Incentivizing Microservices for Online Resource Sharing in Edge Clouds

Samanta, Amit; Jiao, Lei; Mühlhäuser, Max; Lin, Wang

doi:10.1109/icdcs.2019.00049

Cited by 26 publications

(7 citation statements)

References 23 publications

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“…If no servers can satisfy the demands, the algorithm decides that the selected choice is invalid. Thus, it should return to the previous state and remove some other choices based on constraint (3) (lines [25][26][27][28].…”

Section: Solution To Srapmentioning

confidence: 99%

“…These works regard service as granularity and neglect the service degradability in service offloading and resource allocation. Samanta et al 28 proposed an online microservice‐based auction mechanism to encourage microservice to release their occupied edge resources, so that edge data centers can reclaim these resources and reallocate them to other microservices that need resources, so as to realize flexible and dynamic allocation of edge resources. However, it does not fully realize the relationship between microservices.…”

Section: Related Workmentioning

confidence: 99%

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PARA: Performability‐aware resource allocation on the edges for cloud‐native services

Guo

Liu

Xiao

et al. 2022

Int J of Intelligent Sys

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This paper explores resource allocation strategy in the Baidu Over The Edge system to enable mobile edge computing (MEC) datacenters to effectively support cloud-native services downstream to the network edge. There are many challenges to this issue. First, MEC datacenters are resourceconstrained to fully meet resource demands. Second, previous works regard the resource requirements of each service as an indivisible unit, resulting in idle MEC resources, even if the resources can meet the demands of some microservices decoupled by the service. Third, they are confined to optimize the allocation for a single slot, failing to adapt to the dynamic demands. To improve resource utilization, we propose performability-aware resource allocation (PARA), a PARA on the edges for cloud-native services. It takes microservices as the unit of resource allocation and allows services to perform with degraded services when only part of microservices' demands are met. It also considers dependency among microservices, dynamic resource requirements, and resource supply characteristics of MEC and cloud. Performability is a unified

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Section: Solution To Srapmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

PARA: Performability‐aware resource allocation on the edges for cloud‐native services

Guo

Liu

Xiao

et al. 2022

Int J of Intelligent Sys

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show abstract

“…For this reason, we refer to such approaches as direct routing. For example, Samanta et al [13] design a framework for executing applications in a system with edge and cloud compute nodes, according to an auction-based placement algorithm. Each compute node in this system is assumed to be addressable from every access point in the network, so that data can be routed directly to the compute node which hosts a specific application.…”

Section: A Direct Routing Approachesmentioning

confidence: 99%

“…According to this approach, the IoT devices are configured to send the data directly to the compute nodes which perform the required processing [13]. In the flowchart of Fig.…”

Section: A Direct Routingmentioning

confidence: 99%

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edgeRouting: Using Compute Nodes in Proximity to Route IoT Data

Karagiannis

Schulte

2021

IEEE Access

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Due to the proliferation of edge computing, cloud providers have started offering compute nodes at the edge of the network in addition to traditional compute nodes in data centers. So far, various systems have been proposed for processing Internet of Things (IoT) data on both edge and cloud compute nodes in order to reduce the communication latency. However, such systems do not typically consider that the network bandwidth between an edge node and a cloud node can be orders of magnitude higher than the bandwidth between an IoT device and a cloud node. As a result, the IoT data are commonly sent selectively to either edge or cloud nodes disregarding alternative network paths through edge nodes, which may have higher network bandwidth, and lower communication latency. To avoid this, in this paper we analyze the latency of sending data to edge and cloud compute nodes of cloud providers. Based on this analysis, we propose edgeRouting which routes the data through the closest edge compute node. By doing that, edgeRouting exploits both the low propagation delay of nodes at the edge, and the high bandwidth among edge and cloud compute nodes of cloud providers. To evaluate our approach, we perform experiments on a real-world setup with nearby and remote compute nodes of a cloud provider, and we show that edgeRouting reduces the communication latency by up to 55% compared to alternative methods.

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Introduction

Dou,

Xu,

2023

Intelligent Industrial Internet Systems

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Incentivizing Microservices for Online Resource Sharing in Edge Clouds

Cited by 26 publications

References 23 publications

PARA: Performability‐aware resource allocation on the edges for cloud‐native services

PARA: Performability‐aware resource allocation on the edges for cloud‐native services

edgeRouting: Using Compute Nodes in Proximity to Route IoT Data

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