Simulation analysis of single server queuing modelijitjournal
A queue is a line of people or things to be handled in a sequential order. It is a sequence of objects that
are waiting to be processed. Queuing theory is the study of queues for managing process and objects.
Simulation has been applied successfully for modeling small and large complex systems and
understanding queuing behavior. Analysis of the models helps to increases the performance of the system.
In this paper we analyze various models of the Single server queuing system with necessaryimplementation
using Matlab Software.
Improving throughput with the Theory of Constraints and Queuing TheoryAndrew Rusling
Practical advice on how to improve the throughput of your agile team, by using the Theory of Constraints and Queuing Theory. Shows how to apply TOC to your task board. Explains how Queuing Theory is built into Scrum and Kanban, powering you to make the most of them.
Solving Of Waiting Lines Models in the Bank Using Queuing Theory Model the Pr...IOSR Journals
Waiting lines and service systems are important parts of the business world. In this article we describe several common queuing situations and present mathematical models for analyzing waiting lines following certain assumptions. Those assumptions are that (1) arrivals come from an infinite or very large population, (2) arrivals are Poisson distributed, (3) arrivals are treated on a FIFO basis and do not balk or renege, (4) service times follow the negative exponential distribution or are constant, and (5) the average service rate is faster than the average arrival rate. The model illustrated in this Bank for customers on a level with service is the multiple-channel queuing model with Poisson Arrival and Exponential Service Times (M/M/S). After a series of operating characteristics are computed, total expected costs are studied, total costs is the sum of the cost of providing service plus the cost of waiting time. Finally we find the total minimum expected cost.
A Case Study of Employing A Single Server Nonpreemptive Priority Queuing Mode...IJERA Editor
This paper discusses a case study of employing a single server nonpreemptivepriorityqueuing model [1]at ATM
machine which originally operates on M/M/1 model. In this study we have taken two priority classes of people
in following order:-
.Priority class 1- woman
.Priority class 2- man
Sometimea long queue is formed at ATMmachine (single server)but the bank management don’t have enough
money to invest on installing new ATM machine.In this situation we want to apply single server nonpreemptive
priority queuing model.The security guard at the ATM will divide the customers in two category and arrange the
customers in the above said priority order Thuspriority class 1 people willreceive theatm service ahead of
priority class 2 people.This will reduce the waiting time of priority class 1 people. Of course by doing this the
waiting time of priority class 2will increase.
Waiting Line Model is one of the decision line model.Waiting Line Model is one of the decision line model.Waiting Line Model is one of the decision line model.Waiting Line Model is one of the decision line model.
MCM,MCA,MSc, MMM, MPhil, PhD (Computer Applications)
Working as Associate Professor at Zeal Education Society, Pune for MCA Progrmme.
Having 18 Years teaching experience
International Journal of Mathematics and Statistics Invention (IJMSI) inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Developed to provide models for forecasting behaviors of systems subject to random demand
The first problems addressed concerned congestion of telephone traffic
Erlang observed that a telephone system can be modeled by Poisson customer arrivals and exponentially distributed service times
Molina, Pollaczek, Kolmogorov, Khintchine, Palm, Crommelin followed the track
Common phenomenon of everyday life
Line maybe People / Items
Examples
– Grocery shop, Bank, Petrol refilling units, Automobile Service station, Airplane, Train etc.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
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Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. Developed by A.K.Erling in 1903
It analyze the shared facility needs to be
accessed for service by a large number of
jobs or customers
Examples:
Waiting lines in cafeterias,
hospitals, banks, theaters,
airports etc.
3. Queuing theory is the mathematical study of
waiting lines (or queues) that enables
mathematical analysis of several related
processes, including arriving at the (back of
the) queue, waiting in the queue, and being
served by the Service Channels at the front of
the queue.
4. Balking
If a customer decides not to enter the queue
since it is too long is called Balking
Reneging
If a customer enters the queue but after
sometimes loses patience and leaves it is called
Reneging
Jockeying
When there are 2 or more parallel queues and
the customers move from one queue to another
is called Jockeying
5. Where customers are involved such as
restaurants, café, super market, airports etc.
Very useful in Manufacturing units
Applicable for the problem of machine
breakdown & repairs
Applicable for the scheduling of jobs in
production control
Applicable for the minimization of traffic
congestion at tollbooth
Provide solution of inventory control
problems
6. The basic characteristics of
queuing phenomenon are:
Service Center
Service Channel
Major Constituents of a
Queuing System
Customer
Queue
Service Channel
7.
8. The customer arrive for service at a single service
facility at random according to Poisson distribution
with mean arrival rate ג.
The service time has exponential distribution with
mean service rate μ.
The service discipline followed is First Come First
Served.
Customer Behavior is Normal
Service facility behavior is Normal
The calling source has infinite size
The mean arrival rate is less than the mean service
rate
The waiting space available for customer in the queue
is infinite
9. The waiting space for the customer is usually
limited
The arrival rate may be state dependent
The arrival process may not be stationary
The population of customers may not be
infinite and the queuing discipline may not be
First Come First Serve
Services may not be rendered continuously
The Queuing system may not have reached
the steady state. It may be, instead, in
transient state
