Blockchain Technology and Applications

Pethuru Raj, Kavita Saini, Chellammal Surianarayanan, Pethuru Raj, Kavita Saini, Chellammal Surianarayanan

• Editorial: Auerbach Publications
• Año: 2020
• ISBN:
  9781000175257
• Edición: 1
• Categoría: Computer Science
• Categoría: Cryptography

Blockchain is emerging as a powerful technology, which has attracted the wider attention of all businesses across the globe. In addition to financial businesses, IT companies and business organizations are keenly analyzing and adapting this technology for improving business processes. Security is the primary enterprise application. There are other crucial applications that include creating decentralized applications and smart contracts, which are being touted as the key differentiator of this pioneering technology. The power of any technology lies in its ecosystem. Product and tool vendors are building and releasing a variety of versatile and robust toolsets and platforms in order to speed up and simplify blockchain application development, deployment and management. There are other infrastructure-related advancements in order to streamline blockchain adoption. Cloud computing, big data analytics, machine and deep learning algorithm, and connected and embedded devices all are driving blockchain application development and deployment.

Blockchain Technology and Applications illustrates how blockchain is being sustained through a host of platforms, programming languages, and enabling tools. It examines:

• Data confidential, integrity, and authentication
• Distributed consensus protocols and algorithms
• Blockchain systems design criteria and systems interoperability and scalability
• Integration with other technologies including cloud and big data

It also details how blockchain is being blended with cloud computing, big data analytics and IoT across all industry verticals. The book gives readers insight into how this path-breaking technology can be a value addition in several business domains ranging from healthcare, financial services, government, supply chain and retail.

Chapter 1

Distributed Computing and/or Distributed Database Systems

K. P. Arjun, N. M. Sreenarayanan, K. Sampath Kumar, and R. Viswanathan

Contents

1.1 Introduction to Computing

1.2 Evolution of Distributed Computing

1.2.1 Centralized Computing

1.2.2 Decentralized Computing

1.2.3 Parallel Computing

1.3 High-Performance Distributed and Parallel Computing

1.3.1 Parallel Computing

1.3.1.1 Bit-Level, Instruction-Level and Task-Level Parallel Computing

1.3.2 Distributed Computing

1.3.3 Architecture of Distributed Computing

1.3.3.1 Physical Architecture of Distributed Computing

1.3.3.2 Software Architecture of Distributed Computing

1.4 Comparison of Distributed Computing with State of the Art

1.4.1 Distributed Computing versus Parallel Computing

1.4.2 Distributed Database Systems

1.4.3 Traditional versus Distributed Databases

1.4.4 Distributed Computing and Blockchain

1.5 Distributed Computing Environment of Blockchain

1.5.2 Distributed Computing Architecture

1.5.2.1 Client–Server Architecture

1.5.2.2 Peer-to-Peer Architecture

1.6 Distributed Ledger in Blockchain

1.6.1 Computing Power and Breakthroughs in Cryptography

1.6.2 Public and Private Blockchain

1.7 Conclusion

References

1.1 Introduction to Computing

Computing involves process-oriented step-by-step tasks to complete a goal-oriented computation. A goal is not a simple or single rather there may be more than one goal. Normally we can say that a goal is a complex operation that is processed using a computer. A normal computer contains hardware and software; and computing can also involve more than one computing environment in hardware like workstations, servers, clients and other intermediate nodes and software like a workstation Operating System, server operating system and other computing software. The computing in our daily life includes sending emails, playing games or making phone calls; these are different kinds of computing examples at different contextual levels. Depending on the processing speed and size, computers are categorized into different types like supercomputers, mainframes, minicomputers and microcomputers. The computing power of a device is directly proportional to its data-storing capacity.

All software is developed in a sequential way which means that before developing software to solve a large problem, we split the problem into smaller sub problems. These sub problems broken down step by step or in a flowchart are called algorithms. These algorithms are executed by the central processing unit (CPU). We can call this serial computing, as the main task is divided into a number of small instructions, then these instructions are executed one by one. But in the main, this serial communication is a huge waste of the hardware other than the CPU. The CPU is continuously taking instructions and processing those instructions. The hardware contributing to processing that specific hardware is used for that particular time only, and for the remaining time that hardware is idle.

So to overcome the deficiencies in resource utilization and improve the computing power we moved into another era of computing called parallel computing and distributed computing. The insight of distributed computing is in solving more complex and larger computational problems with the help of more than one computational system. The computational problem is divided into many tasks, each of which is executed in different computational systems that are located in different regions.

1.2 Evolution of Distributed Computing

Distributed computing [1] is concurrent processing of multiple processes at the same time. Distributed computing works on various very important concepts like multiprogramming and multitask programming. Finally distributed computing has been included in the branch of computer science and engineering since the 1970s. Since then many international conferences like the Symposium on Principles of Distributed Computing (PODC), International Symposium on Distributed Computing (DISC), etc. and international workshops like the International Workshop on Distributed Algorithms [2] on Graphs have been conducted.

1.2.1 Centralized Computing

The name “centralized computing” refers to computing that occurs in a central situated machine. The specifications of the central computing server machine include high computing capabilities and sophisticated software. All other computers are attached to the central situated machine and communicate through terminals. The centralized machine [3] itself controls and manages the peripherals, some of which are physically connected and some of which are attached via terminals.

The main advantage of centralized system is greater security compared to other types of computing because the processing is only done at the centrally located machine. All the connected machines can access the centralized processing machine and start processing their own task by using terminals. If one terminal goes down, then the user can use another terminal and log in again. All the user-related files are still available with that particular user login. The user can resume their session and complete the task.

The main and most important disadvantage of the centralized computing system is that all computing and storage is done at centrally located machine. If the machine fails or crashes the entire system will go down. It affects the performance evaluation on unavailability of service.

Figure 1.1 shows a block diagram of centralized computing. Centralized systems are somewhat related to client–server programming [5]. The client has minimum computing power, but for advanced and high-level computing, client requests for the server. The server computes the request received from the client and sends the response back to the client.

Figure 1.1 Centralized computing.

1.2.2 Decentralized Computing

In centralized computing, a centrally located powerful system provides computing services to all other nodes connected. The disadvantage is that all processing power is located at one entity. Alternatively, the burden at the central level can be shared by the nodes connected on the network. In decentralized computing [6] a single server is not responsible for the whole task. The whole workload is distributed to the computing nodes so that each computing node has equal processing power.

1.2.3 Parallel Computing

To overcoming the deficiencies in resource utilization and improve the computing power we moved into another era of computing called parallel computing. The name “parallel” means that more than one instruction can be executed simultaneously. It requires the configuration of a number of computing engines (normally called “processors”) and related hardware and also software configuration.

1.3 High-Performance Distributed and Parallel Computing

1.3.1 Parallel Computing

In a CPU, a main task is divided into a number of small instructions, and then these instructions are executed one by one. The main problem with the serial communication is wastage of large amount of resources in terms of hardware and software resources. CPU continuously receives instructions and process them. The hardware involved in serial processing remains idl...