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Delineating the Key Capabilities of Cognitive Cloud Environments
Published in Pethuru Raj, Anupama C. Raman, Harihara Subramanian, Cognitive Internet of Things, 2022
Pethuru Raj, Anupama C. Raman, Harihara Subramanian
Cognitive autoscaling in cloud computing is more of deployment of cognitive applications in safety-critical systems, futuristic networking architectures, and virtualized resource management. Today’s autoscaling solutions utilize control loops, where the observed metric values of the system in conjunction with deployment policies and determine the actions for autoscaling. The deployment policies are configuration driven, and most of the time it’s pre-configured. ML-inspired solutions sharply improve autoscaling by predicting future measured values of the system. Cognitive orchestration is ML-driven orchestration where the orchestrator can handle decision making, reasoning, and problem-solving independently.
Multi-scale hydrological system-of-systems realized through WHOS: the brokering framework
Published in International Journal of Digital Earth, 2022
Enrico Boldrini, Stefano Nativi, Silvano Pecora, Igor Chernov, Paolo Mazzetti
The deployment diagram, depicted in Figure 19, provides further details about the deployment of the WHOS broker in a cloud infrastructure environment. In the instance, represented by Figure 19, two Virtual Machines (VMs) are dedicated to the deployment with auto-scaling rules that allow to increase or decrease the number of dedicated VMs, according to resources needed. Each VM hosts a WHOS broker service, which is composed by an auto-scaling set of containers that provide duplicated brokering services (because instantiated by identical container images). An Application Load Balancer distributes incoming requests amongst the available WHOS broker containers. The auto-scaling feature is managed by a set of upscaling and downscaling rules, triggered by the request execution times. A Healthcheck Monitor constantly checks the health status of each container and remove containers that may start exhibiting a malfunctioning behavior. The container-based architecture addresses a set of important system requirements, including portability, reproducibility, and production level Quality of Service (QoS) – as to availability, reliability, and performance.
Multi-objective auto-scaling scheduling for micro-service workflows in hybrid clouds
Published in Enterprise Information Systems, 2023
Shijia Wang, Xuan Liu, Ming Gao, Mingxia Chen, Kai Leung Yung, Shancheng Jiang
Resource management in clouds has become an important research area (Joseph and Chandrasekaran 2020). Auto-scaling scheduling, a cloud scheduling feature based on the diversity of workload and the scalability of cloud resources, is one of the most critical parts for current applications to run in the cloud at a low cost (Fazio et al. 2016). The auto-scaling is the ability to adaptively scale the corresponding computational capacity to maintain stable, predictable performance for different workload requirements at the lowest cost (Coutinho et al. 2015), which is an important foundation for the widespread application of cloud computing. Compared with VMs, containers provide more flexibility, scalability, and resource utilisation efficiency. Al-Qerem (Al-Qerem et al. 2020) proposed a framework for processing IoT requests in fog layer. They aim to develop an analytical model to formulate service latency in IoT fog-cloud scenarios, minimising communication and computing on the cloud while supporting the scalability of transactional services required by applications running in such an environment. Liu (Liu et al. 2021) used docker technology to build a PaaS platform service that controls the cloud, and verified that the Docker-based PaaS platform is effective in reasonable scheduling, fast response, and flexible resource scheduling. Kukade et al. (Kukade and Kale 2013) refactored platform services into loosely coupled, containerised microservices that can be deployed independently. They used a system design that develops and automatically deploys microservices on a cloud infrastructure to dynamically deploy cloud services to provide high availability and save energy. The scaling agent allowed the container instance to be rotated horizontally. Hadley et al. (Hadley et al. 2015) studied the dynamic container migration technology and proposed a framework called MultiBox to create and migrate containers between different cloud providers. Dhuraibi et al. (Al-Dhuraibi et al. 2017) proposed a system for handling vertical auto-scaling scheduling, whose fine-grained adaptive capabilities greatly improve processing performance, that supports automatic vertical elasticity of Docker containers and live migration when sufficient resources are not available, reduce container customers’ costs, better utilise container providers’ resources, and improve the QoS for end-users. Hoenisch et al. (Hoenisch, Weber, and Schulte et al. 2015) proposed a control architecture that adjusts VMS and container provisioning. In summary, (Kukade and Kale 2013; Hadley et al. 2015) focus on automatic horizontal auto-scale, (Al-Dhuraibi et al. 2017) on vertical auto-scale, and (Hoenisch, Weber, and Schulte et al. 2015) on elastic transfer.