Autonomous Lifecycle Management for Resource-Efficient Workload Orchestration for Green Edge Computing

Guim, Francesc; Metsch, Thijs; Moustafa, Hassnaa; Verrall, Timothy; Carrera, David; Cadenelli, Nicola; Chen, Jiang; Doria, David; Ghadie, Chadie; Prats, Raul Gonzalez

doi:10.1109/tgcn.2021.3127531

Cited by 41 publications

(15 citation statements)

References 18 publications

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“…Francesc Guim et al [18] proposed a resource management and configuration scheme in an intelligent dynamic scenario based on service level objectives (SLOs) to guarantee edge services. This scheme takes autonomous cycle management on the edge platform as the center and achieves the goal of dynamic resource scheduling of multitenant services.…”

Section: Research On Edge Service Deploymentmentioning

confidence: 99%

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SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Song¹,

Sun²,

Yu³

et al. 2022

Preprint

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In recent years, edge computing, as an important pillar for future networks, has been developed rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed and save energy. An efficient and feasible task offloading scheme can not only greatly improve the quality of experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the industrial Internet of Things (IIoT), computing networks and so on. To achieve these goals, this paper proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on the optimization of the energy utilization ratio (EUR) and the processing latency. In the pre-implementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service's popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on the task priority. The task priority is generated by the corresponding service popularity and task offloading direction. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

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Section: Research On Edge Service Deploymentmentioning

confidence: 99%

“…Similar to ϕ(Ω), let Ψ represent the set of services cached in the cloud center layer. The storage space occupied by the services pushed in the network at the cloud center layer is obtained from Formula (18).…”

Section: Edge Service Deploymentmentioning

confidence: 99%

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Song¹,

Sun²,

Yu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Embora a literatura apresente várias soluc ¸ões para lidar com a alocac ¸ão de recursos em redes IoT, em geral estão focadas na reduc ¸ão de métricas específicas como latência [Tsai 2018, Zhao et al 2019, Sangaiah et al 2020] e consumo energético [Guim et al 2022, Xavier et al 2022; na otimizac ¸ão via modelos matemáticos [Li et al 2021, Bashir et al 2022; ou são normalmente voltadas a um ambiente específico como MEC [Xavier et al 2020, Bolettieri et al 2021, Wang et al 2022], entre outros. Adicionalmente, estas propostas se concentram no offloading de aplicac ¸ões como forma de alocac ¸ão de recursos.…”

Section: Trabalhos Relacionadosunclassified

“…Em [Guim et al 2022], os autores propõem o gerenciamento autônomo do ciclo de vida para plataformas de borda convergentes, permitindo a orquestrac ¸ão da carga de trabalho com eficiência de recursos em relac ¸ão à computac ¸ão verde. Eles apresentam uma soluc ¸ão para configurac ¸ão de recursos dinâmicos e inteligentes em plataformas de computac ¸ão de borda que hospedam servic ¸os de múltiplos usuários, garantindo os SLOs para cada servic ¸o.…”

Section: Trabalhos Relacionadosunclassified

Um Esquema para Alocação Justa de Dispositivos IoT Multifuncionais Ciente dos Recursos Computacionais

Rocha

Gois

Silva

et al. 2022

Anais Do XXVII Workshop De Gerência E Operação De Redes E Serviços (WGRS 2022)

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Muitos cenários de IoT demandam uma captura continua de informações de sensores multifuncionais e unidades inteligentes, além do envio desses dados para centros de nuvem. Entretanto, a gestão da alocação das tarefas a esses sensores não é simples em razão da urgência e da prioridade que cada tipo de coleta de dados exige dependendo da necessidade do ambiente urbano. Este artigo apresenta o esquema POSITRON para o gerenciamento da alocação de sensoriamento em uma rede IoT multifuncional a partir de políticas previamente definidas. Tais políticas consideram as características das aplicações executadas na rede, bem como as especificações distintas dos dispositivos disponíveis. A implementação do POSITRON em um simulador de redes visa demonstrar a sua eficiência na alocação dos recursos. Os resultados apontam que levar em conta os requisitos demandados pelas aplicações e as características distintas dos dispositivos IoT multifuncionais traz benefício na alocação dos recursos.

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“…The considerable scale of interconnected things and people generated more data traffic to several characteristics like diversity, scale, velocity, and distribution in the big data model. In addition, it needs more big data analytics and infrastructure (Guim et al, 2021). Resources like the Central Processing Unit (CPU) cycle, I/O data rate, memory, and battery power are limited.…”

Section: Introductionmentioning

confidence: 99%

Adaptive resource allocation for time-critical applications in industrial edge computing using Heuristic-HCMDO approach

Udayakumar

Ramamoorthy

2023

Preprint

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An intensive computation source has become increasingly important in recent years to meet the time-critical and low-latency needs of Industrial Internet of Things (IIoT) systems. Existing IIoT-based devices are built with limited computational resources, delivering results in a limited fashion when used in highly resource-intensive applications. Since then, a novel concept known as Edge Computing (EC) has been introduced to reduce network latency and alleviate strain on cloud data centers using an EC server located at the network's periphery. The EC server only managed to gather a small number of resources compared to the resource cloud. Without prior context about task deadline and load, an EC server could not optimally handle latency-sensitive and computation-intensive tasks. Additionally, the EC server did not significantly improve overhead minimization when sending data to and from the remote cloud and the user's device. Parallel to the development of EC, nonorthogonal multiple access (NOMA) has been identified as a technique with the potential to substantially increase spectrum efficiency. In this paper, a NOMA-based EC framework for IIoT system is examined, in which multiple task nodes transfer their task via NOMA to multiple edge servers in proximity for execution. As such, this paper aims to develop a joint optimization model for making decisions about task offloading and allocating resources in Industrial edge computing. An adaptive resource allocation decision model (ARADM) based on deep reinforcement learning (DRL) and heuristically modified long short-term memory (H-LSTM) using hybrid Cat and Mouse Dingo Optimization (HCMDO) is proposed to allocate the task optimally. We formulate joint optimization by considering multi-constraint objective function with communication, computation, and cache parameters using HCMDO. Further, these optimal parameters are used in training an H-LSTM along with benchmark dataset. The outcome of the H-LSTM network utilized in DRL to improve convergence speed, accuracy and stability by predicting optimal cost and load. The goal is to minimize service delay, energy consumption, balance load and maximize resource utilization. The experimental results validated the developed model and its ability to improve the quality of resource allocation in Industrial edge computing.

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Autonomous Lifecycle Management for Resource-Efficient Workload Orchestration for Green Edge Computing

Cited by 41 publications

References 18 publications

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC

Um Esquema para Alocação Justa de Dispositivos IoT Multifuncionais Ciente dos Recursos Computacionais

Adaptive resource allocation for time-critical applications in industrial edge computing using Heuristic-HCMDO approach

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