Distributed Redundant Placement for Microservice-based Applications at the Edge

Zhao, Hailiang; Deng, Shuiguang; Liu, Zijie; Yin, Jianwei; Dustdar, Schahram

doi:10.1109/tsc.2020.3013600

Cited by 67 publications

(35 citation statements)

References 35 publications

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“…We evaluate the mMAPO application placements using elaborated simulated and real-world Cloud -Edge scenarios against four related methods [9], [10], [11], [12]. We demonstrate the ability of mMAPO to reduce the application completion time by up to 80% and decrease the financial cost by 28% while maintaining a stable energy consumption.…”

Section: Component Execution Time Energy Consumptionmentioning

confidence: 99%

“…Deng et al [22] investigated a nonlinear integer programming optimisation to find deployment schemes that reduce the applications execution costs and meet their average response time. Zhao et al [12] presented edge server selection approach, named GASS, which utilizes a genetic algorithm to place microservice-based applications with sequential combinatorial structure considering the execution time and cost.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

“…Research gap: Current multi-objective approaches consider the Edge in isolation [11], [12], [20], [22] or model the computing continuum from an Edge perspective [21], [23]. Computing good placements has a high computational overhead due to the complexity of multi-objective optimization, which requires exploring efficient metaheuristics.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

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Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

Kimovski

Mehran

Kerth

et al. 2022

IEEE Trans. Serv. Comput.

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The Edge computing extension of the Cloud services towards the network boundaries raises important placement challenges for IoT applications running in a heterogeneous environment with limited computing capacities. Unfortunately, existing works only partially address this challenge by optimizing a single or aggregate objective (e.g., response time), and not considering the edge devices' mobility and resource constraints. To address this gap, we propose a novel mobility-aware multi-objective IoT application placement (mMAPO) method in the Cloud -Edge Continuum that optimizes completion time, energy consumption, and economic cost as conflicting objectives. mMAPO utilizes a Markov model for predictive analysis of the Edge device mobility and constrains the optimization to devices that do not frequently move through the network. We evaluate the quality of the mMAPO placements using simulation and real-world experimentation on two IoT applications. Compared to related work, mMAPO reduces the economic cost by 28% and decreases the completion time by 80% while maintaining a stable energy consumption.

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Section: Component Execution Time Energy Consumptionmentioning

confidence: 99%

Section: Multi-objective Optimizationmentioning

confidence: 99%

Section: Multi-objective Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

Kimovski

Mehran

Kerth

et al. 2022

IEEE Trans. Serv. Comput.

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“…The cost optimization is performed by taking into consideration the resource constraints of MEC servers and application response time. The authors in [31] provide a service placement technique within the Edge environment for micro-service-based applications. This scheme performs service placement by considering the heterogeneity of Edge servers and the uncertainty of end-users.…”

Section: Edge-based Techniques To Reduce Job Response Timementioning

confidence: 99%

Handover authentication latency reduction using mobile edge computing and mobility patterns

et al. 2021

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With the advancement in technology and the exponential growth of mobile devices, network traffic has increased manifold in cellular networks. Due to this reason, latency reduction has become a challenging issue for mobile devices. In order to achieve seamless connectivity and minimal disruption during movement, latency reduction is crucial in the handover authentication process. Handover authentication is a process in which the legitimacy of a mobile node is checked when it crosses the boundary of an access network. This paper proposes an efficient technique that utilizes mobility patterns of the mobile node and mobile Edge computing framework to reduce handover authentication latency. The key idea of the proposed technique is to categorize mobile nodes on the basis of their mobility patterns. We perform simulations to measure the

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“…The microservice architecture involves the development of applications as a collection of small services, wherein each service can implement business functions, run its process, and communicate through HyperText Transfer Protocol (HTTP) application programming interfaces (APIs). Each microservice can be deployed, upgraded, expanded, and restarted independently of other services in the application [ 7 ]. Therefore, the integration of edge computing and microservices can provide a flexible and diverse application development architecture.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Based Microservice Resource Management Platform for Edge Computing in the Internet of Things

Huang

Tseng

et al. 2021

Sensors

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Edge computing exhibits the advantages of real-time operation, low latency, and low network cost. It has become a key technology for realizing smart Internet of Things applications. Microservices are being used by an increasing number of edge computing networks because of their sufficiently small code, reduced program complexity, and flexible deployment. However, edge computing has more limited resources than cloud computing, and thus edge computing networks have higher requirements for the overall resource scheduling of running microservices. Accordingly, the resource management of microservice applications in edge computing networks is a crucial issue. In this study, we developed and implemented a microservice resource management platform for edge computing networks. We designed a fuzzy-based microservice computing resource scaling (FMCRS) algorithm that can dynamically control the resource expansion scale of microservices. We proposed and implemented two microservice resource expansion methods based on the resource usage of edge network computing nodes. We conducted the experimental analysis in six scenarios and the experimental results proved that the designed microservice resource management platform can reduce the response time for microservice resource adjustments and dynamically expand microservices horizontally and vertically. Compared with other state-of-the-art microservice resource management methods, FMCRS can reduce sudden surges in overall network resource allocation, and thus, it is more suitable for the edge computing microservice management environment.

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Distributed Redundant Placement for Microservice-based Applications at the Edge

Cited by 67 publications

References 35 publications

Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

Mobility-Aware IoT Application Placement in the Cloud – Edge Continuum

Handover authentication latency reduction using mobile edge computing and mobility patterns

Fuzzy-Based Microservice Resource Management Platform for Edge Computing in the Internet of Things

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