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Paper: Tempest: Scalable Time-Critical Web Services Platform

An interesting and different implementation approach: Tempest: Scalable Time-Critical Web Services Platform

Tempest is a new framework for developing time-critical web services. Tempest enables developers to build scalable, fault-tolerant services that can then be automatically replicated and deployed across clusters of computing nodes. The platform automatically adapts to load fluctuations, reacts when components fail, and ensures consistency between replicas by repairing when inconsistencies do occur. Tempest relies on a family of epidemic protocols and on Ricochet, a reliable time critical multicast protocol with probabilistic guarantees.

Tempest is built around a novel storage abstraction called the TempestCollection in which application developers store the state of a service. Our platform handles the replication of this state across clones of the service, persistence, and failure handling. To minimize the need for specialized knowledge on the part of the application developer, the TempestCollection employs interfaces almost identical to those used by the Java Collections standard. Elements can be accessed on an individual basis, but it is also possible to access the full set by iterating over it, just as in a standard Collection. The hope is that we can free developers from the complexities of scalability and fault-tolerance, leaving them to focus on application functionality.

Traditionally, services relying on a transactional database backend offer a strong data consistency model in which every read operation returns the result of the latest update that occurred on a data item. With Tempest we take a different approach by relaxing the model such that services offer sequential consistency [10]: Every replica of the service sees the operations on the same data item in the same order, but the order may be different from the order in which the operations were issued. Later, we will see that this is a non-trivial design decision; Tempest services can sometimes return results that would be erroneous were we using a more standard transactional execution model. For applications where these semantics are adequate, sequential consistency buys us scheduling flexibility that enables much better real-time responsiveness.

Reader Comments (2)

How to insure high availability where entire data center goes down for an hour and one can redirect their customers during the downtime hours and push back to the original data center when it is up again with all modification and changes they have done?

November 8, 2013 | Unregistered CommenterKrishan

What is the homepage of the Tempest project? Is it opensource?

November 9, 2013 | Unregistered CommenterSimone Scarduzio

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