Energy-Aware Real-Time Scheduling on Heterogeneous and Homogeneous Platforms in the Era of Parallel Computing

Multi-core processors increasingly appear as an enabling platform for embedded systems, e.g., mobile phones, tablets, computerized numerical controls, etc. The parallel task model, where a task can execute on multiple cores simultaneously, can efficiently exploit the multi-core platform\u27s computational ability. Many computation-intensive systems (e.g., self-driving cars) that demand stringent timing requirements often evolve in the form of parallel tasks. Several real-time embedded system applications demand predictable timing behavior and satisfy other system constraints, such as energy consumption. Motivated by the facts mentioned above, this thesis studies the approach to integrating the dynamic voltage and frequency scaling (DVFS) policy with real-time embedded system application\u27s internal parallelism to reduce the worst-case energy consumption (WCEC), an essential requirement for energy-constrained systems. First, we propose an energy-sub-optimal scheduler, assuming the per-core speed tuning feature for each processor. Then we extend our solution to adapt the clustered multi-core platform, where at any given time, all the processors in the same cluster run at the same speed. We also present an analysis to exploit a task\u27s probabilistic information to improve the average-case energy consumption (ACEC), a common non-functional requirement of embedded systems. Due to the strict requirement of temporal correctness, the majority of the real-time system analysis considered the worst-case scenario, leading to resource over-provisioning and cost. The mixed-criticality (MC) framework was proposed to minimize energy consumption and resource over-provisioning. MC scheduling has received considerable attention from the real-time system research community, as it is crucial to designing safety-critical real-time systems. This thesis further addresses energy-aware scheduling of real-time tasks in an MC platform, where tasks with varying criticality levels (i.e., importance) are integrated into a common platform. We propose an algorithm GEDF-VD for scheduling MC tasks with internal parallelism in a multiprocessor platform. We also prove the correctness of GEDF-VD, provide a detailed quantitative evaluation, and reported extensive experimental results. Finally, we present an analysis to exploit a task\u27s probabilistic information at their respective criticality levels. Our proposed approach reduces the average-case energy consumption while satisfying the worst-case timing requirement

Optimizing Energy in Non-preemptive Mixed-Criticality Scheduling by Exploiting Probabilistic Information

The strict requirements on the timing correctness biased the modeling and analysis of real-time systems towards the worst-case performances. Such focus on the worst-case, however, does not provide enough information to effectively steer the resource/energy optimization. In this paper, we integrate a probabilistic-based energy prediction strategy with the precise scheduling of mixed-criticality tasks, where the timing correctness must be met for all tasks at all scenarios. The Dynamic Voltage and Frequency Scaling (DVFS) is applied to this precise scheduling policy to enable energy minimization. We propose a probabilistic technique to derive an energy-efficient speed (for the processor) that minimizes the average energy consumption, while guaranteeing the (worst-case) timing correctness for all tasks, including lo-criticality ones, under any execution condition. We present a response time analysis for such systems under the non-preemptive fixed-priority scheduling policy. Finally, we conduct an extensive simulation campaign based on randomly generated task sets to verify the effectiveness of our algorithm (w.r.t. energy savings) and it reports up to 46% energy-saving

Application of Ionizing Radiation for Sustainable Textile Effluent Treatment Plant

In the rapid growing world, the demand for clothing especially the readymade garment is one of the key economy factors for the developing countries where cheap manpower is available. But, although having a vital role in the total economy, the textile industry development is not sustainable in the South Asian region causing serious degradation to the environment for its high ground water consumption and large waste discharge. In this research the amount of effluent production was reduced by the application of irradiated chitosan which modify the cotton surface and facilitate it to absorb more color at a lower dye concentration. Raw textile effluent was also treated by gamma radiation and physico-chemical properties were measured. Textile effluent treated by gamma radiation was used for the coloration of cotton fabrics with reactive dyes in exhaust method at 60°C where fresh water served as control. Color fastness to wash, rubbing and perspiration were evaluated. Samples showed very high competitiveness with control samples thus opening a new era for of textile effluent reuse. Irradiated effluent also showed excellent plant growth when irrigated to Malabar spinach plants (80% more dry weight) where raw effluent showed significant growth retardation. Minerals content of the plant was also studied and significant increase of minerals content was found in the samples irrigated with treated effluent than the samples irrigated with fresh water. So, this paper represents a very promising technique for the sustainable development in the textile sector leading to zero waste production facility