Task Scheduling Model Research Articles

Multi-agent planning and coordination for automated aircraft ground handling

Inspired by the vision of fully autonomous airside operations at Schiphol airport, this study aims to contribute to the short-term goal of automated aircraft ground handling. In this research, we design and evaluate a multi-agent system for planning of automated ground handling. There are two main components in the system: task allocation optimization and multi-agent path planning. To allocate tasks to ground support equipment (GSE) vehicles, an auction mechanism inspired by temporal sequential single item (TeSSI) auction is proposed. Ground handling tasks scheduling for GSE vehicles is modeled as several single-vehicle pickup and delivery optimization problems (SPDP), and the values of the objective functions are used to generate bids for GSE vehicle agents in the auction. Prioritized safe interval path planning for large agents (LA-SIPP) is used to plan collision-free paths for GSE vehicle agents in the model to execute tasks. The aim is to increase the success rates of allocating tasks and finding collision free paths without causing flight delays, given the limited resources such as a small number of available GSE vehicles, time windows constraints and conflicting interests of different agents. Due to the results, even for the instances with frequent flights and the most limited resources, the success rates of allocation and path planning were higher than 81% and 98%, respectively. Furthermore, periodic task allocation and path planning of the ground handling tasks for flights in three aircraft stands during a planning time window of the day, as well as replanning in case of disruptions were performed in a short CPU time. There is a lack of research dealing with the complete process of ground handling, since existing studies concerning the automation of ground handling operations involve fleet assignment or task scheduling models without an integration of detailed path planning. Our main contribution is to present a framework that combines task allocation and path planning for automation of ground handling operations and provides solutions using a multi-agent perspective.

Sustainable Internet of Vehicles System: A Task Offloading Strategy Based on Improved Genetic Algorithm

“Smart transportation” promotes urban sustainable development. The Internet of Vehicles (IoV) refers to a network with huge interaction, which comprises location, speed, route information, and other information about vehicles. To address the problems that the existing task scheduling models and strategies are mostly single and the reasonable allocation of tasks is not considered in these strategies, leading to the low completion rate of unloading, a task offloading with improved genetic algorithm (GA) is proposed. At first, with division in communication and calculation models, a system utility function maximization model is objectively conducted. The problem is solved by improved GA to obtain the scheme of optimal task offloading. As GA, in the traditional sense, inclines to a local optimum, the model herein introduces a Halton sequence for uniform initial population distribution. Additionally, the authors also adapt improved GA for the problem model and global optimal solution guarantee, thus improving the rate of task completion. Finally, the proposed method is proven through empirical study in view of scenario building. The experimental demonstration of the proposed strategy based on the built scenario shows that the task calculation completion rate is not less than 75%, and when the vehicle terminal is 70, the high-priority task completion rate also reaches 90%, which can realize reasonable allocation of computing resources and ensure the successful unloading of tasks.

Mixed Reality collaboration environment improves the efficiency of human-centered industrial system: A case study in the mining industry

The emerging information and communication technologies (ICT) promote the intelligence of industrial systems. However, humans still play an important role in industrial systems, and they are required to collaborate in many scenarios to perform complex tasks. Mixed Reality (MR) technology allows the virtual-real fusion and real-time interaction, and has unparalleled advantages in building a collaborative environment. In this study, Theoretical and technical studies have been conducted to provide a universal solution for the industrial applications of MR collaboration. In terms of theory, a framework of the intelligent industrial system is first proposed by combining the human-centered idea and cloud-edge-terminal collaborative architecture. Based on this framework, the MR collaboration environment is then designed. Finally, the MR task scheduling model is developed to illustrate how the MR collaboration environment uses the cloud-edge-terminal collaborative architecture to improve the performance. The technical study is conducted based on the theoretical foundation. The key technologies involved in both co-located and remote collaboration modes, that can be achieved by the MR collaboration environment, are studied. In addition, the human–computer interaction (HCI) technology in MR collaboration is explored. Based on this study, the proposed MR collaboration environment is developed, tested, and evaluated by considering the mining industry as an example. Moreover, user surveys from a human-centered perspective are conducted. The results show that the MR collaboration environment has high applicability in multiple links in industrial scenarios represented by longwall mining. It can improve the quality, increase the efficiency, and reduce the costs. It can also provide a human-centered collaboration experience.

A Scheduling Method for Heterogeneous Signal Processing Platforms Based on Quantum Genetic Algorithm

Currently, many problems such as variable signal resources, complex execution environments, and low efficiency of scheduling algorithms are faced by heterogeneous signal processing platforms. The task scheduling algorithm is one of the key factors that directly affect the performance of the processing platform. In order to solve the problems of low efficiency of task scheduling algorithms and high computational cost of processors, a heterogeneous platform scheduling algorithm based on the quantum genetic algorithm is proposed in this paper. The algorithm constructs a task scheduling model by using a directed acyclic graph. This paper quantifies the mapping relationship between the quantum genetic algorithm and task scheduling. It corresponds qubits to binary, chromosomes to processor numbers, and individuals to processor scheduling strategies. In this paper, a new way of coding chromosomes using quantum coherence properties is designed to reduce the population size and increase population diversity. Crossover operations are performed on all individuals using full-interference crossover to avoid the results falling into local optimal solutions. The population of slow convergence is solved by implementing mutation operations on populations through quantum rotation gates. In addition, a task pre-ordering stage is designed based on the table scheduling algorithm. The task scheduling priority developed at this stage is used as the reference value for the initial encoding of the population, so that the search space for solutions is reduced. Finally, experiments are conducted using randomly generated task graphs. The algorithm is compared with improved genetic algorithms and existing intelligent scheduling algorithms. The results show that the algorithm can still obtain better results when the number of populations and iterations is small. It is more appropriate for heterogeneous platforms and computation-intensive tasks.

Fog Computing Resource-Scheduling Strategy in IoT Based on Artificial Bee Colony Algorithm

As the amount of data input increases, fog devices on IoT edge networks become increasingly inefficient. However, a well-designed fog computing resource-scheduling strategy can help to reduce excessive time delays and energy consumption. Therefore, in this paper, we propose an efficient fog computing resource-scheduling strategy. First, we used particle swarm optimization (PSO) to determine the optimal load balance among fog nodes and to obtain the optimal computation time and energy consumption in a single fog cluster. Second, we designed a particle swarm genetic joint optimization artificial bee colony algorithm (PGABC) to optimize the task scheduling among fog clusters based on the time and energy consumption obtained from load balancing. In addition, PGABC was used to optimize the task-scheduling model, which further reduced the delay and energy consumption of fog computing. The experimental results show that the time delay that was calculated using the proposed PGABC algorithm in the given model was reduced by 1.04%, 15.9%, and 28.5%, compared to GABC, ABC, and PSO, respectively, and the energy consumption was reduced by 3.9%, 6.6%, and 12.6%, respectively. The proposed resource-scheduling strategy reduced the delay by approximately 31.25%, 27.8%, 27.8%, and 25.4%, and the energy consumption by approximately 9.7%, 33.3%, 32%, and 29.6%, compared to SJF–PSO, PGABC-R, HSF.ABC&PSO, and MFO, respectively.

An effective adaptive adjustment model of task scheduling and resource allocation based on multi-stakeholder interests in cloud manufacturing

Cloud manufacturing (CMfg) is a new manufacturing mode emerging in the global manufacturing industry. One of the key issues in CMfg is task scheduling and resource allocation (TSRA), which is to allocate suitable resources for multi-tasks while satisfying the interests of multi-stakeholders. Among them, dynamic TSRA is a challenging but crucial problem to ensure the smooth operation of CMfg system since it involves several exceptions. Under these contexts, this study first analyzes the optimized objectives and conflict situations that may damage the interests of multi-stakeholders in dynamic TSRA process. And then, four adaptive adjustment strategies are designed to deal with these conflict situations and ensure the smooth operation. After that, an adaptive adjustment TSRA model based on multi-stakeholder interests (TSRA-MSI) is proposed. To solve the problem, a multi-objective algorithm called HHO-NSGA2 is proposed by combining the advantages of standard Harris Hawks Optimizer and Non-dominated Sorting Genetic Algorithm-II, which contains several problem-specific optimization strategies. In the numerical experiments, the superiority of HHO-NSGA2 is demonstrated by comparing with other five algorithms in terms of convergence, diversity, and comprehensive performance. Finally, a case study is conducted under the actual auto parts production environment, and the results also demonstrate the effectiveness of the proposed TSRA-MSI model and HHO-NSGA2 algorithm.

Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing

Cloud Computing, the efficiency of task scheduling is proportional to the effectiveness of users. The improved scheduling efficiency algorithm (also known as the improved Wild Horse Optimization, or IWHO) is proposed to address the problems of lengthy scheduling time, high-cost consumption, and high virtual machine load in cloud computing task scheduling. First, a cloud computing task scheduling and distribution model is built, with time, cost, and virtual machines as the primary factors. Second, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; to better find the optimal individual, we use the inertial weight strategy for the Improved whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence. To deliver services and access to shared resources, Cloud Computing (CC) employs a cloud service provider (CSP). In a CC context, task scheduling has a significant impact on resource utilization and overall system performance. It is a Nondeterministic Polynomial (NP)-hard problem that is solved using metaheuristic optimization techniques to improve the effectiveness of job scheduling in a CC environment. This incentive is used in this study to provide the Improved Wild Horse Optimization with Levy Flight Algorithm for Task Scheduling in cloud computing (IWHOLF-TSC) approach, which is an improved wild horse optimization with levy flight algorithm for cloud task scheduling. Task scheduling can be addressed in the cloud computing environment by utilizing some form of symmetry, which can achieve better resource optimization, such as load balancing and energy efficiency. The proposed IWHOLF-TSC technique constructs a multi-objective fitness function by reducing Makespan and maximizing resource utilization in the CC platform. The IWHOLF-TSC technique proposed combines the wild horse optimization (WHO) algorithm and the Levy flight theory (LF). The WHO algorithm is inspired by the social behaviours of wild horses. The IWHOLF-TSC approach's performance can be validated, and the results evaluated using a variety of methods. The simulation results revealed that the IWHOLF-TSC technique outperformed others in a variety of situations.

Parallel Scheduling of Large-Scale Tasks for Industrial Cloud–Edge Collaboration

Industrial Internet of Things is moving toward an intelligent level with large-scale collaborative cloud and edge resources, making it possible for online supervision, fast analysis, and precise control for many manufacturing job shops. However, online processing of large-scale industrial computation brings huge communication overhead and energy consumption among cloud, edge, and end devices. To improve the performance of the cloud–edge collaboration, this article establishes a practical model of task scheduling considering two kinds of cloud–edge collaborative modes. We propose a parallel group-merge evolutionary algorithm to assign thousands of tasks in seconds. The algorithm separates tasks into weakly correlated groups and applies modified evolutionary operators to find a subsolution for each group. Then, the subsolutions are merged to form a complete solution for fine-tuning based on the cross-use of heuristics. Experimental results show that the proposed method could assign thousands of tasks to cloud servers and edge servers in seconds, reduce the overall task computing time by 36.97%, and save the overall energy by 23.71% at most.

Reinforcement Learning-Based Optimization for Mobile Edge Computing Scheduling Game

Task scheduling on edge computing servers is a critical concern affecting user experience. Current scheduling methods attain an overall appealing performance through centralized control. Nevertheless, forcing users to act based on a centralized control is impractical. Hence, this work suggests a game theory-based distributed edge computing server task scheduling model. The proposed method comprehensively considers the mobile device-server link quality and the server’s computing resource allocation and balances link quality and computing resources requirements when selecting edge computing servers. Furthermore, we develop a time series prediction algorithm based on IndRNN and LSTM to accurately predict link quality. Once Nash equilibrium is reached quickly through our proposed acceleration scheme, the proposed model provides various QoS for different priority users. The experimental results highlight that the developed solution provides differentiated services while optimizing computing resource scheduling and ensuring an approximate Nash equilibrium in polynomial time.

Towards Multi-Satellite Collaborative Computing via Task Scheduling Based on Genetic Algorithm

With satellite systems rapidly developing in multiple satellites, multiple tasks, and high-speed response speed requirements, existing computing techniques face the following challenges: insufficient computing power, limited computing resources, and weaker coordination ability. Meanwhile, most methods have more significant response speed and resource utilization limitations. To solve the above problem, we propose a distributed collaborative computing framework with a genetic algorithm-based task scheduling model (DCCF-GA), which can realize the collaborative computing between multiple satellites through genetic algorithm. Specifically, it contains two aspects of work. First, a distributed architecture of satellites is constructed where the main satellite is responsible for distribution and scheduling, and the computing satellite is accountable for completing the task. Then, we presented a genetic algorithm-based task scheduling model that enables multiple satellites to collaborate for completing the tasks. Experiments show that the proposed algorithm has apparent advantages in completion time and outperforms other algorithms in resource efficiency.

An IFWA-BSA Based Approach for Task Scheduling in Cloud Computing

Establishing an efficient cloud computing task scheduling model is the object of many scholars’ research. In view of the low scheduling efficiency in cloud computing task scheduling, we propose a cloud computing task scheduling algorithm based on the fusion of the Fireworks Algorithm and Bird Swarm Algorithm (IFWA-BSA). Firstly, we describe the cloud computing task scheduling model based on time and cost constraint functions, secondly, we use chaotic backward learning and Coasean distribution for optimization in FWA initialization; we set thresholds for the radius of core fireworks and non-core fireworks for optimization; we filter the IFWA individuals after each iteration by BSA algorithm, and finally, we use the IFWA-BSA algorithm is used in cloud computing task scheduling model to solve the optimal solution. In the simulation experiments, IFWA-BSA has obvious advantages over ACO, PSO and FWA in the comparison of execution time and consumption cost indexes, which reduces the scheduling time and cost of cloud computing.

TurboMGNN: Improving Concurrent GNN Training Tasks on GPU With Fine-Grained Kernel Fusion

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Graph Neural Networks</i> (GNN) have evolved as powerful models for graph representation learning. Many works have been proposed to support GNN training efficiently on GPU. However, these works only focus on a single GNN training task such as operator optimization, task scheduling, and programming model. Concurrent GNN training, which is needed in the applications such as neural network structure search, has not been explored yet. This work aims to improve the training efficiency of the concurrent GNN training tasks on GPU by developing fine-grained methods to fuse the kernels from different tasks. Specifically, we propose a fine-grained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sparse Matrix Multiplication</i> (SpMM) based kernel fusion method to eliminate redundant accesses to graph data. In order to increase the fusion opportunity and reduce the synchronization cost, we further propose a novel technique to enable the fusion of the kernels in forward and backward propagation. Finally, in order to reduce the resource contention caused by the increased number of concurrent, heterogeneous GNN training tasks, we propose an adaptive strategy to group the tasks and match their operators according to resource contention. We have conducted extensive experiments, including kernel- and model-level benchmarks. The results show that the proposed methods can achieve up to 2.6X performance speedup.

A Broker-Based Task-Scheduling Mechanism Using Replication Approach for Cloud Systems

The reliability and availability of cloud systems have become major concerns of service providers, brokers, and end-users. Therefore, studying fault-tolerance mechanisms in cloud computing attracts intense attention in industry and academia. The task-scheduling mechanisms can improve the fault-tolerance level of cloud systems. A task-scheduling mechanism distributes tasks to a group of instances to be executed. Much work has been undertaken in this direction to improve the overall outcome of cloud computing, such as improving service quality and reducing power consumption. However, little work on task scheduling has studied the problem of lost tasks from the broker’s perspective. Task loss can happen due to virtual machine failures, server crashes, connection interruption, etc. The broker-based concept means that the backup task can be allocated by the broker on the same cloud service provider (CSP) or a different CSP to reduce costs, for example. This paper proposes a novel fault-tolerant mechanism that employs the primary backup (PB) model of task scheduling to address this issue. The proposed mechanism minimizes the impact of failure events by reducing the number of lost tasks. The mechanism is further improved to shorten the makespan time of submitted tasks in cloud systems. The experiments demonstrated that the proposed mechanism decreased the number of lost tasks by about 13%–15% compared with other mechanisms in the literature.

Modified Particle Swarm Optimization Based on Aging Leaders and Challengers Model for Task Scheduling in Cloud Computing

In cloud computing, a shared, configurable pool of computing resources is made accessible online and allocated to users according to their needs. It is essential for a cloud provider to schedule jobs in the cloud to keep up service quality and boost system efficiency. In this paper, we present a scheduling technique based on modified particle swarm optimization to combat the issues of excessively long scheduling time and high computation costs associated with scheduling jobs in a cloud environment. The modified PSO is used to allocate the jobs to virtual machines in order to minimize the objective function consisting of cost and makespan. The algorithm relies on biological changes that occur in organisms to regulate premature convergence and improve local search capability. The technique is analyzed and simulated using CloudSim, and the simulation results demonstrate that the proposed approach decreases makespan and cost effectively as compared to standard PSO.

Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

Deploying task caching at edge servers has become an effective way to handle compute-intensive and latency-sensitive tasks on the industrial internet. However, how to select the task scheduling location to reduce task delay and cost while ensuring the data security and reliable communication of edge computing remains a challenge. To solve this problem, this paper establishes a task scheduling model with joint blockchain and task caching in the industrial internet and designs a novel blockchain-assisted caching mechanism to enhance system security. In this paper, the task scheduling problem, which couples the task scheduling decision, task caching decision, and blockchain reward, is formulated as the minimum weighted cost problem under delay constraints. This is a mixed integer nonlinear problem, which is proved to be nonconvex and NP-hard. To solve the optimal solution, this paper proposes a task scheduling strategy algorithm based on an improved genetic algorithm (IGA-TSPA) by improving the genetic algorithm initialization and mutation operations to reduce the size of the initial solution space and enhance the optimal solution convergence speed. In addition, an Improved Least Frequently Used algorithm is proposed to improve the content hit rate. Simulation results show that IGA-TSPA has a faster optimal solution-solving ability and shorter running time compared with the existing edge computing scheduling algorithms. The established task scheduling model not only saves 62.19% of system overhead consumption in comparison with local computing but also has great significance in protecting data security, reducing task processing delay, and reducing system cost.

Multiobjective Approach to Schedule DAG Tasks on Voltage Frequency Islands

Scheduling a Directed Acyclic Graph (DAG) on voltage frequency islands involves dividing the available processing units into multiple islands with varying voltage and frequency levels, and then mapping the tasks of the DAG to the islands while minimizing the makespan and overall energy consumption. In this research work, a novel DAG task scheduling model is introduced with the assistance acquired from the deep learning paradigm. The proposed model includes four major phases: (a) DAG modelling, (b) Voltage frequency island partitioning, (c) core temperature prediction and (d) scheduling optimization. Initially, the Directed Acyclic Graph (DAG) model is designed. The nodes of DAG represent tasks and the edges represent dependencies between tasks. Then, in the Voltage frequency island partitioning, the available processing units into multiple voltage frequency islands. This can be done based on the power consumption of each unit, and the task requirements. Subsequently, the Recurrent Neural Network (RERNN) is trained to predict the core temperature of each voltage frequency island based on the multi-objectives like execution time, makespan, overall energy consumption. Then, the scheduling of the DAG on the voltage frequency islands is optimized using the Self-Improved Pelican Optimization Algorithm (SI-POA). The proposed SI-POA model is an extended version of the standard POA model. The SI-POA model is inspired by the behavior by the natural behavior of pelicans during hunting. In the scheduling optimization phase, the SI-POA algorithm to optimize the scheduling of the DAG on the voltage frequency islands while taking into account the predicted core temperature of each island based on the Recalling-enhanced recurrent neural network (RERNN) model. The goal is to minimize the makespan and overall energy consumption of the DAG while keeping the core temperature of each island within safe limits.

IETIF: Intelligent Energy‐Aware Task Scheduling Technique in IoT/Fog Networks

Nowadays, with the advent of various communication technologies such as the internet of things (IoT), a large volume of data is produced that needs to be processed in real‐time. Fog computing is an appropriate solution to address the requirements of different types of IoT applications. In most cases, IoT applications consist of a set of dependent tasks that can be separately processed in a heterogeneous fog environment. Scheduling these tasks in a fog environment is an NP‐hard problem that needs a vast amount of time and computation resources to solve, making it infeasible for real‐time applications. In addition, reducing response time and energy consumption in fog computing is an essential issue that should be taken into account in task scheduling algorithms. To address these challenges, we aim to propose a multiobjective task scheduling model to jointly improve energy efficiency and response time. To solve the model, we also propose an intelligent solution named IETIF which combines and leverages the benefits of simulated annealing and NSGA‐III algorithms. Simulation results show that IETIF outperforms the state‐of‐the‐art methods in terms of energy consumption, response time, and speedup.

NDTAEP: Design of a novel deadline-aware task scheduling model using augmented ensemble pattern analysis

A wide variety of scheduling models have been proposed by researchers over the years, and each of them has varying performance in terms of deadline hit ratio, scheduling effort, efficiency of task mapping, etc. However, these models are highly context-sensitive and cannot be scaled to heterogeneous task types due to their internal mapping characteristics. To improve task scalability, this work proposes a design of a novel deadline-aware task scheduling model that uses augmented ensemble pattern analysis for task clustering. The pattern analysis module uses a combination of K-means, hierarchical, and Fuzzy C Means (FCM) clustering to effectively segregate tasks depending on their completion and deadline parameters. These tasks are given to a modified deadline-aware League Championship Algorithm (LCA) optimizer, which assists in mapping the clustered tasks with worker threads. The modified LCA model uses a combination of task priority, task deadline, and worker capacity for scheduling. The model maps tasks that require higher execution effort with moderately performing worker nodes, while tasks with nearer deadlines are allotted to higher-performance workers. Due to the use of an ensemble augmented pattern analyzer with a modified LCA optimizer, the proposed model can improve execution speed by 8%, deadline hit ratio by 1.5%, and scheduling efficiency by 6.5% when compared with various state-of-the-art scheduling approaches. The proposed model was evaluated and showcased a deadline hit ratio of 99.95%, computational efficiency of 96.26%, and average task-scheduling delay of less than 0.1 ms, which makes it highly useful for a wide variety of task scheduling application scenarios.

Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm.

Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring of Information Technology equipment. This work aims to obtain the most suitable energy-saving strategies to achieve safe, intelligent, and visualized energy management. First, the theory of Convolutional Neural Network (CNN) is discussed. Besides, an intelligent energy-saving model based on CNN is designed to ameliorate the variable energy consumption, load, and power consumption of the CDC data center. Then, the core idea of the policy gradient (PG) algorithm is introduced. In addition, a CDC task scheduling model is designed based on the PG algorithm, aiming at the uncertainty and volatility of the CDC scheduling tasks. Finally, the performance of different neural network models in the training process is analyzed from the perspective of total energy consumption and load optimization of the CDC center. At the same time, simulation is performed on the CDC task scheduling model based on the PG algorithm to analyze the task scheduling demand. The results demonstrate that the energy consumption of the CNN algorithm in the CDC energy-saving model is better than that of the Elman algorithm and the ecoCloud algorithm. Besides, the CNN algorithm reduces the number of virtual machine migrations in the CDC energy-saving model by 9.30% compared with the Elman algorithm. The Deep Deterministic Policy Gradient (DDPG) algorithm performs the best in task scheduling of the cloud data center, and the average response time of the DDPG algorithm is 141. In contrast, the Deep Q Network algorithm performs poorly. This paper proves that Deep Reinforcement Learning (DRL) and neural networks can reduce the energy consumption of CDC and improve the completion time of CDC tasks, offering a research reference for CDC resource scheduling.

Improved Jellyfish Algorithm-based multi-aspect task scheduling model for IoT tasks over fog integrated cloud environment

Corporations and enterprises creating IoT-based systems frequently use fog computing integrated with cloud computing to harness the benefits offered by both. These computing paradigms use virtualization and a pay-as-you-go strategy to provide IT resources, including CPU, memory, network and storage. Resource management in such a hybrid environment becomes a challenging task. This problem is exacerbated in the IoT environment, as it generates deadline-driven and heterogeneous data demanding real-time processing. This work proposes an efficient two-step scheduling algorithm comprising a Bi-factor classification task phase based on deadline and priority and a scheduling phase using an enhanced artificial Jellyfish Search Optimizer (JS) proposed as an Improved Jellyfish Algorithm (IJFA). The model considers a variety of cloud and fog resource parameters, including speed, capacity, task size, number of tasks, and number of virtual machines for resource provisioning in a fog integrated cloud environment. The model has been tested for the real-time task scenario with the number of tasks considering both the smaller workload and the relatively higher workload scenario matching the real-time situation. The model addresses the Quality of Service (QoS) parameters of minimizing the batch’s make-span time, lowering the batch execution costs, and increasing the resource utilization. Simulation results prove the effectiveness of the proposed model.