READ: Robustness-Oriented Edge Application Deployment in Edge Computing Environment

Li, Bo; He, Qiang; Cui, Guangming; Xia, Xiaoyu; Chen, Feifei; Jin, Hai; Yang, Yun

doi:10.1109/tsc.2020.3015316

Cited by 73 publications

(16 citation statements)

References 56 publications

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“…The heterogeneity of edge server platforms also increases the risk of failures, while some failures are inherited from the environment, such as disaster relief ambiences. Multiple research aims at high availability [179], resilience [234], robustness [235],…”

Section: ) High Availabilitymentioning

confidence: 99%

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: ) High Availabilitymentioning

confidence: 99%

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

et al. 2022

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“…For this reason, the deployment of MEHs can be performed by maximizing the failover capability [135] or by considering a 1+1 protection, where the users are able to connect to two MEHs (one is active and the other one is for backup) [136].…”

Section: Challengesmentioning

confidence: 99%

“…As already presented in Section IV-C, to alleviate this problem, recent studies [135], [136] investigate the dependability in the deployment of the MEHs because of its impact on the effectiveness of the failover mechanisms. Of course, considering only dependability target is also not correct.…”

Section: General Challengesmentioning

confidence: 99%

5G Multi-access Edge Computing: Security, Dependability, and Performance

Nencioni,

Garroppo,

Olimid

2021

Preprint

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The main innovation of the Fifth Generation (5G) of mobile networks is the ability to provide novel services with new and stricter requirements. One of the technologies that enable the new 5G services is the Multi-access Edge Computing (MEC). MEC is a system composed of multiple devices with computing and storage capabilities that are deployed at the edge of the network, i.e., close to the end users. MEC reduces latency and enables contextual information and real-time awareness of the local environment. MEC also allows cloud offloading and the reduction of traffic congestion. Performance is not the only requirement that the new 5G services have. New missioncritical applications also require high security and dependability. These three aspects (security, dependability, and performance) are rarely addressed together. This survey fills this gap and presents 5G MEC by addressing all these three aspects. First, we overview the background knowledge on MEC by referring to the current standardization efforts. Second, we individually present each aspect by introducing the related taxonomy (important for the not expert on the aspect), the state of the art, and the challenges on 5G MEC. Finally, we discuss the challenges of jointly addressing the three aspects.

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“…This way, the compute node closest to the data source with adequate available computational resources, is utilized for processing the IoT data with low latency. Typically, the compute nodes at the edge of the network integrate limited computational resources [11], [12]. As a result, only a fraction of the IoT data is accepted by nearby compute nodes (i.e., the first nodes on path), while the rest of the data is more likely to be processed in a remote cloud node [13].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Routing in Fog Computing Systems

Karagiannis¹,

Frangoudis²,

Dustdar³

et al. 2023

IEEE Trans. Cloud Comput.

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Fog computing enables the execution of IoT applications on compute nodes which reside both in the cloud and at the edge of the network. To achieve this, most fog computing systems route the IoT data on a path which starts at the data source, and goes through various edge and cloud nodes. Each node on this path may accept the data if there are available resources to process this data locally. Otherwise, the data is forwarded to the next node on path. Notably, when the data is forwarded (rather than accepted), the communication latency increases by the delay to reach the next node. To avoid this, we propose a routing mechanism which maintains a history of all nodes that have accepted data of each context in the past. By processing this history, our mechanism sends the data directly to the closest node that tends to accept data of the same context. This lowers the forwarding by nodes on path, and can reduce the communication latency. We evaluate this approach using both prototype-and simulation-based experiments which show reduced communication latency (by up to 23%) and lower number of hops traveled (by up to 73%), compared to a state-of-the-art method.

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READ: Robustness-Oriented Edge Application Deployment in Edge Computing Environment

Cited by 73 publications

References 56 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

5G Multi-access Edge Computing: Security, Dependability, and Performance

Context-Aware Routing in Fog Computing Systems

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