An Architecture and Stochastic Method for Database Container Placement in the Edge-Fog-Cloud Continuum - Sakellariou Rizos; Bajec Marko; Drobintsev Pavel; Stankovski Vlado; Kochovski Petar (Zenodo, 2019)
Databases as software components may be used to serve a variety of smart applications. Currently, the Internet of Things (IoT), Artificial Intelligence (AI) and Cloud technologies are used in the course of projects such as the Horizon 2020 EU-Korea DECENTER project in order to implement four smart applications in the domains of Smart Homes, Smart Cities, Smart Construction and Robot Logistics. In these smart applications the Big Data pipeline starts from various sensor and video streams on which AI and feature extraction methods are applied. The resulting information is stored in database containers, which have to be placed on Edge, Fog or Cloud infrastructures. The placement decision depends on complex application requirements, including Quality of Service (QoS) requirements. Information that must be considered when making placement decisions includes the expected workload, the list of candidate infrastructures, geolocation, connectivity and similar. Software engineers currently perform such decisions manually, which usually leads to QoS threshold violations. This paper aims to automate the process of making such decisions. Therefore, the goals of this paper are to: (1) develop a decision making method for database container placement; (2) formally verify each placement decision and provide probability assurances to the software engineer for high QoS; and (3) design and implement a new architecture that automates the whole process. A new optimisation method is introduced, which is based on the theory and practice of stochastic Markov Decision Processes (MDP). It uses as input monitoring data from the container runtime, the expected workload and user-related metrics in order to automatically construct a probabilistic finite automaton. The generated automaton is used for both automated decision making and placement success verification. The method is implemented in Java. It also uses the PRISM model-checking tool. Kubernetes is used in order to automate the whole process when orchestrating database containers across Edge, Fog and Cloud infrastructures. Experiments are performed for NoSQL Cassandra database containers for three representative workloads of 50000 (workload 1), 200000 (workload 2) and 500000 (workload 3) CRUD database operations. Five computing infrastructures served as candidates for database container placement. The new MDP-based method is compared with the widely used Analytic Hierarchy Process (AHP) method. The obtained results are used to analyse container placement decisions. When using the new MDP based method there were no QoS violations in any of the placement cases, while when using the AHP based method the placement results in some QoS threshold violations in the cases of workloads 2 and 3. Due to its properties, the new MDP method is particularly suitable for implementation. The paper also describes a multi-tier distributed computing system that uses multi-level (infrastructure, container, application) monitoring metrics and Kubernetes in order to orchestrate database containers across Edge, Fog and Cloud nodes. This architecture demonstrates fully automated decision making and high QoS container operation.
To be presented at: 33rd IEEE International Parallel & Distributed Processing Symposium (IPDPS 2019)
Decentralised technologies for orchestrated cloud-to-edge intelligence
Industrial-Driven Big Data as a Self-Service Solution
Keywords: cc-by-nc-nd-3.0; Cloud; Fog; Edge; storage; probabilistic decision-making; containers