Orchestration Of Applications Research Articles

Orchestration of service-oriented applications with reactive programming techniques

Orchestration of service-oriented applications with reactive programming techniques

Research and Implementation of Container Based Application Orchestration Service Technology

With the rapid development of cloud computing technology, Kubernetes(K8S), as the main orchestration tool for cloud native applications, has become the preferred choice for enterprises and developers. This article is based on container based application orchestration service technology. Through a set of templates containing cloud resource descriptions, it quickly completes functions such as application creation and configuration, application batch cloning, and application multi environment deployment. It simplifies and automates the lifecycle management capabilities required for cloud applications, such as resource planning, application design, deployment, status monitoring, and scaling. Users can more conveniently complete infrastructure management and operation and maintenance work, In order to focus more on innovation and research and development, and improve work efficiency. The actual application effect of the technology used in this article depends to a certain extent on the ability level of basic service resources, and manual template creation is required for the first use. In production use, a certain professional ability is required to create a good application layout template, adjust and optimize resources according to the production environment, in order to significantly improve the effectiveness and efficiency of practical applications.

Intent-driven orchestration of serverless applications in the computing continuum

Following the emergence of Internet of Things (IoT) and edge computing technologies and the development of distributed applications with strict Quality of Service (QoS) requirements, a transition is underway from centralized computing models to distributed computing continuum systems. The latter provide abstractions of the available resources, while they lead to the development of orchestration mechanisms with increased distributed intelligence and autonomy characteristics. These characteristics make the computing continuum suitable for managing serverless computing applications, considering their loose coupling with the resources and the need for reactiveness and efficiency to serve dynamic workloads in the edge and cloud part of the infrastructure. This paper presents an intent-based approach for the orchestration of a function chain over an edge and cloud infrastructure. We propose a Reinforcement Learning (RL) based autoscaling method that incorporates a reward function that aims to provide optimal utilization of resources and timely autoscaling of the functions, taking advantage of methods for workload prediction. Based on a high-level intent, the proposed optimization problem dictates the scheduling of the functions, minimizing the communication overhead and transformation cost while also considering the energy efficiency of the infrastructure. The proposed solution is evaluated with other state-of-the-art techniques for autoscaling and scheduling of serverless functions in a small edge infrastructure. Our solution provides 1.52% QoS violations with a slight increase in the deployed resources, while also significantly reducing the total cost and power consumption for the deployment of the function chain.

ANIARA Project - Automation of Network Edge Infrastructure and Applications with Artificial Intelligence

Emerging use-cases like smart manufacturing and smart cities pose challenges in terms of latency, which cannot be satisfied by traditional centralized infrastructure. Edge networks, which bring computational capacity closer to the users/clients, are a promising solution for supporting these critical low latency services. Different from traditional centralized networks, the edge is distributed by nature and is usually equipped with limited compute capacity. This creates a complex network to handle, subject to failures of different natures, that requires novel solutions to work in practice. To reduce complexity, edge application technology enablers, advanced infrastructure and application orchestration techniques need to be in place where AI and ML are key players.

Quality of Service Aware Orchestration for Cloud-Edge Continuum Applications.

The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications’ quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud–Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud–Edge continuum. It focuses on the application’s quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications.

Crane Cloud: A resilient multi-cloud service abstraction layer for resource-constrained settings

Developers and users situated in low-resource settings are faced with unique contextual and infrastructure challenges when accessing and consuming cloud-based services. In low-resource settings, access to cloud services and platforms is usually characterized by low-end computing devices and often unreliable and slow mobile broadband Internet connections. In this paper, we discuss key challenges for developing for and accessing cloud services in resource constrained settings, namely, (1) Frequent Internet partitions and bandwidth constraints, (2) Data jurisdiction restrictions, (3) Vendor lock-in, and (4) Poor quality of service. Inspired by these challenges, we propose a set of important design considerations and properties for a resilient multi-cloud service layer, that includes: (1) Containerization and orchestration of applications, (2) Application placement and replication, (3) Portability and multi-cloud migration, (4) Resilience to network partitions and bandwidth constraints, (5) Automated service discovery and load balancing, (6) Localized image registry, and (7) Support for platform monitoring and management. We present an implementation and validation case study, Crane Cloud, an open source multi-cloud service abstraction layer built on-top of Kubernetes that is designed with inherent support for resilience to network partitions, microservice orchestration (deployment, scaling and management of containerized applications), a localized image registry, support for migration of services between private and public clouds to avoid vendor lock-in issues and platform monitoring. We evaluate the performance and user experience of Crane Cloud by implementing and deploying a computational and bandwidth intensive machine learning system. The results show lower response times of the system on Crane Cloud compared with hosting on other public clouds. The Crane Cloud platform is serving as a cloud-service for students and developers in low-resource settings and also as an education platform for cloud computing.

Enabling Role-Based Orchestration for Cloud Applications

With the rapidly growing popularity of cloud services, the cloud computing faces critical challenges to orchestrate the deployment and operation of cloud applications on heterogenous cloud platforms. Cloud applications are built on a platform model that abstracts away underlying platform-specific details, so that their orchestration can benefit from the abstract view and flexibility of the underlying platform configuration. However, considerable efforts are still required to properly manage complicated cloud applications. This paper proposes a model-driven approach to cloud application orchestration which promotes the concerns of distinct roles for cloud system provisioning and operation. By establishing a set of capabilities as modeling constructs, our approach allows TOSCA-based application topology itself and its orchestration needs to be specified in a way to provide a more targeted support for different needs and concerns of application developers and operators. With novel orchestration features like application topology description, platform capability modeling, and role-awareness for cloud application orchestration, it can significantly reduce the complexity of application orchestration in diverse cloud environments. To show the feasibility and effectiveness of our proposal for cloud application orchestration, we present a proof-of-concept orchestration system implementation and evaluate its deployment and orchestration results in a Kubernetes cluster.

IT governance and IT application orchestration capability role on organization performance during the COVID-19 pandemic: An intervening of business-IT alignment

The COVID-19 pandemic has evolved into a multi-faceted global crisis with massive challenges for any organization around the world. With lockdowns and other social distancing protocols implemented in countries worldwide, economic activities and social interactions become increasingly reliant on technology. This study aimed to examine the intervention role of business-IT alignment on the influence of IT governance (ITG) and IT application orchestration capability (IT-AOC) on organization performances. The educational sector was chosen as an object of study as this sector will experience a decade of radical technology-driven change due to COVID-19's stimulus and there were limited IT-AOC studies that used this sector as a research object. This study use a survey method and purposive sampling with a total classified sample of 55 higher education institutions (HEIs) and 110 respondents involved represent the samples. Data analysis was conducted using a Variance-Based Structural Equation Modeling (VB-SEM) approach of Partial Least Square (PLS) technique. The results suggest that both ITG and IT-AOC have a positive effect on organization performances. Moreover, this study proved that business-IT alignment can intervene the influence of ITG and IT-AOC on organization performances.

MR On-SeT: A Mixed Reality Occupational Health and Safety Training for World-Wide Distribution

The current generation of dedicated Mixed Reality (MR) devices can be considered as the first generation, which is truly mobile while also being capable of sufficient tracking and rendering. These improvements offer new opportunities for the on-set use of MR devices enabling new ways of using MR. However, these new use cases raise challenges for the design and orchestration of MR applications as well as how these new technologies influence their field of application. In this paper, we present MR On-SeT, a MR occupational health and safety training application, which is based on the experiences of an operational division of a world-wide operating German company. The intended purpose of MR On-SeT is to increase employees’ awareness of potential hazards at industrial workplaces by using it in occupational health and safety training sessions. Since the application is used at various locations throughout the company’s world-wide subsidiaries, we were able to evaluate it through an expert survey with the occupational health and safety managers of seven plants in France, Germany, Japan, and Romania. They reported the condensed experience of around 540 training sessions collected within three months. The purpose of the evaluation was twofold: 1. to understand their perceived attitudes towards the application-in-use, and 2. to collect feedback they received from respondents in training sessions. The results suggest that MR On-SeT can be used to extend current, predominantly theoretical, methods of teaching occupational health and safety at work, which also motivates existing employees to actively engage in the training sessions. Based on the findings, several further design implications are proposed.

TORCH: a TOSCA-Based Orchestrator of Multi-Cloud Containerised Applications

The growth in the number and types of cloud-based services offered to IT customers is supported by the constant entry of new actors in the market and the consolidation of disruptive technologies such as AI, Big Data and Micro-services. From the customer’s perspective, in a market landscape where the cloud offer is highly diversified due to the presence of multiple competing service providers, picking the service that best accommodate their specific needs is a critical challenge. Once the choice is made, so called “cloud orchestration tools” (orchestrators) are required to take care of the customer application’s life-cycle. While big players offer their customers proprietary orchestrators, in the literature quite a number of open-source initiatives have launched multi-cloud orchestrators capable of transparently managing applications on top of the most representative cloud platforms. In this paper, we propose TORCH, a TOSCA-based framework for the deployment and orchestration of cloud applications, both classical and containerised, on multiple cloud providers. The framework assists the cloud customer in defining application requirements by using standard specification models. Unlike other multi-cloud orchestrators, adopts a strategy that separates the provisioning workflow from the actual invocation of proprietary cloud services API. The main benefit is the possibility to add support to any cloud platforms at a very low implementation cost. In the paper, we present a prototypal implementation of TORCH and showcase its interaction with two different container-based cluster platforms. Preliminary performance tests conducted on a small-scale test-bed confirm the potential of TORCH.

Exploring a Model Network for Orchestation IoT Services Based on Kubernetes

This paper focuses on the implementation of the concept of microservices, which can be used to design and implement distributed edge services for IoT applications. Basically, IoT is a platform where integrat-ed services are connected to a common network, so all devices are able to collect and exchange data with each other. Typically, monolithic user mobility services are developed for the core components of the ETSI MEC Unified Systems Reference Architecture. The ETSI MEC views microservices as a tool for separating monolithic applications into a set of loosely coupled distributed components, i.e. which provides an independent unit that implements a function. This architecture is expected to facilitate dynamic adaptation at runtime. However, increased modularity can also increase the load on orchestration and system management. In MEC, user equipment is connected through gate-ways to microservices running at the edge node. In the work, a distributed service is implemented using Docker containers and is designed to actually adapt to surrounding low-capacity servers. The results demonstrate that low latency for edge applications can be achieved with this kind of implementation. The integration of software technology with a standardized boundary system provides a solid foundation for subsequent development. This article discusses the use of edge computing architecture and Kubernetes for orchestration and management of network applications.

Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systems

As recent heterogeneous systems comprise multi-core CPUs and multiple GPUs, efficient allocation of multiple data-parallel applications has become a primary goal to achieve both maximum total performance and efficiency. However, the efficient orchestration of multiple applications is highly challenging because a detailed runtime status such as expected remaining time and available memory size of each computing device is hidden. To solve these problems, we propose a dynamic data-parallel application allocation framework called ADAMS. Evaluations show that our framework improves the average total execution device time by 1.85× over the round-robin policy in the non-shared-memory system with small data set.

Throughput-Aware Partitioning and Placement of Applications in Fog Computing

Fog computing promises to extend cloud computing to match emerging demands for low latency, location-awareness and dynamic computation. It thus brings data processing close to the edge of the network by leveraging on devices with different computational characteristics. However, the heterogeneity, the geographical distribution, and the data-intensive profiles of IoT deployments render the placement of fog applications a fundamental problem to guarantee target performance figures. This is a core challenge for fog computing providers to offer fog infrastructure as a service, while satisfying the requirements of this new class of microservices-based applications. In this article we root our analysis on the throughput requirements of the applications while exploiting offloading towards different regions. The resulting resource allocation problem is developed for a fog-native application architecture based on containerised microservice modules. An algorithmic solution is designed to optimise the placement of applications modules either in cloud or in fog. Finally, the overall solution consists of two cascaded algorithms. The first one performs a throughput-oriented partitioning of fog application modules. The second one rules the orchestration of applications over a region-based infrastructure. Extensive numerical experiments validate the performance of the overall scheme and confirm that it outperforms state-of-the-art solutions adapted to our context.

Describing and Processing Topology and Quality of Service Parameters of Applications in the Cloud

Typical cloud applications require high-level policy driven orchestration to achieve efficient resource utilisation and robust security to support different types of users and user scenarios. However, the efficient and secure utilisation of cloud resources to run applications is not trivial. Although there have been several efforts to support the coordinated deployment, and to a smaller extent the run-time orchestration of applications in the Cloud, no comprehensive solution has emerged until now that successfully leverages applications in an efficient, secure and seamless way. One of the major challenges is how to specify and manage Quality of Service (QoS) properties governing cloud applications. The solution to address these challenges could be a generic and pluggable framework that supports the optimal and secure deployment and run-time orchestration of applications in the Cloud. A specific aspect of such a cloud orchestration framework is the need to describe complex applications incorporating several services. These application descriptions must specify both the structure of the application and its QoS parameters, such as desired performance, economic viability and security. This paper proposes a cloud technology agnostic approach to application descriptions based on existing standards and describes how these application descriptions can be processed to manage applications in the Cloud.

Cost-aware orchestration of applications over heterogeneous clouds.

The orchestration of applications and their components over heterogeneous clouds is recognized as being critical in solving the problem of vendor lock-in with regards to distributed and cloud computing. There have been recent strides made in the area of cloud application orchestration with emergence of the TOSCA standard being a definitive one. Although orchestration by itself provides a considerable amount of benefit to consumers of cloud computing services, it remains impractical without a compelling reason to ensure its utilization by cloud computing consumers. If there is no measurable benefit in using orchestration, then it is likely that clients may opt out of using it altogether. In this paper, we present an approach to cloud orchestration that aims to combine an orchestration model with a cost and policy model in order to allow for cost-aware application orchestration across heterogeneous clouds. Our approach takes into consideration the operating cost of the application on each provider, while performing a forward projection of the operating cost over a period of time to ensure that cost constraints remain unviolated. This allows us to leverage the existing state of the art with regards to orchestration and model-driven approaches as well as tie it to the operations of cloud clients in order to improve utility. Through this study, we were able to show that our approach was capable of providing not only scaling features but also orchestration features of application components distributed across heterogeneous cloud platforms.

ElasticFog: Elastic Resource Provisioning in Container-Based Fog Computing

The recent increase in the number of Internet of Things (IoT) devices has led to the generation of a large amount of data. These data are generally processed by cloud servers because of their high scalability and ability to provide resources on demand. However, processing large amounts of data in the cloud is an impractical solution for the strict requirements of IoT services, such as low latency and high bandwidth. Fog computing, which brings computational resources closer to the IoT devices, has emerged as a suitable solution to mitigate these problems. Resource provisioning and application orchestration are two of the key challenges when running IoT applications in a Fog computing environment. In this article, we present ElasticFog, which runs on top of the Kubernetes platform and enables real-time elastic resource provisioning for containerized applications in Fog computing. Specifically, ElasticFog collects network traffic information in real time and allocates computational resources proportionally to the distribution of network traffic. The experimental results prove that ElasticFog achieves a significant improvement in terms of throughput and network latency compared with the default mechanism in Kubernetes.

A Service-Defined Approach for Orchestration of Heterogeneous Applications in Cloud/Edge Platforms

Edge Computing is moving resources toward the network borders, thus enabling the deployment of a pool of new applications that benefit from the new distributed infrastructure. However, due to the heterogeneity of such applications, specific orchestration strategies need to be adopted for each deployment request. Each application can potentially require different optimization criteria and may prefer particular reactions upon the occurrence of the same event. This paper presents a Service-Defined approach for orchestrating cloud/edge services in a distributed fashion, where each application can define its own orchestration strategy by means of declarative statements, which are parsed into a Service-Defined Orchestrator (SDO). Moreover, to coordinate the coexistence of a variety of SDOs on the same infrastructure while preserving the resource assignment optimality, we present DRAGON, a Distributed Resource AssiGnment and OrchestratioN algorithm that seeks optimal partitioning of shared resources between different actors. We evaluate the advantages of our novel Service-Defined orchestration approach over some representative edge use cases, as well as measure convergence and performance of DRAGON on a prototype implementation, assessing the benefits compared to conventional orchestration approaches.

Comparison of scheduling algorithms for multiple mobile computing edge clouds

5G networks are anticipated to provide service for vertical industries. Mobile Edge Computing (MEC) framework is a physical infrastructure improvement to host mobile applications for the introduction of new service provisioning in 5G networks. Application Orchestration is responsible for scheduling user sessions to application instances. In this paper, we propose four scheduling algorithms to assign sessions in a system with multiple MEC clouds. Results show that scheduling based on the occupancy level of MECs is quite robust in various scenarios and can be applied with the fractional guard channel policy to provide service.

Orchestrating incomplete TOSCA applications with Docker

Cloud applications typically integrate multiple components, each needing a virtualised runtime environment that provides the required software support (e.g., operating system, libraries). This paper shows how TOSCA and Docker can effectively support the orchestration of multi-component applications, even when their runtime specification is incomplete. More precisely, we first introduce a TOSCA-based representation of multi-component applications, and we illustrate how such representation can be exploited to specify only the application-specific components. We then present TosKeriser , a tool for automatically completing TOSCA application specifications, which can automatically discover the Docker-based runtime environments that provide the software support needed by the application components. We also show how we fruitfully exploited TosKeriser in two concrete case studies. Finally, we discuss how the specifications completed by TosKeriser can be automatically orchestrated by already existing TOSCA engines. • We present a TOSCA-based representation of multi-component applications. • Our representation permits specifying only application-specific components. • We present TosKeriser, a tool to automatically complete TOSCA specifications. • Automatically completed specifications can be executed by existing TOSCA engines. • We discuss the usefulness of TosKeriser by means of two concrete case studies.

Chao: a framework for the development of orchestration technologies for technology-enhanced learning activities using tablets in classrooms

In technology-enhanced learning, orchestration technologies refer to computer systems that support teachers in the orchestration of learning applications. Due to the specificity and diversity of each learning application, the use of these orchestration technologies is often not adequate in situations that they were not designed for in the first place. In this article, we tackle this issue and present the software framework Chao. This framework has been designed to provide a set of classes, methods, and user interfaces to facilitate the development of orchestration technologies for tablets. The evaluation of this framework concerns its design, the usability of its user interfaces, and its ability to be adapted for various learning applications. The results suggest that teachers found the instances of the framework useful in assisting them during their orchestration tasks, and that little work is required to instantiate the framework.