Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization

In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often overlooked.It is frequently assumed that vehicles can be accurately modeled during actual motion processes.However,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios.Taking into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading process.The optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming problem.Due to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and transmission.Finally,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization problem.Simulation results show that the algorithm proposed in this paper is able to achieve lower latency task computation offloading.Meanwhile,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

Coordinated Planning Transmission Tasks in Heterogeneous Space Networks:A Semi-Distributed Approach

This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite systems.Specifically,we first formulate the coordinated planning problem into a mixed integer liner programming(MILP)problem based on time expanded graph.Then,the problem is transferred and reformulated into a consensus optimization framework which can be solved by satellite systems parallelly.With alternating direction method of multipliers(ADMM),a semi-distributed coordinated transmission task planning algorithm is proposed,in which each satellite system plans its own tasks based on local information and limited communication with the coordination center.Simulation results demonstrate that compared with the centralized and fully-distributed methods,the proposed semi-distributed coordinated method can strike a better balance among task complete rate,complexity,and the amount of information required to be exchanged.

Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization

Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.

Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

Well organized datacentres with interconnected servers constitute the cloud computing infrastructure.User requests are submitted through an interface to these servers that provide service to them in an on-demand basis.The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category.Task scheduling in cloud poses numerous challenges impacting the cloud performance.If not handled properly,user satisfaction becomes questionable.More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment.The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution.An improvised seagull optimization algorithm which combines the features of the Cuckoo search(CS)and seagull optimization algorithm(SOA)had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment.The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment.Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization(MO-ACO),ACO and Min-Min algorithms.The proposed SOA-CS technique had produced an improvement of 1.06%,4.2%,and 2.4%for makespan and had reduced the overall cost to the extent of 1.74%,3.93%and 2.77%when compared with PSO,ACO,IDEA algorithms respectively when 300 vms are considered.The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.

Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

Cloud computing plays a significant role in Information Technology(IT)industry to deliver scalable resources as a service.One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling.The main advantage of this scheduling is to max-imize the performance and minimize the time loss.Various researchers examined numerous scheduling methods to achieve Quality of Service(QoS)and to reduce execution time.However,it had disadvantages in terms of low throughput and high response time.Hence,this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines(VMs).For this purpose,the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task(PSO-BATS)scheduling with Multi-Layered Regression Host Employment(MLRHE)to sort out the issues of task scheduling and ease the scheduling operation by load balancing.The proposed efficient sche-duling provides benefits to both cloud users and servers.The performance evalua-tion is undertaken with respect to cost,Performance Improvement Rate(PIR)and makespan which revealed the efficiency of the proposed method.Additionally,comparative analysis is undertaken which confirmed the performance of the intro-duced system than conventional system for scheduling tasks with highflexibility.

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

AI(Artificial Intelligence)workloads are proliferating in modernreal-time systems.As the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be reexamined.In particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline constraints.To cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two observations.First,resource planning for AI workloadsis a complicated search problem that requires much time for optimization.Second,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in advance.Based on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of resources.Instead of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of tasks.Thus,in any case,the workload isimmediately executed according to the resource plan maintained.Specifically,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload changes.The proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its effectiveness.Simulationexperiments show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Task Offloading in Edge Computing Using GNNs and DQN

In a network environment composed of different types of computing centers that can be divided into different layers(clod,edge layer,and others),the interconnection between them offers the possibility of peer-to-peer task offloading.For many resource-constrained devices,the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity.In this scenario,it is worth considering transferring these tasks to resource-rich platforms,such as Edge Data Centers or remote cloud servers.For different reasons,it is more exciting and appropriate to download various tasks to specific download destinations depending on the properties and state of the environment and the nature of the functions.At the same time,establishing an optimal offloading policy,which ensures that all tasks are executed within the required latency and avoids excessive workload on specific computing centers is not easy.This study presents two alternatives to solve the offloading decision paradigm by introducing two well-known algorithms,Graph Neural Networks(GNN)and Deep Q-Network(DQN).It applies the alternatives on a well-known Edge Computing simulator called PureEdgeSimand compares them with the two defaultmethods,Trade-Off and Round Robin.Experiments showed that variants offer a slight improvement in task success rate and workload distribution.In terms of energy efficiency,they provided similar results.Finally,the success rates of different computing centers are tested,and the lack of capacity of remote cloud servers to respond to applications in real-time is demonstrated.These novel ways of finding a download strategy in a local networking environment are unique as they emulate the state and structure of the environment innovatively,considering the quality of its connections and constant updates.The download score defined in this research is a crucial feature for determining the quality of a download path in the GNN training process and has not previously been proposed.Simultaneously,the suitability of Reinforcement Learning(RL)techniques is demonstrated due to the dynamism of the network environment,considering all the key factors that affect the decision to offload a given task,including the actual state of all devices.

Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation.While traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and efficiency.To address these issues,this study proposes a novel two-part invalid detection task allocation framework.In the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data.Compared to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public dataset.In the second step of the framework,task allocation modeling is performed using a twopart graph matching method.This phase introduces a P-queue KM algorithm that implements a more efficient optimization strategy.The allocation efficiency is improved by approximately 23.83%compared to the baseline method.Empirical results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

Online task planning method of anti-ship missile based on rolling optimization

Based on the wave attack task planning method in static complex environment and the rolling optimization framework, an online task planning method in dynamic complex environment based on rolling optimization is proposed. In the process of online task planning in dynamic complex environment,online task planning is based on event triggering including target information update event, new target addition event, target failure event, weapon failure event, etc., and the methods include defense area reanalysis, parameter space update, and mission re-planning. Simulation is conducted for different events and the result shows that the index value of the attack scenario after re-planning is better than that before re-planning and according to the probability distribution of statistical simulation method, the index value distribution after re-planning is obviously in the region of high index value, and the index value gap before and after re-planning is related to the degree of posture change.

Two-Stage IoT Computational Task Offloading Decision-Making in MEC with Request Holding and Dynamic Eviction

The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.

Enhancing Numeracy Skills of Grade 3 Students Through Authentic Performance Tasks

Numeracy is the capacity to use mathematical ideas in all facets of life.It involves activities such as adding and subtracting numbers,counting,number recognition,solving number problems involving various operations,sorting,observing,identifying,and establishing patterns.It is one of the fundamental skills that students should have mastered by the end of their primary schooling.With the notable importance of mastery of numeracy skills,low achievement and performance of the learners were observed in this aspect.This study aimed in enhancing the numeracy skills of Grade 3 learners through authentic performance tasks.The variable in numeracy skills includes the four fundamental operations and problem solving.The quasi-experimental design was utilized wherein purposive sampling or non-randomized sampling was used.In this study,33 Grade 3 learners of Rizal Elementary School were selected to participate in the tests.Pre-test and post-test crafted by the teacher were the main instrument in the study.The result revealed that in the pre-test the learners obtained a mean percentage score(MPS)of 38.20%in four fundamental operations,which implied a non-numerate level.While in terms of problem solving,the learners obtained a MPS of 20.60%which is also in the non-numerate level.It has a grand mean of 29.40%with an interpretation of non-numerate level.In the post-test,it was observed that four fundamental operations have a MPS of 81.10%which is in average numerate level,while problem solving has a MPS of 76.30%with a grand mean of 78.70%with an interpretation of average numerate level.This implied that there is a significant difference between the pre-test and post-test in the four fundamental operations and problem solving.Thus,it can be concluded that the application of authentic performance tasks was effective to bridge the gap on numeracy skills.

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

Multi-Agent Deep Deterministic Policy Gradien-Based Task Offloading Resource Allocation Joint Offloading

With the advancement of technology and the continuous innovation of applications, low-latency applications such as drones, online games and virtual reality are gradually becoming popular demands in modern society. However, these applications pose a great challenge to the traditional centralized mobile cloud computing paradigm, and it is obvious that the traditional cloud computing model is already struggling to meet such demands. To address the shortcomings of cloud computing, mobile edge computing has emerged. Mobile edge computing provides users with computing and storage resources by offloading computing tasks to servers at the edge of the network. However, most existing work only considers single-objective performance optimization in terms of latency or energy consumption, but not balanced optimization in terms of latency and energy consumption. To reduce task latency and device energy consumption, the problem of joint optimization of computation offloading and resource allocation in multi-cell, multi-user, multi-server MEC environments is investigated. In this paper, a dynamic computation offloading algorithm based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed to obtain the optimal policy. The experimental results show that the algorithm proposed in this paper reduces the delay by 5 ms compared to PPO, 1.5 ms compared to DDPG and 10.7 ms compared to DQN, and reduces the energy consumption by 300 compared to PPO, 760 compared to DDPG and 380 compared to DQN. This fully proves that the algorithm proposed in this paper has excellent performance.

Optimizing Spatial Crowdsourcing:A Quality-Aware Task Assignment Approach for Mobile Communication

The widespread use of advanced electronic devices has led to the emergence of spatial crowdsourcing,a method that taps into collective efforts to perform real-world tasks like environmental monitoring and traffic surveillance.Our research focuses on a specific type of spatial crowdsourcing that involves ongoing,collaborative efforts for continuous spatial data acquisition.However,due to limited budgets and workforce availability,the collected data often lacks completeness,posing a data deficiency problem.To address this,we propose a reciprocal framework to optimize task assignments by leveraging the mutual benefits of spatiotemporal subtask execution.We introduce an entropy-based quality metric to capture the combined effects of incomplete data acquisition and interpolation imprecision.Building on this,we explore a quality-aware task assignment method,corresponding to spatiotemporal assignment strategies.Since the assignment problem is NP-hard,we develop a polynomial-time algorithm with the guaranteed approximation ratio.Novel indexing and pruning techniques are proposed to further enhance performance.Extensive experiments conducted on datasets validate the effectiveness of our methods.

QoS-Constrained,Reliable and Energy-Efficient Task Deployment in Cloud Computing

Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.

A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.

云环境中Bag-of-tasks应用的多核虚拟计算资源分配机制研究

云计算环境为用户提供了大量弹性可扩展的基础设施资源,用户可以按需购买和支付服务.如果云平台能够自动分配给一批任务合理规模的计算资源,将会方便用户使用并且较大节省用户服务成本.本文在瀚海星云云计算平台上构建了一个软件应用服务层,提供Bag-of-tasks(BoT)应用公共服务.根据历史信息通过回归方法和BP神经网络方法对BoT中的子任务进行执行时间预测.然后使用最大并发度概念,在虚拟机内存是否满足任务情况下,提出了VMA,NP-IO和NP-DP三种算法.最后,使用图片分割软件作为BoT应用,从资源分配情况、任务完成率和算法时间复杂度方面验证了算法的有效性.