Online Resource Allocation for Arbitrary User Mobility in Distributed Edge Clouds

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

doi:10.1109/icdcs.2017.30

Cited by 104 publications

(49 citation statements)

References 24 publications

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“…Existing work on MEC focuses on generic applications, where solutions have been proposed for application offloading [11], [12], workload scheduling [13], [14], and service migration triggered by user mobility [15], [16]. However, they do not address the relationship among communication, computation, and training accuracy for machine learning applications, which is important for optimizing the performance of machine learning tasks.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Wang

Tuor

Salonidis

et al. 2019

IEEE J. Select. Areas Commun.

1,740

896

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Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradientdescent based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

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Section: Related Workmentioning

confidence: 99%

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Wang

Tuor

Salonidis

et al. 2019

IEEE J. Select. Areas Commun.

1,740

896

View full text Add to dashboard Cite

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“…On the other hand, in the MEC area, authors in [18], [20] propose an approach to dynamically place network functions [16], [20] × (predefined) × × [17], [18], [21] × × [19], [22] × × × Our work on MEC servers available in mobile base stations, according to the handover probability of users for the next time-slot. They formulate two optimization models with the objective of minimizing network function migrations and communication cost (i.e., QoS/QoE) between users (UEs) and network functions.…”

Section: Related Workmentioning

confidence: 99%

Mobility-Aware Joint Service Placement and Routing in Space-Air-Ground Integrated Networks

Varasteh

Hofmann

Ðerić

et al. 2019

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

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People desire to be connected, no matter where they are. Recently, providing Internet access to on-board passengers has received a lot of attention from both industry and academia. However, in order to guarantee an acceptable Quality of Service (QoS) for the passenger services with low incurred cost, the path to route the services, as well as the datacenter (DC) to deploy the services should be carefully determined. This problem is challenging, due to different types of Air-to-Ground (A2G) connections, i.e., satellites and Direct Air-To-Ground (DA2G) links. These A2G connection types differ in terms of cost, bandwidth, and latency. Furthermore, due to the flights' movements, it is important to consider adapting the service location accordingly. In this work, we formulate two Mixed Integer Linear Programs (MILPs) for the problem of Joint Service Placement and Routing (JSPR): i) Static (S-JSPR), and ii) Mobility-Aware (MA-JSPR) in Space-Air-Ground Integrated Networks (SAGIN), with the objective of minimizing the total cost. We compare S-JSPR and MA-JSPR using comprehensive evaluations in a realistic European-based SAGIN. The obtained results show that the MA-JSPR model, by considering the future flight positions and using a service migration control, reduces the long-term total cost notably. Also, we show S-JSPR benefits from a low time-complexity and it achieves lower end-to-end delays comparing to MA-JSPR model.

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“…Tan et al created a generalized model to study job dispatching and scheduling among multiple edge servers and designed a provable approximate algorithm. Wang et al further took user mobility into account and formulated a dynamic resource allocation problem given that tasks are offloaded on edge servers. They introduced a gap‐preserving transformation of the problem, and correspondingly developed an online algorithm that can provide a parameterized competitive ratio.…”

Section: Related Workmentioning

confidence: 99%

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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Online Resource Allocation for Arbitrary User Mobility in Distributed Edge Clouds

Cited by 104 publications

References 24 publications

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Mobility-Aware Joint Service Placement and Routing in Space-Air-Ground Integrated Networks

Dynamic service deployment for budget‐constrained mobile edge computing

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