Learning-Based Task Offloading for Vehicular Cloud Computing Systems

Sun, Yuxuan; Guo, Xueying; Zhou, Sheng; Jiang, Zhiyuan; Liu, Xin; Niu, Zhisheng

doi:10.1109/icc.2018.8422661

Cited by 95 publications

(52 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By contrast, we solve the task replication problem within a MAB framework, which is a general learning framework and does not rely on additional assumptions on traffic model or scheduling process. The most related work is probably [13] where the authors use the MAB framework to help make task offloading decision.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Task Replication for Vehicular Cloud: Contextual Combinatorial Bandit with Delayed Feedback

Chen

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

View full text Add to dashboard Cite

Vehicular Cloud Computing (VCC) is a new technological shift which exploits the computation and storage resources on vehicles for computational service provisioning. Spare on-board resources are pooled by a VCC operator, e.g. a roadside unit, to serve computational tasks using the vehicleas-a-resource framework. This paper investigates timely service provisioning for deadline-constrained tasks in VCC systems by leveraging the task replication technique (i.e., allowing one task to be executed by vehicles). A learning-based algorithm, called DATE-V (Deadline-Aware Task rEplication for Vehicular Cloud), is proposed to address the special issues in VCC systems including uncertainty of vehicle movements, volatile vehicle members, and large vehicle population. The proposed algorithm is developed based on a novel contextual-combinatorial multi-armed bandit learning framework. DATE-V is "contextual" because it utilizes side information (context) of vehicles and tasks to infer the completion probability of a task replication under random vehicle movements. DATE-V is "combinatorial" because it replicates the received task and sends task replications to multiple vehicles to guarantee the service timeliness. When learning with multi-armed bandit, DATE-V also addresses the practical concern of delayed feedbacks caused by the task transmission/computational delay in using VCC. We rigorously prove that our learning algorithm achieves a sublinear regret bound compared to an oracle algorithm that knows the exact completion probability of any task replications. Simulations are carried out based on real-world vehicle movement traces and the results show that DATE-V significantly outperforms benchmark solutions.

show abstract

Section: Related Workmentioning

confidence: 99%

“…While [13] only considers a task offloading problem, we consider both task offloading and task replication for VCC systems. More importantly, the MAB algorithm proposed in [13] only works with a finite arm set. By contrast, the proposed CC-MAB framework is able to learn with an infinitely large arm set which fits the VCC systems.…”

Section: Related Workmentioning

confidence: 99%

Task Replication for Vehicular Cloud: Contextual Combinatorial Bandit with Delayed Feedback

Chen

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

View full text Add to dashboard Cite

show abstract

“…However, authors consider that the number of learning trials is large enough compared to the number of BSs whose performances need to be learnt. Focusing on a vehicular edge computing scenario instead, authors in [7] consider the problem of task offloading from one vehicle to neighboring serving vehicles (SeVs) with the goal of minimizing the overall delay. Similar to [4], a volatile MAB approach is used to deal with the SeVs' volatility, in addition to including loadawareness in the selection decision process.…”

Section: Related Workmentioning

confidence: 99%

“…First, they consider a limited user mobility ( [4], [5], [6]) and as such, they do not address the problems that may arise from longer mobility durations, such as the continuously-changing set of FNs. Second, they consider scenarios with a limited number of FNs, where the user can have enough time to learn the best one among them ( [7], [8]), which may not always be the case. In fact, as will be shown in our collected data, a user usually has to learn the performances of a high number of FNs in a very short time frame, since the set of nearby FNs changes due to mobility.…”

Section: Introductionmentioning

confidence: 99%

A User-Centric Mobility Management Scheme for High-Density Fog Computing Deployments

Rejiba¹,

Masip-Bruin²,

Marín‐Tordera³

2019

2019 28th International Conference on Computer Communication and Networks (ICCCN)

View full text Add to dashboard Cite

The inherent mobility characterizing users in fog computing environments along with the limited wireless range of their serving fog nodes (FNs) drives the need for designing efficient mobility management (MM) mechanisms. This ensures that users' resource-intensive tasks are always served by the most suitable FNs in their vicinity. However, since MM decisionmaking requires control information which is difficult to predict accurately a-priori, such as the users' mobility patterns and the dynamics of the FNs, researchers have started to shift their attention towards MM solutions based on online learning. Motivated by this approach, in this paper, we consider a bandit learning model to address the mobility-induced FN selection problem, with a particular focus on scenarios with a high FN density. Following this approach, a software agent implemented within the user's device learns the FNs' delay performances via trial and error, by sending them the user's computation tasks and observing the perceived delay, with the goal of minimizing the accumulated delay. This task is particularly challenging when considering a high FN density, since the number of unknown FNs that need to be explored is high, while the time that can be spent on learning their performances is limited, given the user's mobility. Therefore, to address this issue, we propose to limit the number of explorations to a small subset of the FNs. As a result, the user can still have time to be served by the FN that was found to yield the lowest delay performance. Using real world mobility traces and task generation patterns, we found that it pays off to limit the number of explorations in high FN density scenarios. This is shown through significant improvements in the cumulative regret as well as the instantaneous delay, compared to the case where all newly-appeared FNs are explored.

show abstract

“…An alternative way is to offload tasks in a distributed manner, i.e., each TaV makes task offloading decisions individually [14]. In this case, TaV may not be able to obtain the global state information of channel states and computation loads of all available SeVs, which can be learned while offloading tasks based on multi-armed bandit (MAB) theory, as shown in our previous work [15].…”

Section: Introductionmentioning

confidence: 99%

Task Replication for Vehicular Edge Computing: A Combinatorial Multi-Armed Bandit Based Approach

Sun

Song

Zhou

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

Self Cite

View full text Add to dashboard Cite

In vehicular edge computing (VEC) system, some vehicles with surplus computing resources can provide computation task offloading opportunities for other vehicles or pedestrians. However, vehicular network is highly dynamic, with fast varying channel states and computation loads. These dynamics are difficult to model or to predict, but they have major impact on the quality of service (QoS) of task offloading, including delay performance and service reliability. Meanwhile, the computing resources in VEC are often redundant due to the high density of vehicles. To improve the QoS of VEC and exploit the abundant computing resources on vehicles, we propose a learning-based task replication algorithm (LTRA) based on combinatorial multiarmed bandit (CMAB) theory, in order to minimize the average offloading delay. LTRA enables multiple vehicles to process the replicas of the same task simultaneously, and vehicles that require computing services can learn the delay performance of other vehicles while offloading tasks. We take the occurrence time of vehicles into consideration, and redesign the utility function of existing CMAB algorithm, so that LTRA can adapt to the time varying network topology of VEC. We use a realistic highway scenario to evaluate the delay performance and service reliability of LTRA through simulations, and show that compared with single task offloading, LTRA can improve the task completion ratio with deadline 0.6s from 80% to 98%.

show abstract

Learning-Based Task Offloading for Vehicular Cloud Computing Systems

Cited by 95 publications

References 18 publications

Task Replication for Vehicular Cloud: Contextual Combinatorial Bandit with Delayed Feedback

Task Replication for Vehicular Cloud: Contextual Combinatorial Bandit with Delayed Feedback

A User-Centric Mobility Management Scheme for High-Density Fog Computing Deployments

Task Replication for Vehicular Edge Computing: A Combinatorial Multi-Armed Bandit Based Approach

Contact Info

Product

Resources

About