Here are the key points about specifying and controlling elasticity of human-based services:
- Human-based services can be specified as elastic service units similar to software services
- Non-functional properties like quality, cost, response time etc. can be specified as for software services
- Constraints can be defined to control when human involvement is needed, e.g. if monitoring accuracy goes below a threshold
- Strategies can trigger human-based services for problem solving, e.g. notifying an incident management system when a constraint is violated
- Programming models allow defining data and control flow between software and human-based services to integrate them into complex processes
- Middleware platforms provide APIs and runtime support
Emerging Dynamic TUW-ASE Summer 2015 - Distributed Systems and Challenges for...Hong-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/
International Journal on Web Service Computing (IJWSC)ijwscjournal
Web Service Computing is a recent evolution in Distributed Computing series and it is an emerging and fast growing paradigm in the present scenario. Web Service Computing is a diversified discipline suite that related to the technologies of Business Process Integration and Management, Grid / Utility / Cloud Computing paradigms, autonomic computing, as well as the business and scientific applications. It applies the theories of Science and Technology for bridging the gap between Business Services and IT Services. Service oriented computing addresses how to enable the technology to help people to perform business processes more efficiently and effectively, ultimately resulting in creating WIN-WIN strategy between the business organizations and end users. The greatest significance of the web services is their interoperability, which allows businesses to dynamically publish, discover, and aggregate a range of Web services through the Internet to more easily create innovative products, business processes and value chains both from organization and end user points of views. Due to these, this cross discipline attracts the variety of researchers from various disciplines to conduct the versatile research and experiments in this area.
A major university provides 47,000 students with anytime, anywhere access to applications they need with Citrix XenApp, saving hundreds of thousands of dollars.
Most downloaded article for an year in academia - Advanced Computing: An Inte...acijjournal
Advanced Computing: An International Journal (ACIJ) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
Today, servitization has reached its saturation point as enterprises in almost every business and continent pursued it as a differentiation strategy. Data analytics may offer the next frontier of innovation and hold the potential for enterprises to create value for their customers. Nevertheless, organizations face a series of barriers when utilizing the technologies. We apply a rigorous qualitative analysis process based on grounded theory and interview data of 15 business-to-business companies that already successfully utilize data analytics to create value for their customers. We analyzed our results in the lights of the barriers organization face in servitization and reveal that data analytics adds an additional layer of complexity. Our work contributes to the fundamental understanding of organizational transformation and should provide concrete guidance to business leaders on how to address transformation regarding the utilization of data and analytics.
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
Emerging Dynamic TUW-ASE Summer 2015 - Distributed Systems and Challenges for...Hong-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/
International Journal on Web Service Computing (IJWSC)ijwscjournal
Web Service Computing is a recent evolution in Distributed Computing series and it is an emerging and fast growing paradigm in the present scenario. Web Service Computing is a diversified discipline suite that related to the technologies of Business Process Integration and Management, Grid / Utility / Cloud Computing paradigms, autonomic computing, as well as the business and scientific applications. It applies the theories of Science and Technology for bridging the gap between Business Services and IT Services. Service oriented computing addresses how to enable the technology to help people to perform business processes more efficiently and effectively, ultimately resulting in creating WIN-WIN strategy between the business organizations and end users. The greatest significance of the web services is their interoperability, which allows businesses to dynamically publish, discover, and aggregate a range of Web services through the Internet to more easily create innovative products, business processes and value chains both from organization and end user points of views. Due to these, this cross discipline attracts the variety of researchers from various disciplines to conduct the versatile research and experiments in this area.
A major university provides 47,000 students with anytime, anywhere access to applications they need with Citrix XenApp, saving hundreds of thousands of dollars.
Most downloaded article for an year in academia - Advanced Computing: An Inte...acijjournal
Advanced Computing: An International Journal (ACIJ) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
Today, servitization has reached its saturation point as enterprises in almost every business and continent pursued it as a differentiation strategy. Data analytics may offer the next frontier of innovation and hold the potential for enterprises to create value for their customers. Nevertheless, organizations face a series of barriers when utilizing the technologies. We apply a rigorous qualitative analysis process based on grounded theory and interview data of 15 business-to-business companies that already successfully utilize data analytics to create value for their customers. We analyzed our results in the lights of the barriers organization face in servitization and reveal that data analytics adds an additional layer of complexity. Our work contributes to the fundamental understanding of organizational transformation and should provide concrete guidance to business leaders on how to address transformation regarding the utilization of data and analytics.
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
Enabling and controlling elasticity of cloud comput-
ing applications is a challenging issue. Elasticity programming directives have been introduced to
delegate elasticity control to infrastructures and to
separate elasticity control from application logic. Since
coordination models provide a general approach to manage interaction and elasticity control entails interactions among cloud infrastructure components, we present a coordination-based approach to elasticity control, supporting delegation and separation of concerns at design and run-time, paving the way towards coordination-aware elasticity.
Context-aware Programming for Hybrid and Diversity-aware Collective Adaptive ...Hong-Linh Truong
Collective adaptive systems (CASs) have been researched intensively since many years. However, the recent emerging developments and advanced models in service-oriented computing, cloud computing and human computation have fostered several new forms of CASs. Among them, Hybrid and Diversity-aware CASs (HDA-CASs) characterize new types of CASs in which a collective is composed of hybrid machines and humans that collaborate together with different complementary roles. This emerging HDA-CAS poses several research chal
lenges in terms of programming, management and provisioning. In this paper, we investigate the main issues in programming HDA-CASs. First, we analyze context characterizing HDA-CASs. Second, we propose to use the concept of hybrid compute units to implement HDA-CASs that can be elastic. We call this type of HDA-CASs h2 CAS (Hybrid Compute Unit-based HDA-CAS). We then discuss a meta-view of h2CAS that describes a h 2 CAS program. We analyze and present program features for h2CAS in four main different contexts.
Interactions spanning multiple organizations have become an important aspect in today’s collaboration landscape. Organizations create alliances to fulfill strategic objectives. The dynamic nature of collaborations increasingly demands for automated techniques and algorithms to support the creation of such alliances. Our approach bases on the recommendation of potential alliances by discovery of currently relevant competence sources and the support of semi-automatic formation. The environment is service-oriented comprising humans and software services with distinct capabilities. To mediate between previously separated groups and organizations, we introduce the broker concept that bridges disconnected networks. Here we present a dynamic broker discovery approach based on interaction mining techniques and trust metrics.
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.
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/
TUW-ASE Summer 2015: Advanced service-based data analytics: Models, Elasticit...Hong-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
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.
TUW-ASE Summer 2015: Data marketplaces: core models and conceptsHong-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/
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.
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.
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.
Enabling and controlling elasticity of cloud comput-
ing applications is a challenging issue. Elasticity programming directives have been introduced to
delegate elasticity control to infrastructures and to
separate elasticity control from application logic. Since
coordination models provide a general approach to manage interaction and elasticity control entails interactions among cloud infrastructure components, we present a coordination-based approach to elasticity control, supporting delegation and separation of concerns at design and run-time, paving the way towards coordination-aware elasticity.
Context-aware Programming for Hybrid and Diversity-aware Collective Adaptive ...Hong-Linh Truong
Collective adaptive systems (CASs) have been researched intensively since many years. However, the recent emerging developments and advanced models in service-oriented computing, cloud computing and human computation have fostered several new forms of CASs. Among them, Hybrid and Diversity-aware CASs (HDA-CASs) characterize new types of CASs in which a collective is composed of hybrid machines and humans that collaborate together with different complementary roles. This emerging HDA-CAS poses several research chal
lenges in terms of programming, management and provisioning. In this paper, we investigate the main issues in programming HDA-CASs. First, we analyze context characterizing HDA-CASs. Second, we propose to use the concept of hybrid compute units to implement HDA-CASs that can be elastic. We call this type of HDA-CASs h2 CAS (Hybrid Compute Unit-based HDA-CAS). We then discuss a meta-view of h2CAS that describes a h 2 CAS program. We analyze and present program features for h2CAS in four main different contexts.
Interactions spanning multiple organizations have become an important aspect in today’s collaboration landscape. Organizations create alliances to fulfill strategic objectives. The dynamic nature of collaborations increasingly demands for automated techniques and algorithms to support the creation of such alliances. Our approach bases on the recommendation of potential alliances by discovery of currently relevant competence sources and the support of semi-automatic formation. The environment is service-oriented comprising humans and software services with distinct capabilities. To mediate between previously separated groups and organizations, we introduce the broker concept that bridges disconnected networks. Here we present a dynamic broker discovery approach based on interaction mining techniques and trust metrics.
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.
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/
TUW-ASE Summer 2015: Advanced service-based data analytics: Models, Elasticit...Hong-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
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.
TUW-ASE Summer 2015: Data marketplaces: core models and conceptsHong-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/
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.
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.
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.
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.
“Semantic Technologies for Smart Services” diannepatricia
Rudi Studer, Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB, presentation “Semantic Technologies for Smart Services” as part of the Cognitive Systems Institute Speaker Series, December 15, 2016.
Introduction to Smart Manufacturing & Manufacturing as a Service presentation.
Three important concepts are presented: Cloud computing, internet of things and advanced data analytics.
TUW- 184.742 Emerging Dynamic Distributed Systems and Challenges for Advanced...Hong-Linh Truong
This presentation is part of the course "184.742 Advanced Services Engineering" at The Vienna University of Technology, in Winter Semester 2012. Check the course at: http://www.infosys.tuwien.ac.at/teaching/courses/ase/
Digital Catapult Centre Brighton - Dr Nour Aliwired_sussex
At The Digital Catapult Centre Brighton event, Tech Beyond The Screen: Connectivity & Infrastructure on Wednesday 2nd March, Dr Nour Ali from The University of Brighton spoke about mobile and self adaptive ambients in service oriented architecture.
Review of Business Information Systems – Fourth Quarter 2013 V.docxmichael591
Review of Business Information Systems – Fourth Quarter 2013 Volume 17, Number 4
2013 The Clute Institute Copyright by author(s) Creative Commons License CC-BY 159
Dimensions Of Security Threats In Cloud
Computing: A Case Study
Mathew Nicho, University of Dubai, UAE
Mahmoud Hendy, University of Dubai, UAE
ABSTRACT
Even though cloud computing, as a model, is not new, organizations are increasingly
implementing it because of its large-scale computation and data storage, flexible scalability,
relative reliability, and cost economy of services. However, despite its rapid adoption in some
sectors and domains, it is evident from research and statistics, that security-related threats are the
most noticeable barrier to its widespread adoption. To investigate the reasons behind these
threats, the authors used available literature to identify and aggregate information about IS
security threats in cloud computing. Based on this information, the authors explored the
dimensions of the nature of threat by interviewing a cloud computing practitioner in an
organization that uses both the private and public cloud deployment models. From these findings,
the authors found that IS security threats in cloud computing must be defined at different levels;
namely, at the business and technical level, as well as from a generic and cloud-specific threat
perspective. Based on their findings, the authors developed the Cloud Computing Threat Matrix
(CCTM) which provides a two-dimensional definition of threat that enables cloud users to fully
comprehend the concerns so that they can make relevant decisions while availing cloud computing
services.
Keywords: Cloud Computing; Security; Cloud Security Issues Taxonomy; Threat Matrix
INTRODUCTION
ecause a cloud is a collection of inter-connected and virtualized computers (Buyya et al., 2008), the
main enabling technology for cloud computing is virtualization. The basic concept of cloud is based
on the premise that instead of having selected information systems (IS) resources, such as software
and data stored locally on a user’s or organization’s computer systems, these resources can be stored on Internet
servers, called “clouds,” and accessed anytime, anywhere as a paid service on the Internet. Cloud computing has the
potential to bring significant benefits to small- and medium-sized businesses by reducing the costs of investment in
information communication technology (ICT) infrastructure because it enables the use of services, such as
computation, software, data access, and storage by end-users, without the need to know the physical location and
configuration of the system that delivers the services (Mujinga & Chipangura, 2011). However, it has been stated
that organizations adopt cloud computing projects and systems cautiously while maximizing benefits and
minimizing risks (Lawler, Joseph, & Howell-Barber, 2012). Cloud computing is expected to play .
A Case for Declarative Process Modelling - Slides on Adaptive Case Managment ...Thomas Hildebrandt
We present the use of Dynamic Condition Response Graphs (www.DCRGraphs.net) developed at Exformatics.com and researchers in the Process and System Models Group at IT University of Copenhagen (www.itu.dk/research/models) for modelling and implementing an Adaptive Case Management system for a grant application process.
New Research Articles 2020 January Issue International Journal of Software En...ijseajournal
Proposing Automated Regression Suite Using Open Source Tools for A Health Care Solution
Anjali Rawat and Shahid Ali, AGI Institute, New Zealand
Quality Assessment Model of the Adaptive Guidance
Hamid Khemissa1 and Mourad Oussalah2, 1USTHB: University of Science and Technology Houari Boumediene, Algeria and 2Nantes University, France
An Application of Physics Experiments of High School by using Augmented Reality
Hussain Mohammed Abu-Dalbouh, Samah Mohammed AlSulaim, Shaden Abdulaziz AlDera, Shahd Ebrahim Alqaan, Leen Muteb Alharbi and Maha Abdullah AlKeraida, Qassim University, Kingdom of Saudi Arabia
On the Relationship between Software Complexity and Security
Mamdouh Alenezi and Mohammad Zarour, Prince Sultan University, Saudi Arabia
Structural Complexity Attribute Classification Framework (SCACF) for Sassy Cascading Style Sheets
John Gichuki Ndia1, Geoffrey Muchiri Muketha1 and Kelvin Kabeti Omieno2, 1Murang’a University of Technology, Kenya and 2Kaimosi Friends University College, Kenya
http://www.airccse.org/journal/ijsea/vol11.html
SETTA'18 Keynote: Intelligent Software Engineering: Synergy between AI and So...Tao Xie
2018 Keynote Speaker, Symposium on Dependable Software Engineering - Theories, Tools and Applications (SETTA 2018). "Intelligent Software Engineering: Synergy between AI and Software Engineering" http://confesta2018.csp.escience.cn/dct/page/65581
CAPSTONE PROJECT LITERATURE REVIEW ASSIGNMENT 1CAPSTONE PROJECTawnaDelatorrejs
CAPSTONE PROJECT: LITERATURE REVIEW ASSIGNMENT 1
CAPSTONE PROJECT: LITERATURE REVIEW ASSIGNMENT 8
LITERATURE REVIEW ASSIGNMENT
Jerry L. Quarles
School of Engineering & Computer Science, Liberty University
Author Note
Jerry L. Quarles
I have no known conflict of interest to disclose.
Correspondence concerning this article should be addressed to Jerry L. Quarles.
Email: [email protected]
Table of Contents
Introduction 3
Problem Statement 5
Research Question(s) 8
Literature Review 9
Literature Review Findings and Gaps……………………………………………………………17
Conclusion 19
References 21
Appendix…..……………………………………………………………………………………..26
Error in red – not booked marked correctly
FAILED THIS ASSIGNMENT ALL THE WORK THAT IS MARKED THRU WAS PLAGARISM COPIED WORK!
Total word count needs to be 4000 words so you can use anything highlighted in yellow again to get a total of 4000 words!
Introduction
DO NOT USE MARKED THRU WORDS -Rapid advancements in modern technology have changed the digital landscape and increased the demand for secure internet and communication Technology and Cloud computing is one of them. Cloud computing has grown in popularity as a study and application field (Rashid, & Chaturvedi, 2019). A growing number of technology companies and manufacturing industries have started to implement cloud-based services or are planning to do so. Nonetheless, there are a few drawbacks to using public and or private cloud technologies. As a result, quite a few institutions are already using private cloud technologies. Day by day cloud computing is in growth as many organizations adopted cloud technology, but in parallel, several security issues are raised. Each organization chooses secure infrastructures when they move its data to remote locations. According to the NIST security, portability and interoperability are the major obstacles to the adoption of cloud computing.
Can Use Highlighted words
Our motivation for creating a new design of a hybrid architecture using key cloud technologies build a new private cloud platform for Amazon Inc. that requires high data availability at a low cost. Big data approaches relying on traditional data warehouses often pose latency problems, making them unsuitable for new data use cases (Hsu, Fox, and Min, 2019). Many cloud services are provided by a trusted third party which arises new security threats. The cloud provider provides its services through the Internet and uses many web technologies that arise new security issues. This paper discussed the basic features of cloud computing, security issues, threats, and their solutions. Additionally, the paper describes several key topics related to the cloud, namely cloud architecture framework, service and deployment model, cloud technologies, cloud security concepts, threats, and attacks.
When data is stored in remote storage, the cloud user loses control over the data, at this time consumers may not be conscious of the details of security policies, vulne ...
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 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.
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.
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.
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.
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.
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.
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
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test and evaluate IoT cloud platforms in multi-cloud environments.
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TUWien - ASE Summer 2015: Engineering human-based services in elastic systems
1. Engineering human-based services in
elastic systems
Hong-Linh Truong
Distributed Systems Group,
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/truong
1ASE Summer 2015
Advanced Services Engineering,
Summer 2015 – Lecture 9
Advanced Services Engineering,
Summer 2015 – Lecture 9
2. Outline
Human service units
Provisoning and employing human sevice units
– frameworks
Human service units in Vienna Elastic
Computing Model (VIECOM)
Evaluating human services
ASE Summer 2015 2
3. Scenario
3
Offers services for
handling IoT Data
Offers services for
handling IoT Data
Offers services for big,
data analytics
Offers services for big,
data analytics
Offers services for
complex problem solving
using human experts
Offers services for
complex problem solving
using human experts
IoT Cloud Platform
Data Analytics
Platform
Expert Provisioning
Platform
Sensors
<<send data>>
<<analyze data>> <<notify possible
problem>>
<<control/configure
sensors>>
Predictive maintenance companyPredictive maintenance company
<<monitor>>
Chillers
<<predict and solve
problems>>
<<control
services>>
<<control
algorithms>>
ASE Summer 2015
4. Near-Realtime
Data Analytics
Offline Data
Analytics CloudLyra
Integrated systems of software, things
and people services
ASE Summer 2015 4
Sensors
NoSQL BigData
Sensor
data
Gateways
EventHandling Web
ServiceLoad
Balancer
MOM
Sensor
data
Cloud-based M2M Platform
Control Analysis Configuration
control, analyze,deploy
COMOT – Elasticity ManagementPlatform
critical situation msg
Smart
Communication
Collective Provisioning
External
Service
task
forms
elasticitiy control/management msg
critical
situation msg
critical
situation
msg critical situation
msg/task
clasticity control/management msg;
task
HDA-CAS
Human Interface
(Mobile, Mail, Web)
Human-based
Services
execute/manage
5. Human-based services for solving
complex problems (2)
5
But how to program human-based services and
software-based services together?
But how to program human-based services and
software-based services together?
ASE Summer 2015
6. Example: some common tasks in
data analytics
ASE Summer 2015 6
Human activities in Data Analytics
Labeling
Annotating
Cleansing
Evaluating
data/content
Dectecting
patterns
Classifying
objects
Steering
analytics
7. Human service units in data
analytics -- functions
Evaluating: is the quality of picture good?
Classifying: is it a man‘s or a woman‘s picture?
Decting: any unidentified object in a picture?
Labeling: adding location information of a picture
Cleansing: remove duplicated pictures
Steering: the quality of picture is bad, should we
continue to merge it with others?
ASE Summer 2015 7
How to model such functions for human units ?
E.g., with WSDL or REST?
How to model such functions for human units ?
E.g., with WSDL or REST?
9. Human service units
ASE Summer 2015 9
Human
service unit
Human
service unit
Functions
Non-
functional
parameters
Service
models
Technical
interfaces
Provisioning
mechanisms
Interaction
models
Human acting as a „service unit“Human acting as a „service unit“
10. Human service units
10
Modeling type of
units (e.g.,
computation, data,
monitor,) and their
dependencies
Consumption,
ownership, provisioning,
price, etc.
Elastic
Service
Unit
Service
model
Unit
Dependency
Elastic
Capability
Function
The functional
capability of the unit
and interface to
access the function
Capabilities to be elastic
under different
requirements
SoftwareSoftware PeoplePeople
VolunteersProfessionals
ThingThing
Resources
ASE Summer 2015
11. Forms of human service
Individual Compute Unit
An individual is treated like „a processor“ or “functional
unit“. A service can wrap human capabilities to support
the communication and coordination of tasks
Social Compute Unit
A set of people and software that are initiated and
provisioned as a service for solving tasks
Web services interfaces can be built
Different pricing models and different
quality models
ASE Summer 2015 11
12. Human service units – provisioning
mechanisms (1)
An infrastructure can be introduced for
accessing many ICUs in a crowd
Allow people to register their service unit capabilities
Facilitate communication, task bidding, retrieval and
result delivery
Act like a marketplace: multiple providers and multiple
consumers
ASE Summer 2015 12
ICU ICU
ICUICU
ICU
ICU ICU
CrowdCrowd
13. Human service units – provisioning
mechanisms (2)
An „infrastructure-as-a-service“ for ICUs
Facilitate communication, task retrieval and result
delivery
Single ICUaaS provider and multiple consumers
ASE Summer 2015 13
ICU ICU
ICUICU
ICU
ICU ICU
Cloud
provisioning
models ICUICU
14. Human service units – provisioning
mechanisms (3)
An „infrastructure-as-a-service“ for SCUs
Facilitate communication, task retrieval and result
delivery
Single SCUaaS provider and multiple consumers
ASE Summer 2015 14
SCU SCU
SCUSCU
SCU
SCU SCU
Cloud
provisioning
models SCUSCU
15. Human service units – technical
interfaces (1)
ASE Summer 2015 15
People
Web Servicce
(REST/SOAP)
People
Web page
Analytics
Activity
Analytics
Activity
16. Human service units – technical
interfaces (2)
ASE Summer 2015 16
Email/SMS/Instant
Messaging
People
Web page
Web Servicce
(REST/SOAP)
Communication
Services
People
Analytics
Activity
Analytics
Activity
17. Human service units – interaction
model
ASE Summer 2015 17
ICU/SCU
Task queue
Scheduler
ICU/SCU
Task queue
Scheduler
Analytics
Activity
Analytics
Activity
ICU/SCU
Analytics
Activity
18. Human service units -- NfPs
ASE Summer 2015 18
Which are important
considerations when
interpreting non-functional
properties for human service
units?
Which are important
considerations when
interpreting non-functional
properties for human service
units?
19. Incorporating human units into
complex processes
How to provision and employ human compute
units?
How to select human units?
Where to place human units in data analytics
and why?
How to monitor and test human units in data
analytics?
ASE Summer 2015 19
21. Qurk system architecture (1)
ASE Summer 2015 21
Source: Adam Marcus, Eugene Wu, David Karger, Samuel Madden, and Robert Miller. 2011. Human-powered sorts and joins. Proc. VLDB
Endow. 5, 1 (September 2011), 13-24.
Source: Adam Marcus, Eugene Wu, David Karger, Samuel Madden, and Robert Miller. 2011. Human-powered sorts and joins. Proc. VLDB
Endow. 5, 1 (September 2011), 13-24.
22. Qurk system architecture (2)
ASE Summer 2015 22
Source: Adam Marcus, Eugene Wu, David Karger, Samuel Madden, and Robert Miller. 2011. Human-powered sorts and joins. Proc. VLDB
Endow. 5, 1 (September 2011), 13-24.
Source: Adam Marcus, Eugene Wu, David Karger, Samuel Madden, and Robert Miller. 2011. Human-powered sorts and joins. Proc. VLDB
Endow. 5, 1 (September 2011), 13-24.
23. Jabberwocky approach (1)
ASE Summer 2015 23
Source: Salman Ahmad, Alexis Battle, Zahan Malkani, Sepandar D. Kamvar: The jabberwocky programming environment for structured
social computing. UIST 2011: 53-64
Source: Salman Ahmad, Alexis Battle, Zahan Malkani, Sepandar D. Kamvar: The jabberwocky programming environment for structured
social computing. UIST 2011: 53-64
24. Jabberwocky approach (2)
ASE Summer 2015 24
Source: Salman Ahmad, Alexis Battle, Zahan Malkani, Sepandar D. Kamvar: The jabberwocky programming environment for structured
social computing. UIST 2011: 53-64
Source: Salman Ahmad, Alexis Battle, Zahan Malkani, Sepandar D. Kamvar: The jabberwocky programming environment for structured
social computing. UIST 2011: 53-64
25. Automan approach
ASE Summer 2015 25
Source: Daniel W. Barowy, Charlie Curtsinger, Emery D. Berger, Andrew McGregor: AutoMan: a platform for integrating human-based
and digital computation. OOPSLA 2012: 639-654
Source: Daniel W. Barowy, Charlie Curtsinger, Emery D. Berger, Andrew McGregor: AutoMan: a platform for integrating human-based
and digital computation. OOPSLA 2012: 639-654
26. SW4H approach (1)
ASE Summer 2015 26
Karastoyanova, Dimka; Dentsas, Dimitrios; Schumm, David; Sonntag, Mirko; Sun, Lina; Vukojevic, Karolina: Service-based Integration of
Human Users in Workflow-driven Scientific Experiments. In: Proceedings of the 8th IEEE International Conference on eScience (eScience
2012
Karastoyanova, Dimka; Dentsas, Dimitrios; Schumm, David; Sonntag, Mirko; Sun, Lina; Vukojevic, Karolina: Service-based Integration of
Human Users in Workflow-driven Scientific Experiments. In: Proceedings of the 8th IEEE International Conference on eScience (eScience
2012
27. SW4H approach (2)
Similar concepts in
collaborative working
environments but
integrated into
workflows
Do not discuss about
where and how to
select human units
ASE Summer 2015 27
Karastoyanova, Dimka; Dentsas, Dimitrios; Schumm,
David; Sonntag, Mirko; Sun, Lina; Vukojevic, Karolina:
Service-based Integration of Human Users in Workflow-
driven Scientific Experiments. In: Proceedings of the 8th
IEEE International Conference on eScience (eScience
2012
Karastoyanova, Dimka; Dentsas, Dimitrios; Schumm,
David; Sonntag, Mirko; Sun, Lina; Vukojevic, Karolina:
Service-based Integration of Human Users in Workflow-
driven Scientific Experiments. In: Proceedings of the 8th
IEEE International Conference on eScience (eScience
2012
29. VieCOM -- incorporate humans into a
programming paradigm (1)
• Abstracting human compute units as program elements
• Extending programming languages to support human
compute units
• Data/control flows via extensible APIs
Programming
languages
• Shared memory (e.g., human –software – human),
message passing (human-to-human), artifact-centric,
etc., via APIs working atop the compute unit abstraction
layer
Multiple
programming
models
• Computing capability /profile management: human
computing power, reputation and incentive models
• Monitoring and enforcing incentives/rewards, quality of
results, availability
• Communication between human-middleware, among
Individual Compute Units (SCU)/Social Compute Units
(SCU) for exchanging artifacts and comprehensing l tasks
Execution
environment
29 http://dsg.tuwien.ac.at/research/viecomASE Summer 2015
30. VieCOM-- incorporate humans into a
programming paradigm (2)
Volunteers ProfessionalsTeamIndividual
Service-based Middleware
CommunicationCommunicationMonitoringMonitoring
Capability/Profile
Management
Capability/Profile
Management
Provisioning/Negotiation/Execution APIProvisioning/Negotiation/Execution API
Abstraction of Human-based Compute Units
SCU
SC
U
SC
U SCU SCU
Program languages and programming models
Program
elements
Software
Compute
Units
program human actions
and dependencies
program incentive condition
and rewarding action
program result evaluation
method
Human-to-middleware
interfaces:
•visualization of collective tasks
•embedding of common forms
•mobile app
30ASE Summer 2015
31. Cloud of hybrid service units
Cloud of HBS: A cloud of HBS includes HBS that can be
provisioned, deployed, and utilized on-demand based on
different pricing and incentive models.
Cloud of HBS: A cloud of HBS includes HBS that can be
provisioned, deployed, and utilized on-demand based on
different pricing and incentive models.
Cloud of hybrid services: A cloud of hybrid services
includes SBS and HBS that can be provisioned, deployed
and utilized on-demand based on different pricing and
incentive models.
Cloud of hybrid services: A cloud of hybrid services
includes SBS and HBS that can be provisioned, deployed
and utilized on-demand based on different pricing and
incentive models.
ASE Summer 2015 31
32. Hybrid compute unit design –
fundamental elements
32ASE Summer 2015
Hong-Linh Truong, Hoa Khanh Dam, Aditya Ghose, Schahram Dustdar "Augmenting Complex Problem Solving with
Hybrid Compute Units",9th International Workshop on Engineering Service-Oriented Application (WESOA's 2013), In
conjunction with ICSOC 2013, Dec 2, 2013, Berlin, Germany, (c)Springer-Verlag
Hong-Linh Truong, Hoa Khanh Dam, Aditya Ghose, Schahram Dustdar "Augmenting Complex Problem Solving with
Hybrid Compute Units",9th International Workshop on Engineering Service-Oriented Application (WESOA's 2013), In
conjunction with ICSOC 2013, Dec 2, 2013, Berlin, Germany, (c)Springer-Verlag
33. Hybrid compute unit design --
Relationships
33
Relationship Type HBS SBS TBS HCU
Similarity Yes Yes Yes Yes
Composition Yes Yes Yes Yes
Data Dependency Yes Yes Yes Yes
Control Dependency Yes Yes Yes Yes
Location Dependency Yes Yes Yes Yes
Forwarding Yes Yes No Yes
Delegation Yes Yes No Yes
Social Relation Yes No No Yes
Elasticity Yes Yes No Yes
ASE Summer 2015
34. Hybrid compute units
34
Hybrid compute unit (HCU): a set of service units
includes software-based services, human-based
services and things-based services that can be
provisioned, deployed and utilized as a collective
on-demand based on different quality, pricing and
incentive models.
Hybrid compute unit (HCU): a set of service units
includes software-based services, human-based
services and things-based services that can be
provisioned, deployed and utilized as a collective
on-demand based on different quality, pricing and
incentive models.
ASE Summer 2015
35. Extensible architecture
Adapters for: email,
Dropbox, REST,
Android
Integrated with
WP4,6,8;
API access for WP5,2
Open source and
documentation:
https://github.com/tuwi
endsg/SmartCom
ASE Summer 2015 35
P. Zeppezauer, O. Scekic, H.-L. Truong and S. Dustdar, "Virtualizing Communication for Hybrid and Diversity-Aware Collective Adaptive Systems,"
10th International Workshop on Engineering Service-Oriented Applications (WESOA'14@ICSOC), Paris, 2014.
Zeppezauer, Virtualizing Communication for Hybrid and Diversity-aware Collective Adaptive Systems, Master thesis, Dec 2014.
P. Zeppezauer, O. Scekic, H.-L. Truong and S. Dustdar, "Virtualizing Communication for Hybrid and Diversity-Aware Collective Adaptive Systems,"
10th International Workshop on Engineering Service-Oriented Applications (WESOA'14@ICSOC), Paris, 2014.
Zeppezauer, Virtualizing Communication for Hybrid and Diversity-aware Collective Adaptive Systems, Master thesis, Dec 2014.
Highlights: Virtualizing Communication
36. Specifying and controling elasticity
of human-based services
What if we need to
invoke a human?
#predictive maintanance analyzing chiller measurement
#SYBL.ServiceUnitLevel
Mon1 MONITORING accuracy = Quality.Accuracy
Cons1 CONSTRAINT accuracy < 0.7
Str1 STRATEGY CASE Violated(Cons1):
Notify(Incident.DEFAULT, ServiceUnitType.HBS)
#predictive maintanance analyzing chiller measurement
#SYBL.ServiceUnitLevel
Mon1 MONITORING accuracy = Quality.Accuracy
Cons1 CONSTRAINT accuracy < 0.7
Str1 STRATEGY CASE Violated(Cons1):
Notify(Incident.DEFAULT, ServiceUnitType.HBS)
ASE Summer 2015 36
37. Utilizing hybrid services for
evolving/dependent task graphs
Hong-Linh Truong, Schahram
Dustdar, Kamal Bhattacharya
"Programming Hybrid Services
in the Cloud", 10th International
Conference on Service-oriented
Computing (ICSOC 2012),
November 12-16, 2012, Shanghai,
China. Best Paper Award.
Hong-Linh Truong, Schahram
Dustdar, Kamal Bhattacharya
"Programming Hybrid Services
in the Cloud", 10th International
Conference on Service-oriented
Computing (ICSOC 2012),
November 12-16, 2012, Shanghai,
China. Best Paper Award.
ASE Summer 2015 37
38. Elastic SCU provisioning atop ICUs
Elastic profile
SCU (pre-)runtime/static formation
Cloud APIs
Muhammad Z.C. Candra, Hong-Linh Truong, and Schahram
Dustdar, Provisioning Quality-aware Social Compute Units in
the Cloud, ICSOC 2013.
Muhammad Z.C. Candra, Hong-Linh Truong, and Schahram
Dustdar, Provisioning Quality-aware Social Compute Units in
the Cloud, ICSOC 2013.
Algorithms
Ant Colony
Optimization
variants
FCFS
Greedy
SCU
extension/reduction
Task reassignment
based on trust, cost,
availability
Mirela Riveni, Hong-Linh Truong, and Schahram
Dustdar, On the Elasticity of Social Compute Units,
CAISE 2014
Mirela Riveni, Hong-Linh Truong, and Schahram
Dustdar, On the Elasticity of Social Compute Units,
CAISE 2014
ASE Summer 2015 38
39. Selecting human units
Do not select at all
Let human units bid the tasks
E.g., in crowdsourcing platforms
Static/fix mapping
E.g., using static information for human-task mapping
Simple selection techniques
Using the requirement of the task to find the suitable
human units based on their capabilities
Complex selection techniques
Utilizing complex dependency graphs to find suitable
human units
ASE Summer 2015 39
40. Selecting SCU based on task
graphs
40
SCU Formation
Algorithms
Business As
Usual
Corrective
Action
HBS
Constraints
Hong Linh Truong, Schahram Dustdar, Kamal Bhattacharya: Programming Hybrid Services in the Cloud. ICSOC 2012: 96-110Hong Linh Truong, Schahram Dustdar, Kamal Bhattacharya: Programming Hybrid Services in the Cloud. ICSOC 2012: 96-110
ASE Summer 2015
41. Placement techniques for human
units
Usually at design time the developer/designer decides
Where to put human units
Where some triggers should be put in order to invoke
human units if needed
At runtime
Find suitable human units
Invoke human units
Placement of human units
Application-specific
Needs automatic algorithms and supporting tools
ASE Summer 2015 41
42. Configuring iSCU
Establish „connectedness“ based on compliance
constraints and network topology
Addional cost might occur!
Program SBS and HBS for the iSCU to have a
complete working environment.
Different connectedness
E.g., ring-based, star-based, and master-slave
topologies
ASE Summer 2015 42
43. Towards programming framework
for HCU
43
Coordination and
Composition Models
Coordination and
Composition Models
[ICSOC12]
ASE Summer 2015
45. Turkalytics
Develop „Interaction
Model“ for human
activities
Monitor and analyze
metrics, e.g.,
performance and
location
ASE Summer 2015 45
Paul Heymann and Hector Garcia-Molina. 2011. Turkalytics: analytics for human computation. In Proceedings of the 20th international
conference on World wide web (WWW '11). ACM, New York, NY, USA, 477-486. DOI=10.1145/1963405.1963473
http://doi.acm.org/10.1145/1963405.1963473
Paul Heymann and Hector Garcia-Molina. 2011. Turkalytics: analytics for human computation. In Proceedings of the 20th international
conference on World wide web (WWW '11). ACM, New York, NY, USA, 477-486. DOI=10.1145/1963405.1963473
http://doi.acm.org/10.1145/1963405.1963473
46. Turkalytics
Track client
detail at client
side (in browser)
Store tracked
information in a
log server
Analyze events
ASE Summer 2015 46
Paul Heymann and Hector Garcia-Molina. 2011. Turkalytics: analytics for human computation. In Proceedings of the 20th international
conference on World wide web (WWW '11). ACM, New York, NY, USA, 477-486. DOI=10.1145/1963405.1963473
http://doi.acm.org/10.1145/1963405.1963473
Paul Heymann and Hector Garcia-Molina. 2011. Turkalytics: analytics for human computation. In Proceedings of the 20th international
conference on World wide web (WWW '11). ACM, New York, NY, USA, 477-486. DOI=10.1145/1963405.1963473
http://doi.acm.org/10.1145/1963405.1963473
47. VieCOM
SCU execution model and lifecycle
management
Metrics for ICUs and SCUs
Mirela Riveni, Hong-Linh Truong, and Schahram Dustdar, On the Elasticity of Social Compute
Units, CAISE 2014
Mirela Riveni, Hong-Linh Truong, and Schahram Dustdar, On the Elasticity of Social Compute
Units, CAISE 2014
ASE Summer 2015 47
48. Exercises
Read mentioned papers
Analyze pros and cons of existing frameworks
for data analytics
Survey existing algorithms for matching human
units to data analytics tasks
Examine requirements for locating places for
human units and implement some algorithms
Examine monitoring techniques for cloud of
human compute units
ASE Summer 2015 48
49. 49
Thanks for
your attention
Hong-Linh Truong
Distributed Systems Group
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/truong
ASE Summer 2015