  Reliable systems have always been built out of unreliable components. Early
on, the reliable components were small such as mirrored disks or ECC (Error
Correcting Codes) in core memory. These systems were designed such that
failures of these small components were transparent to the application. Later,
the size of the unreliable components grew larger and semantic challenges crept
into the application when failures occurred.
  As the granularity of the unreliable component grows, the latency to
communicate with a backup becomes unpalatable. This leads to a more relaxed
model for fault tolerance. The primary system will acknowledge the work request
and its actions without waiting to ensure that the backup is notified of the
work. This improves the responsiveness of the system.
  There are two implications of asynchronous state capture: 1) Everything
promised by the primary is probabilistic. There is always a chance that an
untimely failure shortly after the promise results in a backup proceeding
without knowledge of the commitment. Hence, nothing is guaranteed! 2)
Applications must ensure eventual consistency. Since work may be stuck in the
primary after a failure and reappear later, the processing order for work
cannot be guaranteed.
  Platform designers are struggling to make this easier for their applications.
Emerging patterns of eventual consistency and probabilistic execution may soon
yield a way for applications to express requirements for a "looser" form of
consistency while providing availability in the face of ever larger failures.
  This paper recounts portions of the evolution of these trends, attempts to
show the patterns that span these changes, and talks about future directions as
we continue to "build on quicksand".

Distributed, Parallel, and Cluster Computing