In Linux kernel version 6.6, the Earliest Eligible Virtual Deadline First (EEVDF) scheduler was introduced as the new default scheduler. However, due to its high computational complexity, it may not be suitable for all application scenarios. In cases where there are a large number of short-term tasks or frequent task communication, EEVDF can incur excessive context switch overhead and performance degradation. To address these issues, we propose a lightweight scheduling strategy named SRAND. Leveraging the programmable scheduling framework ‘sched_ext’ and employing BPF technology, we have implemented a strategy based on global and local run queues. This strategy utilizes a five-level BPF mapped queue to partition tasks with different virtual runtimes. Tasks with smaller virtual runtimes are placed into a FIFO-type global queue first, enabling priority scheduling for tasks with smaller virtual runtimes. Additionally, we monitor CPU idle states and allocate tasks in a timely manner, enhancing task responsiveness while reducing scheduling complexity. It is worth noting that our strategy integrates user-space scheduling policies into the kernel via an eBPF program loader, thus eliminating the need for kernel code modifications. By implementing the SRAND strategy, we have observed significant improvements compared to Linux's default scheduling strategy EEVDF. Specifically, our proposed strategy averages an 11.83% reduction in process context switch time and an overall performance improvement of 7.02% in stress tests, while maintaining satisfactory load balancing.

In order to improve performance and avoid overheating on mobile devices, precise thermal control with low overhead is crucial. To achieve this, we propose incorporating a performance monitoring counter (PMC)-based power model into thermal control, which enables a more accurate evaluation of the CPU’s power consumption. We demonstrate the plausibility of this approach using polynomial regression based on Moore’s Law. Additionally, we introduce a lightweight PMC sampling method that can collect multiple PMCs at once in the kernel space, reducing sampling overhead. By replacing the utilization-based model in the original the intelligent power allocation (IPA) with a PMC-based power model, we realize the PMC-based IPA governor can be ported to real mobile devices. After updating the thermal control governor in the Linux kernel, we perform tests on our PMC-based IPA using a mobile phone device. We compare it with Stepwise and IPA, which are commonly used in current mobile phone systems. We choose the CPU-intensive workbench, I/O-intensive workbench, and CPU and I/O-intensive hybrid workbench as workloads. The results show that PMC-based IPA effectively reduces energy consumption while improving performance. In particular, during the CPU and I/O-intensive hybrid experiment, where CPU-intensive and I/O-intensive tasks are executed alternately, PMC-based IPA reduces the running time by 10.0% and energy consumption by 16.6% compared to the original IPA. In order to verify the benefits of PMC-based IPA, mobile phone testing software AI Bench and Antutu are utilized. The results show that our scheme is able to control temperature more precisely than IPA and achieves a better score while consuming less energy, particularly during AI computing. These experiment results suggest that PMC-based IPA is valuable for practical use.

Rendering redundancy consumes a lot of computing resources in mobile devices. Eliminating redundancy can effectively improve system energy efficiency. However, as the premise of redundancy elimination, the existing redundancy identification methods affect the final performance because of the excessive cost. In this article, we propose the CLERR: a high-performance cross-layer method for eliminating rendering redundancy. CLERR decomposes the redundancy detection process into two collaborative steps, thereby reducing detection overhead. The proposed method is compatible with the mainstream Android 12 system. Experimental results indicate that the method can reduce frame drop rates by 14.5% and save SOC energy by 5.1%.

Dynamic multiobjective optimization problems (DMOPs) are characterized by optimization functions that change over time in varying environments. The DMOP is challenging because it requires the varying Pareto-optimal sets (POSs) to be tracked quickly and accurately during the optimization process. In recent years, transfer learning has been proven to be one of the effective means to solve dynamic multiobjective optimization. However, the negative transfer will lead the search of finding the POS to a wrong direction, which greatly reduces the efficiency of solving optimization problems. Minimizing the occurrence of negative transfer is thus critical for the use of transfer learning in solving DMOPs. In this article, we propose a new individual-based transfer learning method, called an individual transfer-based dynamic multiobjective evolutionary algorithm (IT-DMOEA), for solving DMOPs. Unlike existing approaches, it uses a presearch strategy to filter out some high-quality individuals with better diversity so that it can avoid negative transfer caused by individual aggregation. On this basis, an individual-based transfer learning technique is applied to accelerate the construction of an initial population. The merit of the IT-DMOEA method is that it combines different strategies in maintaining the advantages of transfer learning methods as well as avoiding the occurrence of negative transfer; thereby greatly improving the quality of solutions and convergence speed. The experimental results show that the proposed IT-DMOEA approach can considerably improve the quality of solutions and convergence speed compared to several state-of-the-art algorithms based on different benchmark problems.

Many real-world optimization problems involve multiple objectives, constraints, and parameters that may change over time. These problems are often called dynamic multiobjective optimization problems (DMOPs). The difficulty in solving DMOPs is the need to track the changing Pareto-optimal front efficiently and accurately. It is known that transfer learning (TL)-based methods have the advantage of reusing experiences obtained from past computational processes to improve the quality of current solutions. However, existing TL-based methods are generally computationally intensive and thus time consuming. This article proposes a new memory-driven manifold TL-based evolutionary algorithm for dynamic multiobjective optimization (MMTL-DMOEA). The method combines the mechanism of memory to preserve the best individuals from the past with the feature of manifold TL to predict the optimal individuals at the new instance during the evolution. The elites of these individuals obtained from both past experience and future prediction will then constitute as the initial population in the optimization process. This strategy significantly improves the quality of solutions at the initial stage and reduces the computational cost required in existing methods. Different benchmark problems are used to validate the proposed algorithm and the simulation results are compared with state-of-the-art dynamic multiobjective optimization algorithms (DMOAs). The results show that our approach is capable of improving the computational speed by two orders of magnitude while achieving a better quality of solutions than existing methods.

This work proposed a semi-supervised method for automatic texture filtering. Our method leveraged a limited amount of labeled data and a large amount of unlabeled data to train Generative Adversarial Networks (GANs). Separate loss functions were designed for both labeled and unlabeled datasets. Our main contribution is the introduction of knowledge extracted from shallow and deep layers in neural networks. Loss defined within shallow layers preserves the edge, while loss defined within the deep layers identifies the semantic content and conversely removes the small-scale texture variations. This contribution directly addresses the major challenge for texture filtering, distinguishing the structural content from non-structural textures at the pixel level. The extracted information, in our study, improved the content and color consistency before and after the process of filtering, for unlabeled samples in particular. The proposed method offers twofold benefits: first, significant reductions in the amounts of time and effort expended in reconstructing the labeled dataset, especially given the delicate operations required at the pixel level; second, a reduction in over-fitting, in supervised learning with a small amount of labeled data, by utilizing a large amount of unlabeled data. The results confirm that our method can perform comparably with non-learning-based methods, alleviating the demand for the determination of optimal parameter values.

The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments. One of the promising approaches for solving the DMOPs is reusing the obtained Pareto optimal set (POS) to train prediction models via machine learning approaches. In this paper, we train an Incremental Support Vector Machine (ISVM) classifier with the past POS, and then the solutions of the DMOP we want to solve at the next moment are filtered through the trained ISVM classifier. A high-quality initial population will be generated by the ISVM classifier, and a variety of different types of population-based dynamic multi-objective optimization algorithms can benefit from the population. To verify this idea, we incorporate the proposed approach into three evolutionary algorithms, the multi-objective particle swarm optimization(MOPSO), Nondominated Sorting Genetic Algorithm II (NSGA-II), and the Regularity Model-based multi-objective estimation of distribution algorithm(RE-MEDA). We employ experiments to test these algorithms, and experimental results show the effectiveness.