Optimal Task Scheduling Research Articles

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&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

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

The developments of multi-core systems (MCS) have considerably improved the existing technologies in the field of computer architecture. The MCS comprises several processors that are heterogeneous for resource capacities, working environments, topologies, and so on. The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling. At the same time, the task scheduling process is yet to be explored in the multi-core systems. This paper presents a new hybrid genetic algorithm (GA) with a krill herd (KH) based energy-efficient scheduling technique for multi-core systems (GAKH-SMCS). The goal of the GAKH-SMCS technique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation. The GAKH-SMCS model involves a multi-objective fitness function using four parameters such as makespan, processor utilization, speedup, and energy consumption to schedule tasks proficiently. The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset. The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan, processor utilization, speedup, and energy consumption. The overall simulation results depicted that the presented GAKH-SMCS model achieves energy efficiency by optimal task scheduling process in MCS.

Analysis and prediction of UAV-assisted mobile edge computing systems.

As the demand for the internet of things (IoT) continues to grow, there is an increasing need for low-latency networks. Mobile edge computing (MEC) provides a solution to reduce latency by offloading computational tasks to edge servers. However, this study primarily focuses on the integration of back propagation (BP) neural networks into the realm of MEC, aiming to address intricate network challenges. Our innovation lies in the fusion of BP neural networks with MEC, particularly for optimizing task scheduling and processing. Firstly, we introduce a drone-assisted MEC model that categorizes computation offloading into synchronous and asynchronous modes based on task scheduling. Secondly, we employ Markov chains and probability-generation functions to accurately compute parameters such as average queue length, cycle time, throughput, and average delay in the synchronous mode. We also derive the first and second-order derivatives of the probability-generation function to support these computations. Finally, we establish a BP neural network to solve for the average queue length and latency in the asynchronous mode. Our results from the BP neural network closely align with the theoretical values obtained through the probability-generation function, demonstrating the effectiveness of our approach. Additionally, our proposed UAV-assisted MEC model outperforms the synchronous mode. Overall, our MEC scheduling approach significantly reduces latency, enhances speed, and improves throughput, with our model reducing latency by approximately 11.72$ \% $ and queue length by around 9.45$ \% $.

Resource Allocation in Massive Internet of Things-Edge Network with Optimal Path Planning and Scheduling

Although an Ultra Dense deployment is required for 5G services, it will be nearly impossible to achieve 60 service coverage with the dense deployments due to the even shorter transmission range. In light of 5G’s impressive technological advance, 5G and data centers are now capable of handling an increasing number of real-time and complicated computational tasks from Internet of Things (IoT) systems. This paper proposes an optimal mobile resource-sharing approach to confront this underlying limitation of 5G. In contrast to conventional algorithms, the designed optimal path planning and scheduling for mobile edge server (OPPSMES) is proposed that have the advantage of a synchronization among request being received and achieved lower delay and resource demand for as computing this allowed for the parallel processing of task and server in mobile condition. The OPPSMES includes two steps, i.e., path planning and optimal task scheduling, to improve efficiency. According to simulation outcomes, there is a significant increase in resource utilization and a decrease in average response time.

HunterPlus: AI based energy-efficient task scheduling for cloud–fog computing environments

Cloud computing is a mainstay of modern technology, offering cost-effective and scalable solutions to a variety of different problems. The massive shift of organization resource needs from local systems to cloud-based systems has greatly increased the costs incurred by cloud providers in expanding, maintaining, and supplying server, storage, network, and processing hardware. Due to the large scale at which cloud providers operate, even small performance degradation issues can cause energy or resource usage costs to rise dramatically. One way in which cloud providers may improve cost reduction is by reducing energy consumption. The use of intelligent task-scheduling algorithms to allocate user-deployed jobs to servers can reduce the amount of energy consumed. Conventional task scheduling algorithms involve both heuristic and metaheuristic methods. Recently, the application of Artificial Intelligence (AI) to optimize task scheduling has seen significant progress, including the Gated Graph Convolution Network (GGCN). This paper proposes a new approach called HunterPlus which examine the effect of extending the GGCN’s Gated Recurrent Unit to a Bidirectional Gated Recurrent Unit. The paper also studies the utilization of Convolutional Neural Networks (CNNs) in optimizing cloud–fog task scheduling. Experimental results show that the CNN scheduler outperforms the GGCN-based models in both energy consumption per task and job completion rate metrics by at least 17 and 10.4 percent, respectively.

A Particle Swarm Optimization Method for AI Stream Scheduling in Edge Environments

With the development of IoT and 5G technologies, edge computing has become a key driver for providing compute, network and storage services. The dramatic increase in data size and the complexity of AI computation models have put higher demands on the performance of edge computing. Rational and optimal scheduling of AI data-intensive computation tasks can greatly improve the overall performance of edge computing. To this end, a particle swarm algorithm based on objective ranking is proposed to optimize task execution time and scheduling cost by designing a task scheduling model to achieve task scheduling in an edge computing environment. It is necessary to fully understand the concept of symmetry of resource utilization and task execution cost indicators. The method utilizes nonlinear inertia weights and shrinkage factor update mechanisms to improve the optimization-seeking ability and convergence speed of the particle-to-task scheduling solution space. The task execution time and scheduling cost are greatly reduced. Simulation experiments are conducted using the Cloudsim toolkit to experimentally compare the proposed algorithm TS-MOPSO with three other particle swarm improvement algorithms, and the experimental results show that the task execution time, maximum completion time and total task scheduling cost are reduced by 31.6%, 23.1% and 16.6%, respectively. The method is suitable for handling large and complex AI data-intensive task scheduling optimization efforts.

An On-Board Task Scheduling Method Based on Evolutionary Optimization Algorithm

In order to meet the requirements of task scheduling in on-board distributed computing environment, an evolutionary optimization scheduling method for on-board tasks was proposed based on the on-board dynamic heterogeneous computing resource model and task model. In this method, task scheduling priority coding was used to adapt to dynamic changes of computing resources. Heuristic critical path for comprehensive index balance was adopted to implement the evaluation, so as to achieve a balance between task makespan, power consumption and reliability. Multi-group neighborhood search strategy was applied to avoid the algorithm falling into local optimization and simplify the complexity of scheduling algorithm. Fault-tolerant strategy of convergence monitoring and stop loss reconfiguration were designed for scheduling unit fault tolerance. Scheduling strategy based on active and backup redundant subtasks was used for computing unit fault tolerance. The simulation results showed the efficiency of this method to deal with the scenario of on-board distributed computing resources and implement the optimal scheduling of on-board tasks.

Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm

During the last decades, intelligent mobile robots have been recognized as one of the most promising and emerging solutions used for fulfilling material transport demands in intelligent manufacturing systems. One of the most significant characteristics of those demands is their multi-objectivity, where identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic-based material transport system that is simultaneously facing several conflicting objectives is a highly challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an intelligent single mobile robot. The proposed optimization methodology includes the comprehensive analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi-objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outperforms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution. Finally, transportation paths based on obtained scheduling plans are experimentally corroborated by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical model of the intelligent manufacturing environment. The achieved experimental results successfully demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of material transport tasks based on a single mobile robotic system.