Enabling Mobile Service Continuity Across Orchestrated Edge Networks

Abdullaziz, Osamah Ibrahiem; Wang, Li-Chun; Chundrigar, Shahzoob Bilal; Huang, Kuei-Li

doi:10.1109/tnse.2019.2953129

Cited by 8 publications

(4 citation statements)

References 37 publications

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“…Abdullaziz et al focused on the migration aspect of container orchestration by making it a more reliable option [13]. The authors achieved this by leveraging live orchestration as a method of achieving low downtime comparable to reinstantiation.…”

Section: Related Workmentioning

confidence: 99%

“…After the centroid list is generated, we apply a straightforward rigid distance-based clusterer that produces a radial border around the centroid. This captures as many users adhering to equation (13) where S is the user's diameter coordinate space divided over the number of potential clusters. T is an integer variable ranging from 1 to 3 representing the mobility of each user (stationary, pedestrian, vehicular) and I represents the tolerances of the users present in the overall user space.…”

Section: User Solution Spacementioning

confidence: 99%

See 1 more Smart Citation

Optimal Container Migration/Re-Instantiation in Hybrid Computing Environments

Aleyadeh

Moubayed

Heidari

et al. 2022

IEEE Open J. Commun. Soc.

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End-users and service providers have recently favored lower latency services. Edge computing has improved user quality of service (QoS) guarantees through the reduced geographical distance, decreased use of the network backbone, and flexible placement of the container hosting edge devices. The under-utilized isolated edge computing idling nodes are significant for service providers, especially for Internet of Things (IoT) applications. However, the nodes' minimal maintenance remains a hindrance due to related increased failures. Orchestrating over the edge alongside core environments allows tolerant services and more demanding ones to coexist without impacting the user experience. Therefore, the orchestrator's second priority is achieving and maintaining the QoS through optimal recovery method selection by either migrating the live containers or re-instantiating them. This paper proposes an Optimal Container Migration/Re-Instantiation (OC-MRI) model to optimize the orchestration methods focusing on downtime, container dependencies, and latency requirements. Next, we introduce a real-time heuristic-based solution, Edge Computingenabled Container Migration/Re-Instantiation (EC2-MRI). Both models are bench-marked alongside staple greedy approaches. Simulation results showcase the lowest latencies and downtime with the OC-MRI model. Furthermore, the EC2-MRI model shows comparable results to the optimal model with minimal lag.

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Section: Related Workmentioning

confidence: 99%

Section: User Solution Spacementioning

confidence: 99%

Optimal Container Migration/Re-Instantiation in Hybrid Computing Environments

Aleyadeh

Moubayed

Heidari

et al. 2022

IEEE Open J. Commun. Soc.

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“…Thanks to CRIU's support for many popular container runtimes such as Docker, Containerd, runC, it is widely adopted by many containerized service migration works. ARNAB system in [21] enables transparent edge service continuity via double-tier migration. The first-tier hands-off user connectivity, while the secondtier leverages CRIU for a pre-copy stateful migration process.…”

Section: B Current Container Stateful Migration Techniquesmentioning

confidence: 99%

Proactive Stateful Fault-Tolerant System for Kubernetes Containerized Services

Tran

Kim

2022

IEEE Access

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Recently, the development of Kubernetes (K8s) containerization platform has enabled cloudbased, lightweight, highly scalable, and agile services in both general and telco use-cases. Ensuring high availability, reliable and continuous containerized services is a major requirement of service providers to provide fault-tolerance, transparent service experiences to end-users. To satisfy this requirement, fault prediction and proactive stateful service recovery features must be applied in cloud systems. Prior proactive failure recovery approaches mostly focused on either improving fault prediction performance based on different machine learning time series forecasting techniques or optimizing recovery service placement after fault prediction. However, a mechanism that enables stateful containerized service migration from the predicted faulty node to the healthy destination node has not been studied. Service migration in previous proactive works is only simulated or performed by virtual machine (VM) migration techniques. In this paper, we propose a proactive stateful fault-tolerant system for K8s containerized services that pipelines a Bidirectional Long Short-Term Memory (Bi-LSTM) fault prediction framework and a novel K8s stateful service migration mechanism for service recovery. Experimental results show how the Bi-LSTM model improved prediction performance against other time-series forecasting models in prior proactive works. We then combined the Bi-LSTM fault prediction framework with both the default K8s and our stateful migration mechanisms. The comparison between these two proactive systems proves our system efficiency in terms of avoiding Quality of Service (QoS) violation. INDEX TERMS Containerization, proactive fault-tolerant, Kubernetes

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“…The framework dynamically and automatically selects a suitable execution location for processing tasks if those processing tasks should be computed on a Cloud server rather than the local gateway device. Ibrahiem et al [24] proposed an architecture for transparent service continuity via double-tier migration (ARNAB) that is based on container migration. When ARNAB migrates a service (application), this architecture needs two tiers-the first tier migrates user connectivity, while the second tier migrates user containerized service.…”

Section: Service Migration Frameworkmentioning

confidence: 99%

IoT-RECSM—Resource-Constrained Smart Service Migration Framework for IoT Edge Computing Environment

Zhai

Xiang

Zhao

et al. 2020

Sensors

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The edge-based computing paradigm (ECP) becomes one of the most innovative modes of processing distributed Interneit of Things (IoT) sensor data. However, the edge nodes in ECP are usually resource-constrained. When more services are executed on an edge node, the resources required by these services may exceed the edge node’s, so as to fail to maintain the normal running of the edge node. In order to solve this problem, this paper proposes a resource-constrained smart service migration framework for edge computing environment in IoT (IoT-RECSM) and a dynamic edge service migration algorithm. Based on this algorithm, the framework can dynamically migrate services of resource-critical edge nodes to resource-rich nodes. In the framework, four abstract models are presented to quantificationally evaluate the resource usage of edge nodes and the resource consumption of edge service in real-time. Finally, an edge smart services migration prototype system is implemented to simulate the edge service migration in IoT environment. Based on the system, an IoT case including 10 edge nodes is simulated to evaluate the proposed approach. According to the experiment results, service migration among edge nodes not only maintains the stability of service execution on edge nodes, but also reduces the sensor data traffic between edge nodes and cloud center.

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Enabling Mobile Service Continuity Across Orchestrated Edge Networks

Cited by 8 publications

References 37 publications

Optimal Container Migration/Re-Instantiation in Hybrid Computing Environments

Optimal Container Migration/Re-Instantiation in Hybrid Computing Environments

Proactive Stateful Fault-Tolerant System for Kubernetes Containerized Services

IoT-RECSM—Resource-Constrained Smart Service Migration Framework for IoT Edge Computing Environment

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