MODAClouds Multi-Cloud DevOps Toolbox
solutions for Multi-Cloud

MODAClouds Multi-Cloud DevOps Toolbox is a set of tools and best practice methods specifically created "for Clouds", for the most demanding Multi-Cloud scenarios. MODAClouds Toolbox is here to help developers and operators to change and improve the way software is created and operated, in a more agile manner.

MODAClouds MultiCloud DevOps Toolbox - Technology and Tools

The toolbox helps lowering existing barriers between Development and Operation Teams and helps embracing DevOps practices within IT teams. This means that MODAClouds software tools can help organizations of any size to Build and Run Cloud Applications driven by business and technical needs and quality requirements.

However, the impact of MODAClouds Project extends outside the toolbox. Various tools have also been develop to help us use our technologies with other relevant third party tools. These tools are also shared with the community, they will help you use Multi-Cloud with various cutting edge solutions for IT automation with Puppet © PuppetLabs; for BigData with Kundera by Impetus, and for Palladio Optimization Tools. Learn more about MODAClouds By-Products here.

Take a look at all the technologies available to improve the use of MultiClouds learn more about all technologies available

Schedule a Demo of MODAClouds MultiCloud DevOps Toolbox now!

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MODAClouds Toolbox

The toolbox is comprised by a set of components under the same idea "MODAClouds Multi-Cloud DevOps Toolbox": Creator 4Clouds, an Open Source Integrated Development Environment (IDE) for high-level application design, and Energizer 4Clouds, a Multi-Cloud Run-time Environment energized to provide early prototyping, automatic deployment and execution with built-in capabilities such as self-adaptation of applications with guaranteed Quality of Service (QoS) on compatible Multi-Clouds.

The toolbox also includes a separate tool, Venues 4Clouds, which is a decision support system that helps decision makers identify and select the Best Execution Venue for cloud applications not only considering Technical requirements, but more importantly, including the business perspective into the equation when designing and operating cloud applications.

MODAClouds DevOps Toolbox

MultiClouds offers benefits for your organization, not only for technicians

For the CEO and Decision Makers

who need to define Business-Driven Cloud strategies in which cloud applications and services assure performance, quality, cost and other requirement from a provider agnostic perspective.


For Cloud Developers and Architects

who are seeking for agnostic and independent cloud frameworks and building tools and at the same time can help them later address automated runtime monitoring and adaptation based on QoS for their apps.


For IT Operators

who need to use the right tools to automate operations based on QoS SLAs and guarantee that applications are executing over optimal performance quality thresholds or under cost thresholds with a minimum quality regardless of the provider, and migrate if necessary to other providers .

Solving Top Cloud Problems MODAClouds Toolbox

See how MODAClouds technologies and approach help developers, operators, CEOs, CIOs and other managers address cloud computing problems in Multi-Cloud scenarios.

See Solving Top Cloud Problems MODAClouds Toolbox in

MODAClouds DevOps Toolbox Creator 4Clouds

MODAClouds Creator 4Clouds model Multi-Clouds applications with one thing in mind: Business Requirements

The Design-Time Platform provided by MODAClouds Toolbox is a piece of software that allows users to design cloud applications, identify components and key functions of applications, perform performance and cost evaluation, get help concerning the mapping of data to scalable databases, define Business and QoS Requirements, plan the initial deployment strategy of our application on a multi-cloud environment, and finally, choose the service provider that best suits all technical and business requirements defined for the application.

Creator4Clouds will result in an architecture inspired by the OMG Model-Driven Architecture (MDA), which is a model-based approach for the development of software systems. The MDA relies on three types of models for three layers of abstractions. The closer to the system a layer is, the more technical the description. These three MDA layers, from the more abstract to the more detailed are:

  • The Cloud-enabled Computational Independent Model (CCIM), which describes what the system is expected to as well as represent an application independently from the underlying cloud architecture.
  • The Cloud Platform Independent Model (CPIM), which describes views of the systems in the context of a cloud-based architecture, still in a Cloud platform independent manner so that it can be mapped to several cloud platforms at the CPSM levels.
  • The Cloud Platform Specific Model (CPSM), which refines the CPIM with technical details required for specifying how the system can use a specific Cloud platform.

This environment is comprised by a set of tools orchestrated by an Integrated Development Environment (IDE). From the point of view of the user, this IDE is the main piece of software to be used at design time. It realizes the MDA approach proposed by the MODAClouds project, and its main output is the set of models and artefact's required by the runtime components to deploy, monitor and adapt cloud applications. The IDE provides support for a set of models such as requirements models, service definition and orchestration, data model etc. It also provides transformation, reverse engineering, traceability and document and code generation capabilities. As an Integrated Development Environment it provides a common access point for the following set of tools (its core functionality):

  • CloudML 4Clouds: CloudML is a central piece of our software as it is the heart of our DevOps approach supporting a continuous design-development-operation process. It defines a meta-model that is used by the IDE to support the specification of deployment architectures and resource provisioning scripts. Moreover it offers an engine that executes such specifications thus supporting deployment and startup of cloud applications on multiple clouds, both at the IaaS and PaaS level in a seamless way. Moreover, CloudML 4Clouds has been key in different collaboration actions with other projects that want to exploit its potential and in the collaborations with OASIS standardization body.
  • Space 4Clouds for Dev (QoS Analysis and Optimization Tool): The System Performance and Cost Evaluation on cloud scenarios technology developed by MODAClouds project is coined under the name of Space 4Clouds Tool. This technology is present both at design-time, and run-time. At design time, Space 4Clouds for Dev (QoS Analysis and Optimization Tool), it allows the QoS engineer to assess the QoS requirements of the application, assess the fulfillment of such requirements by a given deployment and optimize it by identifying the solution that maximizes fulfillment and minimizes costs. The results of this analysis are then going to be used by the application developer and the application provider to eventually improve, respectively, the design of the application code and data and its deployment.
  • The DataMapping 4Clouds is a provided tool that allows application developer to design the data structures that are manipulated and stored by the application in order to be able to find the data structures that allow for best runtime performance.

Energizer 4Clouds manage, monitor and assure operations of your cloud services in Multi-Cloud scenarios

This environment is comprised by the following complementary tools which provide advanced services to application developers and operators.

MODAClouds DevOps Toolbox Energizer 4Clouds

Energizer 4Clouds Platform includes the following built in capabilities to empower Multi-Cloud DevOps:

  • Multi-Cloud Execution Platform with QoS: The execution platform provisions all services that are needed to deploy on multi-cloud infrastructures, both the application and the corresponding monitoring platform. It provides an abstraction layer that enables organizations take advantage for their Cloud applications to be independently deployed on different clouds in a seamless way through the ADDapters 4Clouds tool. The execution platform also offers the services and the API needed to maintain and change the operational state of the application at runtime, typically under triggers of the self-adaptation platform.
  • Multi-Cloud Support Services: management, coordination, communication and storage generic services focused on distributed aspects of cloud environments.
  • Multi-Cloud Self-Adaptation: It reacts to fluctuations during the execution of multi-cloud applications in performance behavior of service providers to assure QoS by adapting the application based on the defined QoS requirements and the situation of the runtime environment. It addresses problems such as cloud outages or loss of performance/availability by changing the configuration of the application (e.g., by adding new replicas of a component), migrating the application to a different cloud or bursting some parts of the applications due to performance demand.

The capabilities mentioned above are organized within the following subcomponents:

  • ADDapters 4Clouds: It is responsible for connecting MODAClouds Multi-Cloud DevOps Platform to several underlying cloud service providers at IaaS and PaaS levels. By creating an abstraction layer exposing a Vendor-Independent API to add different underlying providers, this tool offers Multi-Cloud agility, interoperability and bursting among several of the most important IaaS and PaaS providers. The platforms currently supported are Google App Engine, Windows Azure, Amazon Web Services and Flexiant as well as all those covered by the jCloud library. Moreover, it has been extended in order to allow the deployment of local applications that make use of Glassifish 4.0 Application Server. As part of ADDapters 4Clouds, the CPIM library exposes APIs for access to the following Cloud services offered by the most common PaaS platforms: NoSQL Service, SQL Service, Blob Service, Message Queue, Task Queue, Mail Service, and Memcache. The platform is offered to give other providers the opportunity to create a connector and become eligible for being used within the MODAClouds platform.
  • Tower 4Clouds: provides a monitoring platform that is part of the runtime environment. It is responsible for establishing a Multi-Cloud monitoring environment for:
    • collecting data from the running application and the underlying infrastructures;
    • process the information gathered by the monitoring collectors and perform an analysis that generates high-level statistics;
    • defining a summary of measures such as application health, QoS, etc. It collects data from running applications and underlining hosting clouds through data collectors and executes monitoring rules that define the QoS constraint to be checked (together with some frequency of the check) and the actions to be executed in case the constraint results to be violated providing triggers and data streams to the Self-Adaptation Platform and sending feedback to the design-time IDE.
    • and finally, automating inputs and outputs to the Self-Adaptation Platform and the IDE.
    The monitoring infrastructure allows monitoring at different levels of abstraction depending on the data collectors that are implemented for each specific case. So far, we have implemented with infrastructure level and application level monitoring. However, new data collectors can be easily incorporated into the infrastructure. Data analysis can be customized by developing specific monitoring rules. Many of them are automatically generated through the MODAClouds IDE starting from the QoS requirements associated to the application and from its main components. Other rules can be added by the application designers and operators. Fine grained monitoring can be dynamically switched on/off depending on the measured application health. It also provides mechanisms to allow other components can notify to this platform their interest in monitoring data generated by the available monitoring rules and the generated events to trigger specific actions.
  • Space 4Clouds for Ops (Self-Adaptation): it is composed of two main parts:
    • The Multi-Cloud Models@runtime engine acquires data from Tower 4Clouds, keeps track and shows to the users the status of the multi-cloud application and of its execution environment and allows operators to perform reconfiguration actions. Moreover, it checks that the proposed actions are possible given the current configuration of the system (such configuration may even have evolved during the reasoning process) and interacts with the execution platform to apply these actions.
    • The self-adaptation reasoner acquires monitoring data from Tower 4Clouds and uses them to run performance and availability models that allow the platform to identify the best self-adaptation actions, which are then proposed to the Models@runtime engine.
  • IDE: The run-time platform IDE is comprised by a “cockpit” that enables runtime management.

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MODAClouds DevOps Toolbox Venues 4Clouds

Venues 4Clouds the tool to identify best cloud for your business

Venues 4Clouds is designed to support organizations on the task of choosing the most suitable cloud provider, or Best Execution Venue, for their Cloud applications, considering different aspects such as application architecture and more importantly form the business perspective, business and technical risk, quality and cost.

Venues 4Clouds Service Platform includes the following built in capabilities:

  • Cloud Service Information Retriever: a partially automated agent in charge of retrieving data needed from service providers to load up the Decision Support (DSS) data base to do its estimations and benchmarking. We assume that the parameters are estimated either based on the feeds from cloud measurement services or based on expert or user judgments. A parameter may be estimated or measured either directly, or through estimation of a measurable indicator which is then aggregated and mapped to the decision model. The dynamics of the indicators and the parameters as well as their relevance and uncertainty will be among the factors for determining whether the data acquisition should be automatic (e.g. real-time retrieval based on a monitoring environment) or manual, and how frequent it should be.
  • Business, Technical and Performance Requirement Analysis Model: is the engine that takes all inputs and rules from the user and provides alternatives in conjunction with the knowledge base of services. It is capable of analyzing and using tangible and intangible technical and business requirements (assets) which are then evaluated by the DSS engine which is capable of suggesting different alternatives which provide certain characteristics or features and are thus able to treat and mitigate these risks to some extent. In a simplified way, it is a complex and unique algorithm that helps to better match requirements to different service offerings.

Venues 4Clouds is a separated component itself with its own IDE, however, deeply integrated into the whole design workflow proposed by Creator 4Clouds. Its IDE facilitates users with the analysis of the Best Execution Venue for running cloud applications, helping not only technologists, but managers collect:

  • the types of cloud services required by the application to be deployed in a multi-cloud environment;
  • the functional and non-functional requirements of this application;
  • and the rules to be used by the DSS in order to detect risk.

MODAClouds Multi-Cloud DevOps Workflow designed to assure that business requirements are taken into account to create agnostic, agile and DevOps Multi-Cloud approaches.

MODAClouds DevOps Workflow

MODAClouds is not only about technology. What it is important is that all MODAClouds technologies and tools provide also a methodology, a workflow that helps on both the design-time and the run-time of cloud applications. The proposed methodology and framework is also a relevant part of the results of the project. This process is detailed in other technical deliverables, however, it is worth highlighting here that this workflow exploits all the pieces described in the previous section to enable a coherent DevOps process for multi-cloud applications.

The previous picture shows an overview of the MODAClouds workflow. Various actors are involved in this process. The Feasibility Study Engineer is the one in charge of assessing if the development or migration of some application on the cloud is possible within the identified time and cost constraints, taking into account all possible risks. Application Developers are the ones in charge of all technical aspects related to the design, development and validation of the application. He/she interacts with the QoS Engineer who makes sure that the application and its allocation to the available resources fulfill the QoS requirements identified for the application. Then, Cloud App Admins or Operators are the ones in charge of overseeing the deployment and operation of the application. All these actors interact with MODAClouds tools by following the best practices and guidelines we are developing (see D11.4.1). There is not a single entry point for our workflow:

  • If we have some pre-existing application, most likely, the entry point into the workflow will be the functional modeling phase using MODACloudML. In this phase the Application Developer will model the organization of the application using Creator 4Clouds. At this point, the Feasibility Study Engineer will be able to acquire these models and import them into Venue 4Clouds so that his/her analysis and selection of possible cloud solutions will be based not only on business considerations but also on the application architecture.
  • In the case we have a brand new application to consider, the entry point will probably be the interaction with Venue 4Clouds by the Feasibility Study Engineer who will start analyzing the problem considering only business terms and will then pass the results, in terms of candidate cloud solutions to the Application Developer.

In both cases, there can be various iterations between feasibility study and the functional modeling phase. At any point of these iterations, the QoS Engineer will be involved to analyze the application model and its possible allocation on specific cloud services in order to verify the fulfillment of the application QoS. If needed, the QoS Engineer will offer to the others some alternative solutions that optimize the high priority QoS requirements.

When all these actors will be satisfied of the achieved design solution, the application code will be developed and a deployment model in CloudML will be derived. At this point, the Cloud App Admin will be able to run the deployment model through the CloudML engine and will be able to control the execution of the application and of its self-adaptation thanks to Energizer 4Clouds.

There are two major aspects that make the work flow interesting:

  • Feed-back Loop for DevOps: All three main components of MODAClouds 4Clouds suite of tools are built with the idea to reduce the gap of Development and Operations teams, according to DevOps philosophy. Therefore, we have included in the design of our architecture what we call “Feed-Back Loop” technologies that extend capabilities offered by Creator, Venues and Energizer 4Clouds. Thanks to the Feed-Back Loop approach, Tower 4Clouds connects with Creator 4Clouds and Venue 4Clouds. The first connector is responsible for providing developers and the QoS engineers with the perspective of the application behavior at runtime to improve the development process and incorporate DevOps techniques and tools into the process. The connector retrieves information from the runtime platform capturing the actual behavior and state of running applications to inform developers and automatically update parameters of the design-time tools models. It also hands information of operative and QoS requirements for the Run-time platform.
  • DevOps Connector for Venues 4CloudsDevOps Connector for Venues 4Clouds: The connection between Tower 4Clouds and Venues 4Clouds allows this second one to adapt its knowledge base according to real live data to help have updated vision of services quality for future recommendations.

Schedule a Demo of MODAClouds MultiCloud DevOps Toolbox now!

Contact our team and schedule a one-to-one demo of MODAclouds Toolbox technologies.

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MODAClouds - Technology and Tools By-Products

MODAClouds By-Products provide a flexible and powerful approach to take advantage of using MODAClouds Technologies in conjunction with first class third party tools.

It is important to highlight that MODAClouds has not only created technology for MODAClouds expected goals. The technical team has created various “products” that have been developed during the project in the form of tools generated with MODAClouds technologies, or to somehow assist MODAClouds project. Some of them are built using other tools, and provide stand-alone value, or in conjunction with those other tools, for end-users with specific demands.

Take a look at how MODAClouds provides value in conjunction with other third party tools

Use MODAClouds Toolbox with other leading third party tools like cutting edge solutions for IT automation Puppet © PuppetLabs, for BigData with Kundera by Impetus, and for Palladio Optimization Tools.

Take a look at MODAClouds' by-Products

DICE Framework
solution for rapid development of Big Data applications

DICE Framework is a set of tools and techniques specifically created to help developers of Data-Intensive Applications speed up the design and deployment process while simultaneously reasoning on their product's quality. DICE Framework is here to help developers automate their workstreams and make product delivery pipeline quality-aware and agile.

DICE Framework

The Framework consists of Development Tools, which primarily focus on the development stage of a Data-Intensive Application, and Runtime Tools, which collect data during the application quality testing to characterise the efficiency, reliability and correctness of the components. This dual approach helps lowering existing barriers between Development and Operation Teams, facilitating shorter lead-times and embracing DevOps practices within IT teams. This means that DICE Framework can help organisations to Build and Run Data-Intensive Applications driven by quality requirements in a more efficient manner.

Take a look at all the technologies available to help speed up application development process.

Learn more about technologies available

DICE Framework

The tools comprising the Framework can be divided into three functional groups:

  • DICE IDE and Quality analysis tools aim to accelerate coding, design and application prototyping through the use of the DICE Integrated Development Environment, with quality analysis via simulation, verification and optimization methods during the early stages of application design;
  • Continuous Delivery and Testing tools support delivery on private and public clouds via a deployment tool, optimal application configuration, continuous integration and quality testing.
  • Feedback and Iterative Enhancement tools are centered around the monitoring platform tailored to Big Data technologies and a set of tools that continuously detect quality anomalies, explain how the Data-Intensive Application utilizes resources and inform the developer of discovered quality issues;

DICE Framework


DICE IDE & Quality Analysis

Model Data-Intensive Applications with one thing in mind: Quality Requirements

DICE Integrated Development Environment (IDE) is the central element of the DICE Framework, where the developer specifies the Data-Intensive Application using a Model-Driven Engineering approach. From the point of view of the user, DICE IDE is the main piece of software to be used at design time.

DICE IDE facilitates creation of an architecture inspired by the Model-Driven Architecture (MDA). MDA is a design approach for the development of software systems. The MDA relies on three types of models for three layers of abstractions. The closer to the system a layer is, the more technical the description. These three MDA layers, from the more abstract to the more detailed are:

  • DICE Platform-Independent Models (DPIM) allow to specify an application with its components and architecture in a technology-independent way.
  • DICE Technology-Specific Models (DTSM) map relevant technologies to the specifications defined with DPIM models.
  • DICE Deployment-Specific Models (DDSM) augment the DTSM models with technical details required for specifying how the application can be deployed in a Cloud testbed.

Such models can be aligned by DICE model-to-model transformations that are automatically executed within the IDE to reduce the amount of manual work required from the developer.

The DICE Profile is a UML Profile based on MARTE (Modeling and Analysis of Real-Time and Embedded Systems) and DAM (Dependability Modeling and Analysis) profiles. The DICE Profile introduces concepts needed for the modeling of DIA at the different abstraction levels of DICE described above (DPIM, DTSM and DDSM).

A set of tools orchestrated by the DICE IDE allows to assess application's quality properties (efficiency, costs, safety/correctness, etc.).

  • Verification Tool allows developers to evaluate their design against user-defined properties, in particular safety ones. The verification process allows the DIA designer to perform verification tasks using a lightweight approach, meaning that formal verification is launched through interfaces that hide the complexity of the underlying models and engines. These interfaces allow the user to easily produce the formal model to be verified and the properties to be checked without the requirement of high technical expertise in the subject.
  • Simulation Tool simulates the behavior of a Data-Intensive Application (DIA) to assess its performance and reliability. The DIA can be defined both at the DPIM level or at DTSM level using a particular technology (e.g. Storm, Spark).
  • Optimization Tool allows the architect to assess the performance and minimize the deployment cost of a DIA against user-defined properties, in particular meeting of deadlines under different levels of cluster congestion. The input is a DICE DDSM model, enriched with the Service Level Agreements to be satisfied and a description of the execution environment (e.g. list of candidate providers, list of virtual machine types, etc.). The optimization's objective is finding the least expensive cluster configuration able to guarantee the application jobs to be executed before a user-defined deadline. The architect can analyze the application behavior under different conditions. For example, they can study pros and cons of public clouds versus private cloud in terms of execution costs.

DICE Deployment Modelling Tool provides a simple visual specification of the DDSM model to produce a fully deployable TOSCA blueprint in a manner of minutes in order to speed up the deployment process.

Continuous Delivery & Testing

Continuously deploy and test Data-Intensive Applications in a testbed.

This environment is comprised of the following complementary tools which provide advanced services to application developers and operators.

DICE Continuous Delivery & Testing logo

  • Delivery Tool consists of a deployment service, complete with a web user interface and command line interface, and a DICE technology library. It can be used for experimenting with various setups of Big Data technology without the need to spend effort on manually installing and configuring the cluster. The tool is also designed to work well in a Continuous Integration workflow, where it assists with the quality assurance of the Data-Intensive Applications being developed.
  • Quality Testing Tool helps to stress-test data-intensive applications based on technologies such as Hadoop/MapReduce, Storm and Cassandra. With Quality Testing Tool developer can run basic load tests on the application throughout the development cycle in order to support the activities of Configuration Optimization and Anomaly Detection Tools across software versions.
  • Fault Injection Tool has been developed to generate faults within Virtual Machines (VMs) and at the Cloud Provider Level. The FIT provides the ability for a user to generate faults at the VM level, and for an administrator at a cloud level. The purpose of the FIT is to allow cloud platform owners/Application/VM owners a means to test the resiliency of a cloud installation. With this approach the designers can use robust testing, showing where to strengthen the application before it reaches production environment. It also allows a user/application owner to test and understand their application design/deployment in the event of a cloud failure or outage, thus helping to mitigate the risk before the cloud-based deployment. This tool will assist developers and cloud operators in offering their best services to all customers and simplify testing within the DevOps paradigm.
  • Configuration Optimization Tool automatically tunes configuration parameters of Data-Intensive Applications (DIA). DIAs are developed with several technologies (e.g., Apache Storm, Hadoop, Spark, Cassandra) each of which has typically dozens of configurable parameters that should be carefully tuned in order for DIA to perform optimally. Configuration Optimization (CO) tool enables end users of such application to auto-tune their application in order to get the best performance. CO tool is already integrated with DICE Delivery Tool (including deployment service and continuous integration), as well as the DICE Monitoring Platform.

The workflow and features of the Continuous Delivery & Testing Toolbox are described below:

After the initial prototyping of the application, the developer will request to deploy the current prototype. After an automatic commit of all models and code to the external repository, the continuous deployment tool will retrieve a copy of them from the repository, build the application, and internally store the outputs and their associated artifacts. The delivery tool will then initialize the deployment environment (if not already created), consisting of VMs and software stack, and deploy (or update the existing deployment of) the application.

The quality testing tool will support the generation of test workloads to the application. Such workloads are those that will be used to assess the quality of prototypes. Similarly, the fault injection tool will generate faults and malicious interferences that can help to verify the application resilience. They will also be exploited by the configuration optimization tool to generate an experimental plan automatically given a time budget. The output of this tool is to confirm the optimal configuration of the deployment for an application in its final stages before being pushed to production. Compared to the optimization tool of the design time, configuration optimization will also deal with technology-specific parameters, which are difficult to model in design-time exploration. Moreover, configuration optimization is black-box and solely measurement-driven, whereas design space exploration is primarily model-driven.

DICE Feedback & Enhancement tool logo

Feedback & Iterative Enhancement Tool

Monitor and iteratively improve your application

DICE monitoring platform (DMon) is at the heart of the DICE Feedback and Iterative Enhancement Tool. It collects, stores, indexes and visualizes monitoring data in real-time from applications running on Big Data frameworks. It supports DevOps professionals in iterative quality enhancements of the source code and with the optimisation of the deployment environment. DMon is a fully distributed, highly available and horizontally scalable platform.

The platform is able to monitor both the infrastructure (memory, CPU, disk, network etc.) and multiple Big Data frameworks, currently supported being Apache HDFS, YARN, Spark, Storm and MongoDB. The core components of the platform and the node components running on the monitored cluster are easily controlled thanks to a Web-based user interface. Visualization of collected data is fully customizable and can be structured in multiple dashboards based on developer's needs, or tailored to specific roles in the organization, such as administrator, quality assurance engineer or software architect. DMon is already integrated with the DICE Continuous Integration tool

The Anomaly Detection and Trace Checking Tools query the monitoring platform for relevant metrics and use them to generate analyses concerning anomaly in performance, reliability or operational behavior of the application at a given release version. The Anomaly Detection Tool reasons on the base of black-box and machine-learning models constructed from the monitoring data. Conversely, the trace checking tools are going to analyze the correctness of traces. These analysis results will be manually retrieved by the developer from the IDE plugin. The Enhancement Tool's objective is to close the loop between runtime (monitoring data) and design time (models and tools) by automatically annotating the DICE models stored in the repository with statistics on the inferred and recorded monitoring data, helping the user to inspect the root-causes of performance or reliability anomalies.

Learn more about the DICE Framework

If you want to learn more about the specific components of the DICE Framework, please visit our Knowledge Repository on GitHub.

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