Performance analysis of non-preemptive priority with application to cloud E-marketplaces

Akingbesote, Alaba Olu; Adigun, Matthew O.; Xulu, S. S.; Sanjay, M.; Ajayi, I. R.

doi:10.1109/icastech.2014.7068069

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Akingbesote et al modeled a typical cloud E‐marketplace under a non‐preemptive policy and evaluated the performance impact on consumer waiting time. They found that as the server utilization became high, the performance of their model based on waiting time was 80% better than the conventional nonpriority model.…”

Section: Primary Studies and Classification Schemesmentioning

confidence: 99%

Applying queue theory for modeling of cloud computing: A systematic review

Ghomi

Rahmani

Qader

2019

Concurrency and Computation

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The cloud computing paradigm is an important service in the Internet for sharing and providing resources in a cost-efficient way. Modeling of a cloud system is not an easy task because of the complexity and large scale of such systems. Cloud reliability could be improved by modeling the various aspects of cloud systems, including scheduling, service time, wait time, and hardware and software failures. The aim of this study is to survey research studies done on the modeling of cloud computing using the queuing system in order to identify where more emphasis should be placed in both current and future research directions. This paper follows the goal by investigating the articles published between 2008 and January 2017 in journals and conferences. A systematic mapping study combined with a systematic literature review was performed to find the related literature, and 71 articles were selected as primary studies that were classified in relation to the focus, research type, and contribution type. We classified the modeling techniques of cloud computing using the queuing theory in seven categories based on their focus area: (1) performance, (2) quality of service, (3) workflow scheduling, (4) energy savings, (5) resource management, (6) priority-based servicing, and (7) reliability. A majority of the primary articles focus on performance (37%), 15% of them focus on resource management, 14% of them focus on quality of service, 13% of them focus on workflow scheduling, 13% of them focus on energy savings, 4% of them focus on priority-based servicing for requests, and 4% of them focus on reliability. This work summarizes and classifies the research efforts conducted on applying queue theory for modeling of cloud computing (AQTMCC), providing a good starting point for further research in this area. KEYWORDScloud computing, cloud modeling, queuing systems, queuing theory, systematic review INTRODUCTIONCloud computing is the recent evolution in the information technology and is growing very fast due to the extensive use of mobile devices, such as PDAs, cell phones, and tablets. 1 To emphasize the goals of cloud computing, we refer to the definition of NIST (National Institute of Standards and Technology); a cloud model is composed of five characteristics, three service models, and four deployment models. 2 Service models of cloud computing are as follows: (1) Software as a Service (SaaS), such as CRM (Customer Relationship Management), virtual desktop, communications, and games; (2) Platform as a Service (PaaS), such as databases, web servers, and deployment tools; and (3) Infrastructure as a Service (IaaS), such as virtual machines (VMs), storages, servers, and load balancers. Deployment models include private clouds, public clouds, community clouds, and hybrid clouds. The characteristics of a cloud are on-demand self-servicing, elasticity, broad network access, resource pooling, and measured service. 1 A data center in cloud computing consists of several servers that are organized in racks and connected through communicati...

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Section: Primary Studies and Classification Schemesmentioning

confidence: 99%

Applying queue theory for modeling of cloud computing: A systematic review

Ghomi

Rahmani

Qader

2019

Concurrency and Computation

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“…Several works have been done which ranges from GUI-SET's implementation [18][19][20] to its performance evaluation [17,21,22] and security [15,16,23] as well as the Pricing strategy to be used on the platform [14]. However, little research has been conducted in relation to providing an optimally satisfactory service especially in the newly integrated ad hoc model system.…”

Section: Introductionmentioning

confidence: 99%

Approaches to Addressing Service Selection Ties in Ad Hoc Mobile Cloud Computing

Akinola

Adigun

2018

Journal of Computer Networks and Communications

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e ad hoc mobile cloud (AMC) allows mobile devices to connect together through a wireless connection or any other means and send a request for web services from one to another within the mobile cloud. However, one of the major challenges in the AMC is the occurrence of dissatisfaction experienced by the users. is is because there are many services with similar functionalities but varying nonfunctional properties. Moreover, another resultant cause of user dissatisfaction being coupled with runtime redundancy is the attainment of similar quality computations during service selection, often referred to as "service selection ties." In an attempt to address this challenge, service selection mechanisms for the AMC were developed in this work.is includes the use of selected quality of service properties coupled with user feedback data to determine the most suitable service. ese mechanisms were evaluated using the experimental method.e evaluation of the mechanisms mainly focused on the metrics that evaluate the satisfaction of users' interest via the quantitative evaluation. e experiments affirmed that the use of the shortest distance can help to break selection ties between potential servicing nodes. Also, a continuous use of updated and unlimited range of users' assessments enhances an optimal service selection.

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