING
4.1. Scheduling objectives
a. When designing a scheduling algorithm, a system designer must take into consideration many factors
such as the kind of systems used and user's needs.
b. Maximize throughput: Maximizing the throughput of a scheduler is made by servicing the maximum
number of processes per unit of time.
c. Avoid an infinite blocking state or starvation: Avoiding an infinite blocking state or starvation is avoiding
process to stuck in waiting state for unbounded time before or while process service.
d. Minimize overhead: Using system resources in an effective manner to reduce system overhead (system
overhead cause resources wastage). So overall system performance improves greatly.
e. Enforcement of priorities: If a system is based on processes priorities, the scheduler shall privilege higher
priority processes.
f. Achieve balance between response and utilization: System resources shall be kept busy by the scheduler.
The scheduler can prevent starvation through the concept of aging and be able to increase
throughput by promoting processes having a short burst time and can be satisfied quickly. Also, processes
whose completion because other processes to run shall be favored by the scheduler to accomplish goals
previously sited.
4.2. Scheduling parameters
Each CPU scheduling algorithm has its own proprieties, and choosing a particular algorithm may
favor one class of process over the other. Many criteria must be considered for comparing CPU scheduling
algorithms performance. Those criteria include the following:
a. CPU utilization: the maximum rate of keeping the CPU busy doing useful work.
b. Throughput: The number of jobs processed per time unit.
c. Turnaround time: The time needed for the execution of one process.
d. Waiting time: The time spent by the process waiting in ready queue.
e. Response time: The time between submission of the request and the first response.
f. The sharing of the processor and resources introduced several states for a task as shown in Figure 13:
1. New: the task is not yet activated.
2. Ready: the task is enabled and has all the resources it needs to run.
3. Waiting: the task is waiting for resources.
4. Running: the task runs.
5. Terminated: the task has no current application, it has terminated.
The scheduler is responsible for the transition from one task state to another. For each invocation,
the scheduler updates the list of ready tasks by including all active tasks and who have their resources and
removing tasks that ended their performances or blocked by waiting a resource. Then among the ready tasks,
the scheduler selects the highest priority task to run. Thus, a task in the new state can move to the ready state.
A task in the ready state may go to running state or waiting state. A task in running status may return to the
ready state if it is preempted by another higher-priority task, it can go to the waiting state if it is waiting for
resource liberation or has finished executing, it passes in the passive state.
Figure 13. Process state transition diagram
new
ready runnig
terminated
waiting
Interrupt Exitadmitted
Scheduler Dispatch
I/O or event waitI/O or event completion
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A task can move from the waiting state to the ready state, and finally a task can move from the
passive state to the ready state. Figure 13 provides an illustration of the different states and their
transitions [2].
5. EXISTING CPU SCHEDULING ALGORITHMS
There are varieties of CPU Scheduling algorithms. The most and commonly used are explained.
a. First-come first-served (FIFO) scheduling policy
It is a policy of seniority without requisition: The CPU is allocated according to the process submission
order. In this policy, processes of low execution time can be penalized because a long process precedes
them in the queue.
b. Scheduling policy "shorter first" scheduling policy
This policy tries to remedy the disadvantage mentioned for the previous policy. The CPU is allocated to
the process of smaller execution time. This policy is also a policy without requisition. It has the property
of minimizing the average response time for all scheduling algorithms without requisitioning.
It penalizes long work. It also makes it necessary to estimate the duration of the processes, which are
not usually known. There is a version with requisition of this policy called "time remaining first
shortest": in this case, the executing process restores the processor when a new execution time process
less than its remaining execution time becomes ready [18].
c. Round robin scheduling policy
Each process present in the queue of ready processes acquires the processor in turn for a maximum of a
time equal to the quantum of time. If the process has completed its execution before the end of the
quantum, it releases the processor and the next process in the queue of the ready processes is elected.
If the process has not completed before the end of the quantum, it loses the processor and is reinserted
at the end of the process. This turnstile policy is usually used in timeshare systems. Its performance
largely depends on the size of the quantum. Too large quantum increases the response times while a too
small quantum multiplies the context switches until they are not negligible [19].
d. Priority-based Scheduling:
A fixed priority rank is assigned to each process and the scheduler sort processes basing on their
priorities. Process with highest priority is putted on the head of the queue and the process with the
lowest priority in the tail [20].
e. Rate Monotonic (RM)
RM scheduling algorithm was introduced by Liu and Layland in 1973 [13]. This is a preemptive
scheduling algorithm which applies to independent periodic tasks, and due upon request (Ti=Di).
The task priority is inversely proportional to its period, that is to say, the longer the period of a task is,
the higher is the priority. This algorithm is optimal in the class of algorithms for fixed priority
preemptive independent tasks due on synchronous request. A sufficient condition for schedulability of
the RM algorithm for a set of periodic tasks Γn maturing on request is given by [13]:
∑ ≤ n(2 – 1) (3)
f. Deadline Monotonic (DM)
The DM scheduling algorithm was introduced by Leung and Whitehead in 1982 for conditioned
deadline tasks [21]. The task priority is inversely proportional to its relative deadline, that is to say, the
longer the relative deadline is thehigher isthe priority. This algorithm is optimal in the class of
preemptive and fixed priority algorithms for independent preemptive and conditioned deadline tasks
(Di ≤ Ti). The sufficient condition for schedulability of the DM algorithm for a set of periodic tasks Γn
due to stress is given by:
∀𝜏 ∈ 𝛤 , 𝐶 + ∑ ∈ ( ) . 𝐶 ≤ 𝐷 (4)
With hp(τi) is the set of Γn tasks of priorities higher than or equal to Γn, not including Γn.
Dynamic priorities. A dynamic priority changes during the execution of an instance [22].
a. Earliest Deadline First (EDF)
The EDF scheduling algorithm was introduced by Lui and Layland in 1973 [13]. It is a scheduling
algorithm which can be preemptive or non-preemptive and applied for independent periodic tasks
(Ti = Di). The highest priority at time t is allocated to the task with the closest absolute deadline.
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EDF is optimal for independent and preemptive tasks. A necessary and sufficient condition of
schedulability of EDF for a set of periodic tasks Γn is given by:
∀τi∈Γn∑ ≤ 1 (5)
b. Least-Laxity First (LLF)
The LLF scheduling algorithm is based on the laxity. The task whose laxity is the lowest compared
to all the ready tasks have the highest priority. This algorithm is optimal for independent and
preemptive tasks.
The condition of schedulability of LLF and the EDF are the same. That is to say that the necessary
and sufficient condition of schedulability of LLF for a set of periodic tasks Γnis given by:
∀τi∈Γn∑ ≤ 1 (6)
A dynamic priority changes during the execution of an instance. The most used scheduling
algorithm for dynamic priorities is "Least laxity Fisrt".
6. DRAWBACKS OF ROUND ROBIN SCHEDULING ALGORITHMS
Round Robin scheduling algorithm has many disadvantages, which are as following:
6.1. High Average Waiting Time
For round robin architecture, the process spends the time in the ready queue waiting his turn to own
the processor. Due to the presence of time quantum, processes are pushed to leave the processor and return to
the waiting state. This procedure produces a high average waiting time, which presents the main
disadvantage.
6.2. Low throughput
Throughput present the number of process completed per time unit. Due to its time slice, Round
Robin is characterized by a high number of context switches, which leads to overall degradation of the
system performance (throughput).
6.3. Context switch
Ones the time slice finished, the process is forced to leave the CPU. The scheduler stores the context
of the current process in stack or a register and allots the CPU to the next process in the ready queue. This
concept is known by context switching which leads to time wastage and scheduler overhead.
6.4. High response time
Response time is defined as the time between submission of a request and the first CPU response. In
general, round robin made larger response time, which causes system performance degradation. To achieve
high performance, the reduction of response time shall be indispensable.
6.5. Very high turnaround time
Turnaround time is the time between submission of a process and its completion. Round Robin
scheduling algorithm is characterized by a high turnaround time. So as a result, to improve the system
performance this parameter should be reduced.
7. EFFICIENCY OF EXISTING APPROACHES
Scheduling processes in a fair manner and producing results in minimum average waiting time and
turnaround time are the main criteria of an efficient algorithm. In different research papers, the issue of time
quantum and priority decision is the bottleneck in various CPU scheduling algorithm. Finding the optimal
time quantum in round robin algorithm is a difficult problem to solve. The real meaning of dynamicity in
CPU scheduling algorithms is not goal to attempt. A variety of objectives are to be taken into consideration
and solutions must satisfy most of the performance criteria. Solutions must be universally accepted by most
of the operating system. Concerning dynamic time quantum and dynamic priority calculation, many
researchers are carried out and some of them paid attention towards the combination of two. Other
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researchers pay big attention to response time and some of them even used artificial intelligence in
determining dynamic time quantum or priority. In general, measurement are not far from outliers. So giving
the optimum results cannot be obvious for the set of processes having CPU burst time. The presence of
outliers in the list is obvious especially when the CPU always tries to keep a mix of CPU bound and I/O
bound processes. Decreasing the outlier makes the data more exact when finding mean or median value for
determining time quantum.
8. PROPOSED ALGORITHM
This approach of Round Robin Scheduling Algorithm emphasizes on simple Round Robin
Algorithm drawbacks. This kind of scheduling gives the same amount of time to all processes. Processes are
executed in first come first served way. Round Robin is not efficient with processes with small execution
time. Therefore, as a result, the waiting time and response time of processes increase and consequently the
system throughput decreases. In this proposed algorithm, we have implemented RR algorithm while taking
into account priority based for tasks management. In fact, a priority index is assigned to each Process.
Processes in ready queue are sorted according to their priorities. Process with small priority index are placed
in the head of the ready queue and so on. The proposed algorithm solves the problem of higher average
waiting time, turnaround time, response time and more context switches and thereby improves the system
performance. The proposed algorithm is described in the following flowchart as shown in Figure 14.
Figure 14. PBRR flowchart
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8.1. PBRR illustration by taking example
We have three processes P0, P1and P2 in ready queue arriving at time 0 with burst time 10, 3 and 2
respectively. Each process has its own priority index 2, 1 and 3respectively (the lowest index is associated to
the highest priority).We consider Time quantum (TQ) is assumed4 milliseconds (ms). In step1, the scheduler
selects the process, which has the highest priority and put it in head of the ready queue and so on. In the end,
the ready queue is sorted according to priority index of each process.
The process P1 has the highest priority, so the scheduler assigns P1 to CPU. After execution of
allocated process for time interval of 3ms, P1 has finished execution, it will be removed from the ready
queue. Next process in the sorted ready queue, which has the highest priority, is P0 with 10ms CPU burst
time. CPU will be allocated to P0 for a time interval of 4ms.P0 has a remaining CPU burst time to 6ms.
It will be compared to time quantum so it is less than the specified TQ value soP1is put at the tail of the ready
queue to be allocated again to CPU for remaining time interval of 6ms. Next process in the ready queue,
which has highest priority is P2 with 2ms CPU burst time. The scheduler will allocate P2 to the CPU for a
time interval of 2ms. Since the CPU burst time of P2 is less than the TQ, P2 has finished execution, it will be
removed from the ready queue. Now, P0 is the only process that didn't finish its execution. So P0 will be
allocated to the CPU again for 4ms. P0 remaining burst time became 2ms. Since there are no other processes
in the ready queue, P0 is reallocated to the CPU for the remaining CPU burst time. P0 finish its executions
and will be removed from the ready queue.
When using PBRR scheduling algorithm, the waiting time is 5 ms for P0, 0ms for P1and 7 ms for
P2, as result the average waiting time is 4ms. It should be noted that the average waiting time is 5.33 ms
when using simple RR scheduling algorithm. This average waiting time was significantly improved when
using PBRR scheduling algorithm for the same set of processes and features.
8.2. Experimental Result
The environment in which the execution takes place is a single processor environment and all the
processes are independent. All the processes have the same arrival time. All the attributes like burst time,
number of processes, priority and the time slice of all the processes are known before submitting the
processes to the processor. The time quantum is taken in milliseconds. For experimental observation it is
considered that the arrival time of all processes is zero.
8.2.1. Case Studies:
a. Example 1
Suppose RR time quantum =4, Table 11 shows processes with their burst times and priorities.
According to simple Round-Robin Algorithm the Gantt chart obtained as in Figure 15.
Table 11. Processes with Its Id, Burst Time and priority
Process ID Burst Time (ms) Priority
P0 10 2
P1 3 1
P2 2 3
Figure 15. Gantt chart for simple RR
Total average waiting time=5.33
Average turnaround time=10.33
According to simple PBRR Algorithm the Gantt chart obtained as in Figure 16.
Figure 16. Gantt chart for PBRR algorithm
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Then we will obtain:
Total average waiting time=4
Average turnaround time=7.33
Results are resumed in Table 12 mentioned,
Table 12. Comparison of simple RR and PBRR
Algorithm
Average waiting
time
Average Turnaround
time
Throughput
Simple RR 5.33 10.33 Low
PBRR 4 7.33 High
b. Example 2
Suppose RR time quantum =4ms, Table 13 shows another combination of processes with their burst
times and priorities. According to simple Round-Robin Algorithm the Gantt chart will be as in Figure 17.
Table 13. Processes with Its Id, Burst Time and priority
Process ID Burst Time (ms) Priority
P0 12 2
P1 25 3
P2 7 1
Figure 17. Gantt chart for simple RR
Total average waiting time=16.66
Average turnaround time=31.33
And According to simple PBRR Algorithm Algorithm the Gantt chart will be as in Figure 18.
Figure 18. Gantt chart for PBRR algorithm
Then we will obtain:
Total average waiting time=14
Average turnaround time=29
Results are resumed in Table 14 mentioned,
Table 14. Processes with Its Id, Burst Time and priority
Algorithm
Average
waiting time
Average
Turnaround time
Throughput
Simple RR 16.66 31.33 Low
PBRR 14 29 High
9. CONCLUSION
In this paper, we introduced a real time operating system and real time tasks. In addition, we present
the scheduling objectives and parameters that must be considered for comparing CPU scheduling algorithms
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performance. We emphasize on studying RR drawbacks like high average turnaround, high context
switching, high response time, high turnaround time and low throughput. After analyzing RR performances
and drawbacks, we proposed a new algorithm named Priority Based Round Robin (PBRR), which deal with
drawbacks of implementing a simple round robin algorithm. Priority index is assigned to each Process.
Processes in ready queue are sorted according to their priorities. This new approach is performing better than
a simple RR in terms of average waiting time, average turnaround time.
For future work, we look further ahead to use hardware architecture based on FPGA. The hardware
accelerators will be used during the tasks scheduling mainly in sorting processes in the ready queue. In fact,
the use of a hybrid scheduler will improve system latency and determinism.
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