Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Oleghe, Omogbai

doi:10.1109/access.2021.3077550

Cited by 44 publications

(13 citation statements)

References 89 publications

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“…However, they focus only on cost, energy, and latency minimization. Omogbai Oleghe [15] highlights the concept of container placement and migration in edge computing. He studied the scheduling problem from the provider perspective by listing the frameworks and algorithms used to model and solve the container placement issues.…”

Section: E Related Workmentioning

confidence: 99%

VNF and CNF Placement in 5G: Recent Advances and Future Trends

Attaoui¹,

Sabir

Elbiaze

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler for the most of mobile network operators. NFV decouples network functions from hardware devices. This decoupling allows network services, called Virtualized Network Functions (VNFs), to be hosted on commodity hardware which simplifies and enhances service deployment and management for providers, improves flexibility, and leads to efficient and scalable resource usage, and lower costs. The proper placement of VNFs in the hosting infrastructures is one of the main technical challenges. This placement significantly influences the network's performance, reliability, and operating costs. The VNF placement is NP-Hard. Therefore, there is a need for placement methods that can cope with the complexity of the problem and find appropriate solutions in a reasonable duration. The primary purpose of this study is to provide a taxonomy of optimization techniques used to tackle the VNF placement problems. We classify the studied papers based on performance metrics, methods, algorithms, and environment. Virtualization is not limited to simply replacing physical machines with virtual machines or VNFs, but may also include microservices, containers, and cloud-native systems. In this context, the second part of our article focuses on the placement of Containers Network Functions (CNFs) in edge/fog computing. Many issues have been considered as traffic congestion, resource utilization, energy consumption, performance degradation, etc. For each matter, various solutions are proposed through different surveys and research papers in which each one addresses the placement problem in a specific manner by suggesting single objective or multi-objective methods based on different types of algorithms such as heuristic, meta-heuristic, and machine learning algorithms.

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Section: E Related Workmentioning

confidence: 99%

VNF and CNF Placement in 5G: Recent Advances and Future Trends

Attaoui¹,

Sabir

Elbiaze

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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“…Oleghe presented in his work [14] the frameworks and algorithms most commonly used in container placement problemm, the types of containers currently dominating the edge space and the heuristic approaches currently favored in the research community to offer real time solutions. Govindaraj et al followed a similar approach in [15].…”

Section: Related Workmentioning

confidence: 99%

“…The second is a preventative step to avoid immediate failures related to mobility of the users. To address this issue, an auditing threshold is first enforced on each cluster guided by the following equation (14) where I is the mobility index the user and i is the cluster number:…”

Section: Axmentioning

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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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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“…However, the two aforementioned solutions exploit virtual machines and they are known for their large image sizes and long start-up times, making service migration a very costly operation. NEPTUNE, as other approaches in the literature [29,44,50], uses containers that are lighter and faster to scale.…”

Section: Related Workmentioning

confidence: 99%

NEPTUNE: Network- and GPU-aware Management of Serverless Functions at the Edge

Baresi¹,

Hu²,

Quattrocchi³

et al. 2022

Preprint

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Nowadays a wide range of applications is constrained by lowlatency requirements that cloud infrastructures cannot meet. Multiaccess Edge Computing (MEC) has been proposed as the reference architecture for executing applications closer to users and reducing latency, but new challenges arise: edge nodes are resourceconstrained, the workload can vary significantly since users are nomadic, and task complexity is increasing (e.g., machine learning inference). To overcome these problems, the paper presents NEP-TUNE, a serverless-based framework for managing complex MEC solutions. NEPTUNE i) places functions on edge nodes according to user locations, ii) avoids the saturation of single nodes, iii) exploits GPUs when available, and iv) allocates resources (CPU cores) dynamically to meet foreseen execution times. A prototype, built on top of K3S, was used to evaluate NEPTUNE on a set of experiments that demonstrate a significant reduction in terms of response time, network overhead, and resource consumption compared to three well-known approaches. CCS CONCEPTS• Theory of computation → Scheduling algorithms; • Computing methodologies → Distributed computing methodologies; • Computer systems organization → Distributed architectures.

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Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Cited by 44 publications

References 89 publications

VNF and CNF Placement in 5G: Recent Advances and Future Trends

VNF and CNF Placement in 5G: Recent Advances and Future Trends

Optimal Container Migration/Re-Instantiation in Hybrid Computing Environments

NEPTUNE: Network- and GPU-aware Management of Serverless Functions at the Edge

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