scholarly journals ACEA: A Queueing Model-Based Elastic Scaling Algorithm for Container Cluster

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kui Li ◽  
Yi-mu Ji ◽  
Shang-dong Liu ◽  
Hai-chang Yao ◽  
Hang Li ◽  
...  
Keyword(s):  
Resource Utilization ◽  
Waiting Time ◽  
Sudden Change ◽  
Solution Space ◽  
Performance Model ◽  
Utilization Rate ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Elastic Scaling ◽  
The Relationship

Elastic scaling is one of the techniques to deal with the sudden change of the number of tasks and the long average waiting time of tasks in the container cluster. The unreasonable resource supply may lead to the low comprehensive resource utilization rate of the cluster. Therefore, balancing the relationship between the average waiting time of tasks and the comprehensive resource utilization rate of the cluster based on the number of tasks is the key to elastic scaling. In this paper, an adaptive scaling algorithm based on the queuing model called ACEA is proposed. This algorithm uses the hybrid multiserver queuing model (M/M/s/K) to quantitatively describe the relationship among number of tasks, average waiting time of tasks, and comprehensive resource utilization rate of cluster and builds the cluster performance model, evaluation function, and quality of service (QoS) constraints. Particle swarm optimization (PSO) is used to search feasible solution space determined by the constraint relation of ACEA quickly, so as to improve the dynamic optimization performance and convergence timeliness of ACEA. The experimental results show that the algorithm can ensure the comprehensive resource utilization rate of the cluster while the average waiting time of tasks meets the requirement.

scholarly journals Small Queuing Restaurant Sustainable Revenue Management

2020 ◽  
Vol 12 (8) ◽  
pp. 3477
Author(s):  
Kwangji Kim ◽  
Mi-Jung Kim ◽  
Jae-Kyoon Jun
Keyword(s):  
South Korea ◽  
Revenue Management ◽  
Waiting Time ◽  
Equilibrium Points ◽  
Management Strategies ◽  
Queuing System ◽  
Utilization Rate ◽  
Trade Off ◽  
Average Waiting Time ◽  
Price Increases

When competitive small restaurants have queues in peak periods, they lack strategies to cope. However, few studies have examined small restaurants’ revenue management strategies at peak times. This research examines how such small restaurants in South Korea can improve their profitability by adapting their price increases, table mix, and the equilibrium points of the utilization rates, and reports the following findings based on the analysis of two studies. In Study 1, improving profitability by increasing prices should carefully consider the magnitude and timing. In Study 2, when implementing the table mix strategy, seat occupancy and profit also increase, and we further find the equilibrium points of the utilization rates. Under a queuing system, the utilization rate and average waiting time are also identified as having a trade-off relationship. The results provide insights into how managers of small restaurants with queues can develop efficient revenue management strategies to manage peak hours.

scholarly journals An Edge Computing Based Smart Healthcare Framework for Resource Management

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4307 ◽  
Author(s):  
Soraia Oueida ◽  
Yehia Kotb ◽  
Moayad Aloqaily ◽  
Yaser Jararweh ◽  
Thar Baker
Keyword(s):  
Resource Utilization ◽  
Waiting Time ◽  
Smart Cities ◽  
Real Life ◽  
Edge Computing ◽  
Utilization Rate ◽  
Resource Assignment ◽  
Smart Healthcare ◽  
Patient Waiting Time ◽  
Patient Waiting

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.

A Hybrid Multiple Parallel Queuing Model to Enhance QoS in Cloud Computing

2020 ◽  
Vol 12 (1) ◽  
pp. 18-34 ◽  
Author(s):  
Shahbaz Afzal ◽  
G. Kavitha
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Waiting Time ◽  
Queue Length ◽  
Utilization Rate ◽  
Queuing Model ◽  
Total Response Time ◽  
Proposed Model ◽  
Mathematical Equations ◽  
And Performance

Among the different QoS metrics and parameters considered in cloud computing are the waiting time of cloud tasks, execution time of tasks in VM's, and the utilization rate of servers. The proposed model was developed to overcome some of the pitfalls in the existing systems among which are sub-optimal markdown in the queue length, waiting time, response time, and server utilization rate. The proposed model contemplates on the enhancement of these metrics using a Hybrid Multiple Parallel Queuing approach with a joint implementation of M/M/1: ∞ and M/M/s: N/FCFS to achieve the desired objectives. A neoteric set of mathematical equations have been formulated to validate the efficiency and performance of the hybrid queuing model. The results have been validated with reference to the workload traces of Bit Brains infrastructure provider. The results obtained indicate the significant reduction in the queue length by 60.93 percent, waiting time in the queue by 73.85 percent, and total response time by 97.51%.

Development of a Heuristics Model for Simplifying a Multi-Channel Queuing System

2011 ◽  
Vol 367 ◽  
pp. 647-652
Author(s):  
B. Kareem ◽  
A. A. Aderoba
Keyword(s):  
Waiting Time ◽  
Channel Model ◽  
Single Channel ◽  
Queuing System ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Queuing Models ◽  
Significant Difference ◽  
Number Of Customers ◽  
Proportionality Principle

Queuing model has been discussed widely in literature. The structures of queuing systems are broadly divided into three namely; single, multi-channel, and mixed. Equations for solving these queuing problems vary in complexity. The most complex of them is the multi-channel queuing problem. A heuristically simplified equation based on relative comparison, using proportionality principle, of the measured effectiveness from the single and multi-channel models seems promising in solving this complex problem. In this study, six different queuing models were used from which five of them are single-channel systems while the balance is multi-channel. Equations for solving these models were identified based on their properties. Queuing models’ performance parameters were measured using relative proportionality principle from which complexity of multi-channel system was transformed to a simple linear relation of the form = . This showed that the performance obtained from single channel model has a linear relationship with corresponding to multi-channel, and is a factor which varies with the structure of queuing system. The model was tested with practical data collected on the arrival and departure of customers from a cocoa processing factory. The performances obtained based on average number of customers on line , average number of customers in the system , average waiting time in line and average waiting time in the system, under certain conditions showed no significant difference between using heuristics and analytical models.

scholarly journals A Study of Service in Restaurant by Using Queuing Model

2013 ◽  
Vol 5 ◽  
pp. 14-18
Author(s):  
G.D. Mishra ◽  
Vijiya Singh Chauhan ◽  
Nikita Chandra
Keyword(s):  
Numerical Model ◽  
Waiting Time ◽  
Queuing Theory ◽  
Arrival Rate ◽  
Utilization Rate ◽  
Service Rate ◽  
Case Scenario ◽  
Queuing Model ◽  
Restaurant Management ◽  
Potential Customers

The restaurants want to avoid losing their customers due to a long wait on the line. This shows a need of a numerical model for the restaurant management to understand the situation better. This paper aims to show that queuing theory satisfies the model when tested with a real-case scenario. We obtained the data from a restaurant. We then derive the arrival rate, service rate, utilization rate, waiting time in queue and the probability of potential customers to balk based on the data using Little’s Theorem and M/M/1 queuing model. We conclude the paper by discussing the benefits of performing queuing analysis to a busy restaurant.

scholarly journals Deskripsi Sistem Antrian pada Klinik Dokter Spesialis Penyakit Dalam

2012 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Deiby T Salaki
Keyword(s):  
Direct Observation ◽  
Waiting Time ◽  
Arrival Rate ◽  
Queuing System ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Number Of Patients

DESKRIPSI SISTEM ANTRIAN PADA KLINIK DOKTER SPESIALIS PENYAKIT DALAM ABSTRAK Penelitian ini dilakukan untuk mengetahui deskripsi sistem antrian pada klinik dokter internist. Pengumpulan data dilakukan secara langsung pada klinik dokter internist JHA selama 12 hari, selama 2 jam waktu pengamatan tiap harinya pada periode sibuk.. Model antrian yang digunakan adalah model (M/M/1) : (FIFO/~/~), tingkat kedatangan bersebaran poisson, waktu pelayanan bersebaran eksponensial, dengan jumlah pelayanan adalah seorang dokter, disiplin antrian yang digunakan adalah pasien yang pertama datang yang pertama dilayani, jumlah pelayanan dalam sistem dan ukuran populasi pada sumber masukan adalah tak berhingga.  Sistem antrian pada klinik ini memiliki kecepatan kedatangan pelayanan anamnesa rata-rata  menit 1 orang pasien datang, kecepatan kedatangan pelayanan pemeriksaan fisik rata-rata  menit 1 orang pasien datang, rata-rata waktu pelayanan anamnesa untuk  seorang pasien  menit, rata-rata waktu pelayanan pemeriksaan fisik untuk  seorang pasien  menit, peluang kesibukan  pelayanan anamnesa sebesar , peluang kesibukan  pelayanan pemeriksaan fisik sebesar , dan peluang pelayanan anamnesa menganggur sebesar , peluang pelayanan pemeriksaan fisik menganggur sebesar . Rata-rata banyaknya pengantri untuk anamnesa adalah  pasien sedangkan untuk pemeriksaan fisik  pasien, rata-rata banyaknya pengantri dalam sistem adalah  pasien, waktu rata-rata seorang pasien dalam klinik adalah  menit, waktu rata-rata seseorang pasien untuk antri adalah  menit. Kata kunci: Sistem Antrian, Klinik Penyakit Dalam  DESCRIPTION OF QUEUING SYSTEM AT THE INTERNIST CLINIC ABSTRACT This research determines the description of queuing system at the internist Clinic. Data collected by direct observation during 12 days and in 2 hours. Queuing model that used is model of (M/M/1): (FIFO /~/~). Based on the research, the clinic has 3.256 minutes per patient in average arrival rate for anamnesys, the average arrival rate for diaagnosys is 3.255 minutes per patient, average service speed for anamnesys is 2.675 minutes per patient, average service speed for diagnosys is 12.635 minutes, the probability of busy periods for anamnesys is 0.864, the probability of busy periods for diagnosys is 0.832 and probability of all free services or no patient in the anamnesys equal to 0.136, probability of all free services or no patient in the anamnesys equal to 0.168. The average number of patients in anamnesys queue is 5 patients, the average number of patients in diagnosys queue is 4 patients, the average number of patients in the system is 10 patients, the average waiting time in the system is 47.078 minutes and the average queuing time is 31.660 minutes. Keywords: Queuing system, internist clinic

scholarly journals An Improved Queuing Model for Reducing Average Waiting Time of Emergency Surgical Patients Using Preemptive Priority Scheduling

2021 ◽  
Vol 9 (6) ◽  
pp. 827-840
Keyword(s):  
Waiting Time ◽  
Time Management ◽  
Arrival Rate ◽  
Preemptive Priority ◽  
Queuing Model ◽  
Priority Scheduling ◽  
Average Waiting Time ◽  
Public Sector Hospital ◽  
Medical Facilities ◽  
The Mean

Our research objective is to reduce the Average Waiting Time for patients in an Emergency Department of public sector hospital. We have based our model on M/M/s Queuing System, our study revealssignificant findings on arrival rate of patients. During this simulation, we have used a preemptive priority scheduling model. In our practice, the arrival rate followed a Poisson distribution, averaging 30 patients per hour, with the Mean Service time of1.5 hours and Average Waiting Time recorded around 12.13 minutes. This research offersvaluable help to achieve better time management in emergency departments of high-density medical facilities.

The Analysis of ATM Queuing Phenomenon on Campus

2012 ◽  
Vol 155-156 ◽  
pp. 1117-1121 ◽  
Author(s):  
Na Song ◽  
Zhi Qin Shang
Keyword(s):  
Commercial Bank ◽  
Waiting Time ◽  
Sojourn Time ◽  
Queue Length ◽  
Queuing System ◽  
Utilization Rate ◽  
Allocation Of Resources ◽  
Average Waiting Time ◽  
Quantity Index ◽  
Working Efficiency

The article focused on the study of the ATM of Industrial and Commercial Bank in Qinhuangdao. To achieve the purposes of improving customers’ satisfaction and optimizing the allocation of resources, the article calculated some major quantity index including average queue length and waiting queue length, average waiting time and sojourn time as well as utilization rate of the system, analyzed ATM working efficiency of queuing system and gave some suggestions for improvement.

scholarly journals An Efficient Grouped Task Scheduling and Resource Allocation in Cloud Computing Environments

2019 ◽  
Vol 8 (4) ◽  
pp. 12203-12206
Keyword(s):  
Resource Allocation ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Waiting Time ◽  
Execution Time ◽  
High Capacity ◽  
Task Type ◽  
Average Waiting Time ◽  
Computing Environments ◽  
Time Average

Task scheduling in cloud is the allocation of resources to a task at a particular time. In cloud, scheduling strategy is defined or adapted by a scheduler according to the changing environment. Allocation of resource with poor capacity to a task may lead to increase in execution time of the task. Problem of resource under utilization may also occur when a resource with high capacity is allocated to a task that requires a resource with lesser capacity. In this paper we proposed an Efficient Grouped Task Scheduling (EGTS) and resource allocation to minimize average waiting time, average execution time and increase resource utilization. EGTS classify Tasks into two groups of similar task type, and sort the tasks in the order of their respective deadlines. Task in each group is allocated Virtual Machine with capacity equal to the average capacity required by tasks in that group. An experiment was conducted using CloudSim to exhibit EGTS and the result shows minimal average execution time, average waiting time and a higher resource utilization compared to Min-Min and Max-Min

Application of Queuing Theory in Production Efficiency of Direct Rolling Process of Long Product

2020 ◽  
Vol 977 ◽  
pp. 34-41
Author(s):  
Hong Liang Zhang ◽  
Guang Hong Feng ◽  
Bao Shan Wang ◽  
Xu Ming Liu ◽  
Xin Liu
Keyword(s):  
Waiting Time ◽  
Queuing Theory ◽  
Production Efficiency ◽  
Temperature Drop ◽  
Rolling Process ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Direct Rolling ◽  
Rolling Rate ◽  
Optimal Average

Based on the mathematical method of queuing theory, the queuing model of billets in the direct rolling process of the long product was established, which represented the conveying process of billets at the casting and rolling interface. Using the billets queuing model, the influence of different steel quantity, length, speed and other factors on the average waiting time of a single billet in the direct rolling production process was analyzed. Combined with the temperature drop of billets, the optimal average waiting time for a single billet was determined. The method improved the conveying connection efficiency of the casting-rolling interface and the direct rolling rate of billets.

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