Service Entity Placement for Social Virtual Reality Applications in Edge Computing

Wang, Lin; Jiao, Lei; He, Ting; Li, Jun; Mühlhäuser, Max

doi:10.1109/infocom.2018.8486411

Cited by 166 publications

(79 citation statements)

References 20 publications

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“…He et al investigated the optimal provisioning of services among edge servers with sharable and non‐sharable resources, and presented efficient algorithms based on maximum flow method to maximize the requests sent by end users without caring about response latency. Wang et al studied service entity placement for VR applications in MEC environments. The service entity here refers to the bundle of user's individual data and processing logics, while in our study, different services correspond to different applications, so their study only works for one application scenario.…”

Section: Related Workmentioning

confidence: 99%

“…In this subsection, we propose an online algorithm called MUSM to solve the multi-slot minimization problem defined above. Since we have divided the long-term optimization problem into multiple per-slot minimization sub-problems, MUSM is consequently designed to iteratively run at every time slot to solve the sub-problem formalized by Equation (25). We assume it runs on the cloud and receives task information from all MEC servers in the current time slot as input.…”

Section: Online Algorithmmentioning

confidence: 99%

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Dynamic service deployment for budget‐constrained mobile edge computing

Zhou

Fan

Wang

et al. 2019

Concurrency and Computation

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Summary Currently, Mobile edge computing (MEC) is facing a great challenge that is how to make full use of edge resources to provide a seamless support for compute‐intensive latency‐sensitive applications. Prior studies often make a simple assumption that tasks can be executed upon every edge server, but the assumption does not hold in practical scenarios. Because a specific application task often corresponds to a certain service that provides the corresponding running environment, whereas an edge server only has limited resources and cannot offer too many services. How to decide service deployment of so many types of services among multiple edge servers is also a big challenge. To address the challenge, we study dynamic service deployment for latency‐sensitive applications. We first model the long‐term budget‐constrained latency minimization problem as a multi‐slot latency minimization problem based on the Lyapunov framework. By doing this, the hardness of a problem is significantly reduced, since we never require future information to solve the long‐term optimization. Furthermore, we extend our study by joining the task scheduling optimization, where every edge server is fully utilized in an even more efficient collaborative manner. Our extensive experiments show that the proposed algorithms can bring short latency with low cost.

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Section: Related Workmentioning

confidence: 99%

Section: Online Algorithmmentioning

confidence: 99%

Dynamic service deployment for budget‐constrained mobile edge computing

Zhou

Fan

Wang

et al. 2019

Concurrency and Computation

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“…It is worth noting that the extension of the delay-limited transmission design to the more specific delay-sensitive applications, e.g., medical packets [21], virtual reality applications [36], multimedia streaming and video telephony will be left as our future work. In this paper, we focus on the UAV-enabled mobile relaying with general buffer constraint and average delay constraint, to describe general UAV-enabled mobile relaying system especially for mixed FSO/RF-based backhaul network.…”

Section: B Proposed Algorithmmentioning

confidence: 99%

“…Proposed Algorithm for Throughput Maximization with a Limited Buffer Input : L Q , L req , B RF , B FSO , γ 0 , γ FSO , V , and a set of parameters related to UAV's flight Output: Optimized values of {q R [n]} N n=1 , {v R [n]} N n=1 , and {a R [n]} N n=0 Initialize the UAV's position vector {q 0 R [n]} N n=1 , and set the iteration number k = 0 ; 2 while the partial increase for the objective value of (P1 ⋆ ) is above a tolerance ε, do the optimal solution {q * R [n]} N n=1 to (P1 ⋆ ) for the local values {q k R [n]} N n=1 at the iteration k;Update the optimal solution as q k R [n] = q * R [n], n = 1, 2, · · · , N; 6 end throughput maximization of delay-tolerant transmission by replacing (P1 ⋆ ) to (P2 ⋆ ) and (33) to(36).…”

mentioning

confidence: 99%

On the Throughput of Mixed FSO/RF UAV-Enabled Mobile Relaying Systems with a Buffer Constraint

Lee

Park

Alouini

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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In this paper, we consider an unmanned aerial vehicle (UAV) aided mobile relaying system under a buffer constraint. We propose a new relaying protocol employing mixed free-space optical/radio frequency (FSO/RF) communication, i.e., the source-relay and relay-destination links utilize FSO and RF links, respectively, under the buffer constraint at the UAV relay node. Taking the conditions of an imbalance in transmission rate between RF and FSO links into consideration, we study the trajectory optimization problem of buffer-constrained UAV relay node in order to maximize the end-to-end data throughput. Especially, we classify two relaying transmission schemes according to the delay requirements, i.e., i) delay-limited transmission and ii) delay-tolerant transmission. We solve the locally optimal trajectory problem of the UAV to maximize the throughput of ground user terminal. As a result, we propose an iterative algorithm that efficiently finds a local optimum solution for the throughput maximization problems. Through this algorithm, we present the resulting trajectories over the the atmospheric condition, the buffer size, and the delay requirement. Also, we show the optimum buffer size and the throughput-delay tradeoff for a given system. Our numerical results validate that the proposed buffer-aided mobile relaying scheme achieves 65.55% throughput gains compared to conventional static relaying scheme. Index TermsThis work was presented in part at ). 2 Mixed FSO/RF communication, UAV-aided mobile relaying, throughput maximization, buffer constraint, delay-considered design. I. INTRODUCTION As it becomes easier to implement unmanned aerial vehicles (UAVs), UAV-aided mobile relaying systems have attracted great research interest [1]. Motivated by the mounting interest in the unmanned flying platforms, a UAV-enabled mobile relaying framework have been proposed to transport the backhaul/fronthaul traffic between the access and core networks [2]-[4]. Compared to a conventional static relaying system, the mobile relaying has several key advantages. Above all, cost-effectiveness and easy deployment make the mobile relaying systems especially suitable for unexpected or temporary events, such as emergency response, disaster recovery, military operation, etc. In addition, its high mobility offers new opportunities for delay-tolerant applications (e.g., periodic sensing, large data uploading/downloading) and performance improvements through the dynamic relay relocations to achieve the better communication environment. In particular, the authors in [5] identified that the mobile relaying offers a new degree of freedom for performance enhancement compared with conventional static relaying, via careful relay trajectory design. Based on the result of [5], a few works including [6]-[8] have focused on utilizing mobile relaying system in various applications. Especially in fifth-generation (5G) and future wireless networks, wireless backhaul/fronthaul networks are required to meet ultra high rate requirements, particularly in the p...

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“…Elastic services that can be partitioned in arbitrary ways is considered in [15], which can be unrealistic in practice because it is usually impossible to split a computer program arbitrarily. The work in [16]- [18] does not consider concrete capacity limits, which therefore cannot capture the strict resource limitation of edge nodes. The work in [19] proposes a greedy service placement algorithm that can be shown to have a constant approximation ratio when all services have the same size and the reward is homogeneous.…”

Section: Introductionmentioning

confidence: 99%

Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems

Pasteris

Wang

Herbster

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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127

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Mobile edge computing (MEC) is a promising technique for providing low-latency access to services at the network edge. The services are hosted at various types of edge nodes with both computation and communication capabilities. Due to the heterogeneity of edge node characteristics and user locations, the performance of MEC varies depending on where the service is hosted. In this paper, we consider such a heterogeneous MEC system, and focus on the problem of placing multiple services in the system to maximize the total reward. We show that the problem is NP-hard via reduction from the set cover problem, and propose a deterministic approximation algorithm to solve the problem, which has an approximation ratio that is not worse than 1 − e −1 /4. The proposed algorithm is based on two subroutines that are suitable for small and arbitrarily sized services, respectively. The algorithm is designed using a novel way of partitioning each edge node into multiple slots, where each slot contains one service. The approximation guarantee is obtained via a specialization of the method of conditional expectations, which uses a randomized procedure as an intermediate step. In addition to theoretical guarantees, simulation results also show that the proposed algorithm outperforms other state-of-the-art approaches.

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Service Entity Placement for Social Virtual Reality Applications in Edge Computing

Cited by 166 publications

References 20 publications

Dynamic service deployment for budget‐constrained mobile edge computing

Dynamic service deployment for budget‐constrained mobile edge computing

On the Throughput of Mixed FSO/RF UAV-Enabled Mobile Relaying Systems with a Buffer Constraint

Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems

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Service Entity Placement for Social Virtual Reality Applications in Edge Computing

Cited by 166 publications

References 20 publications

Dynamic service deployment for budget‐constrained mobile edge computing

Dynamic service deployment for budget‐constrained mobile edge computing

On the Throughput of Mixed FSO&#x002F;RF UAV-Enabled Mobile Relaying Systems with a Buffer Constraint

Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems

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On the Throughput of Mixed FSO/RF UAV-Enabled Mobile Relaying Systems with a Buffer Constraint