Big data processing applications are becoming more and more complex. They are no more monolithic in nature but instead they are composed of decoupled analytical processes in the form of a workflow. One type of such workflow applications is stream workflow application, which integrates multiple streaming big data applications to support decision making. Each analytical component of these applications runs continuously and processes data streams whose velocity will depend on several factors such as network bandwidth and processing rate of parent analytical component. As a consequence, the execution of these applications on cloud environments requires advanced scheduling techniques that adhere to end user’s requirements in terms of data processing and deadline for decision making. In this article, we propose two multicloud scheduling and resource allocation techniques for efficient execution of stream workflow applications on multicloud environments while adhering to workflow application and user performance requirements and reducing execution cost. Results showed that the proposed genetic algorithm is an adequate and effective for all experiments.

An element-by-element preconditioned conjugate gradient (PCG) iterative solver for the solution of 3D finite element analysis has been implemented into a FPGA-based 32-bit floating-point reconfigurable computer. The algorithm formulation has been chosen in order to optimize the match with the capabilities of the FPGA platform. It is capable of giving a speed-up of about 40 times compared to an optimized 64-bit software version running on a fast PC.

The Discrete Element Method (DEM) is a numerical model to describe the mechanical behaviour of discontinuous bodies. It has been traditionally used to simulate particle flows (e.g. sand, sugar), but is becoming more popular as a method to represent solid materials. The DEM is very computationally expensive, but has properties that make it amenable to acceleration by reconfigurable computing. This paper describes the implementation of a dedicated hardware architecture for the DEM implemented on an FPGA, which is capable of giving a speed-zip of about 30 times compared to an optimised software version running on a fast microprocessor.