Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study

Naranjo, Paola G. Vinueza; Pooranian, Zahra; Shamshirband, Shahaboddin; Abawajy, Jemal; Conti, Mauro

doi:10.3390/app7121325

Cited by 27 publications

(6 citation statements)

References 24 publications

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“…Auto-Scaling Function (The Framework without An Open-Source Server) [5][6][7] User Control for Limited Custom Service Quality Requirements [8] Intelligent Automatic Expansion Kubernetes HPA [9] Auto-Scaling Auto-scaling of Cloud Resources [10] Flexible and Skillful Resource Allocation, The Core Technology of Cloud Computing [11,12] Development of Appropriate Threshold-Based Rules [13] Threshold-based Investigate the automatic expansion system [14] Elastic Docker [15] Horizontal automatic scaling [16] Reinforcement Learning-Based Adaptive Fuzzy Logic Controller [17] Threshold-based Solution for Automatic Expansion of Horizontal Containers [18] Reinforce Learning Solution (i.e., Q Learning, Dyna-Q and Model-Based) [19] Dynamic Number of Servers Using A Threshold-Based Expansion Policy [20] Table 1. Cont.…”

Section: Microservice Computingmentioning

confidence: 99%

Improved Q Network Auto-Scaling in Microservice Architecture

et al. 2022

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Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service. A cloud provider requires flexible resource management to meet the continually changing demands, such as auto-scaling and provisioning. A common approach used in both commercial and open-source computing platforms is workload-based automatic scaling, which expands instances by increasing the number of incoming requests. Concurrency is a request-based policy that has recently been proposed in the evolving microservice framework; in this policy, the algorithm can expand its resources to the maximum number of configured requests to be processed in parallel per instance. However, it has proven difficult to identify the concurrency configuration that provides the best possible service quality, as various factors can affect the throughput and latency based on the workloads and complexity of the infrastructure characteristics. Therefore, this study aimed to investigate the applicability of an artificial intelligence approach to request-based auto-scaling in the microservice framework. Our results showed that the proposed model could learn an effective expansion policy within a limited number of pods, thereby showing an improved performance over the underlying auto expansion configuration.

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Section: Microservice Computingmentioning

confidence: 99%

Improved Q Network Auto-Scaling in Microservice Architecture

et al. 2022

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“…In the scope of Fog computing, novel optimization algorithms are also proposed to resolve the selection problems of when 5G mobile users should offload computing workload to Wi-Fi [19] and when Fog servers should cluster the nearby cloudlets for offloading the task of a mobile [20]. In the same vein, energy-efficient and real-time management of the distributed resources available along the Fog strata is taken into consideration in [21]. The authors propose an energy-efficient adaptive scheduler for Vehicular Fog Computing (VFC), which optimizes the energy by taking advantage of the heterogeneity of Foglets (FIs).…”

Section: Related Workmentioning

confidence: 99%

Flexible deployment of component-based distributed applications on the Cloud and beyond

Pham¹,

Nguyen²

2019

KSII TIIS

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In an effort to minimize operational expenses and supply users with more scalable services, distributed applications are actually going towards the Cloud. These applications, sent out over multiple environments and machines, are composed by inter-connecting independently developed services and components. The implementation of such programs on the Cloud is difficult and generally carried out either by hand or perhaps by composing personalized scripts. This is extremely error prone plus it has been found that misconfiguration may be the root of huge mistakes. We introduce AutoBot, a flexible platform for modeling, installing and (re)configuring complex distributed cloud-based applications which evolve dynamically in time. AutoBot includes three modules: A simple and new model describing the configuration properties and interdependencies of components; a dynamic protocol for the deployment and configuration ensuring appropriate resolution of these interdependencies; a runtime system that guarantee the proper configuration of the program on many virtual machines and, if necessary, the reconfiguration of the deployed system. This reduces the manual application deployment process that is monotonous and prone to errors. Some validation experiments were conducted on AutoBot in order to ensure that the proposed system works as expected. We also discuss the opportunity of reusing the platform in the transition of applications from Cloud to Fog computing.A preliminary version of this paper appeared in IEEE SoICT 2016, December 08-09, Ho Chi Minh City, Vietnam. This version extends the D&C protocol significantly to cover deployment and dynamic reconfiguration for distributed applications not only in Cloud computing but also beyond the Cloud such as Fog computing. Current related work is also updated to cover the state-of-the-art. New experiments were conducted to validate the flexibility and portability of our approach in both Cloud and Fog environment.

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“…For bridging the gap between complex manufacturing tasks and underlying resources, Liu et al proposed novel multi-granularity resource virtualization and sharing strategies, including a resource clustering algorithm and cloud service specifications [46]. Naranjo et al presented an energy-efficient adaptive scheduler for Vehicular Fog Computing (VFC), focusing on the optimization of energy by leveraging the heterogeneity of foglets [47]. As one cyber-physical example, tactical cloud is a novel conception composed of a fleet of heterogeneous aircraft that cooperate below the resource level [48].…”

Section: Resource Sharing and Virtualizationmentioning

confidence: 99%

Multi-Constraint Optimized Planning of Tasks on Virtualized-Service Pool for Mission-Oriented Swarm Intelligent Systems

et al. 2019

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With the emergence of swarm intelligent systems, especially the swarming of aircraft and ground vehicles, cooperation in multiple dimensions has becoming one of the great challenges. How to dynamically schedule the resources within a swarm intelligent system and optimize the execution of tasks are all vital aspects for such systems. Focusing on this topic, in this paper, one new task planning mechanism with multiple constraints is proposed to solve such dynamic programming problems. Concretely, several fundamental models, covering three-level task models and resource-service pool models, are put forward and defined first. Considering the limitations of swarm systems running within complicated cyber-physical space, multi-dimension constraints for tasks scheduling and execution are further modeled and established. On this basis, we mapped this planning problem to an optimization searching problem, and then proposed a Genetic-Algorithm-based mechanism. All these works have been verified with simulated cooperation scenes. Experimental results show that this new mechanism is efficient to solve such resource-related and mission-oriented cooperation problems in complicated environments.

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Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study

Cited by 27 publications

References 24 publications

Improved Q Network Auto-Scaling in Microservice Architecture

Improved Q Network Auto-Scaling in Microservice Architecture

Flexible deployment of component-based distributed applications on the Cloud and beyond

Multi-Constraint Optimized Planning of Tasks on Virtualized-Service Pool for Mission-Oriented Swarm Intelligent Systems

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