Open Cloud Computing Interface Research Articles

Extending OCCI for an autonomous SLA negotiation strategy in federated clouds

SummaryEach cloud service provider provides only an interface of its own cloud infrastructure for enabling clients to use its cloud resources. However, there is a number of difficulties for cloud providers to ensure proper functioning. One of the main problems of a cloud provider is the lack of resources to support a huge number of on‐demand resources provisioning. Thus, resources cannot be distributed among different cloud providers since the federation is not the basic operation of the cloud provider. The most efficient way to overcome this problem is to extend the interface's cloud provider with an automatic negotiation to dynamically form the best agreement between the different cloud providers based on the service level agreement. In this article, we propose an extension for the Open Cloud Computing Interface which is the standardized interface for the cloud computing to support the automatic negotiation between the different cloud providers. To prove the efficiency and the effectiveness of our approach, we implement a prototype to evaluate the key presented in this article.

Scientific workflow execution in the cloud using a dynamic runtime model

To explain specific phenomena, scientists perform a sequence of tasks, e.g., to gather, analyze and interpret data, forming a scientific workflow. Depending on the complexity of the workflow, scientists require access to various kinds of tools, applications and infrastructures for individual tasks. Current approaches are often limited to managing these resources at design time, requiring the scientist to preemptively set up applications essential for their workflow. Therefore, a dynamic provisioning and configuration of computing resources are required that fulfills these needs at runtime. In this paper, we present a dynamic runtime model that couples workflow tasks with their individual applications and infrastructure requirements. This runtime model is used as a knowledge base by a model-driven workflow execution engine orchestrating the sequence of tasks and their infrastructure. We exhibit that the simplicity of the runtime model supports the creation of highly tailored infrastructures, the integration of self-developed applications, as well as a human-in-the-loop allowing scientists to monitor and interact with the workflow at runtime. To tackle the heterogeneity of cloud provider interfaces, we implement the workflow runtime model by extending the Open Cloud Computing Interface cloud standard, which provides an extensible data model as well as a uniform interface to manage cloud resources. We demonstrate the applicability of our approach using three case studies and discuss the benefits of the runtime model from a user and system perspective.

Towards Automated and Optimized Security Orchestration in Cloud SLA

In cloud computing, providers pool their resources and make them available to customers. Next-generation computer scientists are flocking to the cutting-edge field of cloud computing for their research and exploration of uncharted territory. There are still several barriers that cloud service providers must overcome in order to provide cloud services in accordance with service level agreements. Each cloud service provider aspires to achieve maximum performance as per Service Level Agreements (SLAs), and this is especially true when it comes to the delivery of services. A cloud service level agreement (SLA) guarantees that cloud service providers will satisfy the needs of large businesses and offer their clients with a specified list of services. The authors offer a web service level agreement–inspired approach for cloud service agreements. We adopt patterns and antipatterns to symbolize the best and worst practices of OCCI (Open Cloud Computing Interface Standard), REST (Representational State Transfer), and TOSCA (Topology and Orchestration Specification for Cloud Applications) with DevOps solutions, all of which API developers should bear in mind when designing APIs. When using this method, everything pertaining to the cloud service, from creation to deployment to measurement to evaluation to management to termination, may be handled mechanically. When distributing resources to cloud apps, our system takes into account the likelihood of SLA breaches and responds by providing more resources if necessary. We say that for optimal performance, our suggested solution should be used in a private cloud computing setting. As more and more people rely on cloud computing for their day-to-day workloads, there has been a corresponding rise in the need for efficient orchestration and management strategies that foster interoperability.

Model-Driven Elasticity Management with OCCI

Elasticity is considered as a fundamental feature of cloud computing where the system capacity can adjust to the current application workloads by provisioning or de-provisioning computing resources automatically and timely. Many studies have been already conducted to elasticity management systems, however, almost all lack to offer a complete modular solution. In this article, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoDEMO</small> , a new elasticity management system powering both vertical and horizontal elasticities, both VM and Container virtualization technologies, multiple cloud providers simultaneously, and various elasticity policies. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoDEMO</small> is characterized by the following features: it represents ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) the first system that manages elasticity using Open Cloud Computing Interface (OCCI) model with respect to the OCCI standard specifications, ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) the first unified system which combines the functionalities of the worldwide cloud providers: Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), and ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i> ) allows a dynamic configuration at runtime during the execution of the application. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoDEMO</small> permits to timely adapt resource capacity according to the workload intensity and increase application performance without introducing a significant overhead.

Model-based cloud resource management with TOSCA and OCCI

With the advent of cloud computing, different cloud providers with heterogeneous cloud services (compute, storage, network, applications, etc.) and their related Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable cloud system. Several standards have been proposed to address this challenge and provide a unified interface to cloud resources. The Open Cloud Computing Interface (OCCI) thereby focuses on the standardization of a common API for Infrastructure-as-a-Service (IaaS) providers, while the Topology and Orchestration Specification for Cloud Applications (TOSCA) focuses on the standardization of a template language to enable the proper definition of the topology of cloud applications and their orchestrations on top of a cloud system. TOSCA thereby does not define how the application topologies are created on the cloud. Therefore, we analyze the conceptual similarities between the two approaches and we study how we can integrate them to obtain a complete standard-based approach to manage both Cloud Infrastructure and Cloud application layers. We propose an automated extensive mapping between the concepts of the two standards, and we provide TOSCA Studio, a model-driven tool chain for TOSCA that conforms to OCCI. TOSCA Studio allows to graphically design cloud applications as well as to deploy and manage them at runtime using a fully model-driven cloud orchestrator based on the two standards. Our contribution is validated by successfully transforming and deploying three cloud applications: WordPress, Node Cellar and Multi-Tier.

Model-Driven Simulation of Elastic OCCI Cloud Resources

Abstract Deploying a cloud configuration in a real cloud platform is mostly cost- and time- consuming, as large number of cloud resources have to be rented for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test cloud configuration. However, most of the existing cloud simulation tools require extensive technical skills and do not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a model-driven engineering approach based on the Open Cloud Computing Interface(OCCI) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for the supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results are compared with the ones computed from AWS.

Model-driven cloud resource management with OCCIware

Abstract Cloud computing has emerged as the main paradigm for hosting and delivering computing resources as services over Internet. It provides a delivery model for computing resources at infrastructure, platform, and software levels. However, there is a plethora of cloud providers offering different resource management interfaces. Due to this diversity, the consumption, provisioning, management, and supervision of cloud resources are subjected to four key issues, i.e., heterogeneity, interoperability, integration, and portability. To cope with these issues, Open Cloud Computing Interface (OCCI) is proposed as a community-based and open recommendation standard for managing any kind of cloud resources. Currently, only runtime implementations exist for OCCI, and each one targets a specific cloud service model such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or Software as a Service (SaaS). Thus, OCCI lacks an approach to model and execute different OCCI artifacts. Our approach provides a generic modeling framework coupled with a generic runtime implementation. In this article, we propose the OCCIware approach, which represents the first approach to design, validate, generate, implement, deploy, execute, and supervise everything as a service with OCCI. This approach provides OCCIware Studio , the first model-driven tool chain for OCCI. It is built around OCCIware Metamodel , which defines the static semantics for the OCCI standard in Ecore and OCL. In addition, it proposes OCCIware Runtime , the first generic OCCI runtime implementation targeting all the cloud service models (IaaS, PaaS, and SaaS). OCCIware provides a unique and unified framework to manage OCCI artifacts and, at the same time, it represents a factory to build cloud domain-specific modeling frameworks where each framework targets a specific cloud domain. OCCIware has been applied in various cloud domains and use-cases, which validate its applicability.

On semantic detection of cloud API (anti)patterns

ContextOpen standards are urgently needed for enabling software interoperability in Cloud Computing. Open Cloud Computing Interface (OCCI) provides a set of best design principles to create interoperable REST management APIs. Although OCCI is the only standard addressing the management of any kind of cloud resources, it does not support a range of best principles related to REST design. This often worsens REST API quality by decreasing their understandability and reusability. ObjectiveWe aim at assisting cloud developers to enhance their REST management APIs by providing a compliance evaluation of OCCI and REST best principles and a recommendation support to comply with these principles. MethodFirst, we leverage patterns and anti-patterns to drive respectively the good and poor practices of OCCI and REST best principles. Then, we propose a semantic-based approach for defining and detecting REST and OCCI (anti)patterns and providing a set of correction recommendations to comply with both REST and OCCI best principles. We validated this approach by applying it on cloud REST APIs and evaluating its accuracy, usefulness and extensibility. ResultsWe found that our approach accurately detects OCCI and REST(anti)patterns and provides useful recommendations. According to the compliance results, we reveal that there is no widespread adoption of OCCI principles in existing APIs. In contrast, these APIs have reached an acceptable level of maturity regarding REST principles. ConclusionOur approach provides an effective and extensible technique for defining and detecting OCCI and REST (anti)patterns in Cloud REST APIs. Cloud software developers can benefit from our approach and defined principles to accurately evaluate their APIs from OCCI and REST perspectives. This contributes in designing interoperable, understandable, and reusable Cloud management APIs. Thank to the compliance analysis and the recommendation support, we also contribute to improving these APIs, which make them more straightforward.

The Potential Use of Service-Oriented Infrastructure Framework to Enable Transparent Vertical Scalability of Cloud Computing Infrastructure

Cloud computing technology has become familiar to most Internet users. Subsequently, there has been an increased growth in the use of cloud computing, including Infrastructure as a Service (IaaS). To ensure that IaaS can easily meet the growing demand, IaaS providers usually increase the capacity of their facilities in a vertical IaaS increase capability and the capacity for local IaaS amenities such as increasing the number of servers, storage and network bandwidth. However, at the same time, horizontal scalability is sometimes not enough and requires additional strategies to ensure that the large number of IaaS service requests can be met. Therefore, strategies requiring horizontal scalability are more complex than the vertical scalability strategies because they involve the interaction of more than one facility at different service centers. To reduce the complexity of the implementation of the horizontal scalability of the IaaS infrastructures, the use of a technology service oriented infrastructure is recommended to ensure that the interaction between two or more different service centers can be done more simply and easily even though it is likely to involve a wide range of communication technologies and different cloud computing management. This is because the service oriented infrastructure acts as a middle man that translates and processes interactions and protocols of different cloud computing infrastructures without the modification of the complex to ensure horizontal scalability can be run easily and smoothly. This paper presents the potential of using a service-oriented infrastructure framework to enable transparent vertical scalability of cloud computing infrastructures by adapting three projects in this research: SLA@SOI consortium, Open Cloud Computing Interface (OCCI), and OpenStack.

Extending OCCI for autonomic management in the cloud

Cloud Computing is an emerging paradigm involving different kinds of Information Technologies (IT) services. One of the major advantages of this paradigm resides on its pay-as-you-go economic model. To remain efficient, it becomes necessary to couple this model with autonomic computing. By autonomic computing we mean the ability of the system to automatically and dynamically manage its resources to respond to the requirements of the business based on Service Level Agreement (SLA). In this paper, we propose an extension for Open Cloud Computing Interface (OCCI) to support the different aspects of autonomic computing. This OCCI extension describes new Resources and Links that are generic Kinds and are specialized using OCCI Mixins. We introduce the Autonomic Manager as a special Resource that starting from a SLA instantiates all needed entities to automatically establish an infrastructure to enable an autonomic management of Cloud resources. The other introduced OCCI Resources are: Analyzer to analyze monitoring data based on specific analysis rules and Reconfiguration Manager to generate reconfiguration actions based on reconfiguration strategies. These Resources are linked using new defined Link entities. We describe herein, a real use case to show that we can apply our approach to the different levels of the Cloud (i.e., IaaS, PaaS and SaaS) at the same time. We present also the implementation details as well as evaluation preliminary results that are encouraging.

Application level interface for a cloud monitoring service

We introduce a new paradigm, based on an extension of the Open Cloud Computing Interface (OCCI), for the on-demand monitoring of the cloud resources provided to a user. We have extended the OCCI with two new sub-types of core entities: one to collect the measurements and the other to process them. The user can request instances of such entities to implement a monitoring infrastructure.The paradigm does not target a specific cloud model, and is therefore applicable to any kind of resource provided as a service. The specifications include only the minimum needed to describe a monitoring infrastructure, thus making this standard extension simple and easily adoptable. Despite its simplicity, the model is able to describe complex solutions, including private/public clouds, and covers both infrastructure and application monitoring.To highlight the impact of our proposal in practice, we have designed an engine that deploys a monitoring infrastructure using its OCCI-compliant descriptions. The design is implemented in a prototype that is available as open source.

Automated Deployment of a Microservice-based Monitoring Infrastructure

We explore the specification and the automated deployment of a monitoring infrastructure in a container-based distributed system. This result shows that highly customizable monitoring infrastructures can be effectively provided as a service, and that a key step in this process is the definition of an expandable abstract model for them. So we start defining a simple model of the monitoring infrastructure that provides an interface between the user and the cloud management system. The interface follows the guidelines of Open Cloud Computing Interface (OCCI), the cloud interface standard proposed by the Open Grid Forum. The definition is simple and generic and it is a first step towards the definition of a standard interface for Monitoring Services. It allows the definition of complex, hierarchical monitoring infrastructure by composing multiple instances of two basic components, one for measurement and another for data distribution,. We illustrate how the monitoring functionalities that are defined through the interface are implemented as microservices embedded in containers. The internals of each microservice reflects the distinction between core functionalities which are bound to the standard, and custom plugin modules. We describe the engine that automatically deploys a system of microservices that implements the monitoring infrastructure. Special attention is paid to preserve the distinction between core and custom functionalities, and the on demand nature of a cloud service. A proof of concept demo is available through the Docker hub and consists of two multi-threaded Java appli- cations that implement the two basic components.

An OCCI Compliant Model for PaaS Resources Description and Provisioning

The proliferation of Cloud Computing platforms has led to the emergence of a large number of heterogeneous platform resources, services, application programming interfaces and supported frameworks and technologies. On one hand, supported frameworks vary from one Platform-as-a-Service (PaaS) to another depending on their implementations and capacities. On the other hand, each PaaS provides a specific and proprietary scenario to provision adequate resources and to deploy applications. In this paper, we propose a new PaaS-independent approach to provision in a unified way, appropriate PaaS resources for applications deployment in Cloud platforms. Our approach aims at addressing vendor lock-in restrictions and applications portability issues in existing Cloud platforms. To that end, we define a unified description model based on the Open Cloud Computing Interface (OCCI) core model that allows describing and managing an abstract Cloud resource. Our defined model consists of two main parts: (1) an OCCI platform extension which describes PaaS resources that can be provisioned by a PaaS to set-up an appropriate environment and (2) an OCCI application extension which describes application resources to deploy an application in this environment. We show the feasibility of our approach with an implementation and a use case of a realistic deployment scenario in Cloud Foundry PaaS.

An autonomic approach to manage elasticity of business processes in the Cloud

Cloud Computing is gaining more and more importance in the Information Technologies (IT) scope. One of the major assets of this paradigm is its economic model based on pay-as-you-go model. Cloud Computing gets more attention from IT users when it fits their required QoS and reduces their expenses. This task cannot be done without increasing the autonomy of the provisioned Cloud resources. In this paper, we propose a holistic approach that allows to dynamically adding autonomic management facilities to Cloud resources even if they were designed without these facilities. Based on the Open Cloud Computing Interface (OCCI) standard, we propose a generic model that allows describing the needed resources to render autonomic a given Cloud resource independently of the service level (Infrastructure, Platform or Software). Herein, we define new OCCI Resources, Links and Mixins that allow provisioning autonomic Cloud Resources. In order to illustrate our approach, we propose a use case that specializes our autonomic infrastructure to ensure the elasticity of Service-based Business Processes (SBPs). The elasticity approach that we are using is based on a formal model that features duplication/consolidation mechanisms and a generic Controller that defines and evaluates elasticity strategies. To validate our proposal, we present an end to end scenario of provisioning an elastic SBP on a public PaaS. Evaluation of our approach on a realistic situation shows its efficiency.

A RESTFull interface for scalable agents based cloud services

Agent technology provides asynchronous mechanisms that could represent the best choice for effective programming of Cloud, due to the unpredictable behaviour of the network. Cloud Agency is a collection of agent based services for provisioning, monitoring and autonomic reconfiguration of Cloud resources at infrastructure level, which go beyond the common offer by commercial providers and by open Cloud technologies. Its service interface provides methods for orchestrated executions of agents that implement a scalable solution for provisioning and management of Cloud resources at infrastructures level. Cloud Agency extends the open cloud computing interface (OCCI) proposal of standard. We present the design of Cloud Agency Interface and the implementation of an RESTfull to/from ACL gateway that allows for the communication between the Cloud world and the agents' one, being compliant with OCCI and extending its model and services. We investigate performance and scalability of the proposed solution.

Toward an Open Cloud Standard

Today's cloud ecosystem features several increasingly divergent management interfaces. Numerous bridging efforts attempt to ameliorate the resulting vendor lock-in for customers. However, as the number of providers continues to grow, the drawback of this approach becomes apparent: the need to maintain adapter implementations. The Open Cloud Computing Interface builds on the fundamentals of modern Web-based services to define a standardized interface for cloud environments while enabling service providers to differentiate their service offerings at the same time.

WNoDeS, a tool for integrated Grid and Cloud access and computing farm virtualization

INFN CNAF is the National Computing Center, located in Bologna, Italy, of the Italian National Institute for Nuclear Physics (INFN). INFN CNAF, also called the INFN Tier-1, provides computing and storage facilities to the International High-Energy Physics community and to several multi-disciplinary experiments. Currently, the INFN Tier-1 supports more than twenty different collaborations; in this context, optimization of the usage of computing resources is essential. This is one of the main drivers behind the development of a software called WNoDeS (Worker Nodes on Demand Service).WNoDeS, developed at INFN CNAF and deployed on the INFN Tier-1 production infrastructure, is a solution to virtualize computing resources and to make them available through local, Grid or Cloud interfaces. It is designed to be fully integrated with a Local Resource Management System; it is therefore inherently scalable and permits full integration with existing scheduling, policing, monitoring, accounting and security workflows. WNoDeS dynamically instantiates Virtual Machines (VMs) on-demand, i.e. only when the need arises; these VMs can be tailored and used for purposes like batch job execution, interactive analysis or service instantiation. WNoDeS supports interaction with user requests through traditional batch or Grid jobs and also via the Open Cloud Computing Interface standard, making it possible to allocate compute, storage and network resources on a pay-as-you-go basis. User authentication is supported via several authentication methods, while authorization policies are handled via gLite Argus. WNoDeS is an ambitious solution aimed at virtualizing cluster resources in medium or large scale computing centers, with up to several thousands of Virtual Machines up and running at any given time.In this paper, we descrive the WNoDeS architecture.

Virtual pools for interactive analysis and software development through an integrated Cloud environment

WNoDeS, an acronym for Worker Nodes on Demand Service, is software developed at CNAF-Tier1, the National Computing Centre of the Italian Institute for Nuclear Physics (INFN) located in Bologna. WNoDeS provides on demand, integrated access to both Grid and Cloud resources through virtualization technologies. Besides the traditional use of computing resources in batch mode, users need to have interactive and local access to a number of systems. WNoDeS can dynamically select these computers instantiating Virtual Machines, according to the requirements (computing, storage and network resources) of users through either the Open Cloud Computing Interface API, or through a web console. An interactive use is usually limited to activities in user space, i.e. where the machine configuration is not modified. In some other instances the activity concerns development and testing of services and thus implies the modification of the system configuration (and, therefore, root-access to the resource). The former use case is a simple extension of the WNoDeS approach, where the resource is provided in interactive mode. The latter implies saving the virtual image at the end of each user session so that it can be presented to the user at subsequent requests. This work describes how the LHC experiments at INFN-Bologna are testing and making use of these dynamically created ad-hoc machines via WNoDeS to support flexible, interactive analysis and software development at the INFN Tier-1 Computing Centre.