A novel design is proposed to implement highly parallel stream join operators on a field-programmable gate array (FPGA), by examining handshake join algorithm for hardware implementation. The proposed design is evaluated in terms of the hardware resource usage, the maximum clock frequency, and the performance. Experimental results indicate that the proposed implementation can handle considerably high input rates, especially at low match rates. Results of simulation conducted to optimize size of buffers included in join and merge units give a new intuition regarding static and adaptive buffer tuning in handshake join.

We present an economical and fault-tolerant load balancing strategy (EFTLBS) based on an operator replication mechanism and a load shedding method, that fully utilizes the network resources to realize continuous and highly-available data stream processing without dynamic operator migration over wide area networks. In this paper, we first design an economical operator distribution (EOD) plan based on a bin-packing model under the constraints of each stream bandwidth as well as each server's CPU capacity. Next, we devise super-operator (SO) that load balances multi-degree operator replicas. Moreover, for improving the fault-tolerance of the system, we color the SOs based on a coloring bin-packing (CBP) model that assigns peer operator replicas to different servers. To minimize the effects of input rate bursts upon the system, we take advantage of a load shedding method while keeping the QoS guarantees made by the system based on the SO scheme and the CBP model. Finally, we substantiate the utility of our work through experiments on ns-3.

A data stream is a series of massive unbounded tuples continuously generated at a rapid rate. Continuous queries for data streams should be processed continuously, so that a strict time constraint is required. In most previous research studies, in order to guarantee this constraint, the evaluation order of join predicates in a continuous query is optimized using a greedy strategy. However, because a greedy strategy traces only the first promising plan, it often finds a suboptimal plan. To reduce the possibility of producing a suboptimal plan, in this paper, we propose an improved scheme, k-Extended Greedy Algorithm (k-EGA), that simultaneously examines a set of promising plans and reoptimize an execution plan adaptively. The number of promising plans is flexibly controlled by a user-defined range variable. The scheme verifies the performance of the current plan periodically. If the plan is no longer efficient, a newly optimized plan is generated. The performance of the proposed scheme is verified through various experiments to identify its various characteristics.