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Monitoring Data Integrity in Big Data Analytics Services
Enabled by advances in Cloud technologies, Big Data Analytics Services (BDAS) can improve many processes and identify extra information from previously untapped data sources. As our experience with BDAS and its benefits grows and technology for obtaining even more data improves, BDAS becomes ever more important for many different domains and for our daily lives. Most efforts in improving BDAS technologies have focused on scaling and efficiency issues. However, an equally important property is that of security, especially as we increasingly use public Cloud infrastructures instead of private ones. In this paper we present our approach for strengthening BDAS security by modifying the popular Spark infrastructure so as to monitor at run-time the integrity of data manipulated. In this way, we can ensure that the results obtained by the complex and resource-intensive computations performed on the Cloud are based on correct data and not data that have been tampered with or modified through faults in one of the many and complex subsystems of the overall system
Checkpointing as a Service in Heterogeneous Cloud Environments
A non-invasive, cloud-agnostic approach is demonstrated for extending
existing cloud platforms to include checkpoint-restart capability. Most cloud
platforms currently rely on each application to provide its own fault
tolerance. A uniform mechanism within the cloud itself serves two purposes: (a)
direct support for long-running jobs, which would otherwise require a custom
fault-tolerant mechanism for each application; and (b) the administrative
capability to manage an over-subscribed cloud by temporarily swapping out jobs
when higher priority jobs arrive. An advantage of this uniform approach is that
it also supports parallel and distributed computations, over both TCP and
InfiniBand, thus allowing traditional HPC applications to take advantage of an
existing cloud infrastructure. Additionally, an integrated health-monitoring
mechanism detects when long-running jobs either fail or incur exceptionally low
performance, perhaps due to resource starvation, and proactively suspends the
job. The cloud-agnostic feature is demonstrated by applying the implementation
to two very different cloud platforms: Snooze and OpenStack. The use of a
cloud-agnostic architecture also enables, for the first time, migration of
applications from one cloud platform to another.Comment: 20 pages, 11 figures, appears in CCGrid, 201
On reducing the complexity of matrix clocks
Matrix clocks are a generalization of the notion of vector clocks that allows
the local representation of causal precedence to reach into an asynchronous
distributed computation's past with depth , where is an integer.
Maintaining matrix clocks correctly in a system of nodes requires that
everymessage be accompanied by numbers, which reflects an exponential
dependency of the complexity of matrix clocks upon the desired depth . We
introduce a novel type of matrix clock, one that requires only numbers to
be attached to each message while maintaining what for many applications may be
the most significant portion of the information that the original matrix clock
carries. In order to illustrate the new clock's applicability, we demonstrate
its use in the monitoring of certain resource-sharing computations
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