Scheduling Of Applications Research Articles

Evaluation of fluopyram for disease and root knot nematode control along with yield response on peanut

Efficacy of in-furrow application of fluopyram + imidacloprid alone or followed by fluopyram + prothioconazole at-pegging and aldicarb for managing peanut root-knot nematode populations, root and pod galling due to nematodes, and yield, along with non-target effects on leaf spot diseases and stem rot were compared on the root-knot susceptible cultivar Georgia-06G over seven years on an irrigated site with an established population of Meloidogyne arenaria race 1. In all years, fungicides were applied to all plots following a Peanut Rx application schedule. Root-knot nematode populations, nematode damage, and yield along with leaf spot severity and stem rot incidence significantly differed among years; except for nematode populations, these measured variables also differed by nematicide treatment. Averaged over years, galling and root damage was significantly reduced with aldicarb compared to the no-nematicide control but not with either of the fluopyram treatments. While similar late-season root-knot nematode populations were noted for all nematicide programs and the non-treated control, pooled over years, significant yield gains were obtained with aldicarb and fluopyram programs with the two-treatment program providing greater yields than the aldicarb treatment. Leaf spot severity, averaged over years, was similar in aldicarb and control treatments; however, both fluopyram treatments had significantly lower leaf spot severity than the aldicarb treatment. Stem rot incidence did not differ among nematicide treatments. In addition, pooled over years, there was a negative relationship between juvenile populations and yield, but this was relatively weak. Weather, particularly extreme rain conditions, impacted yield directly as well as indirectly by affecting leaf spot severity and nematode activity.

Feasibility/Flexibility-based optimization for process design and operations

This paper provides an overview of concepts and computational approaches for the evaluation of feasibility/flexibility and how they can be used for process design and process operations optimization. It emphasizes more recent topics in this area, in particular feasible region evaluation, feasibility-based optimization, and optimization with flexibility requirements. The description of process feasibility and the feasibility-based optimization problem are presented as a way to efficiently incorporate multiple constraints and avoid unnecessary exploration of the infeasible space in the black-box optimization context. The relationship between flexibility analysis and robust optimization is also highlighted, and opportunities in exploring synergies therein are outlined. Applications in pharmaceutical design and process scheduling are used to provide context in the utilization of the presented approaches.

Double-layer home energy management strategy for increasing PV self-consumption and cost reduction through appliances scheduling, EV, and storage

Maximizing self-consumption of the photovoltaic (PV) generation is an important factor to increase the penetration of PV in the residential grid. It can improve PV system profitability, save energy and reduce grid stress. This study proposes a double-layer home energy management strategy to increase PV self-consumption and reduce household electricity costs. The first layer involves rescheduling shiftable appliances to operate during surplus PV generation hours, while the second layer employs a multi-objective energy management strategy based on Jaya and particle swarm optimization (PSO) algorithms to optimize power exchange between the energy storage system (ESS) and electric vehicle (EV), with smart home. Six different scenarios are simulated to investigate the role of household appliances, ESS, and EV technology in increasing PV self-consumption. Results scheduling showed that the proposed strategy can increase self-consumption and reduce grid consumption by 17–41% and 27–78%, respectively, depending on the proposed scenarios. The double-layer strategy is found to be more effective than the single-layer approach, with significant economic benefits. The proposed method provides an efficient solution for improving PV system profitability, saving energy, and reducing grid stress. Thus, this study contributes to the development of sustainable energy management systems, paving the way for future research in this area.

An integrated residence smart grid system with enhanced energy management for batteries

Residential consumers can efficiently engage in demand response programs with the help of home energy management systems (HEMSs). The best scheduling of gas, oil, coal, and uranium appliances has yet to be considered by traditional HEMSs, which exclusively control domestic electric devices to lower the cost of electricity usage. The associated demand response (DR) for smart homes is then adjusted to lessen the difference between predicted and actual demand after comparing the actual price to this forecast. Data acquired by Nordpool and publicly available energy information have been used to test the proposed strategy about price demand forecast and DR management. The function is to reduce energy costs for the home and the dissatisfaction brought on by moving, cutting, and replacing loads by changing prices over time. Case studies with accurate data show that the suggested strategy can return on investment bills by up to 85% while maintaining their satisfaction.

P2P power trading of nanogrids for power management in consideration of battery lifetime of ESS

This study presents a peer-to-peer (P2P) trading strategy for power management of nanogrids with renewable energy sources (RESs), taking battery lifetime of energy storage system (ESS) into consideration. Nanogrid requires more analytical power management as individual control of each electric appliance should be carried out, considering its rated power. With the increasing penetration of distributed energy resources, P2P trading can be applied to nanogrids equipped with RESs and ESSs. For the power management of a nanogrid, multi-objective optimization is used to minimize grid power consumption, electricity cost, and delay of using electric appliances in appliance scheduling. In this paper, a battery aging model or degradation cost model is considered in the multi-objective optimization to prolong the ESS lifetime. The electricity from RESs and ESSs used for P2P trading is determined to increase the profitability of nanogrids. Depending on the amount of electricity allocated to P2P trading, a nanogrid can be a seller or buyer in P2P trading. P2P trading is carried out based on the cooperative game theory to maximize the welfare of both sellers and buyers. As compared to power management involving P2P trading without considering ESS lifetime, the proposed one in consideration of ESS lifetime decreases the electricity cost of nanogrid by 2.86% on average, and the degradation cost of ESS by 32.75% on average.

Meta-heuristic optimization based cost efficient demand-side management for sustainable smart communities

The monetary value of grid electricity is inflating significantly due to the staggeringly broadening gap between electricity demand and supply, which arise from the unceasing growth of consumption demands. Although heuristic optimization based demand side management has its merits, incorporating Ant Colony Optimization remains disputable due to its tendency to converge at a local optimum. Therefore, this work presents a hybridized algorithm of Ant Colony Optimization and Genetic Algorithm, which alleviates the drawbacks of Ant Colony Optimization through Genetic Algorithm. The proposed work promotes sustainable energy utilization simultaneously with demand-side optimization. The performance of the proposed algorithm is compared with no scheduling instance, Ant Colony Optimization based energy management controller, and mutated Ant Colony Optimization based appliance scheduling. The proposed algorithm successfully curtails 35.4% from community peak load demand and achieves 33.67% cumulative cost saving for the community. In other words, comparative analysis confirms the supremacy of the proposed algorithm in terms of minimizing peak load, total cost, peak-to-average ratio, and waiting time, while providing prevailing insights about proposed algorithm as a sustainable solution approach.

MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform

Heterogeneous architecture-based systems-on-chip enable the development of flexible and powerful multifunctional RF systems. In complex and dynamic environments where applications arrive continuously and stochastically, real-time scheduling of multiple applications to appropriate processor resources is crucial for fully utilizing the heterogeneous SoC’s resource potential. However, heterogeneous resource-scheduling algorithms still face many problems in practical situations, including generalized abstraction of applications and heterogeneous resources, resource allocation, efficient scheduling of multiple applications in complex mission scenarios, and how to ensure the effectiveness combining with real-world applications of scheduling algorithms. Therefore, in this paper, we design the Multi-Application Scheduling Algorithm, named MASA, which is a two-phase scheduler architecture based on Deep Reinforcement Learning. The algorithm is made up of neural network scheduler-based task prioritization for dynamic encoding of applications and heuristic scheduler-based task mapping for solving the processor resource allocation problem. In order to achieve stable and fast training of the network scheduler based on the actor–critic strategy, we propose optimization methods for the training of MASA: reward dynamic alignment (RDA), earlier termination of the initial episodes, and asynchronous multi-agent training. The performance of the MASA is tested with classic directed acyclic graph and six real-world application datasets, respectively. Experimental results show that MASA outperforms other neural scheduling algorithms and heuristics, and ablation experiments illustrate how these training optimizations improve the network’s capacity.

A Framework for QoS Parameters-Based Scheduling for IoT Applications on Fog Environments

A Framework for QoS Parameters-Based Scheduling for IoT Applications on Fog Environments

A Multi-Objective Improved Cockroach Swarm Algorithm Approach for Apartment Energy Management Systems

The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.

TIVC: An Efficient Local Search Algorithm for Minimum Vertex Cover in Large Graphs.

The minimum vertex cover (MVC) problem is a canonical NP-hard combinatorial optimization problem aiming to find the smallest set of vertices such that every edge has at least one endpoint in the set. This problem has extensive applications in cybersecurity, scheduling, and monitoring link failures in wireless sensor networks (WSNs). Numerous local search algorithms have been proposed to obtain "good" vertex coverage. However, due to the NP-hard nature, it is challenging to efficiently solve the MVC problem, especially on large graphs. In this paper, we propose an efficient local search algorithm for MVC called TIVC, which is based on two main ideas: a 3-improvements (TI) framework with a tiny perturbation and edge selection strategy. We conducted experiments on real-world large instances of a massive graph benchmark. Compared with three state-of-the-art MVC algorithms, TIVC shows superior performance in accuracy and possesses a remarkable ability to identify significantly smaller vertex covers on many graphs.

A sustainable approach for demand side management considering demand response and renewable energy in smart grids

The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort.

Variable Neighborhood Search for precedence-constrained tasks optimization on heterogeneous systems

Supercomputing power is one of the fundamental pillars of the digital society, which depends on the accurate scheduling of parallel applications in High-Performance Computing (HPC) centers to minimize computing times. However, precedence-constraint task scheduling is a well-known NP-Hard optimization problem, and no optimal polynomial-time algorithm exists to solve it. Therefore, as accurate as possible to the optimal values, heuristic algorithms are of relevant interest. Our new scheduling proposal name is EFT-GVNS, which stands for Earliest Finish Time - General Variable Neighborhood Search. EFT-GVNS uses a Composite Local Search (CLS), making our proposal more efficient than traditional GVNS. EFT-GVNS accuracy against four high-performance algorithms from the state-of-the-art (EDA, EFT-ILS, GRASP-CPA, MPQGA) and one reference algorithm in the literature (HEFT) is studied. Experimental results over four real-world applications (Fpppp, LIGO, Robot, Sparse) and 14 synthetic instances from the literature show that EFT-GVNS outperforms in terms of the median achieved results, with a global improvement of 37.6%, 27.4%, 17.8%, 6.1%, 2.2% to HEFT, EDA, EFT-ILS, GRASP-CPA, and MPQGA, respectively. EFT-GVNS achieves all the 14 optimal values of the synthetic benchmark.

Cost-Efficient Fault-Tolerant Workflow Scheduling for Deadline-Constrained Microservice-Based Applications in Clouds

Microservices are becoming increasingly popular in the construction of cloud applications. On the basis of containers, microservice instances can be implemented with high scalability and maintainability. Due to the need of ensuring various quality of service (QoS) requirements and the two-layer resource structure of containers and virtual machines (VMs), microservice workflow scheduling in clouds is a challenging problem to address. This paper proposes a heuristic algorithm GSMS to minimize execution cost of a microservice-based workflow application while satisfying deadline and reliability constraints. GSMS adopts a greedy fault-tolerant scheduling strategy for replicas of each task to select appropriate resources that meet the sub-deadline and minimize the cost until the sub-reliability is guaranteed. Furthermore, a resource adjustment strategy is incorporated into GSMS to further improve resource utilization. By conducting extensive experiments with several realistic workflow applications, in comparison with existing algorithms, the effectiveness and efficiency of GSMS in achieving lower execution cost and meeting deadline and reliability requirements are validated.

Design and Implementation of High QoS 3D-NoC using Modified Double Particle Swarm Optimization on FPGA

One technique to overcome the exponential growth bottleneck is to increase the number of cores on a processor, although having too many cores might cause issues including chip overheating and communication blockage. The problem of the communication bottleneck on the chip is presently effectively resolved by networks-on-chip (NoC). A 3D stack of chips is now possible, thanks to recent developments in IC manufacturing techniques, enabling to reduce of chip area while increasing chip throughput and reducing power consumption. The automated process associated with mapping applications to form three-dimensional NoC architectures is a significant new path in 3D NoC research. This work proposes a 3D NoC partitioning approach that can identify the 3D NoC region that has to be mapped. A double particle swarm optimization (DPSO) inspired algorithmic technique, which may combine the characteristics having neighbourhood search and genetic architectures, also addresses the challenge of a particle swarm algorithm descending into local optimal solutions. Experimental evidence supports the claim that this hybrid optimization algorithm based on Double Particle Swarm Optimisation outperforms the conventional heuristic technique in terms of output rate and loss in energy. The findings demonstrate that in a network of the same size, the newly introduced router delivers the lowest loss on the longest path. Three factors, namely energy, latency or delay, and throughput, are compared between the suggested 3D mesh ONoC and its 2D version. When comparing power consumption between 3D ONoC and its electronic and 2D equivalents, which both have 512 IP cores, it may save roughly 79.9% of the energy used by the electronic counterpart and 24.3% of the energy used by the latter. The network efficiency of the 3D mesh ONoC is simulated by DPSO in a variety of configurations. The outcomes also demonstrate an increase in performance over the 2D ONoC. As a flexible communication solution, Network-On-Chips (NoCs) have been frequently employed in the development of multiprocessor system-on-chips (MPSoCs). By outsourcing their communication activities, NoCs permit on-chip Intellectual Property (IP) cores to communicate with one another and function at a better level. The important components in assigning application duties, distributing the work to the IPs, and coordinating communication among them are mapping and scheduling methods. This study aims to present an entirely advanced form of research in the area of 3D NoC mapping and scheduling applications, grouping the results according to various parameters and offering several suggestions for further research.

Adapted techniques of explainable artificial intelligence for explaining genetic algorithms on the example of job scheduling

Many evolutionary artificial intelligence (AI) technologies have been applied to assist with job scheduling in manufacturing. One of the main approaches is genetic algorithms (GAs). However, due to their complexity, users may need help understanding or communicating GA applications, preventing their widespread acceptance among factory workers. The concept of explainable AI (XAI) has been proposed to address this issue. This study reviews existing XAI techniques for explaining GA applications in job scheduling and summarizes the problems existing XAI techniques face. Several novel XAI techniques are proposed to solve these problems, including decision tree-based interpretation, dynamic transformation and contribution diagrams, and improved bar charts. To illustrate the effectiveness of the proposed methodology, it is applied to a case from the literature. According to the experimental results, the proposed methodology can compensate for the deficiencies of existing XAI methods in handling high-dimensional data and visualizing the contribution of feasible solutions, thereby satisfying all the requirements for an effective XAI technique for explaining GA applications in job scheduling. Additionally, the proposed methodology can be easily extended to explain other evolutionary AI applications in job scheduling, such as ant colony optimization (ACO), particle swarm optimization (PSO), and the artificial bee colony (ABC) algorithm.

AI-empowered game architecture and application for resource provision and scheduling in multi-clouds

Current deep learning technologies used a large number of parameters to achieve a high accuracy rate, and the number of parameters is commonly more than a hundred million for image-related tasks. To improve both training speed and accuracy in multi-clouds, distributed deep learning is also widely applied. Therefore, reducing the network scale or improving the training speed has become an urgent problem to be solved in multi-clouds. Concerning this issue, we proposed a game architecture in multi-clouds, which can be supported by resource provision and service schedule. Furthermore, we trained a deep learning network, which can ensure high accuracy while reducing the number of network parameters. An adapted game, called flappy bird, is used as an experimental environment to test our neural network. Experimental results showed that the decision logic of the flappy bird, including flight planning, avoidance, and sacrifice, is accurate. In addition, we published the parameters of the neural network, so other scholars can reuse our neural network parameters for further research.

Price-based coordination strategy for copper smelting process

Rigorous scheduling that uses hard heuristics and allows forceful shutdown of process units, whether intentionally or unintentionally, in the copper smelting process always harms the process operation. This way of process scheduling often results in sub-optimal process operation. Although a flexible scheduling framework that empowers process units to make independent operational decisions is desirable, the inter-dependencies among these units continue to pose a challenge in its design. This study presents a hierarchical scheduling framework for the copper smelting process, which uses price-based heuristics to resolve process inter-dependencies among the process units. These inter-dependencies arise due to the bottlenecks present in the process. Taking advantage of market theory, the coordinator uses PI controllers to find the appropriate prices to address the inter-dependencies. Two case studies are presented to demonstrate that the proposed framework effectively resolves all the process inter-dependencies, indicating its potential for application in other large-scale scheduling applications.

Efficient Techniques for Residential Appliances Scheduling in Smart Homes for Energy Management Using Multiple Knapsack Problem

The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is formulated using the multiple knapsack technique. Serial and parallel scheduling algorithms of home appliances namely MKSI (Multiple knapsacks with serial implementation) and MKPI (Multiple knapsacks with parallel implementation) are proposed to reduce electricity cost and PAR. Price-based demand response techniques are incorporated to shift appliances from peak hours to off-peak hours to optimize energy consumption. The proposed algorithms are tested on real-time datasets and evaluated based on time of use pricing tariff and critical peak pricing. The performance of both the algorithms is compared with the unscheduled scenario and existing algorithm. Simulations show that both proposed algorithms are efficient methods for home energy management to minimize PAR and electricity bills of consumers. The proposed MKSI algorithm achieves cost reduction of 20.26% and 42.53% for TOU and CPP, respectively as compared to the unscheduled scenario while PAR is reduced by 45.07% and 39.51% for TOU and CPP, respectively. The proposed MKPI algorithm achieves 22.33% and 46.36% cost reduction compared to the unscheduled case for TOU and CPP while the PAR ratio is reduced by 46.47% and 41.16% for TOU and CPP respectively.

Microservice Application Scheduling in Multi-Tiered Fog-Computing-Enabled IoT.

Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application execution. Building upon this premise, it is necessary to investigate idle or underutilized resources that are present at the edge of the network. The utilization of a microservice architecture in IoT application development, with its increased granularity in service breakdown, provides opportunities for improved scalability, maintainability, and extensibility. In this research, the proposed schedule tackles the latency requirements of applications by identifying suitable upward migration of microservices within a multi-tiered fog computing infrastructure. This approach enables optimal utilization of network edge resources. Experimental validation is performed using the iFogSim2 simulator and the results are compared with existing baselines. The results demonstrate that compared to the edgewards approach, our proposed technique significantly improves the latency requirements of application execution, network usage, and energy consumption by 66.92%, 69.83%, and 4.16%, respectively.

Survey on Modeling of Temporally and Spatially Interdependent Uncertainties in Renewable Power Systems

Constructing a renewable energy-based power system has become an important development path for the power industry’s low-carbon transformation. However, as the proportion of renewable energy generation (REG) increases, the power grid gradually changes to uncertainty. Technologies to address this issue have been introduced. However, the majority of existing reviews focus on specific uncertainty modeling approaches and applications, lacking the consideration of temporal and spatial interdependence. Therefore, this paper provides a comprehensive review of the uncertainty modeling of temporal and spatial interdependence. It includes the discrete and continuous stochastic process-based methods to address temporal interdependence, the correlation coefficient and copula functions in modeling spatial interdependence, and the Itô process and random fields theory to describe temporal and spatial interdependence. Finally, their applications in power system stability, control, and economic scheduling are summarized.