EDF-Adaptive: A New Semipartitioned Scheduling Algorithm for Multiprocessor Real-Time

Most of the multiprocessor real-time scheduling algorithms follow the partitioned approach, the global approach, or the semipartitioned approach which is a hybrid of the first two by allowing a small subset of tasks to migrate. EDF-fm (Earliest Deadline First-based Fixed and Migrating) and EDF-os (Earliest Deadline First-based Optimal Semipartitioned) are semipartitioned approaches and were proposed for soft real-time sporadic task systems. Despite their desirable property that migrations are boundary-limited such as they can only occur at job boundaries, EDF-fm and EDF-os are not always optimal and have higher tardiness and cost of overheads due to task migration. To address these issues, in this paper, we classify the systems into different types according to the utilization of their tasks and propose a new semipartitioned scheduling algorithm, earliest deadline first-adaptive, dubbed as EDF-adaptive. Our experiments show that EDF-adaptive can achieve better performance than EDF-fm and EDF-os, in terms of system utilization and tardiness overhead. It is also proved that EDF-adaptive is able to lessen the task migration overhead, by reducing the number of migrating jobs and the number of processors to which a task is migrated.

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

As computing-intensive mobile applications become increasingly diversified, mobile devices’ computing power is hard to keep up with demand. Mobile devices migrate tasks to the Mobile Edge Computing (MEC) platform and improve the performance of task processing through reasonable allocation and caching of resources on the platform. Small cellular networks (SCN) have excellent short-distance communication capabilities, and the combination of MEC and SCN is a promising research direction. This paper focuses on minimizing energy consumption for task migration in small cellular networks and proposes a task migration energy optimization strategy with resource caching by combining optimal stopping theory with migration decision-making. Firstly, the process of device finding the MEC platform with the required task processing resources is formulated as the optimal stopping problem. Secondly, we prove an optimal stopping rule’s existence, obtain the optimal processing energy consumption threshold, and compare it with the device energy consumption. Finally, the platform with the best energy consumption is selected to process the task. In the simulation experiment, the optimization strategy has lower average migration energy consumption and higher average data execution energy efficiency and average distance execution energy efficiency, which improves task migration performance by 10% ∼ 60%.

Run-time thermal management based on task migration techniques in 3D chip multiprocessors

The industry trend of Chip Multiprocessors (CMPs) architecture is to move from 2D CMPs to 3D CMPs architecture for obtain higher performance, more reliability, and reduced memory access latency. However, one key challenge in designing the 3D CMPs is the thermal issue as a result of maximizing the throughput . Therefore, applying Runtime Thermal Management (RTM) has become crucial for controlling thermal hotspots. In this thesis, two methods of run-time task migration are presented to balance the temperature and reduce the number of hotspots in 3D CMPs. The proposed techniques consider hotspots both in the core and the memory layers simultaneously to make the optimum run-time task migration decisions. The first proposed approach is divided into two algorithms working in parallel, which aim at maximizing the throughput on the 3D CMPs while satisfying the peak temperature constraints. Experimental results show that the proposed architecture yields up to 60% reduction in overall chip energy. The proposed architecture improves the IPC for canneal and fluidanimate applications by 18% and 14%, respectively. In the second method, the proposed technique migrates the hottest core with the optimal coldest core instead of the coldest core in the core layer. The optimal coldest core is selected by considering hotspots.