Edge computing is becoming ever more relevant to offload compute-heavy tasks in vehicular networks.In this context,the concept of vehicular micro clouds(VMCs)has been proposed to use compute and storage resources on n...Edge computing is becoming ever more relevant to offload compute-heavy tasks in vehicular networks.In this context,the concept of vehicular micro clouds(VMCs)has been proposed to use compute and storage resources on nearby vehicles to complete computational tasks.As many tasks in this application domain are time critical,offloading to the cloud is prohibitive.Additionally,task deadlines have to be dealt with.This paper addresses two main challenges.First,we present a task migration algorithm supporting deadlines in vehicular edge computing.The algorithm is following the earliest deadline first model but in presence of dynamic processing resources,i.e,vehicles joining and leaving a VMC.This task offloading is very sensitive to the mobility of vehicles in a VMC,i.e,the so-called dwell time a vehicles spends in the VMC.Thus,secondly,we propose a machine learning-based solution for dwell time prediction.Our dwell time prediction model uses a random forest approach to estimate how long a vehicle will stay in a VMC.Our approach is evaluated using mobility traces of an artificial simple intersection scenario as well as of real urban traffic in cities of Luxembourg and Nagoya.Our proposed approach is able to realize low-delay and low-failure task migration in dynamic vehicular conditions,advancing the state of the art in vehicular edge computing.展开更多
Recently,with the increasing complexity of multiplex Unmanned Aerial Vehicles(multi-UAVs)collaboration in dynamic task environments,multi-UAVs systems have shown new characteristics of inter-coupling among multiplex g...Recently,with the increasing complexity of multiplex Unmanned Aerial Vehicles(multi-UAVs)collaboration in dynamic task environments,multi-UAVs systems have shown new characteristics of inter-coupling among multiplex groups and intra-correlation within groups.However,previous studies often overlooked the structural impact of dynamic risks on agents among multiplex UAV groups,which is a critical issue for modern multi-UAVs communication to address.To address this problem,we integrate the influence of dynamic risks on agents among multiplex UAV group structures into a multi-UAVs task migration problem and formulate it as a partially observable Markov game.We then propose a Hybrid Attention Multi-agent Reinforcement Learning(HAMRL)algorithm,which uses attention structures to learn the dynamic characteristics of the task environment,and it integrates hybrid attention mechanisms to establish efficient intra-and inter-group communication aggregation for information extraction and group collaboration.Experimental results show that in this comprehensive and challenging model,our algorithm significantly outperforms state-of-the-art algorithms in terms of convergence speed and algorithm performance due to the rational design of communication mechanisms.展开更多
Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a...Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a pervasive cloud environment. Our approach is based on Colored Petri Net（CPN）. In this research, we expanded the semantics of a CPN and created two task migration models with different task migration policies： one that took account of context information and one that did not. We evaluated the two models using CPN-based simulation and analyzed their task migration accessibility, integrity during the migration process, reliability, and the stability of the pervasive cloud system after task migration. The energy consumption and costs of the two models were also investigated. Our results suggest that CPN with context sensing task migration can minimize energy consumption while preserving good overall performance.展开更多
By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal serv...By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion time.In this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood.To satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites.Simulation results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.展开更多
Most distributed stream processing engines(DSPEs)do not support online task management and cannot adapt to time-varying data flows.Recently,some studies have proposed online task deployment algorithms to solve this pr...Most distributed stream processing engines(DSPEs)do not support online task management and cannot adapt to time-varying data flows.Recently,some studies have proposed online task deployment algorithms to solve this problem.However,these approaches do not guarantee the Quality of Service(QoS)when the task deployment changes at runtime,because the task migrations caused by the change of task deployments will impose an exorbitant cost.We study one of the most popular DSPEs,Apache Storm,and find out that when a task needs to be migrated,Storm has to stop the resource(implemented as a process of Worker in Storm)where the task is deployed.This will lead to the stop and restart of all tasks in the resource,resulting in the poor performance of task migrations.Aiming to solve this problem,in this pa-per,we propose N-Storm(Nonstop Storm),which is a task-resource decoupling DSPE.N-Storm allows tasks allocated to resources to be changed at runtime,which is implemented by a thread-level scheme for task migrations.Particularly,we add a local shared key/value store on each node to make resources aware of the changes in the allocation plan.Thus,each resource can manage its tasks at runtime.Based on N-Storm,we further propose Online Task Deployment(OTD).Differ-ing from traditional task deployment algorithms that deploy all tasks at once without considering the cost of task migra-tions caused by a task re-deployment,OTD can gradually adjust the current task deployment to an optimized one based on the communication cost and the runtime states of resources.We demonstrate that OTD can adapt to different kinds of applications including computation-and communication-intensive applications.The experimental results on a real DSPE cluster show that N-Storm can avoid the system stop and save up to 87%of the performance degradation time,compared with Apache Storm and other state-of-the-art approaches.In addition,OTD can increase the average CPU usage by 51%for computation-intensive applications and reduce network communication costs by 88%for communication-intensive ap-plications.展开更多
文摘Edge computing is becoming ever more relevant to offload compute-heavy tasks in vehicular networks.In this context,the concept of vehicular micro clouds(VMCs)has been proposed to use compute and storage resources on nearby vehicles to complete computational tasks.As many tasks in this application domain are time critical,offloading to the cloud is prohibitive.Additionally,task deadlines have to be dealt with.This paper addresses two main challenges.First,we present a task migration algorithm supporting deadlines in vehicular edge computing.The algorithm is following the earliest deadline first model but in presence of dynamic processing resources,i.e,vehicles joining and leaving a VMC.This task offloading is very sensitive to the mobility of vehicles in a VMC,i.e,the so-called dwell time a vehicles spends in the VMC.Thus,secondly,we propose a machine learning-based solution for dwell time prediction.Our dwell time prediction model uses a random forest approach to estimate how long a vehicle will stay in a VMC.Our approach is evaluated using mobility traces of an artificial simple intersection scenario as well as of real urban traffic in cities of Luxembourg and Nagoya.Our proposed approach is able to realize low-delay and low-failure task migration in dynamic vehicular conditions,advancing the state of the art in vehicular edge computing.
基金supported by the Key Research and Development Program of Jiangsu Province of China(No.BE2022157)the National Natural Science Foundation of China(Nos.62303111,62076060,and 61932007)+1 种基金the Defense Industrial Technology Development Program(No.JCKY2021214B002)the Fellowship of China Postdoctoral Science Foundation(No.2022M720715).
文摘Recently,with the increasing complexity of multiplex Unmanned Aerial Vehicles(multi-UAVs)collaboration in dynamic task environments,multi-UAVs systems have shown new characteristics of inter-coupling among multiplex groups and intra-correlation within groups.However,previous studies often overlooked the structural impact of dynamic risks on agents among multiplex UAV groups,which is a critical issue for modern multi-UAVs communication to address.To address this problem,we integrate the influence of dynamic risks on agents among multiplex UAV group structures into a multi-UAVs task migration problem and formulate it as a partially observable Markov game.We then propose a Hybrid Attention Multi-agent Reinforcement Learning(HAMRL)algorithm,which uses attention structures to learn the dynamic characteristics of the task environment,and it integrates hybrid attention mechanisms to establish efficient intra-and inter-group communication aggregation for information extraction and group collaboration.Experimental results show that in this comprehensive and challenging model,our algorithm significantly outperforms state-of-the-art algorithms in terms of convergence speed and algorithm performance due to the rational design of communication mechanisms.
文摘Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a pervasive cloud environment. Our approach is based on Colored Petri Net（CPN）. In this research, we expanded the semantics of a CPN and created two task migration models with different task migration policies： one that took account of context information and one that did not. We evaluated the two models using CPN-based simulation and analyzed their task migration accessibility, integrity during the migration process, reliability, and the stability of the pervasive cloud system after task migration. The energy consumption and costs of the two models were also investigated. Our results suggest that CPN with context sensing task migration can minimize energy consumption while preserving good overall performance.
基金This work was supported by the National Key Research and Development Program of China(2021YFB2900600)the National Natural Science Foundation of China(61971041+2 种基金62001027)the Beijing Natural Science Foundation(M22001)the Technological Innovation Program of Beijing Institute of Technology(2022CX01027).
文摘By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion time.In this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood.To satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites.Simulation results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.
基金The work was supported by the National Natural Science Foundation of China under Grant Nos.62072419 and 61672479.
文摘Most distributed stream processing engines(DSPEs)do not support online task management and cannot adapt to time-varying data flows.Recently,some studies have proposed online task deployment algorithms to solve this problem.However,these approaches do not guarantee the Quality of Service(QoS)when the task deployment changes at runtime,because the task migrations caused by the change of task deployments will impose an exorbitant cost.We study one of the most popular DSPEs,Apache Storm,and find out that when a task needs to be migrated,Storm has to stop the resource(implemented as a process of Worker in Storm)where the task is deployed.This will lead to the stop and restart of all tasks in the resource,resulting in the poor performance of task migrations.Aiming to solve this problem,in this pa-per,we propose N-Storm(Nonstop Storm),which is a task-resource decoupling DSPE.N-Storm allows tasks allocated to resources to be changed at runtime,which is implemented by a thread-level scheme for task migrations.Particularly,we add a local shared key/value store on each node to make resources aware of the changes in the allocation plan.Thus,each resource can manage its tasks at runtime.Based on N-Storm,we further propose Online Task Deployment(OTD).Differ-ing from traditional task deployment algorithms that deploy all tasks at once without considering the cost of task migra-tions caused by a task re-deployment,OTD can gradually adjust the current task deployment to an optimized one based on the communication cost and the runtime states of resources.We demonstrate that OTD can adapt to different kinds of applications including computation-and communication-intensive applications.The experimental results on a real DSPE cluster show that N-Storm can avoid the system stop and save up to 87%of the performance degradation time,compared with Apache Storm and other state-of-the-art approaches.In addition,OTD can increase the average CPU usage by 51%for computation-intensive applications and reduce network communication costs by 88%for communication-intensive ap-plications.
