Complex Event Processing (CEP) systems are designed to
process large amount of information by simultaneously eval-
uating multiple queries over event streams. Two main re-
quirements imposed by the users of the CEP systems are: (1)
ability to process high throughput event data and (2) ability
to answer queries with very low latency. In order to meet
above requirements CEP systems are becoming increasingly
distributed. Distribution of queries as well as event streams
across multiple nodes facilitates increasing the throughput of
CEP systems while simultaneously maintaining low response
times.
The widespread adoption of cloud computing and the ac-
companying pay-as-you-go model has added new dimensions
to the problem of complex event processing in a distributed
system. Nowadays, it is not only important to be able to
scale the processing out to a large number of nodes, it is also
equally important to be able to scale the processing down,
as soon as the load or user requirements decrease. The abil-
ity to scale processing up and down along with the load and
user requirements is called elasticity.
The goal of the thesis described in this paper is to de-
velop a component allowing for elastic scaling of distributed
CEP systems in response to variations in the load and con-
tractual obligations regarding the quality of service. To this
end, the thesis described in this paper will address following
three major topics: (1) multi query optimization, (2) op-
erator placement in distributed environments, and (3) cost
efficiency. This paper outlines the state of art for the three
aforementioned topics and presents the overall draft of the
solution for the problem of elastic complex event processing.
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