Live Migration of Virtual Machines and Containers over Wide Area Networks with Distributed Mobility Management

Ha, Jaehee; Park, Jaehyun; Han, Sangyup; Kim, Myungchul

doi:10.1145/3286978.3286996

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…It needs to provide agents in the source and destination clusters with the required inter‐cluster management services to complete the migration process. Solutions for such services have been proposed in several other works, for example, References 30,52,53.…”

Section: Proposed Geo‐elastic Deployment Approachmentioning

confidence: 99%

Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

Aldwyan

Sinnott

Jayaputera

2021

Concurrency and Computation

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Containers such as Docker provide a lightweight virtualization technology. They have gained popularity in developing, deploying and managing applications in and across Cloud platforms. Container management and orchestration platforms such as Kubernetes run application containers in virtual clusters that abstract the overheads in managing the underlying infrastructures to simplify the deployment of container solutions. These platforms are well suited for modern web applications that can give rise to geographic fluctuations in use based on the location of users. Such fluctuations often require dynamic global deployment solutions. A key issue is to decide how to adapt the number and placement of clusters to maintain performance, whilst incurring minimum operating and adaptation costs. Manual decisions are naive and can give rise to: over‐provisioning and hence cost issues; improper placement and performance issues, and/or unnecessary relocations resulting in adaptation issues. Elastic deployment solutions are essential to support automated and intelligent adaptation of container clusters in geographically distributed Clouds. In this article, we propose an approach that continuously makes elastic deployment plans aimed at optimizing cost and performance, even during adaptation processes, to meet service level objectives (SLOs) at lower costs. Meta‐heuristics are used for cluster placement and adjustment. We conduct experiments on the Australia‐wide National eResearch Collaboration Tools and Resources Research Cloud using Docker and Kubernetes. Results show that with only a 0.5 ms sacrifice in SLO for the 95th percentile of response times we are able to achieve up to 44.44% improvement (reduction) in cost compared to a naive over‐provisioning deployment approach.

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Section: Proposed Geo‐elastic Deployment Approachmentioning

confidence: 99%

Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

Aldwyan

Sinnott

Jayaputera

2021

Concurrency and Computation

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“…However, when the location of the service is changed, the existing session should be released by changing the IP address of the service, which increases the service downtime for re-discovering the service location and re-establishing the sessions. The traditional method to solve this problem is to adapt IP mobility management to service mobility management, which maintains service IP addresses even when the service location is changed [2][3][4][5], however the traffic path cannot be optimized by anchoring the address at the edge cloud, where the service is initially instantiated. To solve this problem, several methods have been researched to separate the IP context into the ID and location [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

LISP-Based Control Plane for Service Connectivity in Multi-Cluster Cloud Systems

2022

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In mobile networks, the edge cloud environment has emerged to provide various services to users. When the mobile user moves to another location while connecting the edge cloud service, to maintain an optimal service path, it is necessary to migrate the service to a new edge cloud close to the location where the user is newly connected. Several methods have been proposed to integrate edge cloud service migration technology with existing mobility management protocols in mobile networks, and a location/ID separation protocol (LISP)-based service optimal path management method between edge clouds was recently proposed. However, for real implementation of edge cloud with existing platform specified to containerized infrastructure, where the address of service is allocated randomly and locally, it is hard to discover appropriate service from the external client as well as maintain service connectivity when service location is changed. To solve that, this paper proposed the LISP-based service ID management system which provides uniform access for equivalent services running on different edge clouds. To apply the proposed ID management system on real distributed edge cloud systems, the mobility management and edge cloud networking systems were integrated as shown in the testbed implementation. This shows the possibility of combining the isolated mobility management for mobile users and internal identification of edge services using LISP for reducing the interruption and delay of edge service for mobile users. As a result of the experiments conducted on a real testbed, our proposed system was verified to enable a change in the routing path while maintaining a single service ID between different edge clouds, and the delay time for path reconfiguration is reduced compared to the existing method.

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“…To reduce service interruptions from changing the IP addresses, when considering service edge nodes as other mobile end users, general IP mobility management protocols have been proposed as a basis for the service migration method [5][6][7][8]. The basic principles of IP mobility management are to maintain an IP address when a node moves to another location and to redirect the traffic from the IP anchor of the migrated service to the changed location.…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods face certain challenges, such as a bottleneck of the centralized anchor and a non-optimized traffic path. Instead of applying a centralized anchor, the Distributed Mobility Management (DMM)-based solution is proposed, in which the anchor functions are distributed to various locations in the network [7]. However, even if a service is migrated to the optimal location according to the user's movement, the actual path can be made longer if the traffic is redirected to the original anchors.…”

Section: Introductionmentioning

confidence: 99%

Enhanced LISP Mapping System for Optimizing Service Path in Edge Computing Environment

Sun

Kim

2020

IEEE Access

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In the evolution of mobile networks, edge computing is a key technology, which provides services with proximity to users and enables various services to be operated in a virtualized platform for mobile users. Particularly, services commonly used by multiple mobile users can be simultaneously operated at multiple edge nodes, which allows users to use the service of the closest node to their access point. However, to provide session continuity for a service when the user moves, a method for optimizing the service path along with the mobility management of the user is required. In this paper, we propose an enhanced control system using the locator/identifier separation protocol to provide service path optimization with mobility management. This was achieved by integrating the location information of the service and the session information between the service and the users. The operating cost and handoff latency were analyzed. The analysis results show that the proposed system can provide an optimal service path to mobile users through fewer message exchanges and less delay than existing approaches.

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Live Migration of Virtual Machines and Containers over Wide Area Networks with Distributed Mobility Management

Cited by 5 publications

References 16 publications

Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

LISP-Based Control Plane for Service Connectivity in Multi-Cluster Cloud Systems

Enhanced LISP Mapping System for Optimizing Service Path in Edge Computing Environment

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