Total Execution Cost Research Articles

A predictive energy-aware scheduling strategy for scientific workflows in fog computing

Fog computing paradigm provides diverse processing resources and storage close to the edge of Internet of Things (IoT) networks. Workflow scheduling is an open issue in fog computing as it addresses the deployment of IoT workflow tasks on fog cells. Deploying workflow tasks on fog cells in an inefficient manner can have negative consequences. It can lead to bandwidth waste, resource depletion, and significant operating expenses. Additionally, the majority of scheduling evolutionary algorithms suffer from premature convergence and sticking in local optima. In this paper, a predictive energy-aware scheduling framework is proposed as a MAPE-K control model consisting of the four Monitor, Analyzer, Planner and Executer components with a shared Knowledge base in fog computing. First, a prediction method applying an Adaptive Network-based Fuzzy Inference System (ANFIS) into an analyzer component is introduced to predict future resource load. Second, a resource management strategy based on predicted resource load is presented to reduce energy consumption. Third, the Improved Ant Lion Optimizer (ALO) and weighted Grey Wolf Optimizer (GWO) are combined into a planner component called I-ALO-GWO for workflow scheduling. In the end, the decisions made in the previous steps are executed on the fog cells. The effectiveness of the I-ALO-GWO evolutionary algorithm has been tested on the IEEE CEC2019 benchmark functions. In addition, the proposed approach is evaluated practically under several famous scientific workflows using the iFogSim tool. Experimental results indicate that I-ALO-GWO improves the makespan up to 18 %, the energy consumption up to 17 %, the total execution cost up to 11 % and 26 % in terms of efficiency in comparison with the second-best results.

Unmanned surface vehicles (USVs) scheduling method by a bi-level mission planning and path control

To enhance the operational efficiency of container terminals, we propose a bi-level scheduling method that employs unmanned surface vehicles (USVs) to transport containers exclusively between different berths on the waterside of terminals. In the upper level, Considering time windows, berth coordination, energy replenishment, etc., we formulate the mission decision model to obtain a cost-effective USVs shipping solution by minimizing unplanned delay and total execution cost. In the lower level, considering path distance, path smoothness, and motion constraints, we develop the USV path control model to achieve the USV path planning and reduce path tracking errors. Finally, we integrate the advantages of the chaotic Electron Search and multi-population Genetic Algorithm (ES-mGA) to solve the bi-level USVs scheduling model. Experimental results demonstrate the effectiveness of our approach and algorithm in guiding the USVs to efficiently accomplish container transshipment and acquire stable USV sailing states.

A Hybrid Particle Whale Optimization Algorithm with application to workflow scheduling in cloud–fog environment

Workflow scheduling aims to optimize task allocation and execution in cloud–fog​ computing environments. Finding an optimal algorithm for workflow scheduling poses a significant challenge due to the complexity and variability of tasks and resources involved. Although meta-heuristic algorithms have been proposed to address these challenges, they often suffer from being trapped in local optima, failing to achieve the global optimal solution. Our work used a hybrid approach “Particle Whale Optimization Algorithm” (PWOA) to overcome the above-mentioned problem by combining Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA).PSO’s limitation in high-dimensional search spaces, characterized by slow convergence toward the global optimum, and the population-based WOA algorithm’s limited exploration of search spaces are overcome by the proposed algorithm PWOA. The proposed algorithm PWOA overcomes the limitations of PSO and WOA.The objective of the PWOA algorithm is to minimize the Total Execution Time (TET) and Total Execution Cost (TEC) associated with dependent tasks in a cloud–fog computing environment. To evaluate the performance of the PWOA algorithm, extensive simulations are conducted using test cases (30, 50, 100 and 1000) from five different scientific workflows: Cybershake, Epigenomics, Inspiral, Montage and Sipht. These workflows encompassed varying numbers of assigned tasks. The simulation results demonstrated that the proposed PWOA algorithm outperformed the standard PSO and WOA algorithms to improve TET and TEC across all the diverse workflows. This study also provides a comparative view of the effectiveness of the PWOA algorithm in enhancing workflow scheduling performance.

Integrated resource-constrained project scheduling and material ordering problem considering storage space allocation

Limited storage space has become a tricky bottleneck for projects that are executed in downtown areas. To relieve this practical dilemma, this paper addresses the concurrent decisions on storage space allocation, activity scheduling and material ordering under limited storage space. Aiming to minimize the total cost of project execution, an integrated model for the resource-constrained project scheduling and material ordering problem with limited storage space (RCPSMOP-LSS) is constructed, and a space-saving material ordering strategy, namely OSTP (Ordering Strategy with Time Period) is utilized. Due to the NP-hardness of the RCPSMOP-LSS, a double-layer heuristic algorithm (DLHA) is designed to solve the model. In the outer layer, an improved genetic algorithm is proposed to obtain a space allocation plan and a schedule of all activities. Based on the outcome from the outer layer, an exact algorithm is developed in the inner layer to determine the material ordering scheme. Furthermore, numerical experiments are carried out to verify the efficiency of the proposed DLHA. Specifically, the Taguchi method is utilized to calibrate the parameters of DLHA. Besides, the experimental results demonstrate that the DLHA can greatly save the total cost compared to a traditional decentralized method (TDM). The efficiency of DLHA is also superior to CPLEX solver and TDM, especially for medium- and large-sized instances. Finally, a sensitivity analysis of key components shows that increasing the storage space exerts an increasing effect on cost reduction.

A Centralized Multi-Criteria Method for Scheduling Tasks in a Cloud Computing Environment

Task scheduling determines the order of mapping tasks to virtual machines to meet objectives. In this paper, a batch mode heuristic method that is centralized, dynamic, and multi-objective has been presented for scheduling independent tasks with a deadline and belonging to several user levels, using the cloud elasticity in the public cloud environment. In this method, it has been intended to improve the objectives of makespan, deadline violation, total execution cost, and load balancing by considering the tasks’ prioritization based on the criteria of user level, deadline, task length, and selection of heterogeneous virtual machines according to processing power, workload and usage cost. The proposed method was simulated using the CloudSim tool. Besides, the method’s ability to achieve the mentioned goals has been evaluated in comparison with similar methods. The evaluation results, established on standard test data, show that the proposed method has a good performance in improving its objectives.

Edge Computing Resource Allocation Algorithm for NB-IoT Based on Deep Reinforcement Learning

Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%,and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.

Optimal multi-user offloading with resources allocation in mobile edge cloud computing

Mobile cloud computing (MCC), as a prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile device (SMD) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich central cloud. Compared to central cloud, edge cloud can provide services to nearby SMDs with lower latency. However, the edge cloud may be mobile and its resources are limited to multiple nearby users. Therefore, how to obtain an optimal offloading policy under the constraints of mobility and limited resource remains a challenging issue. In this paper, we aim to minimize the total execution cost of multiple devices by offloading the computations from SMDs onto edge clouds in edge cloud computing (ECC) system. By considering the mobility of SMDs and edge clouds, we first formulate the total cost minimization problem under the constraints of application completion deadline and connection time between SMDs and edge clouds as well as the limited computation resource of both edge clouds and SMDs. Then, by solving the minimization problem, we propose an optimal offloading selection strategy based on game model, and an edge cloud payoff competition algorithm to optimally allocate edge cloud resource to SMDs to achieve the minimum total execution cost. Experimental results show that our offloading strategy can effectively reduce energy consumption and application completion time compared with the state-of-the-art methods.

Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application

In recent years, the increasing use of the Internet of Things (IoT) has generated excessive amounts of data. It is difficult to manage and control the volume of data used in cloud computing, and since cloud computing has problems with latency, lack of mobility, and location knowledge, it is not suitable for IoT applications such as healthcare or vehicle systems. To overcome these problems, fog computing (FC) has been used; it consists of a set of fog devices (FDs) with heterogeneous and distributed resources that are located between the user layer and the cloud on the edge of the network. An application in FC is divided into several modules. The allocation of processing elements (PEs) to modules is a scheduling problem. In this paper, some heuristic and meta-heuristic algorithms are analyzed, and a Hyper-Heuristic Scheduling (HHS) algorithm is presented to find the best allocation with respect to low latency and energy consumption. HHS allocates PEs to modules by low-level heuristics in the training and testing phases of the input workflow. Based on simulation results and comparison of HHS with traditional, heuristic, and meta-heuristic algorithms, the proposed method has improvements in energy consumption, total execution cost, latency, and total execution time.

Cost-Efficient Workflow Scheduling Algorithm for Applications With Deadline Constraint on Heterogeneous Clouds

In recent years, more and more large-scale data processing and computing workflow applications run on heterogeneous clouds. Such cloud applications with precedence-constrained tasks are usually deadline-constrained and their scheduling is an essential problem faced by cloud providers. Moreover, minimizing the workflow execution cost based on cloud billing periods is also a complex and challenging problem for clouds. In realizing this, we first model the workflow applications as I/O Data-aware Directed Acyclic Graph (DDAG), according to clouds with global storage systems. Then, we mathematically state this deadline-constrained workflow scheduling problem with the goal of minimum execution financial cost. We also prove that the time complexity of this problem is NP-hard by deducing from a multidimensional multiple-choice knapsack problem. Third, we propose a heuristic cost-efficient task scheduling strategy called CETSS, which includes workflow DDAG model building, task subdeadline initialization, greedy workflow scheduling algorithm, and task adjusting method. The greedy workflow scheduling algorithm mainly consists of dynamical task renting billing period sharing method and unscheduled task subdeadline relax technique. We perform rigorous simulations on some synthetic randomly generated applications and real-world applications, such as Epigenomics, CyberShake, and LIGO. The experimental results clearly demonstrate that our proposed heuristic CETSS outperforms the existing algorithms and can effective save the total workflow execution cost. In particular, CETSS is very suitable for large workflow applications.

Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment

IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud–fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms.Graphical

Design and Application of Cloud Resource-Based Ideological and Political Online Course Resource Platform

Online teaching platforms have been popularized and promoted as a result of the development of network information technology, and colleges and universities encourage teachers to use a variety of network teaching platforms to innovate teaching models and improve teaching effectiveness. Using an ideological and political online course as an example, it analyzes the teaching design concepts, instructional effects, and existing problems on the online learning platform, and extracts recommendations for online course construction that have a specific reference for online course teaching. Additionally, aiming at the multiobjective cloud resource scheduling problem, this article aims to optimize the total completion time and total execution cost of the task. It does so by utilizing fuzzy mathematics, establishing a fuzzy cloud resource scheduling model, and proposing a hybrid intelligent optimization algorithm CO. The CO algorithm is validated by randomly generating cloud-computing resource scheduling data using the CloudSim simulation platform. The experimental results indicate that the CO algorithm outperforms traditional cloud resource scheduling algorithms in terms of optimization and load balancing performance.

Comparative Analysis of PSO Algorithm in Cloud Computing

Abstract: Cloud computing makes it possible to access applications and data from anywhere so this has become new technology. The goals of the paper are to provide additional insights to suggest ways in which performance might be improved by incorporating features from one paradigm into the other. The Reasearch Paper focus on particle swarm optimization (PSO) heuristic-based algorithm based on Scheduling. By scheduling, applications are scheduled to the cloud resources that are used for computation of cost and transmission cost of data in the cloud. by using PSO total cost of execution is minimized. Since the inception of Inertia Weight in PSO, a large number of variations of the Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies that are efficient than others, this paper studies popular Inertia Weight strategies and compares their performance on optimization test problems. Keywords: Particle Swarm Optimization, Inertia weight, Population, Cognitive component, Optimization Function.

Fog Managed Data Model for IoT based Healthcare Systems

<p>In Internet of things enabled healthcare system, sensors create vast volumes of data that are analyzed in the cloud. Transferring data from the cloud to the application takes a long time. An effective infrastructure can reduce latency and costs by processing data in real-time and close to the user devices. Fog computing can solve this issue by reducing latency by storing, processing, and analyzing patient data at the network edge. Placing the resources at fog layer and scheduling tasks is quite challenging in Fog computing. This paper proposes a Fog Managed Data Model (FMDM) with three layers namely Sensor, Fog and cloud to solve the aforementioned issue. Sensors generate patient data and that are managed and processed by Fog and cloud layers. Tasks are scheduled using a Weighted Fog Priority Job Scheduling algorithm (WFPJS) and fog nodes are allocated based on Priority based Virtual Machine Classification Algorithm (PVCA). The performance of this model is validated with static scheduling techniques with variable patient counts and network configurations. The proposed FMDM with WFPJS reduces response time, total execution cost, network usage, network latency, computational latency and energy consumption.</p> <p> </p>

A Multi Objective Time-Cost-Quality Optimization Model fot Project Scheduling and Human Resource Assignment Considering Manpower Competency

Today, information and knowledge are so intertwined with human life that the present age has been called the information era. In this situation, the role of human in the organization and the way of looking at him/her will have a significant contribution to the success or failure of the organization. The role of human resources is becoming more and more prominent and it is now mentioned as the only factor in gaining a sustainable competitive advantage. Furthermore, due to dramatic developments in the field of human resource management, competency-based human resource planning has found a special place. In this study, a project scheduling problem considering multi-skilled resource and manpower competency is investigated. To do this end, an integrated multi-objective optimization model of project scheduling and human resource assignment including total completion time and execution cost minimization and project quality maximization is presented. To solve problem exactly, a mixed-integer programming model is developed and then solved by e-constraint method in GAMS software. As the proposed model is NP-hard, a non-dominated sorting genetic (NSGA-II) algorithm is developed to solve the large scale problem. Furthermore, in order to validate the proposed algorithm, its results compare with exact method for instance problem in small scale. Results indicate that the proposed algorithm is outperformed and it can be used for real case problems.

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Cloud Computing is emerging as a major trend in ICT industry. However, as with any new technology, new major challenges lie ahead, one of them concerning the resource provisioning. Indeed, modern Cloud applications deal with a dynamic context that requires a continuous adaptation process in order to meet satisfactory Quality of Service (QoS) but even the most titled Cloud platform provide just simple rule-based tools; the rudimentary autoscaling mechanisms that can be carried out may be unsuitable in many situations as they do not prevent SLA violations, but only react to them. In addition, these approaches are inherently static and cannot catch the dynamic behavior of the application and are unsuitable to manage multi-Cloud/data center deployments required for mission critical services. This situation calls for advanced solutions designed to provide Cloud resources in a predictive and dynamic way. This work presents capacity allocation algorithms, whose goal is to minimize the total execution cost while satisfying some constraints on the average response time of multi-Cloud based applications. This paper proposes a joint load balancing and receding horizon capacity allocation techniques, which can be employed to handle multiple classes of requests. An extensive evaluation of the proposed solution against an Oracle with perfect knowledge of the future and well-known heuristics proposed in the literature is provided. The analysis shows that our solution outperforms the heuristics producing results very close to the optimal ones, and reducing the number of QoS violations (in the worst case QoS constraints violation rate is 4.26 percent versus up to 17.25 percent of other approaches and can easily reduced by roughly a factor of four by exploiting the receding horizon approach). Furthermore, a sensitivity analysis over two different time scales indicates that finer grained time scales are more appropriate for spiky workloads. Analytical results are validated through simulation, which also analyzes the impact of Cloud environment random perturbations. Finally, experiments on a prototype environment demonstrate the effectiveness of the proposed approach under real workloads.

Cost-effective Heuristic Workflow Scheduling Algorithm in Cloud Under Deadline Constraint

Background: Cloud computing is used to deliver IT resources over the internet. Due to the popularity of cloud computing, nowadays, most of the scientific workflows are shifted towards this environment. Many algorithms have been proposed in the literature to schedule scientific workflows in the cloud, but their execution cost is very high as they are not meeting the user-defined deadline constraint. Aim: This paper focuses on satisfying the user-defined deadline of a scientific workflow while minimizing the total execution cost. Methods: So, to achieve this, we proposed a Cost-Effective under Deadline (CEuD) constraint workflow scheduling algorithm. Results: The proposed CEuD algorithm considers all the essential features of the Cloud and resolves the major issues such as performance variation and acquisition delay. Conclusion: We compared the proposed CEuD algorithm with the existing literature algorithms for scientific workflows (i.e., Montage, Epigenomics, and CyberShake) and obtained better results for minimizing the overall execution cost of the workflow while satisfying the user-defined deadline.

Coordinated global and private job-flow scheduling in grid virtual organizations

In this work, an approach for a preference-based job-flow scheduling in Grid virtual organizations is proposed and studied. Virtual organizations provide uniform rules of the resources sharing and consumption and should respect individual and common preferences of its stakeholders: users, resource providers and administrators. In most cases, a virtual organization’s stakeholders pursue contradictory market-based goals, which may be expressed in terms of time and cost. Still, mutually beneficial job-flow scheduling scenarios are possible in such multi-agent computing environments when the different optimization criteria are coordinated. For this purpose we consider different resources allocation heuristics and scenarios based on a simple linear combination of global (group) and private (user) job scheduling criteria. We study backfilling-based job queue scheduling procedure with a secondary preference-based optimization performed only on the resources allocation step. This approach allows us to respect backfilling-based jobs execution priorities together with more specific individual jobs criteria, including total execution cost or time minimization.

Integrated resource leveling and material procurement with variable execution intensities

This paper addresses the concurrent planning of resource leveling and material ordering, aiming to minimize the completion time of the project and the total cost of project execution. The latter is comprised of material ordering, purchase and holding costs as well as the cost of fluctuations in resource utilization. All activities of the project are performed regarding variable execution intensities, which means that the effort spent to execute an activity is considered to vary over time. A bi-objective mixed-integer linear programming model is proposed to deal with the above-mentioned time-cost trade-off problem. The proposed model determines the start time of activities, the fraction of executing activities in each time period as well as the quantity and timing of orders for materials, while satisfying precedence relationships among activities. The problem belongs to the class of NP-hard problems. Hence, a genetic algorithm as well as two multi-objective meta-heuristic algorithms, namely non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective vibration damping optimization (MOVDO) algorithm, calibrated by Taguchi method, are utilized to solve 30 different sized test problems. Then, four performance metrics are used to assess the performance of both NSGA-II and MOVDO algorithms. Results show that MOVDO outperforms NSGA-II in the small-, medium- and large-sized test problems. Moreover, a sensitivity analysis on different-sized test problems, using MOVDO, shows that when the durations of activities are considered to be flexible, the Pareto front is significantly improved in terms of objective values, and the number of Pareto-optimal solutions (NPS) increases. This helps the project manager(s) make decisions on the schedule and the execution intensity of activities and material procurement in a more flexible manner.

MEACC: an energy-efficient framework for smart devices using cloud computing systems

Rapidly increasing capacities, decreasing costs, and improvements in computational power, storage, and communication technologies have led to the development of many applications that carry increasingly large amounts of traffic on the global networking infrastructure. Smart devices lead to emerging technologies and play a vital role in rapid evolution. Smart devices have become a primary 24/7 need in today’s information technology world and include a wide range of supporting processing-intensive applications. Extensive use of many applications on smart devices results in increasing complexity of mobile software applications and consumption of resources at a massive level, including smart device battery power, processor, and RAM, and hinders their normal operation. Appropriate resource utilization and energy efficiency are fundamental considerations for smart devices because limited resources are sporadic and make it more difficult for users to complete their tasks. In this study we propose the model of mobile energy augmentation using cloud computing (MEACC), a new framework to address the challenges of massive power consumption and inefficient resource utilization in smart devices. MEACC efficiently filters the applications to be executed on a smart device or offloaded to the cloud. Moreover, MEACC efficiently calculates the total execution cost on both the mobile and cloud sides including communication costs for any application to be offloaded. In addition, resources are monitored before making the decision to offload the application. MEACC is a promising model for load balancing and power consumption reduction in emerging mobile computing environments.

HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user’s tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user’s tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data- and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).