  Emerging smart grid applications analyze large amounts of data collected from
millions of meters and systems to facilitate distributed monitoring and
real-time control tasks. However, current parallel data processing systems are
designed for common applications, unaware of the massive volume of the
collected data, causing long data transfer delay during the computation and
slow response time of smart grid systems. A promising direction to reduce delay
is to jointly schedule computation tasks and data transfers. We identify that
the smart grid data analytic jobs require the intermediate data among different
computation stages to be transmitted orderly to avoid network congestion. This
new feature prevents current scheduling algorithms from being efficient. In
this work, an integrated computing and communication task scheduling scheme is
proposed. The mathematical formulation of smart grid data analytic jobs
scheduling problem is given, which is unsolvable by existing optimization
methods due to the strongly coupled constraints. Several techniques are
combined to linearize it for adapting the Branch and Cut method. Based on the
topological information in the job graph, the Topology Aware Branch and Cut
method is further proposed to speed up searching for optimal solutions.
Numerical results demonstrate the effectiveness of the proposed method.
