Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

Many of the currently existing solutions for cloud cost optimisation are aimed at cloud infrastructure providers, and they often deal only with specific types of application services, leaving the providers of cloud applications without the suitable cost optimization solution, especially concerning the wide range of different application types. In this paper, we present an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, with the aim of minimising the operational cost of the cloud service, while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, the knowledge is gained about the effects that the cloud application context parameters have on the service cost and quality of service, which is then used to determine the optimised service deployment option. The service models are validated using cloud application services deployed in laboratory conditions, and the optimisation method is validated using the simulations based on proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service, and use this information in the optimisation aimed at reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain useful insights regarding service deployment decision without acquiring extensive datasets for the analysis.

EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX

With the exponential growth of the Internet of Things (IoT), edge computing is in the limelight for its ability to quickly and efficiently process numerous data generated by IoT devices. EdgeX Foundry is a representative open-source-based IoT gateway platform, providing various IoT protocol services and interoperability between them. However, due to the absence of container orchestration technology, such as automated deployment and dynamic resource management for application services, EdgeX Foundry has fundamental limitations of a potential edge computing platform. In this paper, we propose EdgeX over Kubernetes, which enables remote service deployment and autoscaling to application services by running EdgeX Foundry over Kubernetes, which is a product-grade container orchestration tool. Experimental evaluation results prove that the proposed platform increases manageability through the remote deployment of application services and improves the throughput of the system and service quality with real-time monitoring and autoscaling.

Service Deployment Challenges in Cloud-to-Edge Continuum

This position paper aims to identify the current and future challenges in application, workload or service deployment mechanisms in Cloud-to-Edge environments. We argue that the adoption of the microservices and unikernels on large scale is adding new entries on the list of requirements of a deployment mechanism, but offers an opportunity to decentralize the associated processes and improve the scalability of the applications. Moreover, the deployment in Cloud-to-Edge environment needs the support of federated machine learning.

Collaborative Engineering and Design Leads to Optimized MPD Land Rig Concept

This paper presents a land rig concept optimized for managed pressure drilling (MPD) service deployment, achieved through close partnership between an MPD technology provider and a drilling contractor. An initial scoping phase identified high-level requirements based on the Operator's planned drilling plans. After the initial concept selection engineering teams continued to optimize MPD rig integration. The engineering teams collaborated closely on optimal placement and configuration for maximum operational efficiency. The system was designed to facilitate fast rig moves and walking within each pad with minimum disruption to other processes. Safety and handling issues were identified in the detailed design stage and allowed optimizing field deployment and operability. The equipment was paired with an MPD control system that was fully integrated in the rigs’ drilling automation platform, enabling consistent, reliable, and repeatable performance. This paper will outline the concept selection process, the design and deployment phase, and further optimization that was implemented after initial learnings.