Average waiting time

3/E2/2 Queue.. This approximation overestimates the average waiting time but improves in accuracy as p increases. Table 2 shows th~ av.rage waiting time in the E3/E2/2 ~ueue as giv.n by [r]

For evaluating the performance it is assumed that the environment where all the experiments are performed is a single processor and the burst time for all processes is known prior to submitting of process to the scheduler. Moreover all the processes have equal priority. For doing this, the proposed algorithm is implemented in C programming language. Various numbers of experiments are also carried out of which output of three cases are shown in this paper.

The alternative scenarios developed in partnership with the SEMOB will be described next. The current situation will be called base scenario and its main results are: average vehicle crossing time (in seconds), average waiting time in queue (in seconds) and total count of vehicles leaving the avenue after 4 hours. The results of this scenario, obtained after 30 simulation model replications are indicated in Table 2, from both flow directions: São Paulo-Santo André (SP-SA) and Santo André-São Paulo (SA-SP).

Queueing theory is the mathematical study of waiting lines or queue. The theory enables mathematical analysis of several related processes, including arriving at the queue, waiting in the queue and being served by the server(s) at the front of the queue. The theory permits the derivation and calculation of several performance measures including the average waiting time in the queue or the system, the expected number of customers (waiting or receiving service) and the probability of encountering the system in certain states, such as empty, full, having an available server or having to wait for a certain time to be served.

Round Robin (RR) Scheduling is the basis of time sharing environment. It is the combination of First Come First Served (FCFS) scheduling algorithm and preemption among processes. It is basically used in a time sharing operating system. It switches from one process to another process in a time interval. The time interval or Time Quantum (TQ) is fixed for all available processes. So, the larger process suffers from Context Switches (CS). To increase efficiency, we have to select different TQ for processes. The main objective of RR is to reduce the CS, maximize the utilization of CPU and minimize the turn around and the waiting time. In this paper, we have considered different TQ for a group of processes. It reduces CS as well as enhancing the performance of RR algorithm. TQ can be calculated using min-max dispersion measure. Our experimental analysis shows that Group Based Time Quantum (GBTQ) RR algorithm performs better than existing RR algorithm with respect to Average Turn Around Time (ATAT), Average Waiting Time (AWT) and CS.

Jyotirmay Patel1 and A.K. Solanki, “CPU Scheduling: A Comparative Study”, 2011 [5], discuss about scheduling policies of Central processing unit (CPU) for computer System. A number of problems were solved to find the appropriate among them. Therefore, based on performance, the shortest job first (SJF) algorithm is suggested for the CPU scheduling problems to decrease either the average waiting time or average turnaround time. Also, the first come first serve (FCFS) algorithm is suggested for the CPU scheduling problems to reduce either the average CPU utilization or average throughput.

Based on Al-Jumaily and Al-Jobori (2011), many researchers try to get a full advantage to invent a new technology to increase customer satisfaction. They try to reduce an average waiting time that customer need to wait before the service begin and to improve a quality of service (Mutingi et al., 2015). According to Al-Jumaily and Al-Jobori (2011) and Dai and He (2011), dealing with all customers fairly should be equal to the performance of the system. The most important variable need to be considered is the arrival time and the service time for each customer. When more than one customer arrives at the same time, the queue will start to occur. Other than that, if service times for each customer take too long, it will cause a queue length for another customer.

We have designed simulator using C and C ++ for different scheduling algorithms to calculate their respective average waiting time and average turnaround time. First come First serve (FCFS), SJF, Priority, Round Robin have been calculated for the 12 processes on the basis of designed scheduling programs. These scheduling algorithms have been implemented through FIS. The results obtained after comparative study have been presented in tables (1-8) taking variable time quanta for round robin algorithm. After the aggregation process we find fuzzy variables for each output variables which needs defuzzification. The defuzzification has been done by Yager and Chang graded mean integration formulae The analytical study has been made for both triangular and trapezoidal functions for the defuzzified value of burst time. The Mamdani model is used as a processing model in order to generate the inference rules for fuzzy inference system(FIS). This model expects the output parameters to be fuzzy. The fuzzy model design for comparative study of CPU scheduling policies has been depicted in the following figures through MATLAB (fig 1-8(c)).

can decrease effectively the average waiting customers. It can be seen that the average facilities in service of Tradition1 and Tradition2 are equal according to the discussion above and Table 3. As a consequence, we think about facility utilization rate, the ratio of F and facilities. Fig. 3(e) manifests that the average utilization rates of the three queueing systems all increase linearly, and those of Tradition1 and Tradition2 are max and min on the same value of , respectively. Fig. 3(f) shows that the average queue length L of Tradition1 first increases slowly if  is less than 2, and then increases vigorously. This is why there is a long queue in front of women’s. However, the average queue lengths of Tradition2 and unisex public toilet present basically linear growth and they become increasingly less than the one of Tradition1 with the increase of . Finally, Fig. 3(g) and (h) demonstrate that the average waiting time T w and the average sojourn time T s of Tradition1 increase

After an analysis of the results about the average waiting time and average waiting number of jobs before different operations of the production line, another simulation model is to be c[r]

ABSTRACT: In Multiprogrammed Operating System, process scheduling is one of the important task to decide which of the processes in the ready queue is to be allocated to cpu . There are different Cpu scheduling algorithms li ke FCFS, Shortest Job First (SJF), Round Robin(RR),priority scheduling algorithms etc. All these scheduling algorithms has drawbacks in reducing waiting time, turnaround time ,context switch .The main objective of the proposed work is to compute time quantum for Round Robin scheduling algorithm to maximize cpu utilization and throughput in terms of reducing average waiting time(AWT), average turnaround time (TAT)and total number of context switches(CS) . The paper also presents the analysis of proposed algorithm with existing RoundRobin scheduling algorithm in terms of reducing average waiting time, average turnaround time and in reducing the number of context switches.

Abstract— This paper presents an analytical model of an optical burst switching ring network capable of handling WDM traffic intelligently. The network protocol and efficient architecture increases the data transport capability of a congested network. Here we propose an architecture to ease the traffic congestion in a ring network. The backbone of the proposed model is the use of a proxy node which is connected to a particular number of nodes, depending upon the traffic, then diverting their traffic and thereby increasing throughput. A probabilistic model for the proposed network architecture is developed employing packet queuing control to estimate the average waiting time of packets in the buffer and the average number of packets in the buffer for different incoming traffic arrival rate.

the result by 80 percentages. In [6], the researchers, recommended an algorithm namely, fuzzy round robin algorithm that tried to remove the problem with the CPU round robin scheduling that is, if the time requisite for the running process is some extent more than time quantum even by a fraction value, then the process gets forestalled and context switch occurs. This algorithm also eliminated the problem with the time quantum of RR scheduling, that is if it is too large, the algorithm degenerates to FCFS or it worsens the performance otherwise. The authors in [7], made slight changes to the conventional round robin algorithm where the time quantum of the scheduled processes is augmented to some amount whose remaining time in its last turn is less than or equal to a given threshold value, which is one–fourth of the time quantum. In [8], the novelists talked about dynamic quantum precision wherein a processor is said to be optimally implemented if it has highest throughput, low wait time, low turnaround time and less no of context switches for process coming to execution. This proposal worked by finding the left out B.T. of processes in the last but one turn of each processed to get an optimal threshold value also the processes has been divided into two categories, first category modifies time quantum and second will be processed as per classical RR algorithm. The researchers of [9] followed a mathematical approach which attempts to mathematically formularize the computation of waiting time of any process in a static n processes, CPU bound a round robin scheme which also calculated other performance measures as well. The approach followed uses two ready queue, wherein a process is returned to second ready queue after completion of last round. This reduces average waiting time and increases throughput while level of C.P.U utilization is preserved and there is no substantial increase in overheads.

The proposed fuzzy based scheduling algorithm is an efficient scheduling algorithm that is obtained batter result rather than other algorithm. Figure 5 and 6 shows the comparison between SJF, Priority scheduling algorithm, Fuzzy based CPU scheduling algorithm and proposed new fuzzy based scheduling algorithm. The average waiting time and average turnaround time of proposed fuzzy based algorithm is much better than the Priority algorithm, Fuzzy based CPU scheduling algorithm and closer to obtain by SJF algorithm, but SJF algorithm doesn’t deal with task priority. Figure 7 shows the comparison between SJF, Fuzzy based CPU scheduling algorithm and proposed new fuzzy based scheduling algorithm. Results prove that algorithm proposed in this paper is much better than existing algorithms.

An expected average patient waiting time of 12.69 minutes, against an actual of 45.55 minutes, is a challenging target to achieve. Waiting time in our clinics is comparable to that found in other family medicine clinics, where an average waiting time of upto 80.5 minutes has been reported. 7 Hiring of non-medical staff such as helpers to assist with the patient flow can help reduce patient waiting time. 11 Perceived ambulatory visit duration and meeting or exceeding patient expectation of time needed to be spent with the physician are determinants of patient satisfaction. 12 Further work is recommended to clarify the factors influencing patient waiting time and their relationship with patient satisfaction. 12 Further work is recommended to clarify the factors influencing patient waiting time and their relationship with patient satisfaction.

The Round Robin (RR) CPU scheduling algorithm is a fair scheduling algorithm that gives equal time quantum to all processes. The choice of the time quantum is critical as it affects the algorithm’s performance. This paper proposes a new algorithm that enhanced on the Improved Round Robin CPU (IRR) scheduling algorithm. The proposed algorithm was implemented and benchmarked against basic round robin algorithms available in the literature. The proposed algorithm compared with the basic round robin algorithms, produces minimal average waiting time (AWT), average turnaround time (ATAT), and number of context switches (NCS). From the obtained results we observed that proposed algorithm met better scheduling criterion than the basic round robin scheduling algorithm.

There are four jobs (1,2,3,4,5) which requires processing time ( 5,15,12,25,5). So the sequence of the jobs may vary and also we analyze the GA by changing the process time of different jobs as shown in the Table 2. we are comparing FCFS,SJF,RR and GA, By apply the genetic operator’s crossover, fitness function and inversion, we get the final population. out of the final population we get the job schedule, who have the minimum average waiting time Figure 3 focus on comparison based average waiting time algorithms.

The researcher has found out the causes of delay through observation which are recorded in cause and effect diagram of the departments and through the analysis done in each department. Queuing model was used to know the average waiting time per person in each department. It also helped to identify where the delay occurs in the process.

In each case our algorithm minimized context switch, average turnaround time and average waiting time. Even this performs better for without given priority processes (for case 8 shown in Table 11). In every comparison our algorithm gives better performance except a very few comparison such as shown in table 5 for quantum time 5 only (Equal performance to IRR and better than RRWP and RRP), table 7 for quantum time 2 ,5 and table 10 for quantum 100 and 1000(Equal performance to IRR and better than RRWP and RRP) . This can be neglected as it gives equal performances on those comparisons. It also performs better than RRWP in the case of without given priority. Provided all comparisons in above proofs that, proposed algorithm gives better performance than RRP, RRWP, and IRR with accuracy.

The optimum average waiting time is 5.99 hours by using 9 berths and 7 units of crane. Increase in number of berth indicates the expansion of the port itself. Expansion can be done gradually to ensure that all berthing operations run smoothly. Sharing of quay cranes with adjacent berth can be done for ships that requires more numbers of cranes. This can reduce the cost incurred by the port. The data from Table 1 are plotted in Figure 3, Figure 4 and Figure 5