Cloud Service Orchestration Research Articles

Intelligent Service Orchestration in Edge Cloud Networks

The surge in data traffic is challenging for network infrastructure owners coping with stringent service requirements (e.g., high bandwidth, ultralow latency) as well as shrinking per-gigabyte revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In parallel, there is an increasing demand for network virtualization and container-based services. In this study, we investigate the management of software defined networking (SDN)-based transport network and edge cloud service orchestration. To this end, we use a machine learning (ML)-based design to manage both transport and edge cloud resources of a mobile network effectively. To generate and use real-world data inside our ML platform, we use the Graphical Network Simulator-3 (GNS3) emulator environment. Our emulation results indicate that almost all of the trained ML models can accurately select the correct edge clouds (ECs) (i.e., with high test accuracy) under the considered two scenarios when transport and EC network parameters are considered in comparison to models trained via only transport or cloud-based parameters. At the end of the article, we also provide an evolved architecture where the proposed ML platform can be embedded in an end-to-end mobile network architecture and H2020 5Growth project's baseline management platform.

On QoE-Oriented Cloud Service Orchestration for Application Providers

New virtualization technologies allow Infrastructure Providers (InPs) to lease their resources to Application Service Providers (ASPs) for highly scalable delivery of cloud services to end-users. However, existing literature lacks knowledge on Quality of Experience (QoE)-oriented cloud service orchestration algorithms that can guide ASPs on how to plan their budget to enhance satisfactory QoE delivery to end-users. In contrast to the InP's cloud service orchestration, the ASP's orchestration should not rely on expensive infrastructure control mechanisms such as Software-Defined Networking (SDN), or require aprioriknowledge on the number of services to be instantiated and their anticipated placement location within InP's infrastructure. In this paper, we address this issue of delivering satisfactory user QoE by synergistically optimizing both ASP's management and data planes. The optimization within the ASP management planefirst maximizes Service Level Objective (SLO) coverage of users when application services are being deployed, and are not yet operational. The optimization of the ASP data plane then enhances satisfactory user QoE delivery when applications services are operational with real user access. Our evaluation of QoE-oriented algorithms using realistic numerical simulations, real-world cloud testbed experiments with actual users and ASP case studies show notably improved performance over existing cloud service orchestration solutions.

Decentralized and Fault Tolerant Cloud Service Orchestration

This paper proposes a decentralized framework for the orchestration of Cloud Services using heterogeneous resources residing in the homes of private individuals or small-scale clusters. The framework makes use of Ethereum Smart Contracts to provide a decentralized mechanism for discovering the different interfaces exposed by Cloud Components. The paper introduces a novel concept of Component Administration Networks, which are peer-to-peer networks that monitor and ensure the availability of the software components. The concept applied for the Orchestration process to ensure that the deployment of an Application continues in the presence of Orchestrator component failure. Checkpoints are used to address the continuity of the Management components, in general, and of the Orchestrator, in particular. In our proposal, checkpoint metadata is stored in a Smart Contract to assess the execution time of a Service to reimburse the participants that ensure its execution.

Multi-cloud provisioning of business processes

The Cloud offers enhanced flexibility in the management of resources for any kind of application while it promises the reduction of its cost as well as its infinite scalability. In this way, due to these advantages, there is a recent move towards migrating business processes (BPs) in the Cloud. Such a move is currently performed in a manual manner and only in the context of one Cloud. However, a multi- & cross-Cloud configuration of a BP can be beneficial as it can allow exploiting the best possible offers from multiple Clouds and enable to avoid the lock-in effect by also having the ability to deploy different instances of the BP in different Clouds close to the locations of BP customers. In this respect, this article presents a novel architecture of an environment which realises the vision of multi-Cloud BP provisioning. This environment involves innovative components which support the cross-level orchestration of cloud services as well as the cross-level monitoring and adaptation of BPs. It also relies on a certain language called CAMEL which has been extended to support the adaptive provisioning of multi-Cloud BPs.

Middleware, framework and novel computing models for grid and cloud service orchestration

Highly scalable, resilient commodity hardware along with the availability of huge amounts of data allow consumers and companies to access to a wide range of high-value services through a pay-per-use, pay-as-you-go model. The increasing need for intelligent services at large scale is bringing providers to optimise the use of resources in order to meet the increasing web-scale demand. Programming models are evolving to address the high complexity of service composition, which need resiliency and efficiency. Algorithms and technologies for intelligent orchestration allow multiple owners of cloud and grid infrastructures to satisfy customer requirements and business constraints. We assist in the development of new architectures, frameworks, middlewares and computing models, as well as innovative applications, aimed at supporting scientists and business analysts in the development of unified computing frameworks and data analysis at large scale. This paper summarises a number of contributions given in the area of distributed cloud and grid computing, with particular emphasis on service orchestrations and emergent applications for high-value services.

Middleware, framework and novel computing models for grid and cloud service orchestration

Middleware, framework and novel computing models for grid and cloud service orchestration