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No other text on the market takes this approach, nor offers the comprehensive and up-to-date treatment that Koren and Krishna provide. This book incorporates case studies that highlight six different computer systems with fault-tolerance techniques implemented in their design.

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A complete ancillary package is available to lecturers, including online solutions manual for instructors and PowerPoint slides. Students, designers, and architects of high performance processors will value this comprehensive overview of the field. Purchase the book. We use cookies to help provide and enhance our service and tailor content and ads.

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By continuing you agree to the use of cookies. Here are a couple of basic solutions:. Each solution to byzantine failures has its disadvantages, but they seem to outweigh the alternative, which is having corrupted data in a system. Unfortunately, there may be no solution to byzantine failure where all data is stored and processed by a single process. Another way to handle failures is to design a distributed system, but with it, things get more complicated. A distributed system is the one where a state and processing are shared by multiple computers — unlike a centralized system, where everything is stored in a single piece of hardware — that appears to a user as a single coherent system.

Distributed systems can be found everywhere.

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  • Fault-tolerant computer system.

Here are just a few examples:. While distributed systems may help to tolerate some of the typical failures of centralized systems, they increase complexity of a solution and comes with their own set of problems such as:. Network partition happens when some of the nodes of a distributed system lose connectivity but continue to run independently and end up in two or more disjoint clusters.

In such a case, the state of the system might diverge because each cluster continues to change its own state but fails to synchronize with others. There are two common solutions:. In the case of network partitioning, a distributed system can maintain only one of the two following characteristics: consistency or availability. A consistency can be maintained but at the expense of availability and vice versa.

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This trade-off is commonly known as the CAP theorem. For example, fault-tolerant systems with backup components in the cloud can restore mission-critical systems quickly, even if a natural or human-induced disaster destroys on-premise IT infrastructure.

Five nines, or In most cases, a business continuity strategy will include both high availability and fault tolerance to ensure your organization maintains essential functions during minor failures, and in the event of a disaster. Consider the following analogy to better understand the difference between fault tolerance and high availability.

Fault tolerance

A twin-engine airplane is a fault tolerant system — if one engine fails, the other one kicks in, allowing the plane to continue flying. Conversely, a car with a spare tire is highly available. A flat tire will cause the car to stop, but downtime is minimal because the tire can be easily replaced. Some important considerations when creating fault tolerant and high availability systems in an organizational setting include:.

Some of your systems may require a fault-tolerant design, while high availability might suffice for others. Load balancing and failover are both integral aspects of fault tolerance.

Coordinated and Improved Fault Tolerance for High Performance Computing Systems

Load balancing solutions allow an application to run on multiple network nodes, removing the concern about a single point of failure. Most load balancers also optimize workload distribution across multiple computing resources, making them individually more resilient to activity spikes that would otherwise cause slowdowns and other disruptions.

In addition, load balancing helps cope with partial network failures.