A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Pharmacokinetic and tissue distribution analysis of bioactive compounds from Fuke Qianjin capsules in rats by a validated UPLCMS/MS method

Fuke Qianjin capsules (FKQJ) exhibit obvious advantages and characteristics in the treatment of pelvic inflammatory disease. At present, information regarding the in vivo process of FKQJ is lacking, which has become a bottleneck in further determining the therapeutic effect of this traditional Chinese medicine. In the present study, a sensitive, simple and reliable method was developed and validated for the simultaneous quantification of 12 main components (4 flavonoids, 4 alkaloids, 2 phthalides and 2 diterpene lactones) in plasma and seven tissues of rats to study the pharmacokinetic and distribution characteristics of these components in vivo by using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) for the first time. Plasma and tissue were prepared by protein precipitation with acetonitrile and methanol, followed by its separation on a Waters Acquity UPLC BEH C18 column. The quantification was performed via multiple reaction monitoring (MRM) by a triple quadrupole mass spectrometer under positive electrospray ionization (ESI) mode. The method was validated to demonstrate its selectivity, linearity, accuracy, precision, recovery, matrix effect and stability. For 12 analytes, the low limit of quantification (LLOQs) reached 0.005-2.44 ng/mL, and all calibration curves showed good linearity (r2 ≥ 0.990) in linear ranges. The intra-day and inter-day precision (relative standard deviation) for all analytes was less than 14.96%, and the accuracies were in the range of 85.29%-114.97%. Extraction recoveries and matrix effects of analytes were acceptable. The pharmacokinetic results showed that the main components could be absorbed quickly, had a short residence time, and were eliminated quickly in vivo. At different time points, the 12 components were widely distributed with uneven characteristics in the body, which tended to be distributed in the liver, kidney and lung and to a lesser extent in the uterus, brain and heart. The pharmacokinetic process and tissue distribution characteristics of FKQJ were expounded in this study, which can provide a scientific theory for in-depth development of FKQJ and guide FKQJ use in the clinic.

EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework

Cloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitments, the provider assigns appropriate resources and employs scheduling techniques to efficiently manage the execution of received IoT tasks in fog or cloud systems. The effectiveness of cloud services is directly impacted by some other important criteria, such as energy usage and cost, which are not taken into account by many of the existing methodologies. To resolve the aforementioned problems, an effective scheduling algorithm is required to schedule the heterogeneous workload and enhance the quality of service (QoS). Therefore, a nature-inspired multi-objective task scheduling algorithm called the electric earthworm optimization algorithm (EEOA) is proposed in this paper for IoT requests in a cloud-fog framework. This method was created using the combination of the earthworm optimization algorithm (EOA) and the electric fish optimization algorithm (EFO) to improve EFO's potential to be exploited while looking for the best solution to the problem at hand. Concerning execution time, cost, makespan, and energy consumption, the suggested scheduling technique's performance was assessed using significant instances of real-world workloads such as CEA-CURIE and HPC2N. Based on simulation results, our proposed approach improves efficiency by 89%, energy consumption by 94%, and total cost by 87% over existing algorithms for the scenarios considered using different benchmarks. Detailed simulations demonstrate that the suggested approach provides a superior scheduling scheme with better results than the existing scheduling techniques.

Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment

Inadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A container-based TS algorithm (CBTSA) determines the symmetry relationship of the task/workload with the fog node (FN) or the cloud to decide the scheduling workloads (whether in the fog or a cloud). Furthermore, by allocating and de-allocating resources, the RA algorithm reduces workload delays while increasing resource utilization. However, the unbounded cloud resources and the computational difficulty of finding resource usage have not been considered in CBTSA. Hence, this article proposes an enhanced CBTSA with intelligent RA (ECBTSA-IRA), which symmetrically balances energy efficiency, cost, and the performance-effectiveness of TS and RA. Initially, this algorithm determines whether the workloads are accepted for scheduling. An energy-cost–makespan-aware scheduling algorithm is proposed that uses a directed acyclic graph (DAG) to represent the dependency of tasks in the workload as a graph. Workloads are prioritized and selected for the node to process the prioritized workload. The selected node for processing the workload might be a FN or cloud and is decided by an optimum efficiency factor that trades off the schedule length, cost, and energy. Moreover, a Markov decision process (MDP) was adopted to allocate the best resources using the reinforcement learning scheme. Finally, the investigational findings reveal the efficacy of the presented algorithms compared to the existing CBTSA in terms of various performance metrics.

Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories

Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.