From Traditional Fault Tolerance to Blockchain. Wenbing Zhao

A classification of blockchain applications based on token usage.

      10.13 The impossibility trinity hypothesis.

      List of Tables

      7.1 Messages received and final decisions in two cases for OM(1,4).

      7.2 Messages received and step (3) calculation in two cases for instances of OM(1) at G1.

      7.3 Messages received and step (3) calculation in two cases for instances of OM(1) at G2.

      7.4 Messages received and step (3) calculation in two cases for instances of OM(1) at G3.

      7.5 Messages received and step (3) calculation in two cases for instances of OM(1) at G4.

      7.6 Messages received and step (3) calculation in two cases for instances of OM(1) at G5.

      7.7 Final decision made at each lieutenant in step (3) of OM(2).

      10.1 Blockchain-enabled IoT-based applications.

      10.2 Blockchain-enabled supply chain applications.

      10.3 Blockchain-enabled manufacturing applications.

      10.4 Blockchain-enabled automobile production.

      10.5 Blockchain-enabled energy systems.

      10.6 Blockchain-enabled healthcare systems.

      10.7 Blockchain-enabled smart city.

      10.8 Blockchain-enabled workplace.

      10.9 General discussions on blockchain-enabled CPS applications.

      Acknowledgments

      This book is dedicated to my parents. They tried their best to help me pursue my dreams through so many years’ financial hardship. They took their life savings to pay the government so that I could be free to emigrate to the greatest country on earth. When I stumbled and had nowhere else to go, they took me under their wings and took care of me. I am forever in their debt.

      I also would like to thank my beautiful wife, Hao, and my lovely children Dorothy, Emily, and Arthur. It is them that make my life so enjoyable and meaningful.

      W. Z.

      Preface

      Cloud services are playing an ever increasingly important role in all aspects of our society, governments, businesses, and individuals alike. We depend on these services on a daily basis, such as financial (e.g., online banking and stock trading), e-commerce (e.g., online shopping), civil infrastructure (e.g., electric power grid and traffic control), entertainment (e.g., online gaming and multimedia streaming), and personal data storage (e.g., various cloud services such as Dropbox, Google Drive, and OneDrive). Behind these cloud services is distributed computing, which addresses many critical issues in making the services dependable and trustworthy. The most important of all is to build consensus in its internal operations that span many different computing nodes.

      Distributed consensus has been studied for several decades, at least starting in 1970s. The reason why distributed consensus is important is that a distributed system would span over many computing nodes, and these nodes must maintain a common view on the system state so that each can operate as planned towards the objectives of the system. Prolonged inconsistency among different components of the system would damage the integrity of the system and ultimately would result in system-level failures that are visible to end users.

      The cost of system failures is enormous. If a data center is brought down by a system failure, the average cost for downtime may range from $42,000 to about $300,000 per hour [2, 6]. The cost can be estimated by summing up the wasted expenses and the loss of revenue. While the labor cost of downtime may be estimated relatively easily (i.e., roughly, wasted expenses per hour = number of employees × average salary per hour) [13], it is much harder to estimate the loss of revenue, especially due to the damages on the reputation of the business and the loyalty of its potential customers [2].

Ensuring high availability of distributed systems is not cheap. In [7], the cost of data center is estimated to range from $450 per square foot for 99.671% availability (i.e., 28.8 hours of downtime per year), to $1,100 per square foot for 99.995% availability (i.e., 0.4 hours of downtime per year). That is perhaps one reason why about 59% of Fortune 500 companies suffer from 1.6 hours or more of downtime per week [2].

      All classical consensus algorithms rely on a concept referred to as membership, that is, every node would know how many nodes are in the current membership, the logical role of each node, and how to reach other nodes. Another important construct is voting via the sending of messages to each other. Typically, one of the members would assume a special role, which is referred to as the primary or the coordinator. The coordinator might fail or become compromised, in which case, a new coordinator would be elected through voting. As such, classical distributed consensus algorithms are expensive, highly complex, and not scalable due to the heavy use of multiple rounds of message exchanges among the members.

      In January 2009, the launch of the first practical cryptocurrency, Bitcoin [12], has completely changed the picture. The most essential prerequisite for a cryptocurrency is the assurance that it is virtually impossible for anyone to double-spend the money (i.e., cryptocurrency) one has. Bitcoin addressed this requirement by introducing an immutable distributed ledger in the form of a chain of blocks where each block aggregates hundreds or even thousands of transactions. This distributed ledger is often referred to as the blockchain. The immutability of the blockchain is achieved by several means: (1) cryptographic protection of the blockchain, such as digital signature, one-way hash function, and chaining of the blocks; (2) massive degree of replication of the blockchain across many nodes in the Bitcoin network; and (3) a novel probabilistic consensus algorithm that is completely different from classical consensus algorithms.

The consensus algorithm used in Bitcoin does not involve any explicit form of voting, therefore, there is no extra message exchange among the nodes in the Bitcoin network for the purpose of reaching consensus. In Bitcoin, the consensus building process is converted into a lottery-like stochastic process where the winner of the lottery gets the right to create a new block of transactions and collects an award [22]. To ensure fairness and to ensure the process to be a stochastic process, every participating node would work on a Proof-of-Work (PoW) based puzzle, and the first one that finds a solution becomes the winner. The PoW puzzle has a predefined target difficulty, and a participating node would experiment with different ways of making the hash of the block header meet the target difficulty. This is a CPU-intensive process. Hence, the only way a node could gain advantage over other nodes is to invest in better hardware that can perform the hash operation faster. The Bitcoin consensus algorithm is referred to as PoW and sometimes as the Nakamoto algorithm, named after Bitcoin’s creator, which is apparently a pseudonym. This novel form of consensus algorithm has aroused huge interest in the research and application of the blockchain technology [20]. Some even expressed the hope that the blockchain technology would lead to a new-form of economy, just like what the Internet has transformed our society [16].

      This book contains two parts. The first part consists of the first 7 chapters and it covers the most essential techniques for building dependable distributed systems. The last 3 chapters form the second part, which covers the blockchain technology.

      Chapter 1 introduces the basic concepts and terminologies of dependable distributed computing, as well as the primary means to achieve dependability.

      Chapter