Menu OPTIMIS Toolkit

OPTIMIS Components in detail


OPTIMIS Toolkit can be broken down into three main groups of components: the OPTIMIS Base Toolkit with functionalities common to all components; the OPTIMIS Service Providers' Tools that enable service providers to implement, package, deploy and operate services, and finally the OPTIMIS Infrastructure Providers' Tools with functionality to manage the infrastructure (VMs, servers, data storage, etc.) required to operate services.




OPTIMIS Base toolkit allows to make your deployment and infrastructure decisions based on TREC (Trust, Risk, Eco-Efficiency and Cost) metrics that allow real-time cloud intelligence. The toolkit helps make optimal service deployment and operation decisions, and also provides fundamental services such as monitoring and security and its integrated on other major components of the toolkit (SP Tools and IP Tools).




Want to learn more about the Toolkit?

Read the Documentation

OPTIMIS Infrastructure Providers Tools optimizing infrastructure management

OPTIMIS allows IPs to effectively and efficiently manage and optimize infrastructure resources at a higher level of abstraction.



Infrastructure Providers Tools

  • Admission Control

    Role: AC is responsible for checking whether a service or a set of services can be accepted in the OPTIMIS Cloud and to generate an optimal TREC-driven allocation pattern.

    Key Features:

        TREC-driven optimization
        Probabilistic elasticity
        Affinity rules
        Deployment federation
        Heuristic solver
        Anti-affinity rules
        TREC Constraints for HM
        IP-Dashboard integration

    Innovations:

        TREC-driven optimization: Enables trade-offs among TREC based objectives
        Probabilistic elasticity: Incorporates a probabilistic framework that decreases the resources by taking advantage of statistical knowledge of the service's usage
        Federation of services: Able to partially allocate services and components when resources are not sufficient or a TREC constraint is broken

  • CloudQoS

    Role: Negotiating and creating the OPTIMIS SLA between SP and IP, SP and Broker, Broker and IP.

    Key Features:

        Creation of agreements based on Y1 Service Manifest
        Multi-round negotiations, e.g. with the OPTIMIS Broker, or upon rejection of the AC, support for federation, multi-cloud, bursting and broker
        Support for non-OPTIMIS enabled IPs
        If requested by the SP an IPR statement is included in the agreement template and the created agreement
        The IP can indicate its conformance to the European data protection regulation
        Through the SCC the IP indicates its conformance regarding regulations of data transfers to countries outside EU/EEA

    Innovations:

        Standards-based SLA negotiation and creation using the Service Manifest
        SP’s IPR statement in SLAs with the IP: The SP can inhibit undesired IPR transfer to the IP as a result of processing SP’s data
        Conformance to the legal requirements regarding data protection and data transfers: Including BCR and SCC in the SLA indicated the IP’s conformance with regulations and legal requirements of the EU regarding data protection and data transfers to countries outside the EU member states and the EEA states

  • Cloud Optimizer

    Role: Enables self-management of IP infrastructure using BLOs in complex Cloud scenarios.

    Key Features:

        Coordination of LLMs at IP side during service deployment and operation
        Allow IP Manager specify BLOs at runtime
        TREC support for managing LLMs

    Innovations:

        Allows IP Manager to specify BLOs and TREC constraints
        Considers TREC assessments and notifications within the Holistic Management process
        Direct mapping between objective function and Low-Level Managers (LLMs) configuration

  • VM Manager

    Role: Virtualization-level manager: efficient management of VMs running in a Cloud IP (during their whole lifecycle).

    Key Features:

        Coordination of Low-Level Managers at IP side during service deployment and operation
        TREC support
        HM's BLOs support
        Dynamic reallocation of VMs
        Switches nodes on/off when Eco/Energy-Efficiency mode is set

    Innovations:

        Management of VMs according to TREC factors during their whole lifecycle (deployment, operation and undeployment)
        Interoperability with many Cloud Infrastructure solutions (OpenNebula, EMOTIVE, OpenStack…)
        Placement optimization policies (based on TREC and BLOs)

  • Fault Tolerance Engine

    Role: Detection and recovery of failures during IP operation.

    Key Features:

        VM/Host Failure detection
        Reactive (based on monitoring)
        Proactive (based on risk of future failures)
        Advising corrective actions
        Migrate
        Restart

    Innovations:

        Failures detection through proactive behavior
        Advising corrective actions to upper layers (CO): VM Restart or Migration

  • Monitoring Infrastructure

    Role: Collect & aggregate monitoring data from heterogeneous sources and deliver data to client components.

    Key Features:

        Data collection from heterogeneous sources
        OPTIMIS and non-OPTIMIS environments. Also, aggregate and categorize it according to its origin (energy, physical, virtual, service)

    Innovations:

        Deliver a web site for viewing data stored in the MI database
        Has a set of RESTFul web services for data extraction
        Allows a flexible and individual data collection frequency for each and every collector script
        Manage life cycle of the monitoring data

  • Elasticity Engine

    Role: Performs proactive auto-scaling of VMs to meet load peaks.

    Key Features:

        Proactive Elasticity
        Composite KPIs
        Different modes of operation

    Innovations:

        Proactive auto-scaling: Predicts service workload and allocates capacity to make it available when needed
        Custom KPIs: Service agnostic scaling algorithms that can make use of any Key Performance Indicator

  • Data Manager

    Role: Offer distributed storage to services spanning over different providers.

    Key Features:

        Location based data monitoring
        Secure storage and key management
        Seamless and interoperable exploitation of federated resources
        Accept and enforce proactive suggestions for risk mitigation
        Online predictions for future data activity
        Validation of federated provider legal status

    Innovations:

        Shared storage clusters with increased security and legal enforcement aspects
        Data Location that is transparent to user

  • Trust Framework

    Role: Determine trust level for services, IPs and SPs, performing predictions and sending notifications for optimization purposes.

    Key Features:

        Customized trust calculation for SPs and IPs
        Heterogeneous aspects and calculations
        Understandable trust values aggregation
        More accurate forecasting algorithms applied at different levels
        Forecasting methods for service operation

    Innovations:

        Calculate trust for SPs and IPs using heterogeneous aspects and based on resources usage forecasting
        Trust forecast for services, IPs and SPs depending on potential operations (adding/removing VMs and new services deployed)

  • Infrastructure Provider’s risk Assessment

    Role: The IPRA tools allow the IP to reason about certain assets of service operation, the risk factors associated with these, and estimate the potential consequences.

    Key Features:

        Flexible model and inventory to assess extended risk factors
        Risk standard based semantic attached definition of API
        Proactive and forecasting ability

    Innovations:

        Risk assessment at service operation phase of physical host failure, VM failure, SLA failure and IP failure
        Holistic Manager support with proactive and forecasting assessment and mitigation strategy provision

  • Eco-Efficiency Tool

    Role: Provide energy/ecological efficiency information/notifications to optimize placement.

    Key Features:

        Uses real data coming from the MI to calculate the energy/ecological efficiency
        Assesses/forecasts energy efficiency
        Assesses/forecasts ecological efficiency
        Proactive behavior
        Consider energy credits

    Innovations:

        Assessment/Forecast of energy/ecological efficiency at different levels
        Notification when a given threshold is surpassed

  • Economic (Cost) Framework

    Role: Provides online cost assessment and prediction using traces of hardware (CPU, Network, Memory I/O) utilization

    Key Features:

        Assessment Feature for assessment of service-, VM- and node costs
        Model for integrating risk information into an SLA-penalty model
        Prediction model to anticipate future trends of hardware resource utilization
        Weighing model for relating service cost to Total Cost of Ownership (TCO)

    Innovations:

        Non-linear cost assessment and prediction models per service, VM and physical node
        A configurable cost assessment software service: resource, polling time, interval and assessment DB


OPTIMIS Service Providers Tools to create, deploy, manage and operate

OPTIMIS enables Cloud Service Providers to create, deploy, and operate services with assessed and guaranteed TREC levels through the following tools.



Service Providers Tools

  • Programming Model

    Role: Allow service developers to create new complex services.

    Key Features:

        Cloud-unaware programming model to create composite
        Services: No specific API or cloud oriented calls used
        Compositions formed by services and pieces of code mixed together: Third party services, own developed services, and code deployed in the cloud
        Automatic data dependency detection and maintenance: User doesn't need to specify data dependencies
        Automatic elasticity definition using the workflow: Application-level information used for elasticity

    Innovations:

        Orchestrate a mix of code and services
          Publish group of Orchestration Elements as a service (SaaS)
          Stateful Core Elements support
          Follow operation of the service
          Set of services (Service Provider) sharing resources
          Software License support
          Include own developed services in compositions

  • Integrated Development Environment

    Role:GUI to develop and deploy new services.

    Key Features:

        Eclipse
        Wizards to create new services
        Code generation and compilation
        Service Manifest generation (TREC, legal constraints, elasticity rules)
        Easy deployment
        Support legal definitions for data (location, encryption, property rights)
        Enhancements in porting legacy applications

    Innovations:

        Showcase for the programming model and TREC: Enables to use PM and TREC
          Complete and integrated service cloudification:Compilation, instrumentation, OVF generation, packaging, etc.

  • Image Creation Service

    Role:A RESTFul web service for creating custom images.

    Key Features:

        Image customization
        Match the image characteristics with the requirements in the Service Manifest
        Interoperability: the customized images can be run on OpenStack and OpenNebula VM Managers

    Innovations:

        Customize an image: Able to upload files (war, zip and text), extract archive files, and set up file permissions inside the image
        Select an image according to the Service Manifest: Matching the requirements from a list of available base images

  • License management

    Role:Support for running license–protected applications in Clouds.

    Key Features:

        Conceptualization of License Tokens
        License Delegation
        Trusted Instance
        Implementation of a new mechanism to secure the License Token in the Cloud

    Innovations:

        Token-based authorization: Authorizations may move to the execution environment as needed
        License delegation mechanism: Allowing ISV-supported use of a subset of the site-owned licenses through a dynamically deployed license server in the Cloud

  • Virtual Machine Contextualizer

    Role: Gathers and aggregates context data from multiple sources for the purpose of configuring a VM within a cluster of virtual resources. The VMC has the following features: Ability to insert context data into a VM & Prepare VM for receipt of context data.

    Key Features:

        Automated image maintenance Functionality to whip (compact) images maintaining compact images sizes
        Full interoperability support for non-OPTIMS compliant, e.g. VMware
        VMC presented as CLI developer tool as well an API
        Re-contextualization Mechanism supporting operation time provision of VMs to IPs not known at deployment

    Innovations:

        Flexible deployment time contextualization enabling self-discovery of application resources
        Contextualization of Security, Data and Network aspects `at the VM level
        Elasticity-aware service
        Recontextualization of applications and services enabling autonomic Cloud Computing with self-properties

  • Service Deployment Optimizer

    Role:Optimizes deployment of a service, and coordinating the deployment process in order to provision a service according to the deployment plan.

    Key Features:

        Automated deployment/undeployment process with support for multiple scenarios
        Separates coordination from optimization
        Multiple optimization algorithms
        Deployment in all multi-cloud scenarios
        Compliant with non-Optimis IPs
        Greedy optimization algorithm for improved scalability

    Innovations:

        General approach to service deployment and undeployment
        Coordination and automation of the service the deployment/undeployment process: No human operator involvement is needed. The SDO manages the whole process.
        Supports a range of optimization algorithms with different properties: Different policies: cost min, risk, min, etc. Different algorithms: scalability, quality of solutions, etc.

  • Trust Framework

    Role:Determine trust level for services, IPs and SPs, performing predictions for trust aspects and new services to be deployed.

    Key Features:

        Customized trust calculation for SPs and IPs
        Heterogeneous aspects and calculations
        Understandable trust values aggregation
        More accurate forecasting algorithms applied at different levels

    Innovations:

        Trust Aspects Calculation: Calculate trust for SPs and IPs using heterogeneous aspects and based on resources usage forecasting
        Forecast for new Services: Trust forecast for new services to be deployed in an IP, based on similarities with existing services

  • Service Provider’s Risk Assessment Tools

    Role:Allow the SP to reason about certain assets of service deployment and operation, the risk factors associated with these, and estimate the potential consequences.

    Key Features:

        SP ability to assess the risk associated with IP during SLA negotiation
        SP ability to assess the risk associated with accepting an IP SLA offer
        DS-AHP based assessment supporting multi-criteria for IP assessment
        Adjusted risk of failure for a given IP proposed reliability

    Innovations:

        Risk-based optimized service deployment and coordination across clouds
        IP assessment – How risky it is to deal with IP
        IP SLA offer risk assessment

  • Eco-Efficiency Tool

    Role:Evaluates the energy/ecological efficiency impact which a new service/VM deployment will have if deployed in the infrastructure.

    Key Features:

        Forecast energy efficiency upon service/VM deployment
        Forecasts ecological efficiency upon service/VM deployment
        Considers multiple environmental certifications in the Service Manifest.
        Certifications support
        Consider energy credits

    Innovations:

        Estimate the energy/ecological efficiency impact that the deployment of a new service will have at the different levels (service and infrastructure)

  • Economic (Cost) Framework

    Role:Provides online cost assessment and prediction using traces of hardware (CPU, Network, Memory I/O) utilization.

    Key Features:

        Assessment of service-, VM- and node costs
        Model for integrating risk information into an SLA-penalty mode
        Prediction model to anticipate future trends of hardware resource utilization
        Weighing model for relating service cost to Total Cost of Ownership (TCO

    Innovations:

        Non-linear cost assessment and prediction models: Novel approach to supporting assessment and prediction of service, VM and physical node costs in a cloud data center
        A configurable cost assessment software service: The target resource, polling time, interval and assessment data base can be configured for maximum flexibility

  • Service Manager

    Role:Provides functionality to deploy, undeploy and redeploy services to local and remote locations via interactions with IP.

    Key Features:

        Keeps track of currently running services
        Manage service lifecycle: deploy, redeploy, undeploy
        A GUI front-end for remote service management
        Redeployment via trigger mechanisms with IP blacklisting

    Innovations:

        Innovation 1: IP Redeployment Blacklist
        Mechanism to blacklist IPs used in a previously deployment to prevent redeployment to a know bad provider
        Innovation 2: Triggered Redeployment
        Automatic redeployment of a service to a new IP when reaching TREC thresholds.