Budget-Constrained Edge Service Provisioning With Demand Estimation via Bandit Learning

Chen, Lixing; Xu, Jie

doi:10.1109/jsac.2019.2933781

Cited by 37 publications

(17 citation statements)

References 32 publications

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“…Since these edge servers can implement edge caching by collecting the local service requests in real-time and then use the dynamic information to make intelligent decisions about service reconfiguration with the help of powerful computing ability, including the support for futuristic computationally intensive deep learning algorithms. server in their proposed methods [3], [114], while some others [115], [116] consider a collection of a single MBS with multiple small base stations.…”

Section: ) Heterogeneous Network (Het-net)mentioning

confidence: 99%

“…In this approach, the local caching gain is obtained when a requested service is locally available in the cache (either at the SBS or the devices) by serving this service from the cache without connecting to the MBS. In some work, the network model comprises multiple SBSs along with an MBS [115]- [118]. However, the researchers in [84], [119] • Pico base station (PBS): A PBS is a type of SBS that can be used both indoor and outdoor, supporting 32-100 users [24].…”

Section: ) Heterogeneous Network (Het-net)mentioning

confidence: 99%

“…The location information can be the cell where the user is located, the GPS information, or a tuple of (Position, Position error, Speed, Speed error, Direction, Direction error). Time is the next attribute of the mobile user entity [115], [116] that is exploited to uncover service demand pattern either in combination with other context information or solely. Finally, several works [82], [116], [204], [205] consider user preferences.…”

Section: ) Context-awarenessmentioning

confidence: 99%

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Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

et al. 2022

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The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the third step, we concentrate on the application layer and introduce the applications that can take advantage of dynamic service placement. The fourth step investigates evaluation environments used to validate the solutions, including simulators and testbeds. We introduce real-world datasets such as edge server locations, mobility traces, and service requests used to evaluate the methods. We compile a list of open issues and challenges categorised by various viewpoints in the last step.

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Section: ) Heterogeneous Network (Het-net)mentioning

confidence: 99%

Section: ) Heterogeneous Network (Het-net)mentioning

confidence: 99%

Section: ) Context-awarenessmentioning

confidence: 99%

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Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

et al. 2022

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“…In [25], an adaptive learning task offloading policy was proposed for vehicle edge computing based on the MAB theory. In [26], the authors considered an edge service replacement problem, where they applied contextual combinatorial MAB to estimate users' demand based on side information. An MAB online learning algorithm referred to as utility-table learning was proposed in [27] to determine the optimal workload balance among MEC servers.…”

Section: Related Workmentioning

confidence: 99%

Task Offloading for Large-Scale Asynchronous Mobile Edge Computing: An Index Policy Approach

Xu,

Cheng,

Chen

et al. 2020

Preprint

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Mobile-edge computing (MEC) offloads computational tasks from wireless devices to network edge, and enables realtime information transmission and computing. Most existing work concerns a small-scale synchronous MEC system. In this paper, we focus on a large-scale asynchronous MEC system with random task arrivals, distinct workloads, and diverse deadlines. We formulate the offloading policy design as a restless multi-armed bandit (RMAB) to maximize the total discounted reward over the time horizon. However, the formulated RMAB is related to a PSPACEhard sequential decision-making problem, which is intractable. To address this issue, by exploiting the Whittle index (WI) theory, we rigorously establish the WI indexability and derive a scalable closedform solution. Consequently, in our WI policy, each user only needs to calculate its WI and report it to the BS, and the users with the highest indices are selected for task offloading. Furthermore, when the task completion ratio becomes the focus, the shorter slack time less remaining workload (STLW) priority rule is introduced into the WI policy for performance improvement. When the knowledge of user offloading energy consumption is not available prior to the offloading, we develop Bayesian learning-enabled WI policies, including maximum likelihood estimation, Bayesian learning with conjugate prior, and prior-swapping techniques. Simulation results show that the proposed policies significantly outperform the other existing policies.

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“…When the system serves a large number of users, the group of user profiles is likely to be very diverse. As another example, contextual MAB has been adopted in service placement of mobile edge computing, which utilizes the time of day and mobile user types as the context (Chen and Xu, 2019).…”

Section: Introductionmentioning

confidence: 99%

Stochastic Linear Contextual Bandits with Diverse Contexts

Wu,

Yang,

Shen

2020

Preprint

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In this paper, we investigate the impact of context diversity on stochastic linear contextual bandits. As opposed to the previous view that contexts lead to more difficult bandit learning, we show that when the contexts are sufficiently diverse, the learner is able to utilize the information obtained during exploitation to shorten the exploration process, thus achieving reduced regret. We design the LinUCB-d algorithm, and propose a novel approach to analyze its regret performance. The main theoretical result is that under the diverse context assumption, the cumulative expected regret of LinUCB-d is bounded by a constant. As a by-product, our results improve the previous understanding of LinUCB and strengthen its performance guarantee.

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Budget-Constrained Edge Service Provisioning With Demand Estimation via Bandit Learning

Cited by 37 publications

References 32 publications

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

Task Offloading for Large-Scale Asynchronous Mobile Edge Computing: An Index Policy Approach

Stochastic Linear Contextual Bandits with Diverse Contexts

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