c) i If an M/M/1 queuc has utilization of 90%, what do you think will be the averagequeuc length and the average response time? If the arrival rate is 100 jobs per secondii. If an M/D/1 queue has utilization of 80% do you expect its mean qucue length andresponse time to be less, same, or greater than that of an M/M/1? Explain youranswer.

c) 1. If an M/M/1 queue has utilization of 90%, what do you think will be the average queuc length and the average response time? If the arrival rate is 100 jobs per second ii. If an M/D/1 queuc has utilization of 80% do you expect its mean queue length and response time to be less, samc, or greater than that of an M/M/1? Explain your answer.

c) 1. If an M/M/1 queue has utilization of 90%, what do you think will be the average queuc length and the average response time? If the arrival rate is 100 jobs per second ii. If an M/D/1 queuc has utilization of 80% do you expect its mean queue length and response time to be less, samc, or greater than that of an M/M/1? Explain your answer.

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter20: Queuing Theory

Section20.8: The M/g/1/gd/∞/∞ Queuing System

Problem 4P

See similar textbooks

Similar questions

Calculate average waiting time (AWT), average turnaround time (ATAT) and average response time (ARST) for the following Ready Queue scenario by using following algorithms. Round Robin where Time Quantum = 3 Analyze and tell that which one is efficient in term of AWT, ATAT and ARST. Process Burst Time Arrival Time Priority P1 9 2 3 P2 12 0 6 P3 10 2 0 P4 6 1 7 P5 5 2 3
The Manhattan Tourist Problem (1) Given setting in Figure 6.4, calculate exact number of different paths by dynamic programming. (2) For a general setting, i.e., from source(0,0) to sink (n, m), how many different paths by dynamic programming?1 4 3 3 4 1 0 4 3 N 5 1
Please answer the following question in detail and explain all the proofs and assumptions for all parts. The question has three parts, (a), (b) and (c). Iterative lengthening search is an iterative analogue of uniform-cost search. The basic idea is to use increasing limits on path cost. If a node is generated whose path cost exceeds the current limit, it is immediately discarded. For each new iteration, the limit is set to the lowest path cost of any node discarded in the previous iteration. (a) Show that this algorithm is optimal for general path costs. You may assume that all costs are integers (this is not a loss of generality if the search space is finite). You may wish to consider the minimal path cost C; what happens when we set the path cost to be some limit l < C? (b) Consider a uniform tree with branching factor b, solution depth d, and unit step costs (each action costs one unit). How many iterations will iterative lengthening require? (c) (7 points) Now consider the…
It is a commmon understanding that waiting lines can be of different types. For example, one such typpe is the 'single queue/multipple server' (SQMS) waiting line. Annother type is the 'multiple queue/multiple servers' (MQMS) waiting line. In comparing the waiting times in SQMS and MQMS: a. the average waiting time in the SQMS is shorter. b. the average waiting time in the SQMS is longer. c. the average waiting times of SQMS and MQMS are equal although the variances associated with waiting times in the two systems are different. d. the averages and the variances associated with the waiting times of the two systems are the same.
If a network is to be successful and efficient, what three conditions must be met? Let's separate them out and examine them separately.
In the Erdös-Rényi random network model, suppose N=101 and p=1/20, that is, there are 101 vertices, and every pair of vertices has a probability of 1/20 of being connected by an edge. For the network model given what is the probability that a network generated with those parameters has exactly 400 edges? No need to give the decimal value, the mathematical expression will suffice
3. Knapsack problem (4’)a. (1’) Show the dynamic programming table when applying the bottom-up dynamicprogramming algorithm to the instance of the knapsack problem? (Capacity W=6)b. (1’) Show the dynamic programming table when applying MemoryKnapsack(i, j)dynamic programming algorithm to the instance of the knapsack problem? (CapacityW=6)c. (1’) What is the optimal subset?d. (1’) What is the maximal value of a feasible subset?
What are the three prerequisites for a network to function optimally? Let's break them down and examine them separately.
Consider a stable single-station queue. Suppose it costs $c to hold a customer in the system for one unit of time. Show that the long-run holding cost rate is given by cL, where L is the mean number of customers in steady state.
Calculate average waiting time (AWT), average turnaround time (ATAT) and average response time (ARST) for the following Ready Queue scenario by using following algorithms. Priority Algorithm (Preemptive) Shortest Job First Algorithm (Preemptive) Process Burst Time Arrival Time Priority P1 9 2 3 P2 12 0 6 P3 10 2 0 P4 6 1 7 P5 5 2 3
Consider the Omega network and Butterfly network from p nodes in the leftmost column to p nodes in the rightmost column for some p=2^k. The Omega network is defined in Chapter 2 of the text book such that Si is connected to element S j if j=2i for or j=2i+1-p for See Chapter 2 in text book for its definition. The Butterfly network is an interconnection network composed of log p levels (as the omega network). In a Butterfly network, each switching node i at a level l is connected to the identically numbered element at level l + 1 and to a switching node whose number differs from itself only at the lth most significant bit. Therefore, switching node Si is connected to element S j at level l if j = i or j . Prove that for each node Si in the leftmost column and a node Sj in the rightmost column, there is a path from Si to Sj in the Omega network. Prove that for each node Si in the leftmost and a node Sj in the rightmost, there is a path from Si to Sj in the Butterfly network.
T/F 1).In the aggregate analysis different operations may have different amortized costs,while in the accounting method all operations have the same amortized cost. 2).BFS finds the shortest distance to a node from the starting point in unweighted graphs. 3).Given a graph G. If the edge e is not part of any MST of G, then it must be the maximum weight edge on some cycle in G. 4).Assume that no two men have the same highest-ranking woman. If the women carried out the proposal to men, then the Gale-Shapley algorithm will contain a matching set where every man gets their highest-ranking woman. 5).If a path P is the shortest path from u to v and w is a node on the path, then the part of the path from u to w is also the shortest path from u to w.