Optimal Container Migration/Re-Instantiation in Hybrid Computing Environments

Aleyadeh, Sam; Moubayed, Abdallah; Heidari, Parisa; Shami, Abdallah

doi:10.1109/ojcoms.2022.3140272

Cited by 16 publications

(9 citation statements)

References 27 publications

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“…Instead of taking a holistic view of all registered apps and available resources in the cloud, applications are often scheduled separately. This can lead to longer application execution times, resource wastage due to underutilized container instances, and a reduction in the number of apps that can be implemented considering the available resources, whereby the necessity of an optimal container placement approach that provides resource efficiency in a cloud environment [109]. In addition to resource utilization, the network traffic of containers should also be addressed to ensure the QoS and reduce the total energy consumption in a cloud environment [110].…”

Section: A Container Placement Challengesmentioning

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: A Container Placement Challengesmentioning

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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“…The proposed BACP algorithm for placement and ATCM algorithm for migration solve the optimization problem. An optimal container migration model has been presented in [23] by considering latency, downtime, and dependencies in the edge cloud environment. Table 1 present the detailed review on VM sizing techniques.…”

Section: Literature Surveymentioning

confidence: 99%

Task grouping and optimized deep learning based VM sizing for hosting containers as a service

et al. 2023

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Containers as a service (CaaS) are a kind of services that permits the organization to handle the containers more effectively. Containers are lightweight, require less computing resources, portable, and facilitate better support for microservices. In the CaaS model, containers are deployed in virtual machines, and the virtual machine runs on the physical machine. The objective of this paper is to estimate the resource required by a VM to execute a number of containers. VM sizing is a directorial process where the system administrators have to optimize the allocated resources within the permitted virtualized space. In this work, the VM sizing is carried out using the Deep Convolutional Long Short Term Memory Network (Deep-ConvLSTM), where the weights are updated by Fractional Pelican Optimization (FPO) Algorithm. Here, the FPO is configured by hybridizing the concept of Fractional Calculus (FC) within the updated location of the Pelican Optimization Algorithm (POA). Moreover, the task grouping is done with Deep Embedded Clustering (DEC), where the grouping is established with respect to the various task parameters, such as task length, submission rate, scheduling class, priority, resource usage, task latency, and Task Rejection Rate (TRR). In addition, the performance analysis of VM sizing is done by taking 100, 200, 300, and 400 tasks. We got the best resource utilization of 0.104 with 300 tasks, a response time of 262ms with 100 tasks, and a TRR of 0.156 with 100 tasks and makespan of 0.5788 with 100 tasks.

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“…In [1], an edge based migration strategy is proposed for containerized applications. An Integer Linear Programming (ILP) model aims at minimizing the service downtime and latency occurring while performing the migration across edge nodes.…”

Section: Optimization Based Approachesmentioning

confidence: 99%

Dynamic migration of microservices for end-to-end latency control in 5G/6G networks

Kaur

Guillemin

Sailhan

2023

Preprint

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With the adoption of the microservice paradigm by the telecom industry in the design of 5G/6G networks, complex network functions are decomposed into sets of chained sub-functions, which are further deployed using containerized technologies over geographically distributed cloud clusters. Latency-sensitive applications require to carefully orchestrate the allocation and re-arrangement of (micro)services to prevent from a largely segmented placement of microservices. To address this issue in the context of network functions, we introduce a novel placement and migration strategy that chooses those specific microservice(s) to migrate and the optimal destination (data center) while considering the impact of the migration on other microservices.We devise fast and effective heuristics that are extensively studied via simulation experiments, that show that our proposed approach significantly reduces the service latency.

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Optimal Container Migration/Re-Instantiation in Hybrid Computing Environments

Cited by 16 publications

References 27 publications

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

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

Task grouping and optimized deep learning based VM sizing for hosting containers as a service

Dynamic migration of microservices for end-to-end latency control in 5G/6G networks

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