Integrating scale out and fault tolerance in stream processing using operator state management
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A multi-resource load balancing algorithm for cloud cache systems
Proceedings of the 28th Annual ACM Symposium on Applied Computing
STONE: a stream-based DDoS defense framework
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Adaptive input admission and management for parallel stream processing
Proceedings of the 7th ACM international conference on Distributed event-based systems
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Many applications in several domains such as telecommunications, network security, large-scale sensor networks, require online processing of continuous data flows. They produce very high loads that requires aggregating the processing capacity of many nodes. Current Stream Processing Engines do not scale with the input load due to single-node bottlenecks. Additionally, they are based on static configurations that lead to either under or overprovisioning. In this paper, we present StreamCloud, a scalable and elastic stream processing engine for processing large data stream volumes. StreamCloud uses a novel parallelization technique that splits queries into subqueries that are allocated to independent sets of nodes in a way that minimizes the distribution overhead. Its elastic protocols exhibit low intrusiveness, enabling effective adjustment of resources to the incoming load. Elasticity is combined with dynamic load balancing to minimize the computational resources used. The paper presents the system design, implementation, and a thorough evaluation of the scalability and elasticity of the fully implemented system.