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Real-time systems are often designed using preemptive scheduling and worst-case execution time estimates to guarantee the execution of high priority tasks. There is, however, an interest in exploring nonpreemptive scheduling models for real-time systems, particularly for soft real-time multimedia applications. In this paper, we propose a new algorithm that uses multiple scheduling strategies for efficient nonpreemptive scheduling of tasks. Our goal is to improve the success ratio of the well-known Earliest Deadline First (EDF) approach when the load on the system is very high and to improve the overall performance in both underloaded and overloaded conditions. Our approach, known as group-EDF (gEDF) is based on dynamic grouping of tasks with deadlines that are very close to each other, and using Shortest Job First (SJF) technique to schedule tasks within the group. We will present results comparing gEDF with other real-time algorithms including, EDF, Best-effort, and Guarantee, by using randomly generated tasks with varying execution times, release times, deadlines and tolerance to missing deadlines, under varying workloads. We believe that grouping tasks dynamically with similar deadlines and utilizing a secondary criteria, such as minimizing the total execution time (or other metrics such as power or resource availability that can be easily extended) for scheduling tasks within a group, can lead to new and more efficient real-time scheduling algorithms.

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Processing-in-Memory (PIM) is the concept of moving computation as close as possible to memory. This decreases the need for the movement of data between central processor and memory system, hence improves energy efficiency from the reduced memory traffic. In this paper we present our approach on how to embed processing cores in 3D-stacked memories, and evaluate the use of such a system for Big Data analytics. We present a simple server architecture, which employs several energy efficient PIM cores in multiple 3D-DRAM units where the server acts as a node of a cluster for Big Data analyses utilizing MapReduce programming framework. Our preliminary analyses show that on a single node up to 23% energy savings on the processing units can be achieved while reducing execution time by up to 8.8%. Additional energy savings can result from simplifying the system memory buses. We believe such energy efficient systems with PIM capability will become viable in the near future because of the potential to scale the memory wall.

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