“Eight fallacies of distributed computing,” all [of which] “prove to be false in the long run and all [of which] cause big trouble and painful learning experiences” (https://blogs.oracle. com/jag/resource/Fallacies.html). Accounting for and understanding the implications of network behavior is key to designing robust distributed programs— in fact, six of Deutsch’s “fallacies” directly pertain to limitations on networked communications. This should be unsurprising: the ability (and often requirement) to communicate over a shared channel possibility and impossibility of perform- ing distributed computations under particular sets of network conditions. For example, the celebrated FLP impossibility result9 demonstrates the inability to guarantee consensus in an asynchronous network (that is, one facing indefinite communication partitions between processes) with one faulty process. This means that, in the presence of unreliable (untimely) mes- sage delivery, basic operations such as modifying the set of machines in a cluster (that is, maintaining group membership, as systems such as Zoo- keeper are tasked with today) are not guaranteed to complete in the event of both network asynchrony and indi- vidual server failures. Related results describe the inability to guarantee the progress of serializable transactions,7 linearizable reads/writes,11 and a variety of useful, programmer-friendly guar- antees under adverse conditions.3 The implications of these results are not simply academic: these impossibility results have motivated a proliferation of systems and designs offering a range of alternative guarantees in the event of network failures.5 However, under a friendlier, more reliable network that guarantees timely message delivery, FLP and many of these related results no longer hold:8 by making stronger guarantees about network behavior, we can circumvent the programmabil- ity implications of these impossibility proofs. Therefore, the degree of reliability in deployment environments is critical in robust systems design and directly determines the kinds of operations that systems can reliably perform with- out waiting. Unfortunately, the degree to which networks are actually reliable in the real world is the subject of con- siderable and evolving debate. Some have claimed that networks are reliable (or that partitions are rare enough in practice) and that we are too concerned with designing for theoretical failure The Network Is Reliable DOI:10.1145/2643130 Article development led by queue.acm.org An informal survey of real-world communications failures. BY PETER BAILIS AND KYLE KINGSBURY CACM, September 2014 issue