Abstract
Automated deployment and run-time management of microservices-based applications in cloud computing environments is relatively well studied with several mature solutions. However, managing such applications and tasks in the cloud-to-edge continuum is far from trivial, with no robust, production-level solutions currently available. This paper presents our first attempt to extend an application-level cloud orchestration framework called MiCADO to utilise edge and fog worker nodes. The paper illustrates how MiCADO-Edge can automatically deploy complex sets of interconnected microservices in such multi-layered cloud-to-edge environments. Additionally, it shows how monitoring information can be collected from such services and how complex, user- defined run-time management policies can be enforced on application components running at any layer of the architecture. The implemented solution is demonstrated and evaluated using two realistic case studies from the areas of video processing and secure healthcare data analysis.
Highlights
1.1 Background and MotivationCloud computing has immensely changed the provision of computing, both for personal and business users
This case study demonstrates evidence that MiCADO-Edge fulfills requirements R1, R4, R5, and R6
This case study provides evidence for fulfilling requirements R1, R2, R3, and R6
Summary
1.1 Background and MotivationCloud computing has immensely changed the provision of computing, both for personal and business users. Adoption of cloud services has continued to grow and it is expected that worldwide public cloud service revenue will grow by 33 percent, from 266.4 billion dollars in 2020 to 354.6 billion dollars in 2022 [1]. This is not surprising considering the inherent characteristics of cloud computing that offer economic benefits as well as operational efficiencies to enterprises [2]. The need for developing this capability has emerged from the proliferation of connected devices via the Internet, known as Internet of Things (IoT), which in turn has caused exponential growth in data that needs processing, storing and analysing. The number of connected IoT devices worldwide are expected to grow up to 36 billion by 2025 [7, 8] and they are expected to generate 79.4 ZB of data [9]
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