Due to recent demand for playback of high quality video on mobile devices, there is the need for a scalable, error resilient framework with the ability to adjust to the network and receiver's specification. To cope with the bandwidth fluctuations in the network, the framework's scalability allows for high bit-rate video to be transcoded to a low bit-rate format while preserving quality as much as possible. The new High Efficiency Video Coding (HEVC/H.265) standard allows for high compression rates, however, it is computationally intensive. We propose a novel Development Infrastructure for Video coding/transcoding Applications (DIVA). This framework is capable of providing different quality of services (e.g., Bronze, Silver, Gold), and has some error-resilient capability. Taking advantage of IBM Platform Symphony, the computationally intensive task of HEVC encoding can be distributed on available local or cloud resources. Our experiments illustrate the feasibility of this approach.

In order to improve the performance of MapReduce, we design DynMR. It addresses the following problems that persist in the existing implementations: 1) difficulty in selecting optimal performance parameters for a single job in a fixed, dedicated environment, and lack of capability to configure parameters that can perform optimally in a dynamic, multi-job cluster; 2) long job execution resulting from a task long-tail effect, often caused by ReduceTask data skew or heterogeneous computing nodes; 3) inefficient use of hardware resources, since ReduceTasks bundle several functional phases together and may idle during certain phases.