Dynamic immune cooperative scheduling of agricultural machineries

Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.

Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability.

Heterogeneous CPU-GPU Epsilon Grid Joins: Static and Dynamic Work Partitioning Strategies

Given two datasets (or tables) A and B and a search distance $$\epsilon$$ ϵ , the distance similarity join, denoted as $$A \ltimes _\epsilon B$$ A ⋉ ϵ B , finds the pairs of points ($$p_a$$ p a , $$p_b$$ p b ), where $$p_a \in A$$ p a ∈ A and $$p_b \in B$$ p b ∈ B , and such that the distance between $$p_a$$ p a and $$p_b$$ p b is $$\le \epsilon$$ ≤ ϵ . If $$A = B$$ A = B , then the similarity join is equivalent to a similarity self-join, denoted as $$A \bowtie _\epsilon A$$ A ⋈ ϵ A . We propose in this paper Heterogeneous Epsilon Grid Joins (HEGJoin), a heterogeneous CPU-GPU distance similarity join algorithm. Efficiently partitioning the work between the CPU and the GPU is a challenge. Indeed, the work partitioning strategy needs to consider the different characteristics and computational throughput of the processors (CPU and GPU), as well as the data-dependent nature of the similarity join that accounts in the overall execution time (e.g., the number of queries, their distribution, the dimensionality, etc.). In addition to HEGJoin, we design in this paper a dynamic and two static work partitioning strategies. We also propose a performance model for each static partitioning strategy to perform the distribution of the work between the processors. We evaluate the performance of all three partitioning methods by considering the execution time and the load imbalance between the CPU and GPU as performance metrics. HEGJoin achieves a speedup of up to $$5.46\times$$ 5.46 × ($$3.97\times$$ 3.97 × ) over the GPU-only (CPU-only) algorithms on our first test platform and up to $$1.97\times$$ 1.97 × ($$12.07\times$$ 12.07 × ) on our second test platform over the GPU-only (CPU-only) algorithms.

Hybrid Scheduling Scheme for Real Time Systems

Systems as asymmetric multiprocessor platforms are considered power-efficient multiprocessor architectures, efficient task partitioning (assignment) and play a crucial role in achieving more energy efficiency at these multiprocessor platforms. This paper addresses the problem of energy-aware static partitioning of periodic real time tasks on heterogeneous multiprocessor platforms. A hybrid approach of Particle Swarm Optimization variant and priority assignment based Min-Min algorithm for task partitioning is proposed. The proposed approach aims to minimize the overall energy consumption, meanwhile avoid deadline violations. An energy-aware cost function is proposed to be considered in the proposed approach. Extensive simulated experiments and comparisons with related approaches are conducted in order to validate the effectiveness of the proposed technique. The achieved results demonstrate that the proposed partitioning scheme significantly outperforms in terms of the number of executed iterations to accomplish a specific task in addition to the energy savings.