Big Data Analytics in Cloud Computing

In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.

A Study on Capabilities and Challenges of Fog Computing

Fog computing is a new paradigm believed to be an extension of cloud computing and services to the sting of the network. Similarly, like Cloud, Fog provides computing, data, storage, and various application services to the connected end-users. Fog computing uses one or a lot of combined end users or nearby end users edge devices to perform the configuration, communication, storage, control activity, and management functions over the infrastructure supported. This new paradigm solves the latency and information measure limitation issues encountered from the cloud computing. Primarily, the architecture of the fog computing is discussed and analyzed during this work and then indicates the connected potential security and trust problems. Then, however such problems are tackled within the existing literature is systematically reportable. Finally, the open challenges, analysis, trends, and future topics of security and trust in fog computing are mentioned.

Load Balancing and Prudential Hypervisor in Cloud Computing

Cloud computing is a service model in internet that provides virtualized resources to its clients. These types of servicing give a lot of benefits to the cloud users where they can pay as per their use. Even though they have benefits, they also face some problems like receiving computing resources, which is guaranteed on time. This time delay may affect the service time and the makespan. Thus, to reduce such problems, it is necessary to schedule the resources and then allocate it to using an optimized hypervisor. Here, the proposed method is used to do the above-mentioned problem. First, the available resources are clustered with respect to their characteristics. Then the resources are scheduled using this method. Finally, with respect to that of the clients request the resources, the resources are allocated. Here, the cost is the fitness of the allocation.

Impact of Cloud of Clouds in Enterprises Applications

In recent times, the cloud has become a leading technology demanding its functionality in every business. According to research firm IDC and Gartner study, nearly one-third of the worldwide enterprise application market will be SaaS-based by 2018, driving annual SaaS revenue to $50.8 billion, from $22.6 billion in 2013. Downtime is treated as the primary drawback which may affect great deals in businesses. The service unavailability leads to a major disruption affecting the business environment. Hence, utmost care should be taken to scale the availability of services. As cloud computing has plenty of uncertainty with respect to network bandwidth and resources accessibility, delegating the computing resources as services should be scheduled accordingly. This chapter proposes a study on cloud of clouds and its impact on a business enterprise. It is also decided to propose a suitable scheduling algorithm to the cloud of cloud environment so as to trim the downtime problem faced by the cloud computing environment.

Leveraging the Internet of Things (IoT) Paradigm Towards Smarter Applications

Intelligent processing with smart devices and informative communications in everyday tasks brings an effective platform for the internet of things (IOT). Internet of things is seeking its own way to be the universal solution for all the real-life scenarios. Even though many theoretical studies pave the basic requirement for the internet of things, still the evidence-based learning (EBL) is lacking to deal with the application of the internet of things. As a contribution of this chapter, the basic requirements to study about internet of things with its deployment architecture for mostly enhanced applications are analyzed. This shows researchers how to initiate their research focus with the utilization of internet of things.

Resource Management Techniques to Manage the Load Balancing in Cloud Computing

Load balancing is one of the vital issues in cloud computing that needs to be achieved using proper techniques as it is directly related to higher resource utilization ratio and user satisfaction. By evenly distributing the dynamic local workload across all the nodes in the whole cloud, load balancing makes sure that no single node is overwhelmed, and some other nodes are kept idle. Hence, the technique helps to improve the overall performance resource utility of the system which will lead to high user satisfaction and resource utilization ratio. It also ensures the fair and effective distribution of each and every computing resource in the distributed system. Furthermore, the various load balancing techniques prevent the possible bottlenecks of the system created by the load imbalance. Maximization of the throughput, minimization of the response time, and avoidance of the overload are the other major advantages of the load balancing. Above all, by keeping resource consumption at the minimum, the load balancing techniques help to reduce costs.

An Efficient Framework Based on Cloud Computing Integrated With Internet of Things Technology for Intelligent Waste Management

This chapter presents techniques based on internet of things and cloud computing-driven waste management. The data of the World Bank says that the municipal solid waste generation by the year of 2025 will be 1.42 kg/capital per day in the urban residential areas, with the increase in cost of about $375.5 billion that has a major rise from an annual of $205.4 billion in the year 2012. Due to the high population with the extreme consumption of goods and services, this leads to a strong association among the income levels, quality of life, and waste generation. In the present situation, more than 50% of the total population is living in the cities. In the governance aspect, it is said that the cost of waste management will be highly expensive. This chapter deals with the effective waste management with the implementation of internet of things (IoT)-based cloud technology with the machine learning algorithm that could be highly intellectual in the management of waste.

Cloud-Based Predictive Maintenance and Machine Monitoring for Intelligent Manufacturing for Automobile Industry

The concept of predictive analysis plays complex information retrieval and categorization systems are needed to process queries, filter, and store, and organize huge amount of data, which are mainly composed of texts. As soon as datasets becomes large, most information combines with algorithms that might not perform well. Moreover, prediction is important in today's industrial purposes since that could reduce the issues of heavy asset loss towards the organization. The purpose of prediction is necessary in every field since it could help us to stop the cause of occurring the error before any vulnerable activities could happen. Predictive maintenance is a method that consumes the direct monitoring of mechanical condition of plant equipment to decide the actual mean time to malfunction for each preferred machine. The authors try to estimate the fault that could occur in the machines and decide the time that could cause a critical situation. This prediction should be done effectively, and for this purpose, they have stepped into the concept of machine learning.

Edge Cloud

With the proliferation of new technologies such as augmented and virtual reality, autonomous cars, 5G networks, drones, and IOT with smart cities, consumers of cloud computing are becoming the producers of data. Large volume of data is being produced at the edge of the network. This scenario insists the need for efficient real-time processing and communication at the network edge. Cloud capabilities must be distributed across the network to form an edge cloud, which places computing resources where the traffic is at the edge of the network. Edge cloud along with 5G services could also glint the next generation of robotic manufacturing. The anticipated low latency requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy are also inscribed by edge cloud. A number of giants like Nokia, AT&T, and Microsoft have emerged in the market to support edge cloud. This chapter wraps the features of edge cloud, the driving industries that are providing solutions, the use cases, benefits, and the challenges of edge cloud.

An Intelligent Grid Network Based on Cloud Computing Infrastructures

This chapter covers important topics in development of efficient energy girds. Inefficient power generation, unbalanced consumption patterns that lead to underutilization of expensive infrastructure on the one hand, and severe overload on the other, as well as urgent issues of national and global concern such as power system security and climate change are all driving this evolution. As the smart grid concept matures, we'll see dramatic growth in green power production: small production devices such as wind turbines and solar panels or solar farms, which have fluctuating capacity outside of the control of grid operators. Small companies that specialize in producing power under just certain conditions will boom in forthcoming years. Energy is stored in the storage during low-cost periods, and the stored energy is used during high-cost periods to avoid the expensive draw from the grid. The authors evaluate the impact of large-scale energy storage adoption on grid electricity demand.