Comparison of cloud computing providers for development of big data and internet of things application

The improved technology of big data and the internet of things (IoT) increases the number of developments in the application of smart city and Industry 4.0. Thus, the need for high-performance cloud computing is increasing. However, the increase in cloud computing service providers causes difficulties in determining the chosen service provider. Therefore, the purpose of this study is to make comparisons to determine the criteria for selecting cloud computing services following the system architecture and services needed to develop IoT and big data applications. We have analyzed several parameters such as technology specifications, model services, data center location, big data service, internet of things, microservices architecture, cloud computing management, and machine learning. We use these parameters to compare several cloud computing service providers. The results present that the parameters able to use as a reference for choosing cloud computing for the implementation of IoT and big data technology.

A Hybrid Fuzzy Multi-Criteria Decision-Making Approach for Data Center Location Selection

In today's world, the fact that information applications have become an indispensable part of life with the effect of the developments in information technologies has led to a huge rate of data production and usage. As a result of this, the need for data centers has increased. Although Turkey is a country with advantages that can play a leading role in the field of data centers in the region where it is located, it has some disadvantages too. Some of these disadvantages are natural disasters index, climate index, energy index, accessibility index, human capital and quality of life index (HCLQ). In this context, these disadvantages are considered as criteria for data center location selection problem. In this study, criteria weights were determined by fuzzy DEMATEL (The Decision Making Trial and Evaluation Laboratory) method in the problem solving and alternatives (81 provinces) were ranked using EDAS (Evaluation based on Distance from Average Solution) method. According to the results, it was found that Istanbul is the best alternative in data center location selection.

A Hybrid Fuzzy Multi-Criteria Decision-Making Approach for Data Center Location Selection

In today's world, the fact that information applications have become an indispensable part of life with the effect of the developments in information technologies has led to a huge rate of data production and usage. As a result of this, the need for data centers has increased. Although Turkey is a country with advantages that can play a leading role in the field of data centers in the region where it is located, it has some disadvantages too. Some of these disadvantages are natural disasters index, climate index, energy index, accessibility index, human capital and quality of life index (HCLQ). In this context, these disadvantages are considered as criteria for data center location selection problem. In this study, criteria weights were determined by fuzzy DEMATEL (The Decision Making Trial and Evaluation Laboratory) method in the problem solving and alternatives (81 provinces) were ranked using EDAS (Evaluation based on Distance from Average Solution) method. According to the results, it was found that Istanbul is the best alternative in data center location selection.

Free Cooling in High Heat Density Data Centers

Data centers comprised of racks and corresponding servers form the backbone of today’s cloud computing. Continuous operation of large numbers of servers can generate large amounts of heat which in turn requires high capacity cooling systems. These cooling systems can consume a significant portion of the energy required to run a data center and can negatively impact data center efficiency. With increasing operational costs, the data center industry has started to actively search for efficiency improvements to slow down the rising cost of running services. One of the most prominent ways to cut power consumption is free cooling, which uses outside air for cooling IT equipment completely or part of the time. This system is often used in colder climates and requires less energy since it doesn’t use compressors for cooling incoming air. This paper presents an evaluation of Shanghai as a data center location. As temperature affects data center cooling consumption, focus of the paper is on evaluating optimal operating conditions for local data centers.

Cloud Computing and Operations Research

The aim of this chapter is twofold. On one hand, it shows how some classes of optimization problems can be efficiently solved on a cloud platform, especially in terms of storage capacity and computing power. Since an exhaustive treatment of this topic is beyond the purpose of the book, the attention is focused on the following classes of optimization problems: Linear Programming, Integer Linear Programming, Stochastic Optimization, and Logistics Management. On the other hand, the chapter also shows how some problems that arise in designing and managing the clouds can be mathematically formulated as optimization problems. Among these, the attention is focused on the Data Center Location Problem, the Virtual Machines Allocation Problem, and the Partner Provider Selection Problem. Finally, some useful conclusions are derived on the relation between Simulation-based Optimization and cloud computing.