Stochastic performance model for web server capacity planning in fog computing
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Stochastic performance model for web server capacity planning in fog computing Paulo Pereira1 · Jean Araujo2 · Matheus Torquato3 · Jamilson Dantas1 · Carlos Melo1 · Paulo Maciel1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Cloud computing is attractive mostly because it allows companies to increase and decrease available resources, which makes them seem limitless. Although cloud computing has many advantages, there are still several issues such as unpredictable latency and no mobility support. To overcome these problems, fog computing extends communication, storage, and computation toward the edge of network. Therefore, fog computing may support delay-sensitive applications, which means that the application latency from end users can be improved, and it also decreases energy consumption and traffic congestion. The demand for performance, availability, and reliability in computational systems grows every day. To optimize these features, it is necessary to improve the resource utilization such as CPU, network bandwidth, memory, and storage. Although fog computing extends the cloud computing resources and improves the quality of service, executing capacity planning is an effective approach to arranging a deterministic process for web-related activities, and it is one of the approaches of optimizing web performance. The goal of capacity planning in fog computing is preparing the system for an incoming workload, so we are able to optimize the system’s utilization while minimizing the total task execution time, which happens before sending the load toward the cloud environment or not sending it at all. In this paper, we evaluate the performance of a web server running in a fog environment. We also use QoS metrics to plan its capacity. We proposed performance closed-form equations extracted from a continuous-time Markov chain model of the system. Keywords Cloud computing · Fog computing · Performance evaluation · Continuous-time Markov chain · Capacity planning · Analytical model
* Paulo Pereira [email protected] Extended author information available on the last page of the article
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1 Introduction Cloud computing allowed computing resources to be available as services, which created a new model of business [22]. Cloud providers usually charge for the resource utilization during a time period (e.g., hourly) and also permit customers to use computing resources that are in any geographic location through the Internet [8]. Currently, cloud services consumption is compared to utilities (e.g., electricity, water, and gas) because the customer pays for the service without knowing about the infrastructure that provides it [9]. Most cloud services operate with the pay-as-yougo model, which has no boundaries on capacity or utilization [22]. However, cloud computing substantial yet unsolved challenges such as large end-to-end response time, which may cause lower throughput and higher discard rate. These problems are generally caused by the large dist
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