Adaptive Resource Efficient Microservice Deployment in Cloud-Edge Continuum

Fu, Kaihua; Zhang, Wei; Chen, Quan; Zeng, Deze; Guo, Minyi

doi:10.1109/tpds.2021.3128037

Cited by 62 publications

(26 citation statements)

References 44 publications

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“…The type of FSs acting as service provider in the Fog computing environment ranges from IoT devices with additional resources to resource-rich data centers. Several works have considered a speciic type of FS and discuss their properties in their works such as 1) Base Station (BS) and Macro-cell Station (MS) [16,115,126], 2) femtocells [17,29,34], 3) Rpi [27] and 4) access points (AP) [25,80]. Moreover, several works consider 5) general FSs containing a set of FSs with diferent types such as [28,84].…”

Section: Typementioning

confidence: 99%

“…As a large number of IoT devices and servers exist in the Fog environment, mechanisms and algorithms used in the decision engine should be highly scalable and provide well-suited performance when the system size grows. Several works have studied the scalability feature of their techniques when the number of IoT applications and servers increases or discussed how their distributed techniques work eiciently in large systems such as [27,49,69].…”

Section: High Scalabilitymentioning

confidence: 99%

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Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya

2022

ACM Comput. Surv.

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Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This paper presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described.

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Section: Typementioning

confidence: 99%

Section: High Scalabilitymentioning

confidence: 99%

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya

2022

ACM Comput. Surv.

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“…In [19], an architecture for micro-service deployment is designed. Micro-services that communicate with each other are collocated in order to reduce the communication overhead between them.…”

Section: Related Workmentioning

confidence: 99%

Quality of Service Aware Orchestration for Cloud–Edge Continuum Applications

Orive

Agirre

Truong

et al. 2022

Sensors

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The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications’ quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud–Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud–Edge continuum. It focuses on the application’s quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications.

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“…The type of FSs acting as service provider in the Fog computing environment ranges from IoT devices with additional resources to resource-rich data centers. Several works have considered a specific type of FS and discuss their properties in their works such as 1) Base Station (BS) and Macro-cell Station (MS) [18,127,138], 2) femtocells [19,32,37], 3) Rpi [30] and 4) access points (AP) [27,88]. Moreover, several works consider 5) general FSs containing a set of FSs with different types such as [31,94].…”

Section: Fog Servers (Fss)mentioning

confidence: 99%

“…As a large number of IoT devices and servers exist in the Fog environment, mechanisms and algorithms used in the decision engine should be highly scalable and provide well-suited performance when the system size grows. Several works have studied the scalability feature of their techniques when the number of IoT applications and servers increases or discussed how their distributed techniques work efficiently in large systems such as [30,53,73].…”

Section: High Scalabilitymentioning

confidence: 99%