Hong-Linh Truong presents on managing and testing ensembles of IoT, network functions, and clouds. The document discusses the necessity of blending these resources into ensembles to support various application scenarios. It covers challenges around modeling, composing, configuring, monitoring, and testing the heterogeneous resources. The talk proposes approaches to harmonize the diverse resources, develop monitoring capabilities for the ensembles, and address uncertainties through a combination of model-driven engineering and elastic execution techniques.
In good olden days our predecessors have invented several ways of passing information in hidden form with other objects like papyrus scroll, cryptic etc. As generations crossed through earth's vein we are getting matured and invented several stenographic systems for message passing. The availability of internet in every corner of the universe forced the user of stenographic systems to invent and implement a better secured algorithm for encryption and decryption of text. Here framework will embed text string into digital colour images and the text that is embedded is perceptually invisible to Human Visual System (HVS). Many text stenographic systems are available that are passing the text with digital media as a form of message digest that can be hacked easily. Here this algorithm supplements the conventional algorithms. Instead of forming message digest first a 32-bit secret key will be provided by the encrypted and that is applied on the text with a hash function. On the other end if a snooper tries to perform the extraction of the text with a wrong secret key, he will not be succeeded. In the proposed framework the information of Red (R), Green (G) & Blue (B) values of the pixels of the host colour image are retrieved.
Summary
The Cytoscape Cyberinfrastructure (CI) extends the successful Cytoscape development and community model by enabling network biologists to contribute and leverage microservices deployable at scale. The CI solves many of Cytoscape’s limitations while also delivering novel and dynamic functionality to both Cytoscape and standalone workflows, thus further empowering the already vital network biology community.
Abstract
Cytoscape is an indispensable tool for network data analysis and visualization. One of Cytoscape’s greatest strengths is that it is powered by a vibrant array of developer-contributed apps. However, as network biologists’ requirements evolve, Cytoscape is challenged not only to keep pace, but to lead new and existing developers to create even greater value. Currently, multiscale and multifaceted networks push the memory limits of a Cytoscape workstation, while complex calculations such as Network Based Stratification and Network Based GWAS strain workstation processors. Increasingly, users demand support for collaborative projects, reproducible workflows, and interoperability with external tool chains. Finally, economic pressures favor solutions that promote code and algorithm reusability and evolvability.
In response, we have created the Cytoscape Cyberinfrastructure (CI), which is both an Internet-scale distributed system (based on Microservices [1]) and the network biology community it serves. Its mission is to enable and encourage network biologists to create and deploy high quality, innovative and scalable services focusing on network-based computation, collaboration and visualization.
Microservices can be written in any language, and are highly testable and evolvable. They can run on servers ranging from a single thread to a large cloud-based cluster. They can easily be reused in reproducible workflows or can serve as components in larger services. The CI links microservices via a light weight REST-based aspect-oriented interchange protocol (called CX), which enables tailored data streams while supporting service innovation via evolvable standards. CI infrastructure services support user authentication, long duration job execution, and a service repository that enables researchers to publish their services or discover services published by others. This model builds on the successful Cytoscape app community, which is based on similar mechanisms though at the scale of individual workstations.
Prominent examples of microservices include NDEx [2] (a repository for biological networks), NodeWalker (which uses heat dispersion to identify the most relevant subnetworks containing a given set of genes), cyNetShare [3] (which visualizes a network in a browser) and Cytoscape itself (which can also call CI services). Interfaces are available for Python, IPython, R and Matlab. Future work includes adding clustering, analysis, layout, publishing and display microservices and interfaces to Galaxy and Taverna workflows.
My presentation at The 2nd Portugal|UT Austin summer school in systems and networking and
EMJD-DC spring event 2016
June 3, 2016. Costa da Caparica, Portugal describing my thesis work
The Presentation Slides of the Third OpenDaylight Lisboa Meetup held at FCUL, Lisboa, Portugal. This gives some basic outlines to SDN and OpenDaylight project.
In good olden days our predecessors have invented several ways of passing information in hidden form with other objects like papyrus scroll, cryptic etc. As generations crossed through earth's vein we are getting matured and invented several stenographic systems for message passing. The availability of internet in every corner of the universe forced the user of stenographic systems to invent and implement a better secured algorithm for encryption and decryption of text. Here framework will embed text string into digital colour images and the text that is embedded is perceptually invisible to Human Visual System (HVS). Many text stenographic systems are available that are passing the text with digital media as a form of message digest that can be hacked easily. Here this algorithm supplements the conventional algorithms. Instead of forming message digest first a 32-bit secret key will be provided by the encrypted and that is applied on the text with a hash function. On the other end if a snooper tries to perform the extraction of the text with a wrong secret key, he will not be succeeded. In the proposed framework the information of Red (R), Green (G) & Blue (B) values of the pixels of the host colour image are retrieved.
Summary
The Cytoscape Cyberinfrastructure (CI) extends the successful Cytoscape development and community model by enabling network biologists to contribute and leverage microservices deployable at scale. The CI solves many of Cytoscape’s limitations while also delivering novel and dynamic functionality to both Cytoscape and standalone workflows, thus further empowering the already vital network biology community.
Abstract
Cytoscape is an indispensable tool for network data analysis and visualization. One of Cytoscape’s greatest strengths is that it is powered by a vibrant array of developer-contributed apps. However, as network biologists’ requirements evolve, Cytoscape is challenged not only to keep pace, but to lead new and existing developers to create even greater value. Currently, multiscale and multifaceted networks push the memory limits of a Cytoscape workstation, while complex calculations such as Network Based Stratification and Network Based GWAS strain workstation processors. Increasingly, users demand support for collaborative projects, reproducible workflows, and interoperability with external tool chains. Finally, economic pressures favor solutions that promote code and algorithm reusability and evolvability.
In response, we have created the Cytoscape Cyberinfrastructure (CI), which is both an Internet-scale distributed system (based on Microservices [1]) and the network biology community it serves. Its mission is to enable and encourage network biologists to create and deploy high quality, innovative and scalable services focusing on network-based computation, collaboration and visualization.
Microservices can be written in any language, and are highly testable and evolvable. They can run on servers ranging from a single thread to a large cloud-based cluster. They can easily be reused in reproducible workflows or can serve as components in larger services. The CI links microservices via a light weight REST-based aspect-oriented interchange protocol (called CX), which enables tailored data streams while supporting service innovation via evolvable standards. CI infrastructure services support user authentication, long duration job execution, and a service repository that enables researchers to publish their services or discover services published by others. This model builds on the successful Cytoscape app community, which is based on similar mechanisms though at the scale of individual workstations.
Prominent examples of microservices include NDEx [2] (a repository for biological networks), NodeWalker (which uses heat dispersion to identify the most relevant subnetworks containing a given set of genes), cyNetShare [3] (which visualizes a network in a browser) and Cytoscape itself (which can also call CI services). Interfaces are available for Python, IPython, R and Matlab. Future work includes adding clustering, analysis, layout, publishing and display microservices and interfaces to Galaxy and Taverna workflows.
My presentation at The 2nd Portugal|UT Austin summer school in systems and networking and
EMJD-DC spring event 2016
June 3, 2016. Costa da Caparica, Portugal describing my thesis work
The Presentation Slides of the Third OpenDaylight Lisboa Meetup held at FCUL, Lisboa, Portugal. This gives some basic outlines to SDN and OpenDaylight project.
ISWC 2016 Tutorial: Semantic Web of Things M3 framework & FIESTA-IoT EU projectFIESTA-IoT
Amelie Gyrard presents a tutorial on SWOT - the Semantic Web of Things.
For further information about this work. Please visit:
http://semantic-web-of-things.appspot.com
A predictive model for network intrusion detection using stacking approach IJECEIAES
Due to the emerging technological advances, cyber-attacks continue to hamper information systems. The changing dimensionality of cyber threat landscape compel security experts to devise novel approaches to address the problem of network intrusion detection. Machine learning algorithms are extensively used to detect intrusions by dint of their remarkable predictive power. This work presents an ensemble approach for network intrusion detection using a concept called Stacking. As per the popular no free lunch theorem of machine learning, employing single classifier for a problem at hand may not be ideal to achieve generalization. Therefore, the proposed work on network intrusion detection emphasizes upon a combinative approach to improve performance. A robust processing paradigm called Graphlab Create, capable of upholding massive data has been used to implement the proposed methodology. Two benchmark datasets like UNSW NB-15 and UGR’ 16 datasets are considered to demonstrate the validity of predictions. Empirical investigation has illustrated that the performance of the proposed approach has been reasonably good. The contribution of the proposed approach lies in its finesse to generate fewer misclassifications pertaining to various attack vectors considered in the study.
Deep learning is not merely an AI technique or a software program, but a new class of smart network information technology that is changing the concept of the modern technology project by offering real-time engagement with reality
Deep learning is a data automation method that replaces hard-coded software with a capacity, in the form of a learning network that is trained to perform a task
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
AN EFFICIENT SECURE CRYPTOGRAPHY SCHEME FOR NEW ML-BASED RPL ROUTING PROTOCOL...IJNSA Journal
Internet of Things (IoT) offers reliable and seamless communication for the heterogeneous dynamic lowpower and lossy network (LLNs). To perform effective routing in IoT communication, LLN Routing Protocol (RPL) is developed for the tiny nodes to establish connection by using deflaut objective functions: OF0, MRHOF, for which resources are constraints like battery power, computation capacity, memory communication link impacts on varying traffic scenarios in terms of QoS metrics like packet delivery ratio, delay, secure communication channel. At present, conventional Internet of Things (IoT) are having secure communication channels issue for transmission of data between nodes. To withstand those issues, it is necessary to balance resource constraints of nodes in the network. In this paper, we developed a security algorithm for IoT networks with RPL routing. Initially, the constructed network in corporates optimizationbased deep learning (reinforcement learning) for route establishment in IoT. Upon the establishment of the route, the ClonQlearn based security algorithm is implemented for improving security which is based onaECC scheme for encryption and decryption of data. The proposed security technique incorporates reinforcement learning-based ClonQlearnintegrated with ECC (ClonQlearn+ECC) for random key generation. The proposed ClonQlearn+ECCexhibits secure data transmission with improved network performance when compared with the earlier works in simulation. The performance of network expressed that the proposed ClonQlearn+ECC increased the PDR of approximately 8% - 10%, throughput of 7% - 13%, end-to-end delay of 5% - 10% and power consumption variation of 3% - 7%.
eCAS 2021: Towards Pulverised Architectures for Collective Adaptive Systems t...Gianluca Aguzzi
Engineering large-scale Cyber-Physical Systems–like robot swarms, augmented crowds, and smart cities – is challenging, for many issues have to be addressed, including specifying their collective adaptive behaviour and managing the connection of the digital and physical parts. In particular, some approaches propose self-organising mechanisms to actually program global behaviour while fostering decentralised, asynchronous execution. However, most of these approaches couple behavioural specifications to specific network architectures (e.g.,peer-to-peer), and therefore do not promote flexible exploitation of the underlying infrastructure. Conversely, pulverisation is a recent approach that enables self-organising behaviour to be defined independently of the available infrastructure while retaining functional correctness. Currently, however, no tools are available to formally specify and verify concrete architectures for pulverised applications. Therefore, in this work we propose to combine pulverisation with multi-tier programming, a paradigm that supports the specification of the architecture of distributed systems in a single code base, and enables static checks for the correctness of actual deployments. The approach can be seamlessly implemented by combining the ScaFi aggregate computing tool-chain with the ScalaLoci multi-tier programming language, paving the path fora coherent support to the development of self-organising cyber-physical systems, addressing both functional (behaviour) and non-functional concerns (deployment) in a single code base and modular fashion.
ScaFi-Web, A Web-Based application for Field-based CoordinationGianluca Aguzzi
Field-based coordination is a model for expressing the coordination logic of large-scale adaptive systems, composing functional
blocks from a global perspective. As for any coordination model, a proper toolchain must be developed to support its adoption across all development phases. Under this point of view, the ScaFi toolkit provides a coordination language (field calculus) as a DSL internal in the Scala
language, a library of reusable building blocks, and an infrastructure
for simulation of distributed deployments. In this work, we enrich such
a toolchain by introducing ScaFi-Web, a web-based application allowing in-browser editing, execution, and visualisation of ScaFi programs.
ScaFi-Web facilitates access to the ScaFi coordination technology by
flattening the learning curve and simplifying configuration and requirements, thus promoting agile prototyping of field-based coordination specifications. In turn, this opens the door to easier demonstrations and experimentation, and also constitutes a stepping stone towards monitoring
and control of simulated/deployed systems.
Repository: https://github.com/scafi/scafi-web
Fog Computing: Implementation of a Simple Fog Scenario Through IoT Public Ser...Teodoro Montanaro
Presentation of the paper "Fog Computing: Implementation of a Simple Fog Scenario Through IoT Public Services" at the IEEE Splitech 2021 Conference - https://2021.splitech.org/
Modeling and Provisioning IoT Cloud Systems for Testing UncertaintiesHong-Linh Truong
Modern Cyber-Physical Systems (CPS) and Internet of Things (IoT)
systems consist of both loosely and tightly interactions among
various resources in IoT networks, edge servers and cloud data
centers. These elements are being built atop virtualization layers
and deployed in both edge and cloud infrastructures. They also deal
with a lot of data through the interconnection of different types of
networks and services. Therefore, several new types of uncertainties
are emerging, such as data, actuation, and elasticity uncertainties.
This triggers several challenges for testing uncertainty in such
systems. However, there is a lack of novel ways to model and
prepare the right infrastructural elements covering requirements
for testing emerging uncertainties. In this paper, first we present
techniques for modeling CPS/IoT Systems and their uncertainties
to be tested. Second, we introduce techniques for determining and
generating deployment configuration for testing in different IoT
and cloud infrastructures. We illustrate our work with a real-world
use case for monitoring and analysis of Base Transceiver Stations.
A framework for improving security in cloud computingAJIT M KARANJKAR
Paper Presentation on cloud Computing, we have present the architecture diagram , As well as We have provides in cloud many advantages for individuals and small organizations; it can also create some serious security issues with personal and confidential data.
Towards a Resource Slice Interoperability Hub for IoTHong-Linh Truong
Interoperability for IoT is a challenging problem
because it requires us to tackle (i) cross-system interoperability
issues at the IoT platform sides as well as relevant network
functions and clouds in the edge systems and data centers
and (ii) cross-layer interoperability, e.g., w.r.t. data formats,
communication protocols, data delivery mechanisms, and perfor-
mance. However, existing solutions are quite static w.r.t software
deployment and provisioning for interoperability. Many middle-
ware, services and platforms have been built and deployed as
interoperability bridges but they are not dynamically provisioned
and reconfigured for interoperability at runtime. Furthermore,
they are often not considered together with other services as a
whole in application-specific contexts. In this paper, we focus
on dynamic aspects by introducing the concept of Resource
Slice Interoperability Hub (rsiHub). Our approach leverages
existing software artifacts and services for interoperability to
create and provision dynamic resource slices, including IoT,
network functions and clouds, for addressing application-specific
interoperability requirements. We will present our key concepts,
architectures and examples toward the realization of rsiHub.
Testing Uncertainty of Cyber-Physical Systems in IoT Cloud Infrastructures: C...Hong-Linh Truong
Today’s cyber-physical systems (CPS) span IoT and cloud-based
datacenter infrastructures, which are highly heterogeneous with
various types of uncertainty. Thus, testing uncertainties in these
CPS is a challenging and multidisciplinary activity. We need several
tools for modeling, deployment, control, and analytics to test and
evaluate uncertainties for different configurations of the same CPS.
In this paper, we explain why using state-of-the art model-driven
engineering (MDE) and model-based testing (MBT) tools is not
adequate for testing uncertainties of CPS in IoT Cloud infrastruc-
tures. We discus how to combine them with techniques for elastic
execution to dynamically provision both CPS under test and testing
utilities to perform tests in various IoT Cloud infrastructures.
HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions...Hong-Linh Truong
Effective resource management in IoT systems must
represent IoT resources, edge-to-cloud network capabilities, and
cloud resources at a high-level, while being able to link to diverse
low-level types of IoT devices, network functions, and cloud
computing infrastructures. Hence resource management in such
a context demands a highly distributed and extensible approach,
which allows us to integrate and provision IoT, network functions,
and cloud resources from various providers. In this paper, we
address this crucial research issue. We first present a high-
level information model for virtualized IoT, network functions
and cloud resource modeling, which also incorporates software-
defined gateways, network slicing and data centers. This model
is used to glue various low-level resource models from different
types of infrastructures in a distributed manner to capture
sets of resources spanning across different sub-networks. We
then develop a set of utilities and a middleware to support
the integration of information about distributed resources from
various sources. We present a proof of concept prototype with
various experiments to illustrate how various tasks in IoT cloud
systems can be simplified as well as to evaluate the performance
of our framework.
Information Technology in Industry(ITII) - November Issue 2018ITIIIndustries
IT Industry publishes original research articles, review articles, and extended versions of conference papers. Articles resulting from research of both theoretical and/or practical natures performed by academics and/or industry practitioners are welcome. IT in Industry aims to become a leading IT journal with a high impact factor.
BDE-BDVA Webinar: BigDataEurope Overview & Synergies with BDVABigData_Europe
Short outline of the project's mission and current status & summary of the identified synergies between BDVA and the project, included those at a technical level.
ISWC 2016 Tutorial: Semantic Web of Things M3 framework & FIESTA-IoT EU projectFIESTA-IoT
Amelie Gyrard presents a tutorial on SWOT - the Semantic Web of Things.
For further information about this work. Please visit:
http://semantic-web-of-things.appspot.com
A predictive model for network intrusion detection using stacking approach IJECEIAES
Due to the emerging technological advances, cyber-attacks continue to hamper information systems. The changing dimensionality of cyber threat landscape compel security experts to devise novel approaches to address the problem of network intrusion detection. Machine learning algorithms are extensively used to detect intrusions by dint of their remarkable predictive power. This work presents an ensemble approach for network intrusion detection using a concept called Stacking. As per the popular no free lunch theorem of machine learning, employing single classifier for a problem at hand may not be ideal to achieve generalization. Therefore, the proposed work on network intrusion detection emphasizes upon a combinative approach to improve performance. A robust processing paradigm called Graphlab Create, capable of upholding massive data has been used to implement the proposed methodology. Two benchmark datasets like UNSW NB-15 and UGR’ 16 datasets are considered to demonstrate the validity of predictions. Empirical investigation has illustrated that the performance of the proposed approach has been reasonably good. The contribution of the proposed approach lies in its finesse to generate fewer misclassifications pertaining to various attack vectors considered in the study.
Deep learning is not merely an AI technique or a software program, but a new class of smart network information technology that is changing the concept of the modern technology project by offering real-time engagement with reality
Deep learning is a data automation method that replaces hard-coded software with a capacity, in the form of a learning network that is trained to perform a task
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
AN EFFICIENT SECURE CRYPTOGRAPHY SCHEME FOR NEW ML-BASED RPL ROUTING PROTOCOL...IJNSA Journal
Internet of Things (IoT) offers reliable and seamless communication for the heterogeneous dynamic lowpower and lossy network (LLNs). To perform effective routing in IoT communication, LLN Routing Protocol (RPL) is developed for the tiny nodes to establish connection by using deflaut objective functions: OF0, MRHOF, for which resources are constraints like battery power, computation capacity, memory communication link impacts on varying traffic scenarios in terms of QoS metrics like packet delivery ratio, delay, secure communication channel. At present, conventional Internet of Things (IoT) are having secure communication channels issue for transmission of data between nodes. To withstand those issues, it is necessary to balance resource constraints of nodes in the network. In this paper, we developed a security algorithm for IoT networks with RPL routing. Initially, the constructed network in corporates optimizationbased deep learning (reinforcement learning) for route establishment in IoT. Upon the establishment of the route, the ClonQlearn based security algorithm is implemented for improving security which is based onaECC scheme for encryption and decryption of data. The proposed security technique incorporates reinforcement learning-based ClonQlearnintegrated with ECC (ClonQlearn+ECC) for random key generation. The proposed ClonQlearn+ECCexhibits secure data transmission with improved network performance when compared with the earlier works in simulation. The performance of network expressed that the proposed ClonQlearn+ECC increased the PDR of approximately 8% - 10%, throughput of 7% - 13%, end-to-end delay of 5% - 10% and power consumption variation of 3% - 7%.
eCAS 2021: Towards Pulverised Architectures for Collective Adaptive Systems t...Gianluca Aguzzi
Engineering large-scale Cyber-Physical Systems–like robot swarms, augmented crowds, and smart cities – is challenging, for many issues have to be addressed, including specifying their collective adaptive behaviour and managing the connection of the digital and physical parts. In particular, some approaches propose self-organising mechanisms to actually program global behaviour while fostering decentralised, asynchronous execution. However, most of these approaches couple behavioural specifications to specific network architectures (e.g.,peer-to-peer), and therefore do not promote flexible exploitation of the underlying infrastructure. Conversely, pulverisation is a recent approach that enables self-organising behaviour to be defined independently of the available infrastructure while retaining functional correctness. Currently, however, no tools are available to formally specify and verify concrete architectures for pulverised applications. Therefore, in this work we propose to combine pulverisation with multi-tier programming, a paradigm that supports the specification of the architecture of distributed systems in a single code base, and enables static checks for the correctness of actual deployments. The approach can be seamlessly implemented by combining the ScaFi aggregate computing tool-chain with the ScalaLoci multi-tier programming language, paving the path fora coherent support to the development of self-organising cyber-physical systems, addressing both functional (behaviour) and non-functional concerns (deployment) in a single code base and modular fashion.
ScaFi-Web, A Web-Based application for Field-based CoordinationGianluca Aguzzi
Field-based coordination is a model for expressing the coordination logic of large-scale adaptive systems, composing functional
blocks from a global perspective. As for any coordination model, a proper toolchain must be developed to support its adoption across all development phases. Under this point of view, the ScaFi toolkit provides a coordination language (field calculus) as a DSL internal in the Scala
language, a library of reusable building blocks, and an infrastructure
for simulation of distributed deployments. In this work, we enrich such
a toolchain by introducing ScaFi-Web, a web-based application allowing in-browser editing, execution, and visualisation of ScaFi programs.
ScaFi-Web facilitates access to the ScaFi coordination technology by
flattening the learning curve and simplifying configuration and requirements, thus promoting agile prototyping of field-based coordination specifications. In turn, this opens the door to easier demonstrations and experimentation, and also constitutes a stepping stone towards monitoring
and control of simulated/deployed systems.
Repository: https://github.com/scafi/scafi-web
Fog Computing: Implementation of a Simple Fog Scenario Through IoT Public Ser...Teodoro Montanaro
Presentation of the paper "Fog Computing: Implementation of a Simple Fog Scenario Through IoT Public Services" at the IEEE Splitech 2021 Conference - https://2021.splitech.org/
Modeling and Provisioning IoT Cloud Systems for Testing UncertaintiesHong-Linh Truong
Modern Cyber-Physical Systems (CPS) and Internet of Things (IoT)
systems consist of both loosely and tightly interactions among
various resources in IoT networks, edge servers and cloud data
centers. These elements are being built atop virtualization layers
and deployed in both edge and cloud infrastructures. They also deal
with a lot of data through the interconnection of different types of
networks and services. Therefore, several new types of uncertainties
are emerging, such as data, actuation, and elasticity uncertainties.
This triggers several challenges for testing uncertainty in such
systems. However, there is a lack of novel ways to model and
prepare the right infrastructural elements covering requirements
for testing emerging uncertainties. In this paper, first we present
techniques for modeling CPS/IoT Systems and their uncertainties
to be tested. Second, we introduce techniques for determining and
generating deployment configuration for testing in different IoT
and cloud infrastructures. We illustrate our work with a real-world
use case for monitoring and analysis of Base Transceiver Stations.
A framework for improving security in cloud computingAJIT M KARANJKAR
Paper Presentation on cloud Computing, we have present the architecture diagram , As well as We have provides in cloud many advantages for individuals and small organizations; it can also create some serious security issues with personal and confidential data.
Towards a Resource Slice Interoperability Hub for IoTHong-Linh Truong
Interoperability for IoT is a challenging problem
because it requires us to tackle (i) cross-system interoperability
issues at the IoT platform sides as well as relevant network
functions and clouds in the edge systems and data centers
and (ii) cross-layer interoperability, e.g., w.r.t. data formats,
communication protocols, data delivery mechanisms, and perfor-
mance. However, existing solutions are quite static w.r.t software
deployment and provisioning for interoperability. Many middle-
ware, services and platforms have been built and deployed as
interoperability bridges but they are not dynamically provisioned
and reconfigured for interoperability at runtime. Furthermore,
they are often not considered together with other services as a
whole in application-specific contexts. In this paper, we focus
on dynamic aspects by introducing the concept of Resource
Slice Interoperability Hub (rsiHub). Our approach leverages
existing software artifacts and services for interoperability to
create and provision dynamic resource slices, including IoT,
network functions and clouds, for addressing application-specific
interoperability requirements. We will present our key concepts,
architectures and examples toward the realization of rsiHub.
Testing Uncertainty of Cyber-Physical Systems in IoT Cloud Infrastructures: C...Hong-Linh Truong
Today’s cyber-physical systems (CPS) span IoT and cloud-based
datacenter infrastructures, which are highly heterogeneous with
various types of uncertainty. Thus, testing uncertainties in these
CPS is a challenging and multidisciplinary activity. We need several
tools for modeling, deployment, control, and analytics to test and
evaluate uncertainties for different configurations of the same CPS.
In this paper, we explain why using state-of-the art model-driven
engineering (MDE) and model-based testing (MBT) tools is not
adequate for testing uncertainties of CPS in IoT Cloud infrastruc-
tures. We discus how to combine them with techniques for elastic
execution to dynamically provision both CPS under test and testing
utilities to perform tests in various IoT Cloud infrastructures.
HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions...Hong-Linh Truong
Effective resource management in IoT systems must
represent IoT resources, edge-to-cloud network capabilities, and
cloud resources at a high-level, while being able to link to diverse
low-level types of IoT devices, network functions, and cloud
computing infrastructures. Hence resource management in such
a context demands a highly distributed and extensible approach,
which allows us to integrate and provision IoT, network functions,
and cloud resources from various providers. In this paper, we
address this crucial research issue. We first present a high-
level information model for virtualized IoT, network functions
and cloud resource modeling, which also incorporates software-
defined gateways, network slicing and data centers. This model
is used to glue various low-level resource models from different
types of infrastructures in a distributed manner to capture
sets of resources spanning across different sub-networks. We
then develop a set of utilities and a middleware to support
the integration of information about distributed resources from
various sources. We present a proof of concept prototype with
various experiments to illustrate how various tasks in IoT cloud
systems can be simplified as well as to evaluate the performance
of our framework.
Information Technology in Industry(ITII) - November Issue 2018ITIIIndustries
IT Industry publishes original research articles, review articles, and extended versions of conference papers. Articles resulting from research of both theoretical and/or practical natures performed by academics and/or industry practitioners are welcome. IT in Industry aims to become a leading IT journal with a high impact factor.
BDE-BDVA Webinar: BigDataEurope Overview & Synergies with BDVABigData_Europe
Short outline of the project's mission and current status & summary of the identified synergies between BDVA and the project, included those at a technical level.
SINC – An Information-Centric Approach for End-to-End IoT Cloud Resource Prov...Hong-Linh Truong
We present SINC –
Slicing IoT, Network Functions, and Clouds – which enables designers to dynamically create/update end-to-end slices of the overall IoT network in order to simultaneously meet multiple user needs.
2nd International Conference on Cloud, Big Data and IoT (CBIoT 2021)ijccsa
2nd International Conference on Cloud, Big Data and IoT (CBIoT 2021) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research
projects in the areas of Cloud, Big Data and IoT. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and IoT.
2nd International Conference on Cloud, Big Data and IoT (CBIoT 2021)IJCNCJournal
2nd International Conference on Cloud, Big Data and IoT (CBIoT 2021) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and IoT. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and IoT.
The slides defines IoT and show the differnce between M2M and IoT vision. It then describes the different layers that depicts the functional architecture of IoT, standard organizations and bodies and other IoT technology alliances, low power IoT protocols, IoT Platform components, and finally gives a short description to one of IoT low power application protocols (MQTT).
2 nd International Conference on Cloud, Big Data and IoT (CBIoT 2021)ijwscjournal
2
nd International Conference on Cloud, Big Data and IoT (CBIoT 2021) will act as a
major forum for the presentation of innovative ideas, approaches, developments, and research
projects in the areas of Cloud, Big Data and IoT. It will also serve to facilitate the exchange
of information between researchers and industry professionals to discuss the latest issues and
advancement in the area of Cloud, Big Data and IoT.
CPaaS.io Y1 Review Meeting - Holistic Data ManagementStephan Haller
Data management and governance aspects of the CPaaS.io platform as presented at the first year review meeting in Tokyo on October 5, 2017.
Disclaimer:
This document has been produced in the context of the CPaaS.io project which is jointly funded by the European Commission (grant agreement n° 723076) and NICT from Japan (management number 18302). All information provided in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission and NICT have no liability in respect of this document, which is merely representing the view of the project consortium. This document is subject to change without notice.
Security Analysis of IEEE 802.21 Standard in Software Defined Wireless Networ...Asma Swapna
Software Defined Networking (SDN) is the best
choice in establishing a software controlled inter-domain network. Convergence of different Wireless link technologies bring the mobile users to choose the network being in any geographical location. IEEE 802.21 is such a standard for exchanging networking information for connecting with the network being at any region in the world. Integrated with SDN wireless network this functionality of IEEE 802.21 standard can discover programmable network services with profound resource utilization. However, the information exchange should circulate through a reliable source. Hence, the security analysis of IEEE 802.21 Media Independent Handover (MIH) mechanism for Software Defined Wireless Network (SDWN) is the primary concern of this research work. This study, conducts architectural and functional analysis of MIH integrated with SDWN interface for mobility management of the wireless nodes. The outcome specifies a possible integration with future deployment opportunities in information exchange of IEEE 802.21 MIH for programmable network devices.
Integrated Analytics for IIoT Predictive Maintenance using IoT Big Data Cloud...Hong-Linh Truong
For predictive maintenance of equipment with In-
dustrial Internet of Things (IIoT) technologies, existing IoT Cloud
systems provide strong monitoring and data analysis capabilities
for detecting and predicting status of equipment. However, we
need to support complex interactions among different software
components and human activities to provide an integrated analyt-
ics, as software algorithms alone cannot deal with the complexity
and scale of data collection and analysis and the diversity of
equipment, due to the difficulties of capturing and modeling
uncertainties and domain knowledge in predictive maintenance.
In this paper, we describe how we design and augment complex
IoT big data cloud systems for integrated analytics of IIoT
predictive maintenance. Our approach is to identify various
complex interactions for solving system incidents together with
relevant critical analytics results about equipment. We incorpo-
rate humans into various parts of complex IoT Cloud systems
to enable situational data collection, services management, and
data analytics. We leverage serverless functions, cloud services,
and domain knowledge to support dynamic interactions between
human and software for maintaining equipment. We use a real-
world maintenance of Base Transceiver Stations to illustrate our
engineering approach which we have prototyped with state-of-
the art cloud and IoT technologies, such as Apache Nifi, Hadoop,
Spark and Google Cloud Functions.
On Supporting Contract-aware IoT Dataspace ServicesHong-Linh Truong
Advances in the Internet of Things (IoT) enable a
huge number of connected devices that produce large amounts
of data. Such data is increasingly shared among various
stakeholders to support advanced (predictive) analytics and
precision decision making in different application domains like
smart cities and industrial internet. Currently there are several
platforms that facilitate sharing, buying and selling IoT data.
However, these platforms do not support the establishment and
monitoring of usage contracts for IoT data. In this paper we
address this research issue by introducing a new extensible
platform for enabling contract-aware IoT dataspace services,
which supports data contract specification and IoT data flow
monitoring based on established data contracts. We present
a general architecture of contract monitoring services for
IoT dataspaces and evaluate our platform through illustrative
examples with real-world datasets and through performance
analysis.
Towards the Realization of Multi-dimensional Elasticity for Distributed Cloud...Hong-Linh Truong
As multiple types of distributed, heterogeneous cloud computing environments have proliferated, cloud software can leverage
diverse types of infrastructural, platform and data resources with di
erent cost and quality models. This introduces a multi-
dimensional elasticity perspective for cloud software that would greatly meet changing demands from the user. However, we argue
that current techniques are not enough for dealing with multi-dimensional elasticity in distributed cloud environments. We present
our approach to the realization of multi-dimensional elasticity by introducing novel concepts and a roadmap to achieve them.
On Engineering Analytics of Elastic IoT Cloud SystemsHong-Linh Truong
Developing IoT cloud platforms is very challenging, as IoT
cloud platforms consist of a mix of cloud services and IoT elements, e.g.,
for sensor management, near-realtime events handling, and data analyt-
ics. Developers need several tools for deployment, control, governance
and analytics actions to test and evaluate designs of software compo-
nents and optimize the operation of di erent design con gurations. In
this paper, we describe requirements and our techniques on support-
ing the development and testing of IoT cloud platforms. We present our
choices of tools and engineering actions that help the developer to design,
test and evaluate IoT cloud platforms in multi-cloud environments.
Governing Elastic IoT Cloud Systems under UncertaintiesHong-Linh Truong
we introduce U-GovOps – a novel framework for
dynamic, on-demand governance of elastic IoT cloud systems under
uncertainty. We introduce a declarative policy language to simplify
the development of uncertainty- and elasticity-aware governance
strategies. Based on that we develop runtime mechanisms, which
enable mitigating the uncertainties by monitoring and governing
the IoT cloud systems through specified strategies.
SmartSociety – A Platform for Collaborative People-Machine ComputationHong-Linh Truong
We present the SmartSociety Platform for Collaborative People-Machine computation carried out in the FET SmartSociety project: http://www.smart-society-project.eu/
On Developing and Operating of Data Elasticity Management ProcessHong-Linh Truong
The Data-as-a-Service (DaaS) model enables data analytics
providers to provision and offer data assets to their consumers. To achieve quality of results for the data assets, we need to enable DaaS elasticity by trading off quality and cost of resource usage. However, most of the current work on DaaS is focused on infrastructure elasticity, such as scaling
in/out data nodes and virtual machines based on performance and usage, without considering the data assets' quality of results. In this talk, we introduce an elastic data asset model for provisioning data enriched with quality of results. Based on this model, we present techniques to generate and operate data elasticity management process that is used to
monitor, evaluate and enforce expected quality of results. We develop a runtime system to guarantee the quality of resulting data assets provisioned on-demand. We present several experiments to demonstrate the usefulness of our proposed techniques.
TUW-ASE Summer 2015 - Quality of Result-aware data analyticsHong-Linh Truong
This is a lecture from the advanced service engineering course from the Vienna University of Technology. See http://dsg.tuwien.ac.at/teaching/courses/ase
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Managing and Testing Ensembles of IoT, Network functions, and Clouds
1. Managing and Testing Ensembles of IoT,
Network functions, and Clouds
Hong-Linh Truong
Distributed Systems Group, TU Wien
truong@dsg.tuwien.ac.at
http://rdsea.github.io
CCI@USC, Los Angeles, 21 Sep 2017 1
2. CCI@USC, Los Angeles, 21 Sep 2017 2
Acknowledgements:
Many results are from the joint work with students
and colleagues:
Luca Berardinelli, Duc-Hung Le, Nanjangud C.
Narendra, Christian Proinger, Daniel Moldovan, Ivan
Pavkovic, Georgiana Copil, Stefan Nastic
and from
H2020 U-test (www.u-test.eu)
H2020 Inter-IoT (http://www.inter-iot-project.eu/)
Note: some work are under submission.
3. Outline
Application-oriented ensembles of IoT, Network
functions and Cloud resources
Resource Management
Monitoring
Uncertainty and Testing Uncertainties of
Infrastructures
Conclusions and Future work
CCI@USC, Los Angeles, 21 Sep 2017 3
4. CCI@USC, Los Angeles, 21 Sep 2017 4
Application-oriented Ensembles of IoT,
Network functions, & Clouds: Necessity?
5. Systems of IoT, Network Functions,
and Clouds
5
CCI@USC, Los Angeles, 21 Sep 2017
6. Moving to blending IoT, network
functions and cloud resources
Type 1
Mainly focus on IoT networks: sensors, IoT gateways, IoT-to-
cloud connectivity (e.g., connect to predix.io, IBM Bluemix,
Azure IoT, Google Cloud, Amazon IoT, etc.)
Type 2
Mainly focus on (public/private) services in data centers: e.g.,
IoT data hubs, NoSQL databases, and big data ingest systems
Type 3
Equally focus on both IoT and cloud sides and have the need
to control at both sides
Highly interactions between the two sides, including the network
in the middle
CCI@USC, Los Angeles, 21 Sep 2017 6
7. All types of service models
Cloud resources as services are known
NFV/5G coming and more service providers at
the edge are available
Network function virtualization
IoT infrastructure-as-a-service
Pay-per-use IoT communication
IoT data-as-a-service Cloud service models
Public and private providers
CCI@USC, Los Angeles, 21 Sep 2017 7
8. Examples of IoT networks
CCI@USC, Los Angeles, 21 Sep 2017 8
http://www.sktelecom.com/en/press/detail.do?idx=1172
9. Some application scenarios
Emergency responses, on-demand crowd sensing, Geo
Sports monitoring, cyber-physical systems testing, etc.
CCI@USC, Los Angeles, 21 Sep 2017 9
Geo Sports: Picture courtesy
Future Position X, Sweden
10. Emergency in a Seaport
CCI@USC, Los Angeles, 21 Sep 2017 10
Built a top existing INTER-IoT scenarios
Coordinating activities when an accident happens
Example of actions in a resource slice in a seaport:
1) activating monitoring containers with sensitive goods in the port
2) analyzing and controlling robotic cranes and trucks to make sure that
they do not prevent the emergency responses as well as ready to
support the responses
3) sending alarms and controlling vessel arrivals and revising transport
planning,
4) providing information for operational assistance for the emergency
responses
5) activating systems to support the monitoring of people impacted by the
accident using devices and platforms for chronic disease and cognitive
decline prevention.
11. Examples of IoT data services
IoT data as a service can be offered by different types
of providers
11
Florin-Bogdan Balint, Hong-Linh Truong, On Supporting Contract-aware IoT Dataspace Services, the 5th IEEE International Conference on Mobile
Cloud Computing, Services, and Engineering (MobileCloud 2017), 6-8 April 2017 in San Francisco, USA
.
CCI@USC, Los Angeles, 21 Sep 2017
12. Ensembles of IoT, Network
functions and clouds
CCI@USC, Los Angeles, 21 Sep 2017 12
13. Challenges
Modeling distributed IoT, network functions and
cloud capabilities in an ensemble
Slicing end-to-end network of resources
Composing resources in ensembles of IoT,
network functions and clouds
(Re-)configuring composed resources
Testing and monitoring
CCI@USC, Los Angeles, 21 Sep 2017 13
The SINConcept: http://sincconcept.github.io/
Hong-Linh Truong, Nanjangud Narendra, SINC - An Information-Centric Approach for End-to-End IoT Cloud Resource Provisioning, 2016
International Conference on Cloud Computing Research & Innovation (ICCCRI2016), CloudAsia 2016, May 3-5, 2016, Singapore
14. CCI@USC, Los Angeles, 21 Sep 2017 14
Tools treating IoT, network functions,
and cloud services in an isolated
manner are not enough
15. Service engineering analytics for
IoT Cloud Systems
15CCI@USC, Los Angeles, 21 Sep 2017
http://tuwiendsg.github.io/iCOMOT/
We started in 2011 without network functions in our mind!
16. Monitoring, Controlling and Testing
IoT Cloud Systems
CCI@USC, Los Angeles, 21 Sep 2017 16
Check: http://tuwiendsg.github.io/iCOMOT/demo.html
17. Configuration is not done easy for
the ensembles
CCI@USC, Los Angeles, 21 Sep 2017 17
Sensors/gateways and cloud services have different
management systems and interfaces
Deploy different topologies and configure topologies
to work together is hard
IoT sensors Cloud Service
18. Cross IoT and cloud controls
CCI@USC, Los Angeles, 21 Sep 2017 18
IoT gateways control is very different from cloud controls
Low-level REST API in IoT gateways management and high
level elasticity rules for cloud services
Different protocols for communicating with resource managers
Control for IoT Gateways Control for Clouds
19. Too low-level Infrastructure-level
Testing
CCI@USC, Los Angeles, 21 Sep 2017 19
(From modeling/description)
*testing
strategy=testing plan
Daniel Moldovan,Hong-Linh Truong, A Platform for Run-time Health Verification of Elastic Cyber-physical Systems,
The IEEE International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication
Systems (MASCOTS 2016), September 19-21, Imperial College, London, UK
20. HARMONIZING RESOURCES
FOR ENSEMBLES?
Duc-Hung Le, Nanjangud C. Narendra, Hong Linh Truong:
HINC - Harmonizing Diverse Resource Information across IoT,
Network Functions, and Clouds. FiCloud 2016: 317-324
CCI@USC, Los Angeles, 21 Sep 2017 20
21. Integrating diverse types of resources
Make a Resource Grid ready for creating ensembes
Harmonize IoT, network functions and cloud resources
API Integration and Communication
Use REST API for obtaining metadata and control of resources
Sensoring data can be transferred through different middleware
Work with existing metamodel (IoTivity, OpenHAB, IoTDM, ETSI
MANO, OCCI, CIMI, etc.)
Rely on scalable cloud communication middleware (e.g., AMQP &
MQTT)
CCI@USC, Los Angeles, 21 Sep 2017 21
IoT networks Network Function Services Clouds
Resource Grid
22. Examples of existing
providers/models
Provider Category APIs Information models
FIWare Orion IoT RESTful (NGSI10), one-time
query or subscription
High level attributes on
data and context
FIWare IDAS IoT RESTful for read/write custom
models and assets
Low level resource
model catalogs
IoTivity IoT REST-like OIC protocol, support
C++, Java and JavaScript
Multiple OIC model
OpenHAB IoT RESTful for query and control
IoT resources
Low level resource
model catalogs
OpenDayLight Network Dynamic REST generated from
Yang model (model-driven)
Low level resource
model catalogs
OpenBaton Network RESTful for network service
description
ETSI MANO v1.1.1 data
model
OpenStack Cloud RESTful, multiple language via
SDK, OCCI, CIMI
OpenStack model,
OCCI, CIMI
22
CCI@USC, Los Angeles, 21 Sep 2017
23. Information model
Physical: Sensor/actualtor/devices in providers’ models
Virtual IoT: SD-Gateway and capabilities.
Network functions: edge-to-edge, edge-to-cloud network.
Clouds: VM, data services, data analytics.
23
CCI@USC, Los Angeles, 21 Sep 2017
26. Reducing complexity in accessing
and control resources
26
CCI@USC, Los Angeles, 21 Sep 2017
1. Query data points
2. Control the
resource
3. Query network
functions and clouds
27. CCI@USC, Los Angeles, 21 Sep 2017 27
As we able to get resources deploy and
configuration
29. Key questions in monitoring
Which metrics make sense? Memory/CPU usage?
We focus on: response time, delay, cost, availability and
reliability
Collect monitoring data from multiple resources of
sub-systems
Gathering and correlating monitoring
Deal with different integration models: through provider
interfaces versus instrumentation
Analytics and visualization
CCI@USC, Los Angeles, 21 Sep 2017 29
30. Monitoring
components
CCI@USC, Los Angeles, 21 Sep 2017 30
Hong-Linh Truong, Christian Proinger, SANALYTICS – On Monitoring and Analytics of IoT, Network
Functions and Clouds. 2017. working paper.
31. Example of an ensemble
CCI@USC, Los Angeles, 21 Sep 2017 31
32. Example of low level metrics
CCI@USC, Los Angeles, 21 Sep 2017 32
34. Infrastructure uncertainties
CPS: includes IoT, network functions and clouds
An ensemble represents a virtual infrastructure
Cross-system and cross-layer
Goals
Characterize and specify possible uncertainties associated
with interactions among elements in CPS infrastructures
Supporting testing uncertainties and uncertainties
analytics
Conventional aspects, e.g., infrastructural physical
resources and typical system operations
Emerging novel aspects: data uncertainties (data/data-
centric CPS), elasticity of CPS resources (w.r.t function
and composition), and Governance (related to
business/trustworthiness)
CCI@USC, Los Angeles, 21 Sep 2017 34
37. Examples – UC1 Geo Sports
Data Delivery uncertainties
Uncertainties affect the infrastructure capabilities
for generating, processing and delivering data
Located in Software in Cyber
Environment, e.g., software components
(execution environments, OS of gateways) in
the infrastructure
At Runtime but Sporadic
Caused by Resource Competition (☒
Technological process)
Indirect impact on Software
Non-functional
aspects
Very Low Low Medium High Extrem
e
Dependability
Legal/complian
ce
Quality
Risk of this type of uncertainties
CCI@USC, Los Angeles, 21 Sep 2017 37
38. Key observations:
how to combine topdown approach
and bottom-up approach to leverage
strengths of both models and elastic
systems?
CCI@USC, Los Angeles, 21 Sep 2017 38
Combining MDE and Elastic Execution
39. Key issues: Two separate worlds
CCI@USC, Los Angeles, 21 Sep 2017 39
SUT Infrastructure
Development
Deployment Description
Development
IoT/Cloud Infrastructures
Infrastructure Configurations
Resource
Information
Adaptor/Tool
Deployment Scripts
Preparing CPS under Test
Requirements
Artifact
Repository
Figure source:
Mark Utting and Bruno Legeard. 2006. Practical Model-Based Testing: A Tools
Approach. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
?
40. Combining MDE with Elastic
Execution
CCI@USC, Los Angeles, 21 Sep 2017 40
Hong-Linh Truong, Luca Berardinelli, Testing Uncertainty of Cyber-physical Systems in IoT Cloud
Infrastructures – Combining Model-Driven Engineering and Elastic Execution, Workshop on Testing
Embedded and Cyber-Physical Systems, ISSTA 2017, Santa Barbara, 10-14 July, 2017.
41. Interwoven test execution and
provisioning
CCI@USC, Los Angeles, 21 Sep 2017 41
IoT Cloud Infrastructures
Modeling
Uncertainties
and SUT
Uncertainty
Profile
System Under
Test (SUT)
Models
Generating
Uncertainty
Test cases
Deploying/
Configuring
SUT
System Under
Test (SUT)
Test cases
Executing
Tests
IoT/Cloud
Resource
Information
Deploying/
Configuring
Testing Utilities
Test
Utilities
42. Tool pipelines: from MDE to elastic
execution for testing
CCI@USC, Los Angeles, 21 Sep 2017 42
Key thoughts:
Different algorithms to create suitable deployment configurations based
on data uncertainties, cost, and time
Interactions between testing and elasticity control of IoT and Cloud
services
43. Our current progress
CCI@USC, Los Angeles, 21 Sep 2017 43
Consider to generate provisioning configurations
from SUT models
Extracting
model
information &
Generating test
configuration
test
strategies
Infrastructural
IoT Cloud
resources
Provisioning &
Configuring
SUT
Executing
Tests
Test plans &
executors
IoT Units
Cloud
T
E
S
T
S
ProviderA
ActuatorsSensors
VMsAnalytics GWs
IoT Units
Cloud
T
E
S
T
S
ProviderB
ActuatorsSensors
VMsAnalytics GWs
Infrastructure
(Class Diagrams)
Behaviour
(State Machine
diagrams)
Uncertainty
Hong-Linh Truong, Luca Berardinelli, Ivan Pavkovic and Georgiana Copil, Modeling and Provisioning IoT Cloud
Systems for Testing Uncertainties, 14th EAI International Conference on Mobile and Ubiquitous Systems:
Computing, Networking and Services (MobiQuitous 2017), Nov 7–10, 2017,Melbourne, Australia
44. IoT and Cloud Resource Profile
CCI@USC, Los Angeles, 21 Sep 2017 44
46. Generic models for Task Executor
Tasks for testing
also including
deployment and
reconfiguration
of ensembles.
CCI@USC, Los Angeles, 21 Sep 2017 46
47. Configuration Generation and
Deployment
Reuse well-known tools for deployment
Utilize different information services
Runtime and (future) uncertainty analytics
CCI@USC, Los Angeles, 21 Sep 2017 47
48. Example of BTS monitoring
CCI@USC, Los Angeles, 21 Sep 2017 48
51. Summary
Ensembles of IoT, network functions and clouds
Important for various types of applications
SINC: a conceptual framework
Elasticity and Uncertainties
We need to consider more data and network aspects.
Adaptation and optimization under uncertainties
Modeling and testing uncertainties are very challenging
Testing uncertainties require fundamental changes in testing
techniques: deal with elasticity and virtualization
We need new set of tools and techniques for
managing and testing ensembles.
CCI@USC, Los Angeles, 21 Sep 2017 51
52. Future work
Current research topics
APIs for programming resource queries and controls
(http://sincconcept.github.io/HINC/)
Configuration tools (http://tuwiendsg.github.io/SALSA/)
Uncertainty testing and analytics
(https://github.com/tuwiendsg/COMOT4U/)
Monitoring and analytics
Ensemble requirement modeling, composition algorithms and
optimization
Interoperability issues (http://www.inter-iot-project.eu/)
Policy execution
Check http://rdsea.github.io and
https://github.com/tuwiendsg/COMOT4U for new update
CCI@USC, Los Angeles, 21 Sep 2017 52