Towards Max-Min Fair Resource Allocation for Stream Big Data Analytics in Shared Clouds - 2018


Distributed stream huge information analytics platforms have emerged to tackle the continuously generated knowledge streams. In stream massive knowledge analytics, the info processing workflow is abstracted as a directed graph referred to as a topology. Data are browse from the storage and processed tuple by tuple, and these processing results are updated dynamically. The performance of a topology is evaluated by its throughput. This Project proposes an economical resource allocation scheme for a heterogeneous stream huge data analytics cluster shared by multiple topologies, so as to achieve max-min fairness within the utilities of the throughput for all the topologies. We have a tendency to initial formulate a novel resource allocation problem, that is a mixed 0-one integer program. The NP-hardness of the matter is rigorously proven. To tackle this problem, we tend to remodel the non-convex constraint to many linear constraints using linearization and reformulation techniques. Based mostly on the analysis of the problem-specific structure and characteristics, we have a tendency to propose an approach that iteratively solves the continuous drawback with a fastened set of discrete variables optimally, and updates the discrete variables heuristically. Simulations show that our proposed resource allocation theme remarkably improves the max-min fairness in utilities of the topology throughput, and is low in computational complexity.

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