scholarly journals Improvement in Task Scheduling Capabilities for SaaS Cloud Deployments Using Intelligent Schedulers

2021 ◽  
Vol 6 (2) ◽  
pp. 1-12
Author(s):  
Supriya Sawwashere
Keyword(s):  
Task Scheduling ◽  
Waiting Time ◽  
Utilization Efficiency ◽  
Large Set ◽  
Q Learning ◽  
Initial Task ◽  
Mean Waiting Time ◽  
Standard Task ◽  
Machine Utilization ◽  
Saas Cloud

Task scheduling on the cloud involves processing a large set of variables from both the task side and the scheduling machine side. This processing often results in a computational model that produces efficient task to machine maps. The efficiency of such models is decided based on various parameters like computational complexity, mean waiting time for the task, effectiveness to utilize the machines, etc. In this paper, a novel Q-Dynamic and Integrated Resource Scheduling (DAIRS-Q) algorithm is proposed which combines the effectiveness of DAIRS with Q-Learning in order to reduce the task waiting time, and improve the machine utilization efficiency. The DAIRS algorithm produces an initial task to machine mapping, which is optimized with the help of a reward & penalty model using Q-Learning, and a final task-machine map is obtained. The performance of the proposed algorithm showcases a 15% reduction in task waiting time, and a 20% improvement in machine utilization when compared to DAIRS and other standard task scheduling algorithms.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

493 Evaluation of Patient Waiting Times for Plain Radiographs Whilst Attending Outpatient Clinics in A Tertiary Orthopaedic Centre

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
Z Hayat ◽  
E Kinene ◽  
S Molloy
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Time Frame ◽  
Outpatient Clinics ◽  
Contributing Factors ◽  
Time Data ◽  
Work Related ◽  
Mean Waiting Time ◽  
Appointment Time ◽  
Related Stress

Abstract Introduction Reduction of waiting times is key to delivering high quality, efficient health care. Delays experienced by patients requiring radiographs in orthopaedic outpatient clinics are well recognised. Method To establish current patient and staff satisfaction, questionnaires were circulated over a two-week period. Waiting time data was retrospectively collected including appointment time, arrival time and the time at which radiographs were taken. Results 84% (n = 16) of radiographers believed patients would be dissatisfied. However, of the 296 patients questioned, 56% (n = 165) were satisfied. Most patients (89%) felt the waiting time should be under 30 minutes. Only 36% were seen in this time frame. There was moderate negative correlation (R=-0.5); higher waiting times led to increased dissatisfaction. Mean waiting time was 00:37 and the maximum 02:48. Key contributing factors included volume of patients, staff shortages (73.7%), equipment shortages (57.9%) and incorrectly filled request forms. Eight (42.1%) had felt unwell from work related stress. Conclusions A concerted effort is needed to improve staff and patient opinion. There is scope for change post COVID. Additional training and exploring ways to avoid overburdening the department would benefit. Numerous patients were open to different days or alternative sites. Funding requirements make updating equipment, expanding the department and recruiting more staff challenging.

HOW TO REPORT AND MONITOR THE PERFORMANCE OF WAITING LIST MANAGEMENT

2002 ◽  
Vol 18 (3) ◽  
pp. 611-618
Author(s):  
Markus Torkki ◽  
Miika Linna ◽  
Seppo Seitsalo ◽  
Pekka Paavolainen
Keyword(s):  
Hallux Valgus ◽  
Waiting Time ◽  
Varicose Vein ◽  
Elective Surgery ◽  
Waiting Times ◽  
Waiting List ◽  
Mean Waiting Time ◽  
Number Of Patients ◽  
Potential Problems ◽  
Queue Discipline

Objectives: Potential problems concerning waiting list management are often monitored using mean waiting times based on empirical samples. However, the appropriateness of mean waiting time as an indicator of access can be questioned if a waiting list is not managed well, e.g., if the queue discipline is violated. This study was performed to find out about the queue discipline in waiting lists for elective surgery to reveal potential discrepancies in waiting list management. Methods: There were 1,774 waiting list patients for hallux valgus or varicose vein surgery or sterilization. The waiting time distributions of patients receiving surgery and of patients still waiting for an operation are presented in column charts. The charts are compared with two model charts. One model chart presents a high queue discipline (first in—first out) and another a poor queue discipline (random) queue. Results: There were significant differences in waiting list management across hospitals and patient categories. Examples of a poor queue discipline were found in queues for hallux valgus and varicose vein operations. Conclusions: A routine waiting list reporting should be used to guarantee the quality of waiting list management and to pinpoint potential problems in access. It is important to monitor not only the number of patients in the waiting list but also the queue discipline and the balance between demand and supply of surgical services. The purpose for this type of reporting is to ensure that the priority setting made at health policy level also works in practise.

Estimating waiting and idle times for special queuing situations of tree harvesting machines

1981 ◽  
Vol 11 (1) ◽  
pp. 99-104 ◽  
Author(s):  
C. H. Meng
Keyword(s):  
Waiting Time ◽  
Historical Data ◽  
Idle Time ◽  
Processing Times ◽  
Mean Waiting Time ◽  
Analytical Formulae ◽  
Input Time ◽  
Time Required ◽  
Tree Harvesting ◽  
Normally Distributed

The purpose of this study is to develop analytical formulae for special queuing situations which occur during the operations of the felling and processing devices of a tree harvester, and the pickup and processing devices of a tree processor. Analytical formulae are used to estimate mean waiting time and mean idle time; in case 1 both "input" times and processing times are normally distributed; in case 2 "input" times are normally distributed and processing times are Poisson distributed. "Input" time is a term used for convenience to denote time required to fell a tree by a harvester or time required to pick up a tree by a processor. Methods of choosing distributions for representing "input" times and processing times are provided. In addition, there are two examples, using historical data, which demonstrate the applications of the analytical formulae.

Relations between queue-size and waiting-time distributions

1980 ◽  
Vol 17 (03) ◽  
pp. 822-830
Author(s):  
Masao Mori
Keyword(s):  
Waiting Time ◽  
Queue Size ◽  
Waiting Time Distributions ◽  
Mean Waiting Time

Two types of representations for relation between queue-size and waiting-time distributions are studied. By using these, an incomplete but conceptually nice generalization of Pollaczek–Khinchine formula for mean waiting time forM/G/cis obtained.

scholarly journals The influence of water intake on waiting time prior to uroflowmetry: a prospective, randomized, double-blind trial

2017 ◽  
Vol 26 (3) ◽  
pp. 212-7
Author(s):  
Nur Rasyid ◽  
Donny E. Putra ◽  
Widi Atmoko ◽  
Adianti Khadijah ◽  
Dyandra Parikesit ◽  
...  
Keyword(s):  
Water Intake ◽  
Waiting Time ◽  
Final Analysis ◽  
Study Groups ◽  
Eligibility Criteria ◽  
Double Blind ◽  
Mean Waiting Time ◽  
Influence Of Water ◽  
Superiority Trial ◽  
Male Patients

Background: In uroflowmetry examination, patients are usually instructed to intake a large volume of water and wait until the bladder is full. The association between the volume of water intake and the waiting time before uroflowmetry is unknown. The aim of this study is to investigate the relationship between the volume of water intake and the waiting time prior to uroflowmetry.Methods: This trial was designed as a randomized, researchers, caregivers and patients blinded, superiority trial with three parallel groups and primary endpoint of waiting time prior to the uroflowmetry study based on the volume of patients’ water intake. Randomization was performed by block randomization with a 1:1:1 allocation. Patients scheduled for uroflowmetry at the Urology Clinic of Cipto Mangunkusumo Hospital were enrolled from March 2013 until December 2013. The eligibility criteria were male patients with ages above 50 years and body mass index 18.5–24.9 kg/m2.Results: A total of 83 patients was randomly assigned into 3 study groups: 300 ml (28 patients), 400 ml (28 patients), and 500 ml (27 patients). All patients were included in final analysis. Mean waiting time were 85.1±59.8 min, 107.2±70.4 min, and 66±28.4 min for patients intake 300, 400, and 500 ml of water respectively (p=0.07). The final bladder volumes for three groups were statistically different (262.4±130.8 ml, 289.4±126.2 ml, 359.2±137 ml; p=0.02).Conclusion: The volume water intake of 300–500 ml did not affect waiting time before uroflowmetry. Increasing water intake at least 500 ml added the final bladder volume and shorter the waiting time.

scholarly journals Estimation of the mean waiting time of a customer subject to balking: a simulation study

2007 ◽  
Vol 19 (1) ◽  
pp. 63-63
Author(s):  
Jaejin Jang ◽  
Jaewoo Chung ◽  
Jungdae Suh ◽  
Jongtae Rhee
Keyword(s):  
Waiting Time ◽  
Simulation Study ◽  
Mean Waiting Time ◽  
The Mean

The relative efficiency of direct and maximum likelihood estimates of mean waiting time in the simple queue, M/M/1

1972 ◽  
Vol 9 (2) ◽  
pp. 396-403 ◽  
Author(s):  
John H. Jenkins
Keyword(s):  
Maximum Likelihood ◽  
Waiting Time ◽  
Continuous Time ◽  
Relative Efficiency ◽  
Asymptotic Variance ◽  
Complete Information ◽  
Maximum Likelihood Estimates ◽  
Time Study ◽  
Direct Estimate ◽  
Mean Waiting Time

A problem of estimating waiting time in the statistical analysis of queues is investigated. The continuous time study of the M/M/1 queue made by Bailey is adapted to obtain the asymptotic variance of a direct estimate of waiting time as obtained under conditions of incomplete information. This is then compared with the asymptotic variance of the maximum likelihood estimate as obtained under conditions of complete information and based on the results of Clarke.

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