Live Migration of Virtual Machine and Container Based Mobile Core Network Components: A Comprehensive Study

Ramanathan, Shunmugapriya; Kondepu, Koteswararao; Razo, Miguel; Tacca, Marco; Valcarenghi, L.; Fumagalli, Andrea

doi:10.1109/access.2021.3099370

Cited by 32 publications

(24 citation statements)

References 24 publications

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“…This poses several issues associated with the job latency constraints and the network energy migration costs. A paper addressing the latency challenge, and proposing strategies to reduce the migration time is [14]. The main focus is on how to migrate the virtual entity, by defining protocols to transfer the container/VM from the current location to the target one.…”

Section: Related Workmentioning

confidence: 99%

“…Jobs are executed through the instantiation of containers, which reserve the required computing and memory resources. Here, containers are favored over VMs due to their lower memory footprint, which permits a faster migration process -a desirable feature in the considered scenario [14]. Jobs that are being executed on one MEH but associated with vehicles that are about to leave the eNB/MEH coverage area are assessed by the migration controller.…”

Section: High Level System Overviewmentioning

confidence: 99%

See 1 more Smart Citation

EASE: Energy-Aware Job Scheduling for Vehicular Edge Networks With Renewable Energy Resources

Perin

Meneghello

Carli

et al. 2023

IEEE Trans. on Green Commun. Netw.

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The energy sustainability of multi-access edge computing (MEC) platforms is here addressed by developing Energy-Aware job Scheduling at the Edge (EASE), a computing resource scheduler for edge servers co-powered by renewable energy resources and the power grid. The scenario under study involves the optimal allocation and migration of time-sensitive computing tasks in a resource-constrained Internet of Vehicles (IoV) context. This is achieved by tackling, as the main objective, the minimization of the carbon footprint of the edge network, whilst delivering adequate quality of service (QoS) to the end users (e.g., meeting task execution deadlines). EASE integrates i) a centralized optimization step, solved through model predictive control (MPC), to manage the renewable energy that is locally collected at the edge servers and their local computing resources, estimating their future availability, and ii) a distributed consensus step, solved via dual ascent in closed form, to reach agreement on service migrations. EASE is compared with four existing migration strategies. Quantitative results demonstrate its greater energy efficiency, which often gets close to complete carbon neutrality, while also improving the QoS.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: High Level System Overviewmentioning

confidence: 99%

EASE: Energy-Aware Job Scheduling for Vehicular Edge Networks With Renewable Energy Resources

Perin

Meneghello

Carli

et al. 2023

IEEE Trans. on Green Commun. Netw.

View full text Add to dashboard Cite

show abstract

“…Some of the optimizations mentioned above are orthogonal to our work, and hence could be incorporated into our solution to boost performance. Finally, implementation issues and performance overhead of VM live migration are discussed in [44].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Service Provisioning in the Edge-cloud Continuum with Provable Guarantees

Cohen¹,

Chiasserini²,

Giaccone³

et al. 2022

Preprint

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We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing the corresponding chain properly and allocating a suitable amount of computing resources. Furthermore, chain migration may be necessary to meet the services' target delay, or convenient to keep the service provisioning cost low. We tackle such issues by formalizing the problem of optimal chain placement and resource allocation in the edge-cloud continuum, taking into account migration, bandwidth, and computation costs. Specifically, we first envision an algorithm that, leveraging resource augmentation, addresses the above problem and provides an upper bound to the amount of resources required to find a feasible solution. We use this algorithm as a building block to devise an efficient approach targeting the minimum-cost solution, while minimizing the required resource augmentation. Our results, obtained through trace-driven, large-scale simulations, show that our solution can provide a feasible solution by using half the amount of resources required by state-of-the-art alternatives.

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“…A paper addressing the latency challenge, and proposing strategies to reduce the migration time is [14]. The main focus is on how to migrate the virtual entity, by defining protocols to transfer the container/VM from the current location to the target one.…”

Section: Related Workmentioning

confidence: 99%

“…With phase 2, taking the desired workload to be migrated from phase 1 as input ("neighbors' resource information"), the MEHs collectively reach an agreement ("workload migration agreement") about how many and which jobs are to be actually migrated, as well as about the target MEH for their migration ("job selection and migration"). to their lower memory footprint, which permits a faster migration process -a desirable feature in the considered mobile scenario [14]. Jobs that are being executed on one MEH but associated with vehicles that are about to leave the eNB/MEH coverage area are assessed by the migration controller.…”

Section: Solution Workflowmentioning

confidence: 99%

EASE: Energy-Aware Job Scheduling for Vehicular Edge Networks With Renewable Energy Resources

Perin¹,

Meneghello²,

Carli³

et al. 2021

Preprint

View full text Add to dashboard Cite

The energy sustainability of multi-access edge computing (MEC) platforms is addressed in this paper, by developing Energy-Aware job Scheduling at the Edge (EASE), a computing resource scheduler for edge servers co-powered by renewable energy resources and the power grid. The scenario under study involves the optimal allocation and migration of time-sensitive computing tasks in a resource-constrained internet of vehicles (IoV) context. This is achieved by tackling, as a main objective, the minimization of the carbon footprint of the edge network, whilst delivering adequate quality of service (QoS) to the end users (e.g., meeting task execution deadlines). EASE integrates a i) centralized optimization step, solved through model predictive control (MPC), to manage the renewable energy that is locally collected at the edge servers and their local computing resources, estimating their future availability, and ii) a distributed consensus step, solved via dual ascent in closed form, to reach agreement on service migrations. EASE is compared with existing strategies that always and never migrate the computing tasks. Quantitative results demonstrate the greater energy efficiency achieved by EASE, which often gets close to complete carbon neutrality, while also improving the QoS.

show abstract

Live Migration of Virtual Machine and Container Based Mobile Core Network Components: A Comprehensive Study

Cited by 32 publications

References 24 publications

EASE: Energy-Aware Job Scheduling for Vehicular Edge Networks With Renewable Energy Resources

EASE: Energy-Aware Job Scheduling for Vehicular Edge Networks With Renewable Energy Resources

Dynamic Service Provisioning in the Edge-cloud Continuum with Provable Guarantees

EASE: Energy-Aware Job Scheduling for Vehicular Edge Networks With Renewable Energy Resources

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