An QPSL Queuing Model for Load Balancing in Cloud Computing

Siddiqui, Shadab; Darbari, Manuj; Yagyasen, Diwakar

doi:10.4018/ijec.2020070103

Cited by 12 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Civelek [28] studied the impact of bivariate and temporal dependencies among interarrival and service times on the performance of single-server queues. Siddiqui [29] proposed an QPSL queue model for load balancing in cloud computing. In the construction field, a related study was conducted to apply the concept of customer-server queuing with regard to construction equipment for concrete placement and excavation [30].…”

Section: Research Methods For Quantifying Uncertaintymentioning

confidence: 99%

Quantitative Analysis of Waiting Length and Waiting Time for Frame Construction Work Activities Using a Queue Model; Focusing on Korean Apartment Construction

2021

View full text Add to dashboard Cite

The frame construction of an apartment complex that consists of multiple buildings encounters various uncertainties, owing to the complex relationships between units of work. Currently, the period of such a construction is calculated based on the number of floors of the highest building in the complex. This study quantitatively analyzes an apartment frame construction period using a queue model and evaluates the validity of the estimated period. In this regard, a methodology is proposed for analyzing the construction period by applying the concept of a customer and a server. A case study on the duration of an apartment frame construction period is conducted with the Korea Land and Housing Corporation, which has supplied the largest number of apartments in South Korea. It was found that the stable state of a queue system was observed when the rate of server utilization was applied to the basement and above-ground floors. However, a stable state was not reached on the ground floor. This study includes non-working days in its calculation and quantitatively analyzes uncertainty factors during construction. Therefore, the findings can be practically utilized to quantitatively plan the durations of work units in an apartment frame construction.

show abstract

Section: Research Methods For Quantifying Uncertaintymentioning

confidence: 99%

Quantitative Analysis of Waiting Length and Waiting Time for Frame Construction Work Activities Using a Queue Model; Focusing on Korean Apartment Construction

2021

View full text Add to dashboard Cite

show abstract

“…But very low complexity rate is considered as the major disadvantage of this approach. Siddiqui et al (2019) proposed the queue processing for server load based queuing approach under a cloud computing environment. Response time, throughput, waiting time was the simulation metrics used to evaluate this approach.…”

Section: Previous Literature Reviewsmentioning

confidence: 99%

Optimal Load Balancing in Cloud using ANFIS and MGWO based Polynomial Neural Network

Chourasia

Silakari

2021

Preprint

View full text Add to dashboard Cite

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. Here providing an appropriate service is considered a major challenge in the heterogeneous environment. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. In order to provide optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. In addition to this, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. In addition to this, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

show abstract

“…This theory focuses on managing the queue and service/operating time in a way to reduce waiting time and ensures maximum disposed entity from the system. Arrival rate of entity (𝜆) and service rate of server (𝜇) are the input variables to mathematically estimate waiting time, queue length, entity number in the queue and the system (Siddiqui et al, 2020). The queuing theory has many useful applications, including traffic flow (Zhao et al, 2019), (Hurtado Lange and Maguluri, 2022), (Maadi et al, 2022), plant location design (vehicle gas charging stations (Said and Mouftah, 2020), (Xiao et al, 2020)), scheduling (jobs on machine, patients in hospitals, online order taking and computer programs), and facility design (post offices, banks, supermarkets, ticket counter).…”

Section: Introductionmentioning

confidence: 99%

Formulation and Performance Assessment for Multiple Server Queueing Models

Alam¹,

Azrin²,

U³

et al. 2023

Preprint

View full text Add to dashboard Cite

Queueing theory has grown in prominence as it provides the numerical foundation for decision-making assessment. Queueing models with multiple servers provide extensive decision-making data, which is critical for evaluating a server's performance. The purpose of this study is to formulate multiple server queueing models and provides a performance assessment to evaluate the appropriate models. A three-phased structured approach has been used to model and analyze performance for multiple server queueing models. The "Multiple Server Finite Queue Length Infinite Queue Population Model" is most desirable for a customer who has to wait for less time (approximately 43.14%) in the system as well as in the queue (approximately 62.16%) and reduces the system length by approximately 47.02% and the queue length by approximately 64.76%. However, the "Multiple Server Infinite Queue Length Infinite Queue Population Model" is preferable from a managerial perspective, as the "Multiple Server Finite Queue Length Infinite Queue Population Model" has fewer customers in the system, indicating a loss from a managerial standpoint. When the arrival rate of customers, service rate, and the number of servers are increased, length of the system and queue remain nearly constant, whereas the waiting time in the system nearly doubles for both queueing models. The paper develops a performance evaluation of the “Multiple Server Infinite Queue Length Infinite Queue Population Model” and “Multiple Server Finite Queue Length Infinite Queue Population Model”, which are capable of adapting to an unpredictable decision in any service system. Furthermore, this study provides decision-makers with a perspective-based evaluation of the mentioned servers, with a focus on a manager and a customer.

show abstract

An QPSL Queuing Model for Load Balancing in Cloud Computing

Cited by 12 publications

References 13 publications

Quantitative Analysis of Waiting Length and Waiting Time for Frame Construction Work Activities Using a Queue Model; Focusing on Korean Apartment Construction

Quantitative Analysis of Waiting Length and Waiting Time for Frame Construction Work Activities Using a Queue Model; Focusing on Korean Apartment Construction

Optimal Load Balancing in Cloud using ANFIS and MGWO based Polynomial Neural Network

Formulation and Performance Assessment for Multiple Server Queueing Models

Contact Info

Product

Resources

About