Accommodating bursts in distributed stream processing systems
The only way to compare the old and new types of thermometer is to run side by side comparisons in the field and at many sites.
Which is exactly what the bureau were doing, but the data has never been put in an archive, or has been destroyed. We have this in writing after an FOI application by Dr Bill Johnston (see below).
Together, this model-driven scheduling approach gives a predictable application performance and resource utilization behavior for executing a given DSPS application at a target input stream rate on distributed resources.
Accommodating Bursts in Distributed Stream Processing Systems.
Such systems are often challenged by the bursty nature of the applications.
In this paper, we present BARRE (Burst Accommodation through Rate REconfiguration), a system to address the problem of bursty data streams in distributed stream processing systems.
Upon the emergence of a burst, BARRE dynamically reserves resources dispersed across the nodes of a distributed stream processing system, based on the requirements of each application as well as the resources available on the nodes.
These frameworks are designed to adapt to the dynamic input message rate by scaling in/out.So we need some strategy to come up with the optimal resource requirement for a given streaming application.In this article, we propose a model-driven approach for scheduling streaming applications that effectively utilizes a priori knowledge of the applications to provide predictable scheduling behavior.Known as automatic weather sensors (AWS) these are quite different to the old “liquid in glass” type.The electronic ones can pick up very short bursts of heat – so they can measure extremes of temperatures that the old mercury or liquid thermometers would not pick up, unless the spike of heat lasted for a few minutes.