Optimal Task Scheduling Research Articles

Robust and efficient task scheduling for robotics applications with reinforcement learning

Effective task scheduling can significantly impact on performance, productivity, and profitability in many real-world settings, such as production lines, logistics, and transportation systems. Traditional approaches to task scheduling rely on heuristics or simple rule-based methods. However, with the emergence of machine learning and artificial intelligence, there is growing interest in using these methods to optimize task scheduling. In particular, reinforcement learning is a promising task scheduling approach, because it can learn from experience and adapt to changing conditions. One step often missed or neglected is choosing optimal algorithm parameters and different ways the environment could be implemented. The study analyzes the performance possibilities of task scheduling using reinforcement learning. The deep analysis allows to select highly efficient environment models and Q-learning parameters. Moreover, automatic selection based on optimization algorithms has been proposed. Regardless of the selected optimal parameters, the resilience to environmental changes seems poor. The deducted analysis motivated the Authors to develop a novel Hybrid Q-learning approach. It allows to provide superior efficiency regardless of the environmental parameters.

Real-time rolling-horizon energy management of public laundries: A case study in HSB living lab

Energy Management Systems (EMSs) play a vital role in managing energy consumption for both utilities and consumers. By using EMSs, utilities can influence on energy usage and ensure a more reliable and efficient grid operation, while consumers can make informed decisions about their energy consumption, leading to significant cost savings and reduced environmental impact. In this paper, a real-time rolling-horizon model is developed for managing energy consumption in public laundries aiming at minimizing energy costs, peak demand, and CO2 emission under the traditional Energy-Based Tariff (EBT) and the Power-Based Tariff (PBT). The developed model can not only reduce energy costs, peak demand, and CO2 emission by optimal task scheduling for washing machines and tumble dryers but also ensure users' preferences for a comfortable lifestyle. To demonstrate the effectiveness of the proposed EMS, several simulations were performed under different scenarios using real data and by a realistic case study in HSB living lab demonstration site. The simulation results reveal that implementing the proposed EMS can significantly decrease energy costs and peak demand in public laundries by 13.59% and 39.40%, respectively, when using the PBT tariff. However, the reduction in energy costs and peak demand is negligible when using the EBT tariff. Likewise, the results indicate that using the EMS and changing tariffs have a minimal impact on CO2 emissions reduction.

Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approach

Cloud computing, a technology providing flexible and scalable computing resources, faces a critical challenge in task scheduling, directly impacting system performance and customer satisfaction. The task scheduling problem's NP-completeness makes finding solutions difficult. To address this, researchers propose a hybrid algorithm combining the Grey Wolf Optimization Algorithm (GWO) and the Genetic Algorithm (GA). The hybrid GWO-GA algorithm targets multi-objective task scheduling in cloud computing, aiming to minimize makespan, energy consumption, and cost. Enhancements to the proposed algorithm involve leveraging the genetic algorithm's crossover and mutation operator. Furthermore, the GA-based GWO algorithm's faster convergence in large scheduling problems presents an advantage. Evaluation using the Cloudsim toolkit demonstrates the proposed algorithm's efficiency compared to existing methods. We have used both synthetic and real world data set. The results are verified using the Analysis of Variance (ANOVA) statistical tool. Experimental results showcase its effectiveness in minimizing makespan, energy consumption, and computational cost. Particularly, the proposed algorithm outperforms traditional GWO, GA, and PSO algorithms in terms of makespan, cost, and energy consumption, achieving reductions of 19%, 21%, and 15%, respectively, when compared to each approach. Additionally, it yields energy savings of 17%, 19%, and 23% compared to GWO, GA, and PSO, respectively, while reducing total scheduling costs by 13%, 17%, and 22%. These findings demonstrate the efficacy of the proposed algorithm in addressing the task scheduling problem in cloud computing environments.

SCAFE: A Service-Centered Cloud-Native Workflow Engine Architecture

With the rapid development of manufacturing and cloud computing, more and more emerged cloud services provide a promising way to perform complex requirements efficiently. Workflow offers an effective way to assemble disparate services and interacts with them to construct business logic for user requests. Meanwhile, workflow engines are responsible for the control of workflow execution. However, existing engines usually support interaction between workflows and services or computing resources by tight binding approaches, which lack flexibility and scalability. Therefore, a flexible and decoupled architecture is essential to support automatic workflow management. To fill this gap, this paper proposes a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> ervice- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> entered cloud-n <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</b> tive work <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</b> low architectur <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</b> - SCAFE. The introduction of the service layer in SCAFE decouples the upper business services and lower execution resources, facilitating the independent and joint management of three execution objects (request - service - execution instance) involved in the cloud workflow lifecycle. We present a 2-stage scheduling model for the new architecture to support customized service optimization and resource-aware task scheduling. In addition, a fault-tolerant mechanism is integrated into resolving task execution exceptions quickly. We have successfully implemented a prototype tool to verify its flexibility and crucial functions, thus advancing the field workflow engines in cloud-native environments.

Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical Systems

Cyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms.

A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

The advent of cloud computing has dramatically altered how information is stored and retrieved. However, the effectiveness and speed of cloud-based applications can be significantly impacted by inefficiencies in the distribution of resources and task scheduling. Such issues have been challenging, but machine and deep learning methods have shown great potential in recent years. This paper suggests a new technique called Deep Q-Networks and Actor-Critic (DQNAC) models that enhance cloud computing efficiency by optimizing resource allocation and task scheduling. We evaluate our approach using a dataset of real-world cloud workload traces and demonstrate that it can significantly improve resource utilization and overall performance compared to traditional approaches. Furthermore, our findings indicate that deep reinforcement learning (DRL)-based methods can be potent and effective for optimizing cloud computing, leading to improved cloud-based application efficiency and flexibility.

Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing

The explosive growth of users and applications in IoT environments has promoted the development of cloud computing. In the cloud computing environment, task scheduling plays a crucial role in optimizing resource utilization and improving overall performance. However, effective task scheduling remains a key challenge. Traditional task scheduling algorithms often rely on static heuristics or manual configuration, limiting their adaptability and efficiency. To overcome these limitations, there is increasing interest in applying reinforcement learning techniques for dynamic and intelligent task scheduling in cloud computing. How can reinforcement learning be applied to task scheduling in cloud computing? What are the benefits of using reinforcement learning-based methods compared to traditional scheduling mechanisms? How does reinforcement learning optimize resource allocation and improve overall efficiency? Addressing these questions, in this paper, we propose a Q-learning-based Multi-Task Scheduling Framework (QMTSF). This framework consists of two stages: First, tasks are dynamically allocated to suitable servers in the cloud environment based on the type of servers. Second, an improved Q-learning algorithm called UCB-based Q-Reinforcement Learning (UQRL) is used on each server to assign tasks to a Virtual Machine (VM). The agent makes intelligent decisions based on past experiences and interactions with the environment. In addition, the agent learns from rewards and punishments to formulate the optimal task allocation strategy and schedule tasks on different VMs. The goal is to minimize the total makespan and average processing time of tasks while ensuring task deadlines. We conducted simulation experiments to evaluate the performance of the proposed mechanism compared to traditional scheduling methods such as Particle Swarm Optimization (PSO), random, and Round-Robin (RR). The experimental results demonstrate that the proposed QMTSF scheduling framework outperforms other scheduling mechanisms in terms of the makespan and average task processing time.

An intelligent approach of task offloading for dependent services in Mobile Edge Computing

With the growing popularity of Internet of Things (IoT), Mobile Edge Computing (MEC) has emerged for reducing the heavy workload at the multi-cloud core network by deploying computing and storage resources at the edge of network close to users. In IoT, services are data-intensive and event-driven, resulting in extensive dependencies among services. Traditional task offloading schemes face significant challenges in the IoT scenario with service dependencies. To this end, this paper proposes an intelligent approach for minimizing latency and energy consumption which jointly considers the task scheduling and resource allocation for dependent IoT services in MEC. Specifically, we establish the system model, communication model as well as computing model for performance evaluation by fully considering the dependent relationships among services, and an optimization problem is proposed for minimizing the delay and energy consumption simultaneously. Then, we design a layered scheme to deal with the service dependencies, and present detailed algorithms to intelligently obtain optimal task scheduling and resource allocation policies. Finally, simulation experiments are carried out to validate the effectiveness of the proposed scheme.

Multiagent Meta-Reinforcement Learning for Optimized Task Scheduling in Heterogeneous Edge Computing Systems

Mobile edge computing (MEC) brings the potential to address the ever increasing computation demands from the mobile users (MUs). In addition to local processing, the resource-constrained MUs in an MEC system can also offload computation to the nearby servers for remote execution. With the explosive growth of mobile devices, computation offloading faces the challenge of spectrum congestion, which in turn deteriorates the overall quality of computation experience. This paper hence investigates computation task scheduling in a heterogeneous cellular and WiFi MEC system. Such a system provides both licensed and unlicensed spectrum opportunities. Due to the sharing of communication and computation resources as well as the uncertainties, we formulate the problem of computation task scheduling among the competing MUs in a stationary heterogeneous edge computing system as a non-cooperative stochastic game. We propose an approximation-based multi-agent Markov decision process without the global system state observations, under which a multi-agent proximal policy optimization (PPO) algorithm is derived to solve the corresponding Nash equilibrium. When expanding to a non-stationary heterogeneous edge computing system, the obtained algorithm suffers from the slow convergence due to constrained adaptability. Accordingly, we explore meta-learning and propose a multi-agent meta-PPO algorithm, which rapidly adapts the control policy learning to the non-stationarity. Numerical experiments demonstrate performance gains from our proposed algorithms.

A new fog computing resource management (FRM) model based on hybrid load balancing and scheduling for critical healthcare applications

Critical healthcare application tasks require a real-time response because it affects patients’ life. Fog computing is the best solution to get a fast response and less energy consumption in healthcare. However, current solutions face difficulties in scheduling the tasks to the correct computing devices based on their priorities and capacity to meet the tasks’ deadlines and resource limitations with minimal latency. Furthermore, challenges of load balancing and prioritization are raised when dealing with inadequate computing resources and telecommunication networks while obtaining the best scheduling of emergency healthcare tasks. In this study, a fog computing resource management (FRM) model is proposed, which the proposed model has three main solutions. Firstly, resource availability is calculated according to the average execution time of each task. Secondly, load balancing is enhanced by proposing a hybrid approach that combines the multi-agent load balancing algorithm and the throttled load balancing algorithm. Thirdly, task scheduling is done based on priority, resource availability, and load balancing. The results have been acquired using the iFogSim toolkit. Two datasets are used in this study, the blood pressure dataset was acquired from the UTeM clinic, and the ECG dataset was acquired from the University of California at Irvine. Both datasets are integrated to enlarge the attributes and get accurate results. The results demonstrate the effectiveness of managing resources and optimizing task scheduling and balancing in a fog computing environment. In comparison with other research studies, the FRM model outperforms delay by 55%, response time by 72%, cost by 72%, and energy consumption by 70%.

Optimal Task Scheduling under Adverse Selection and Hidden Actions

A Principal owns a project consisting of several tasks. Tasks differ, both in their innate success probabilities and their incremental benefits. Moreover, only specialists can perform these tasks. Subject to moral hazard and adverse selection, in what order should the Principal commission the tasks, and when should she terminate the project? What investments into changing tasks’ characteristics yield the highest marginal profit? These are typical issues that arise in sequencing R&amp;D activities and other sequential production processes. We show that, despite informational constraints, a simple index—a task’s effective marginal contribution—determines the optimal schedule/mechanism. (JEL D82, L23, L24, L65, M11, O31)

AoI-Aware Scheduling for Air-Ground Collaborative Mobile Edge Computing

As a way of providing users flexible computing services, networks exist that can make full use of air and ground computing resources. Such networks are called air-ground collaborative mobile edge computing (AGC-MEC) networks. AGC-MEC supports numerous emerging real-time applications for which timely computed results are critical. Researchers have developed a novel metric “age of information (AoI)” that can capture the freshness of computed results. This is the first paper to study the problem of AoI-aware scheduling for Air-ground Collaborative mobile Edge computing ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., IACE). So as to minimize the weighted AoI of all the terrestrial user equipments (UEs), we have jointly optimized task scheduling, computing resource allocation, and unmanned aerial vehicle (UAV) trajectory taking into account the constraints on the computing resources and the available energy of the UAV. The formulated problem, which is a challenge to solve, is a mixed-integer nonlinear programming (MINLP) problem. To obtain an effective solution, we propose an iterative algorithm based on the alternating optimization approach, which entails dividing the considered problem into three subproblems. Extensive simulations show that the proposed algorithm can achieve lower weighted AoI than five benchmark algorithms, while satisfying the resource constraints. Furthermore, simulation results demonstrate two interesting insights. First, the introduction of an aerial MEC server facilitates a flexible offloading design of the UEs which is critical to guaranteeing the freshness of computed results. Second, by optimizing the scheduling, the proposed design can unlock performance gains, especially in the resource-limited regime.

Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing

With the development of the Internet of Things (IoT), IoT devices are increasingly being deployed in scenarios with large footprints, remote locations, and complex geographic environments. In these scenarios, base stations are usually not easily deployed and are easily destroyed, so unmanned aerial vehicle (UAV)-based edge computing is a good solution. However, the UAV cannot accomplish the computing tasks and efficiently achieve better resource allocation considering the limited communication and computing resources of the UAV. In this paper, a multi-UAV-assisted mobile edge computing (MEC) system is considered where multiple UAVs cooperate to provide a service to IoT devices. We formulate an optimization function to minimize the energy consumption of a multi-UAV-assisted MEC system. The optimization function is a complex problem with non-convex and multivariate coupling. Thus, a joint UAV deployment and task scheduling optimization algorithm are designed to achieve optimal values of UAV numbers, the hovering position of each UAV, and the best strategy for offloading and resource allocation. Experimental results demonstrate that the algorithm has positive convergence performance and can accomplish more tasks under the constraint of delay compared to the two benchmark algorithms. The proposed algorithm can effectively reduce the system energy consumption compared to the two state-of-the-art algorithms.

Collaborative optimization of task scheduling and multi-agent path planning in automated warehouses

AbstractTask scheduling (TS) and multi-agent-path-finding (MAPF) are two cruxes of pickup-and-delivery in automated warehouses. In this paper, the two cruxes are optimized simultaneously. Firstly, the system model, task model, and path model are established, respectively. Then, a task scheduling algorithm based on enhanced HEFT, a heuristic MAPF algorithm and a TS- MAPF algorithm are proposed to solve this combinatorial optimization problem. In EHEFT, a novel rank priority rule is used to determine task sequencing and task allocation. In MAPF algorithm, a CBS algorithm with priority rules is designed for path search. Subsequently, the TS-MAPF algorithm which combines EHEFT and MAPF is proposed. Finally, the proposed algorithms are tested separately against relevant typical algorithms at different scales. The experimental results indicate that the proposed algorithms exhibited good performance.

Escope: An Energy Efficiency Simulator for Internet Data Centers

Contemporary megawatt-scale data centers have emerged to meet the increasing demand for online cloud services and big data analytics. However, in such large-scale data centers, servers of different generations are installed gradually year by year, making the data center heterogeneous in computing capability and energy efficiency. Furthermore, due to different processor architectures, complex and diverse load dynamic changing, business coupling, and other reasons, operators pay great attention to processor hardware power consumption and server aggregation energy efficiency. Therefore, the simulation and analysis of the energy efficiency characteristics of data center servers under different processor architectures can help operators understand the energy efficiency characteristics of data centers and make the optimal task scheduling strategy. This is very beneficial for improving the energy efficiency of the production system and the entire data center. The Escope simulator designed in this study can simulate the online quantity (placement strategy) of different types of servers in the data center and the optimal operating range of the servers. The purpose of this is to analyze the energy efficiency characteristics of all servers in the data center and provide data center operators with the energy efficiency and energy proportionality characteristics of different servers, improve server utilization, and perform reasonable scheduling. Through the simulation experiment of Escope, it can be proved that running the server at the highest energy efficiency point or running the server under full load cannot improve the energy efficiency of the entire data center. The simulation algorithm provided by Escope can select the optimal set of servers and their corresponding utilization. Escope can set up a variety of simulation strategies, and data center operators can simulate data center energy efficiency according to their own needs. Escope can also calculate the power cost savings of introducing new servers in the data center, which provides an essential reference for operators to purchase servers and design data centers.

Parallel Optimization for Large Scale Interferometric Synthetic Aperture Radar Data Processing

Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the handing of SAR big data poses significant challenges for related algorithms and pipeline, particularly in large-scale SAR data processing. In addition, InSAR algorithms are highly complex, and their task dependencies are intricate. There is a lack of efficient optimization models and task scheduling for InSAR pipeline. In this paper, we design parallel time-series InSAR processing models based on multi-thread technology for high efficiency in processing InSAR big data. These models concentrate on parallelizing critical algorithms that have high complexity, with a focus on deconstructing two computationally intensive algorithms through loop unrolling. Our parallel models have shown a significant improvement of 10–20 times in performance. We have also developed a parallel optimization tool, Simultaneous Task Automatic Runtime (STAR), which utilizes a data flow optimization strategy with thread pool technology to address the problem of low CPU utilization resulting from multiple modules and task dependencies in the InSAR processing pipeline. STAR provides a data-driven pipeline and enables concurrent execution of multiple tasks, with greater flexibility to keep the CPU busy and further improve CPU utilization through predetermined task flow. Additionally, a supercomputing-based system has been constructed for processing massive InSAR scientific big data and providing technical support for nationwide surface deformation measurement, in accordance with the framework of time series InSAR data processing. Using this system, we processed InSAR data with the volumes of 500 TB and 700 TB in 5 and 7 days, respectively. Finally we generated two maps of land surface deformation all over China.

An Efficient Firefly Algorithm for Optimizing Task Scheduling in Cloud Computing Systems

An Efficient Firefly Algorithm for Optimizing Task Scheduling in Cloud Computing Systems

Two-Stage Optimal Task Scheduling for Smart Home Environment Using Fog Computing Infrastructures

The connection of many devices has brought new challenges with respect to the centralized architecture of cloud computing. The fog environment is suitable for many services and applications for which cloud computing does not support these well, such as: traffic light monitoring systems, healthcare monitoring systems, connected vehicles, smart cities, homes, and many others. Sending high-velocity data to the cloud leads to the congestion of the cloud infrastructure, which further leads to high latency and violations of the Quality-of-Service (QoS). Thus, delay-sensitive applications need to be processed at the edge of the network or near the end devices, rather than the cloud, in order to provide the guaranteed QoS related to the reduced latency, increased throughput, and high bandwidth. The aim of this paper was to propose a two-stage optimal task scheduling (2-ST) approach for the distribution of tasks executed within smart homes among several fog nodes. To effectively solve the task scheduling, this proposed approach uses a naïve-Bayes-based machine learning model for training in the first stage and optimization in the second stage using a hyperheuristic approach, which is a combination of both Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In addition, the proposed mechanism was validated against various metrics such as energy consumption, latency time, and network usage.

An enhanced ordinal optimization with lower scheduling overhead based novel approach for task scheduling in cloud computing environment

Efficient utilization of available computing resources in Cloud computing is one of the most challenging problems for cloud providers. This requires the design of an efficient and optimal task-scheduling strategy that can play a vital role in the functioning and overall performance of the cloud computing system. Optimal Schedules are specifically needed for scheduling virtual machines in fluctuating & unpredictable dynamic cloud scenario. Although there exist numerous approaches for enhancing task scheduling in the cloud environment, it is still an open issue. The paper focuses on an improved & enhanced ordinal optimization technique to reduce the large search space for optimal scheduling in the minimum time to achieve the goal of minimum makespan. To meet the current requirement of optimal schedule for minimum makespan, ordinal optimization that uses horse race conditions for selection rules is applied in an enhanced reiterative manner to achieve low overhead by smartly allocating the load to the most promising schedule. This proposed ordinal optimization technique and linear regression generate optimal schedules that help achieve minimum makespan. Furthermore, the proposed mathematical equation, derived using linear regression, predicts any future dynamic workload for a minimum makespan period target.

Optimized Task Scheduling and Virtual Object Management Based on Digital Twin for Distributed Edge Computing Networks

Optimized Task Scheduling and Virtual Object Management Based on Digital Twin for Distributed Edge Computing Networks