Performance Analysis of SaaS Ticket Management Systems

Gushev, Pano; Ristov, Sasko; Gušev, Marjan

doi:10.15439/2014f331

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…e price of goods and services will change as a general rule, but the business model will remain stable during the planning period, which is typically 3-5 years, but in certain industries, may be 7-10 years or more [61,62]. A long-term pricing plan was set while keeping a flexible price point in mind.…”

Section: Demand-supply Algorithmmentioning

confidence: 99%

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Rakhra

Bhargava

et al. 2022

Journal of Food Quality

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Several industries have recently seen the replacement of human labor by automated machinery and equipment. Across the globe, farmers’ attitudes on the use of technology in agriculture are divergent. However, although some people are excited and ready to embrace technology, others are cautious and wary of trying new technologies for the first time. The third category is particularly prevalent in underdeveloped nations such as India, owing to a lack of competence, a lack of effective translation, and most crucially, a lack of financial resources. It is fruitless for the government to attempt to resolve these difficulties due to the fact that they do not take into consideration the changing circumstances and input needs of each agricultural group. Smart Tillage is a cutting-edge framework that was developed to solve the challenges listed above. In India, a decision-based smart engine for the rental and sharing of tools and equipment has been developed, which leverages machine learning methods to proceed towards a selection of tools and equipment. The option is entirely reliant on a variety of input variables, including crop kind, harvest time/month, crop equipment needed, harvest type, and the amount of money available for rental. Additionally, an ideal recommendation engine driven by content and collaborative-based filtering will provide the farmer’s requirements depending on their requirements. In terms of escalation, the proposals would be cost-effective and excellent since they would need little changes in training, technique improvements, and resource management via a new rent-share model similar to that used by Uber. In this work, demand and supply algorithms are used to define market equilibrium, and the results are shown in graphs. This includes discussion of a variety of demand and supply parameters, their impact on market equilibrium prices and quantities, and their effect on shifting demand and supply curves. The many sorts of elasticities (demand, cross-price, supply, income, and so on) are examined, as well as the ramifications for pricing systems that may result from these elasticities.

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Section: Demand-supply Algorithmmentioning

confidence: 99%

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Rakhra

Bhargava

et al. 2022

Journal of Food Quality

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“…Gusev et al [9] also achieved opposite results for transactional web services without using worker role on Azure. The authors believe that a great role in this behavior is mostly due to the Azure's balancer and organization of the system using a predefined database.…”

Section: Discussionmentioning

confidence: 97%

Windows Azure: Resource Organization Performance Analysis

Gušev

Ristov

Koteska

et al. 2014

Advanced Information Systems Engineering

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International audienceCloud customers can scale the resources according to their needs in order to avoid application bottleneck. The scaling can be done in two ways, either by increasing the existing virtual machine instance with additional resources, or by adding an additional virtual machine instance with the same resources. Although it is expected that the costs rise proportionally to scaling, we are interested in finding out which organization offers scaling with better performance. The goal of this paper is to determine the resource organization that produces better performance for the same cost, and help the customers decide if it is better to host a web application on a more ”small” instances or less ”large” instances. The first hypothesis states that better performance is obtained by using more and smaller instances. The second hypothesis is that the obtained speedup while scaling the resources is smaller than the scaling factor. The results from the provided experiments have not proven any of the hypotheses, meaning that using less, but larger instances results with better performance and that the user gets more performances than expected by scaling the resources

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“…DaaS is special case to IaaS [1]. A solution itself can be hosted in the cloud, by which it will follow all the elements mentioned above, and use the available cloud services in order to achieve scalability and elasticity [8]. The model of the e-Business solution with ticket management system is not any different from any other solution that uses the cloud computing, but if SaaS model is adopted, the solution will make some changes in order to meet some of the SaaS characteristics.…”

Section: Backgroudmentioning

confidence: 99%

A model of SaaS e-Business solution

Kolic¹,

Ristov

Gušev

2014

2014 22nd Telecommunications Forum Telfor (TELFOR)

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E-Business solutions are information systems mostly used by the Small and Medium Enterprises. To have a robust, scalable and elastic solution, some companies decide to use e-Business software as service that satisfy their needs and at the same time saves money. In addition to this, the infomatization of the company will be accelerated, and further development of the company will be processed. The building of that kind of solution must be adapted for the cloud environment. In that case, the implementation faces the challenges of the cloud environment such as elasticity and scalability.This paper is focused on solving the problems and challenges when implementing a Software as a service (SaaS) e-Business solution. Certain mechanisms must be applied to this solution so it has the characteristics of one SaaS cloud solution and implement the elasticity and scalability of the solution.

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Performance Analysis of SaaS Ticket Management Systems

Cited by 5 publications

References 17 publications

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Windows Azure: Resource Organization Performance Analysis

A model of SaaS e-Business solution

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