Glossary

The above figure presents the most up-to-date view of the iKaaS multi-cloud architecture. A brief description of the main notions in the project and architectural components is as follows (for more details the interested viewer can refer to the iKaaS D4.1 deliverable and its planned update D4.2).

Local Cloud
A Local Cloud comprises sufficient/appropriate computing/storage/networking capabilities and supports requested services in the vicinity of the users. It can be viewed as an implementation of the edge computing paradigm. The main characteristics of a Local Cloud are as follows:
  • It is used as the “attachment point” of IoT devices
  • It can have geographical characteristics
  • It must support virtualization
  • It must support a minimum set of local cloud components. As it will become clearer below, the following components must be supported since they are indispensable for a Local Cloud management and control:
    • Virtual Entity
    • Security Gateway
    • (Local) Service Manager
    • (Local) Resource Catalogue

The other components shown in the figure can be optionally supported depending on the computing/storage/networking capabilities of the Local Cloud.

  • It has an identifiable administrator/owner
  • It adheres to certain regulations/privacy considerations for data access and treatment (region-induced)
  • It must be compliant with the iKaaS API specification
Global Cloud
The Global Cloud can be viewed in the “traditional” cloud view as a “backbone infrastructure”, which increases the business opportunities for service providers and the ubiquity/reliability/performance/efficiency/scalability of service provision by offering additional resources. The main characteristics of a Global Cloud are as follows:
  • It must support virtualization
  • It must support all global cloud components since there does not exist a more powerful alternative to undertake the hosting of these components
  • It can have more than one owners based on the scale and span (geographical) of it
  • It must be compliant with the iKaaS API specification
iKaaS Model
A set of component data models and dependency relations among them that cover the whole lifecycle of data, knowledge and service manipulation in the iKaaS platform.
Knowledge
Data (observations) acquired from individual connected objects or also provided by other stakeholders as well as the outcome of learning and reasoning based on any kind of data. Knowledge is used for both service and platform optimization purposes.
Knowledge-as-a-Service (KaaS)
The provisioning of knowledge in a way that can be queried and exploited for service and platform optimization purposes. This includes both the mechanisms for knowledge generation as well as for storage and query of knowledge.
Security gateway
The security gateway resides at each local cloud and controls the access to data stored in local clouds and IoT devices attached to local clouds subject to privacy preferences and regulations. To do so it refers to the policies stored in the Policy DB and to the privacy certificate issued by the Global Privacy certificate authority (CA).
Service Manager
The Global Service Manager (GSM) works as the core entity that is responsible for the management of the distributed local cloud environments that may be deployed in different geographical places, with different deployment option, network supports, and so on. Essentially, the GSM is the component that manipulates the local clouds heterogeneity and it acts on behalf and/or supports the operation of the external (such as application) and internal (such as Data Processing) entities.
Data Processing
Data Processing provides functionalities for information processing, decision making and learning. Information processing and learning can be applied for both service and platform optimization purposes. Decision making refers to service specific decision making; platform oriented decision making resides with the service managers. Data Processing components can exist in both local and global clouds and can be even migrated among local/global clouds based on service needs, platform constraints as well as regulations.
Trust
In iKaaS, trust refers to two things. Reputation of data sources and human-related trust. The former is a measure of a data source’s ability to provide data that are valid; the latter is a measure of the level of confidence between each pair of users of iKaaS services.
Virtual Entity
A kind of “driver software” that allows an IoT device to become part of an iKaaS platform instantiation. It describes the IoT devices and enables exposing IoT device capabilities in terms of data and control, ensures compatibility of device accessibility as per-service needs.
iKaaS Service
A (Simple) Service corresponds to the functionality provided by the function of a sensor, actuator or other ΙοΤ device e.g. “temperature monitoring” by a temperature sensor. A Complex Service is comprised by one or more simple and/or complex services as well as additional logic/intelligence. It can be composed dynamically for delivering services, required for a certain application in accordance with user/stakeholder requirements.
iKaaS Application
An application exploits one or more (Simple or Complex) services for the benefit of an end user.
Local Cloud DB
Stores data pertaining to a local cloud, such as data acquired from relevant objects/devices (e.g. sensors deployed in a city). The Local Cloud DB also maintains knowledge related to running/executed services and situations in the local cloud. One or more Local Cloud DBs exist in a Local Cloud. Data are stored in the suitable Local Cloud DB since the data management of the Local Cloud DBs must satisfy both a high-speed search of a large volume of data for data analysis and flexible search of data which changes depending on the collection environment.
Global Knowledge DB
This component maintains knowledge related to users and situations in the real world. For example, part of the information maintained in the Global Knowledge DB includes user profile, data and devices repository, which can comprise "static" user information such as age, gender as well as information on current user status based on analysed data acquired from user devices, information on user devices (wearables) being used by the particular user, e.g. type, state (active/non-active), communication details, user relationships, etc. The knowledge maintained in the Global Cloud DB is constantly updated via Data Processing mechanisms. The Global Knowledge DB may be distributed.
Cache DB
This component is used for replicating data retrieved from the Local Cloud DBs, once they are requested by an Application. This is done so that these data can be retrieved faster when needed in the future. As such, the first time specific data are requested from the Local Cloud DBs in order to serve a service request issued by an Application, these data are also cached.
Query Functions
This component has the role of making the queries for data in the Local Cloud DB, understandable by the specific Local Cloud DB they are addressed to. This because there can be one or more Local Cloud DBs in the Local Cloud and data are stored in different Local Cloud DBs. The dispatching mechanism therefore needs to distinguish the type of database in a query and redirect the query to the selected database.
Store Functions
Store Functions collect the data from Virtual Entities, when the Virtual Entities do not implement by themselves appropriate store functions, translate the data format to match the iKaaS data model (translate data to the format of the Local Cloud DBs) and store them in Local Cloud DBs.
Data Conversion
The role of this component is the homogenization of raw static data towards the iKaaS data model.
Policy DB
The policy DB is a database for storing security policies and privacy policies and is used by the Security Gateway.
Static Data DB
Contains raw static data related to city structures such as buildings, roads, bridges etc.
Global Privacy CA
This is an executive agency to address the differences in the regulations between countries.
Resource Catalogue

The resource catalogue constitutes an integration of distributed semantic repositories that include data of the instantiated iKaaS data models, such as the services model, etc. The next subsections provide a short description of each available included component under the resource catalogue federation:

Data catalogue: This type of catalogue includes data of the instantiated datasets model that essentially corresponds to the high-level description of the available datasets across the different integrated local clouds. This catalogue provides an appropriate semantic endpoint that allows to entities the interaction with the stored/available data. Each entity can search and discover particular datasets using different filter parameters, whereas through the exploitation of the instantiated data it is possible to find where the dataset is stored, which measurement types are supported etc. Consequently, the data catalogue is used for the store and management of the datasets information, as a datasets registry.

Service Catalogue: This type of catalogue includes data of the instantiated service model that essentially correspond to the high-level description of the available simple services across the different integrated local clouds. In addition, it stores data related with the description of the complex services. This catalogue provides an appropriate semantic endpoint that allows to entities the interaction with the stored/available data. Each entity can search and discover particular services (simple and complex) using different filter parameters, whereas through the exploitation of the instantiated data it is possible to find where the service is stored, which functional capabilities it supports, etc. Consequently, the service catalogue is used for the store and management of the iKaaS services information, both provided by Virtual Entities but also by the platform back-end.

Platform Catalogue: This type of catalogue includes data of the instantiated platform model that essentially correspond to the high-level description of the available platform that implement the different local clouds. This catalogue provides an appropriate semantic endpoint that allows to entities the interaction with the stored/available data. Each entity can search and discover particular platform information using different filter parameters, whereas through the exploitation of the instantiated data it is possible to find deployment requirements, resource availability for each platform, etc. This catalogue helps to add cloud resources awareness in the service deployment (instantiation and migration) decision making.

Knowledge Catalogue: This type of catalogue includes data of the instantiated knowledge model that essentially correspond to the high-level description of the available knowledge across the different integrated local clouds. This catalogue provides an appropriate semantic endpoint that allows to entities the interaction with the stored/available data. Each entity can search and discover particular datasets using different filter parameters, whereas through the exploitation of the instantiated data it is possible to find where the knowledge is stored, which datasets are associated with this, etc. Consequently, the knowledge catalogue is used for the store and management of the knowledge information, acting as a knowledge registry.

User Catalogue: This component maintains knowledge related to users and situations in the real world. For example, part of the information maintained in the User Catalogue includes user profile, data and devices repository, which can comprise "static" user information such as age, gender as well as information on current user status based on analysed data acquired from user devices, information on user devices (wearables) being used by the particular user.