Microservices Scheduling Model Over Heterogeneous Cloud-Edge Environments As Support for IoT Applications

Filip, Ion-Dorinel; Pop, Florin; Şerbănescu, Cristina; Choi, Chang

doi:10.1109/jiot.2018.2792940

Cited by 87 publications

(30 citation statements)

References 20 publications

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“…In addition, there are many doubts related to the safety of using IoT devices in comparison with the use of conventional devices (e.g., the use of locks for doors controlled via Bluetooth, compared to standard ones) [14]. One solution is the introduction of the edge-of-things approach [15][16][17], which consists of separating at the network level the devices inside the home and putting intelligence at the edge in order to take control of key functionalities of the system. The result is a home automation system (HAS), which keeps at the edge the security, privacy and some management functionalities.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Deployment of smart home management system at the edge: mechanisms and protocols

Batalla

Gonciarz

2018

Neural Comput & Applic

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Due to growing popularity of smart home systems, smart home security is a topic which is becoming increasingly important. Internet of Things (IoT) devices are obtaining increasing access to private data, but very often it does not mean that improved security mechanisms and mechanisms guaranteeing availability are implemented. The main problem is limited computing power and the limited memory of the nodes used in the network. Moreover, IoT systems are increasingly often managed through the cloud, which causes that their interfaces are available over the Internet. Another problem is the lack of expertise of users which can lead to configuration errors potentially causing data loss and hacker attacks. In this paper, we face up security and availability issues in smart homes and propose an edge-of-things solution that focuses on putting the management of the home at the edge. The management is controlled by the network operator in a similar way as occurs with current set-top-boxes for multimedia streaming at home. We propose an architecture for this system, implement the necessary modules and test it from the point of view of security and availability. The results show that the proposed edge-of-things solution is able to solve many of the challenges that current smart home applications present.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Deployment of smart home management system at the edge: mechanisms and protocols

Batalla

Gonciarz

2018

Neural Comput & Applic

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“…They considered the objectives of cloud service cost, network latency, and service reliability. Filip et al [20] proposed a new model for scheduling microservices placement across heterogeneous cloud-edge environments. Wan et al [21] proposed an efficient communication framework and a suboptimal algorithm to determine the data center's container.…”

Section: Related Workmentioning

confidence: 99%

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

2020

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As cloud-native computing is becoming the de-facto paradigm in the cloud field, Microservices Architecture has attracted attention from industries and researchers for agility and efficiency. Moreover, with the popularity of the IoT in the context of edge computing, cloud-native applications that utilize geographically-distributed multiple resources are emerging. In line with this trend, there is an increasing demand for microservices placement that selectively use optimal resources. However, optimal microservices placement is a significant challenge because microservices are dynamic and complex, depending on diversified workloads. Besides, generalizing workloads' characteristics consisting of complex microservices is realistically challenging. Thus, microservices deployment with mathematically structured algorithms based on simulation is less practical. As an alternative, a microservices placement framework is required that can reflect the characteristics of workloads derived from empirical profiling. Therefore, in this research work, we propose a refinement framework for profiling-based microservices placement to identify and respond to workload characteristics in a practical way. To achieve this goal, we perform profiling experiments with selected workloads to derive delicate resource requirements. Then, we perform microservices placement with a greedy-based heuristic algorithm that considers application performance by using resource requirements derived from the profiled results. Finally, we verify the proposed concept by comparing the experimental results that use our work and those that don't.

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“…Existing mobile computing systems are designed for homogeneous network environments or single communication technologies and therefore have limited bandwidth. They also do not support resource‐intensive IoT and 5G applications or consider network characteristics such as link quality and lifetimes.…”

Section: State Of the Artmentioning

confidence: 99%

Private mobile edge cloud for 5G network applications

Shah

2019

Internet Technology Letters

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Resource‐intensive real‐time IoT and 5G network applications require low latency, high data rates, and considerable computing and storage resources. Mobile cloud computing systems cannot fulfill the requirements of these applications due to high communication latency. Consequently, recently proposed edge computing implementations require adding new infrastructure or updating existing infrastructure. In addition, edge computing does not exploit the capabilities of end devices, which have become more powerful than supercomputers in the last decade. To overcome the drawbacks of mobile cloud and edge computing systems, we have proposed a private mobile edge cloud in which multiple mobile and stationary devices such as sensors, robots, home appliances, and smartphones, interconnected through infrastructure‐less and infrastructure‐based wireless local area networks are combined to create a small‐scale cloud data center at a local physical area such as a home. We have also discussed opportunities and research challenges associated with development of proposed system and a high‐level architecture based on a cross layer design for an efficient management of resources.

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Microservices Scheduling Model Over Heterogeneous Cloud-Edge Environments As Support for IoT Applications

Cited by 87 publications

References 20 publications

RETRACTED ARTICLE: Deployment of smart home management system at the edge: mechanisms and protocols

RETRACTED ARTICLE: Deployment of smart home management system at the edge: mechanisms and protocols

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

Private mobile edge cloud for 5G network applications

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