GENETIC ALGORITHM WITH TWO OBJECTIVE FOR REAL-TIME TASK SCHEDULING WITH COMMUNICATION TIME

Purpose of the study:The real-time task scheduling on multiprocessor system is known as an NP-hard problem. This paper proposes a new real-time task scheduling algorithmwhich considers the communication time between processors and the execution order between tasks. Methodology:Genetic Algorithm (GA)with Adaptive Weight Approach (AWA) is used in our approach. Main Findings:Our approach has two objectives. The first objective is to minimize the total amount of deadline-miss. And the second objective is to minimize the total number of processors used. Applications of this study:For two objectives,the range of each objective is readjusted through Adaptive Weight Approach (AWA) and more useful result is obtained. Novelty/Originality of this study:This study never been done before.This study also wasprovided current information about scheduling algorithm and heuristics algorithm

Real-Time Scheduling in Heterogeneous Systems Considering Cache Reload Time Using Genetic Algorithms

Abstract. Since optimal assignment of tasks in a multiprocessor system is, in almost all practical cases, an NP-hard problem, in recent years some algorithms based on genetic algorithms have been proposed. Some of these algorithms have considered real-time applications with multiple objectives, total tardiness, completion time, etc. Here, we propose a suboptimal static scheduler of nonpreemptable tasks in hard real-time heterogeneous multiprocessor systems considering time constraints and cache reload time. The approach makes use of genetic algorithm to minimize total completion time and number of processors used, simultaneously. One important issue which makes this research different from previous ones is cache reload time. The method is implemented and the results are compared against a similar method

A Cloud-Edge-aided Incremental High-order Possibilistic c-Means Algorithm for Medical Data Clustering

Medical Internet of Things are generating a big volume of data to enable smart medicine that tries to offer computer-aided medical and healthcare services with artificial intelligence techniques like deep learning and clustering. However, it is a challenging issue for deep learning and clustering algorithms to analyze large medical data because of their high computational complexity, thus hindering the progress of smart medicine. In this paper, we present an incremental high-order possibilistic c-means algorithm on a cloud-edge computing system to achieve medical data co-clustering of multiple hospitals in different locations. Specifically, each hospital employs the deep computation model to learn a feature tensor of each medical data object on the local edge computing system and then uploads the feature tensors to the cloud computing platform. The high-order possibilistic c-means algorithm (HoPCM) is performed on the cloud system for medical data clustering on uploaded feature tensors. Once the new medical data feature tensors are arriving at the cloud computing platform, the incremental high-order possibilistic c-means algorithm (IHoPCM) is performed on the combination of the new feature tensors and the previous clustering centers to obtain clustering results for the feature tensors received to date. In this way, repeated clustering on the previous feature tensors is avoided to improve the clustering efficiency. In the experiments, we compare different algorithms on two medical datasets regarding clustering accuracy and clustering efficiency. Results show that the presented IHoPCM method achieves great improvements over the compared algorithms in clustering accuracy and efficiency

A Novel Enhanced Quantum PSO for Optimal Network Configuration in Heterogeneous Industrial IoT

A novel enhanced quantum particle swarm optimization algorithm for IIoT deployments is proposed. It provides enhanced connectivity, reduced energy consumption, and optimized delay. We consider heterogeneous scenarios of network topologies for optimal path configuration by exploring and exploiting the hunts. It uses multiple inputs from heterogeneous IIoT into quantum and bio-inspired optimization techniques. The differential evolution operator and crossover operations are used for information interchange among the nodes to avoid trapping into local minima. The different topology scenarios are simulated to study the impact of $p$ -degrees of connectivity concerning objective functions’ evaluation and compared with existing techniques. The results demonstrate that our algorithm consumes a minimum of 30.3% lesser energy. Furthermore, it offers improved searching precision and convergence swiftness in the possible search space for $p$ -disjoint paths and reduces the delay by a minimum of 26.7%. Our algorithm also improves the throughput by a minimum of 29.87% since the quantum swarm inclines to generate additional diverse paths from multiple source nodes to the gateway