Heuristic Optimization, Job Scheduling, Job Allocation, Load Balancing, Multi-Tier Cloud Computing
Public Cloud , Data Protection , storage , Cloud Security , Security Model , Data storage , Data retrieval , retrieval
Cloud Security, Virtualization, Virtual Machine Image, Security Threats, Threat modeling First Section
Sanjay Sareen and Meenu Sareen, Guru Nanak Dev University, india
The growing need of sharing health information of patients between healthcare agencies, doctors, and other authorized persons in order to provide better care of patients has resulted in the cloud-based healthcare system. However, the sharing of health information on the cloud raises major security and privacy issues that need to address. This article introduces a framework that protects health information from unauthorized access and lets the patient as data owner decide who the authorized persons are, i.e., who the patient discloses her health information to. In this article, a framework is proposed that presents a new methodology using a combination of different techniques such as information granulation, information pseudonymization, and secret sharing scheme, allowing privacy-preserving primary and secondary use of the health records. In this model, the privacy of patient’s identity is protected so that users and providers of healthcare services do not need to trust the cloud service provider with privacy related issues. We designed and implemented different privacy preserving algorithms on the Amazon EC2 to evaluate the security and performance analysis of our proposed system. The security analysis showed that the framework is secure and protected against common intruder scenarios.
Pseudonymization; secret sharing scheme; cloud computing; secure data sharing; healthcare systems
Security Considerations For Edge computing
John M. Acken1 and Naresh K. Sehgal2, 1Portland State University, Portland, OR and 2Intel Corp, Santa Clara, CA
Present state of edge computing is an environment of different computing capabilities connecting via awide variety of communication paths. This situation creates both great operational capabilityopportunities and unimaginable security problems. This paper emphasizes that the traditionalapproaches to security of identifying a security threat and developing the technology and policies todefend against that threat are no longer adequate. The wide variety of security levels, computationalcapabilities, and communication channels requires a learning, responsive, varied, and individualizedapproach to information security. We describe a classification of the nature of transactions with respectto security based upon relationships, history, trust status, requested actions and resulting responsechoices. We propose that each element in the edge computing world utilizes a localized ability toestablish an adaptive learning trust model with each entity that communicates with the element
Edge Computing, Security, Adaptive learning, Trust model, Threats, Cloud Computing, InformationSecurity
A New Prediction Framework For Improving the Energy Efficiency in Cloud
Maryam Amiri and Leyli Mohammad-Khanli, University of Tabriz, Iran
Cloud computing relies on sharing pool of computing resources. It delivers services with availability and scalability obligations to users. On the other hand, green computing and energy efficiency are two main challenges of cloud. Therefore, resource should be allocated in a way that energy consumption is reduced and Quality of Service (QoS) dropping is avoided. Due to the point that storage is one of the biggest energy consumers, this paper focuses on the storage system in cloud. For improving energy efficiency of storage, the next access number of data blocks of disks is predicted. Based on predicted results, data blocks can be transmitted to the most appropriate disks and energy consumption is reduced. The goal of this paper is to represent a new method to predict the next access number of data blocks of disks. The proposed framework is composed of four components: fuzzy classifier, Markov chain, base prediction methods and neural network. Indeed, the main predictor component is neural network. After prediction of next state of data block, data can be distributed on the disks in a way that the number of disks is minimized due to energy consumption reduction and avoidance of Service Level Agreements (SLA) violation. The results of experiments show the high accuracy of the proposed method in comparison with the similar methods.
Cloud computing, Prediction framework, Energy efficiency, Neural Network, Fuzzy Classifier, Access number
Trust Modelling for Security of IoT Devices
Naresh K. Sehgal1, Shiv Shankar2 and John M. Acken3, 1Data Centre Group, Intel Corp, Santa Clara, CA, 2Chief Data Scientist, Maphalli, Bangalore, India and 3Portland State University, Portland, OR
IoT (Internet of Things), represents many kinds of devices in the field, connected to data-centers via various networks, submitting data, and allow themselves to be controlled. Connected cameras, TV, media players, access control systems, and wireless sensors are becoming pervasive. Their applications include Retail Solutions, Home, Transportation and Automotive, Industrial and Energy etc. This growth also represents security threat, as several hacker attacks been launched using these devices as agents. We explore the current environment and propose a quantitative and qualitative trust model, using a multi-dimensional exploration space, based on the hardware and software stack. This can be extended to any combination of IoT devices, and dynamically updated as the type of applications, deployment environment or any ingredients change.
Edge Computing, Security, Adaptive learning, Trust model, Threats, Cloud Computing, Information Security
QOS-Driven Job Scheduling: Multi-Tier Dependency Considerations
Husam Suleiman and Otman Basir, Department of Electrical and Computer Engineering, University of Waterloo, Canada
For a cloud service provider, delivering optimal system performance while fulfilling Quality of Service (QoS) obligations is critical for maintaining a viably profitable business. This goal is often hard to attain given the irregular nature of cloud computing jobs. These jobs expect high QoS on an on-demand fashion, that is on random arrival. To optimize the response to such client demands, cloud service providers organize the cloud computing environment as a multi-tier architecture. Each tier executes its designated tasks and passes the job to the next tier; in a fashion similar, but not identical, to the traditional job-shop environments. An optimization process must take place to schedule the appropriate tasks of the job on the resources of the tier, so as to meet the QoS expectations of the job. Existing approaches employ scheduling strategies that consider the performance optimization at the individual resource level and produce optimal single-tier driven schedules. Due to the sequential nature of the multi-tier environment, the impact of such schedules on the performance of other resources and tiers tend to be ignored, resulting in a less than optimal performance when measured at the multi-tier level.
In this paper, we propose a multi-tier-oriented job scheduling and allocation technique. The scheduling and allocation process is formulated as a problem of assigning jobs to the resource queues of the cloud computing environment, where each resource of the environment employs a queue to hold the jobs assigned to it. The scheduling problem is NP-hard, as such a biologically inspired genetic algorithm is proposed. The computing resources across all tiers of the environment are virtualized in one resource by means of a single queue virtualization. A chromosome that mimics the sequencing and allocation of the tasks in the proposed
virtual queue is proposed. System performance is optimized at this chromosome level. Chromosome manipulation rules are enforced to ensure task dependencies are met. The paper reports experimental results to demonstrate the performance of the proposed technique under various conditions and in comparison with
other commonly used techniques.
Cloud Computing, Task Scheduling and Allocation, QoS Optimization, Load Balancing, Genetic Algorithms
Virtual Enterprise Architecture Supply Chain (VEASC) Model On Cloud Computing: A Simulation-Based Study Through OPNET Modelling
Tlamelo Phetlhu, Department of Commerce and Law, University of Zululand, KwaZulu Natal, South Africa
The virtual enterprise architecture supply chain (VEASC) model has been studied in this research employing OPNET modelling and simulations. VEASC requires a synchronous framework of integrated applications and databases, coordination, collaborations, and communications for ensuring high accuracy and responsiveness in a supply chain (SC). The traditional models of electronic data interchange (EDI) and out-of-application methods for messaging and collaborations are not suitable to achieve the full benefits of VEASC because multiple human interventions may be required. In this research, a cloud-based SC application and its distributed databases contributed by multiple supplier and buyer organisations are modelled and simulated on OPNET. The application modelled on the cloud is based on a commercial software called INTEND. The simulation results revealed continuous flow of all the phases of the SC application because the reports reflected continuous interactions between the agents involved and the cloud distributed databases. This model is a good enabler of the VEASC model.
Supply chain, enterprise architecture, integration, collaboration, cloud computing, OPNET, simulations
SECURITY ISSUES IN CLOUD-BASED BUSINESSES
Mohamad Ibrahim AL Ladan, Rafik Hariri University, LEBANON
Cloud-based Business is a Business running and relying on Cloud computing IT paradigm. Cloud computing is an emerging technology paradigm that transfers current technological and computing concepts into utility-like solutions similar to electricity and communication systems. It provides the full scalability, reliability, computing resources configurability and outsourcing, resource sharing, external data warehousing, and high performance and relatively low cost feasible solutions and services as compared to dedicated infrastructures. Cloud-based Businesses store, access, use, and manage their data and software applications over the internet on a set of servers in the cloud without the need to have them stored/installed locally on their local devices. The cloud technology is used daily by many businesses/people around the world from using web based email services to executing heavy complex business transactions. Like any other emerging technology, Cloud computing comes with a baggage of some pros and cons. It is very useful in business development as it brings amazing results in a timely manner; however, it comes with increasing security and privacy concerns and issues. In this paper we will investigate, analyse, classify, and discuss the new security concerns and issues introduced by cloud computing. In addition, we present some security requirements that address and may alleviate these concerns and issues.
Cloud-based Business security issues and concerns; Cloud computing security issues and concerns. Cloud computing security requirements
A MapReduce based Algorithm for Data Migration in a Private Cloud Environment
Anurag Kumar Pandey, Ruppa K. Thulasiram and A. Thavaneswaran, University of Manitoba, Winnipeg, Canada
Use of cloud computing has grown quickly and is now a technology that serves the compute, storage and data management needs of individuals, businesses, governments and other organizations. Large data centers are created to serve large number of clients' workload from various walks of life. When a resource in a data center reaches its end-of-life, instead of investing in upgrading, it is possibly the time to decommission such a resource and migrate workloads to other resources in the data center. Data migration between different cloud servers of a given private cloud is risky due to the possibility of data loss. We have designed a MapReduce based algorithm and have introduced few metrics to test and
evaluate our proposed framework. We show that our algorithm for data migration works efficiently for text, image, audio and video files with minimum data loss and scale well for large data as well.
Cloud Computing, Private Cloud, Data Migration, MapReduce, Data Loss, Cost
INTEGRATING CLOUD COMPUTING TO SOLVE ERP COST CHALLENGE
Amal Alhosban and Anvitha Akurathi, Department of Computer Science, Engineering and Physics
University of Michigan-Flint, MI, USA
Enterprise Resource Planning (ERP) is a popular business management tool used by almost all
companies these days to organize their business. In-spite of the challenges faced by ERP; before, during
and after its implementation into the Enterprise, it fetches greater profits to the organization. This paper
deals with the challenges faced by ERP with a complete literature overview of the challenges from earlier
authors. Then after a brief visit of these factors, a very essential topic to the Enterprises i.e., Costs are
discussed. The costs that are incurred in the project, some unknown or hidden costs are dealt with. A
solution is proposed to solve this cost problem of ERP and to improve the profit margins to the
companies. The solution is Cloud ERP. The latter part deals with the benefits of Cloud ERP in general
and with respect to costs along with the concerns of cloud ERP, the major issue among all the concerns
and few proposed solutions of solving this problem in the cloud ERP.
ERP, Cost, Cloud ERP, Security
Enabling Edge Computing Using Container Orchestration and Software Defined Wide Area Networks
Felipe Rodriguez Yaguache1 and Kimmo Ahola2, 1School of Electrical Engineering, Aalto University, Espoo, Finland and 25G Networks & Beyond, Technical Research Centre of Finland (VTT), Espoo, Finland
With SD-WAN being increasingly adopted by corporations, and Kubernetes becoming the de-facto
container orchestration tool, the opportunities for deploying edge-computing applications running over a
SD-WAN scenario are vast. In this document, an in-house service discovery solution that works alongside
Kubernetes’ master node for allowing an improved traf ic handling and better user experience, is
developed. First, a proof-of-concept SD-WAN topology was implemented alongside a Kubernetes cluster
and the in-house service discovery solution. Next, the implementation's performance is tested based on
the required times for updating the discovery solution according to service updates. Finally, some
conclusions and modifications are pointed out based on the results, while also discussing possible
SD-WAN, Edge computing, Virtualization, Kubernetes, Services
Context-Aware Trust-Based Access Control For Ubiquitous Systems
Malika Yaici, Faiza Ainennas and Nassima Zidi, Computer Department, University of Bejaia, Bejaia, Algeria
The ubiquitous computing and context-aware applications experience at the present time a very important development. This has led organizations to open more of their information systems, making them available anywhere, at any time and integrating the dimension of mobile users. This cannot be done without taking into account thoughtfully the access security: a pervasive information system must henceforth be able to take into account the contextual features to ensure a robust access control.
In this paper, access control and a few existing mechanisms have been exposed. It is intended to show the importance of taking into account context during a request for access. In this regard, our proposal incorporates the concept of trust to establish a trust relationship according to three contextual constraints (location, social situation and time) in order to decide to grant or deny the access request of a user to a service
Pervasive systems, Access Control, RBAC, Context-awareness, Trust management
An Ontology Based Approach to Improve Process Mining Result In Univer sity Information system
Maryem Dellai and Yemna Sayeb, Research Laboratory, ISAMM, Manouba, Tunisia
Process mining algorithms use event logs to extract process-related information, to discover, analyze conformance, or to enhance processes. Event logs can be used to analyze and visualize the processes with better insight and improved formal access to the data. Most process mining (PM) applications are based on event logs with keyword-based activity and resource descriptions. In recent years, lots of efforts are dedicated to explore logic-based ontology formalisms.In this research work, we use ontologies that are intended to define the semantics of recorded events. The highest quality of event logs requires the existence of ontologies to which events and attributes point. Many human-designed processes are based on explicit workflow or lifecycle models which can be described using taxonomies or more complicated ontologies. Ontologies have been successfully applied to represent the knowledge in many domains. In this paper, we introduce an approach for enriching event logs using Process mining with associated ontology structures. Our proposal is to provide features that help integrating event logs from event sources in order to extract data and put it into a suitable format semantically enriched so that the data can be exploited with process mining tools (ProM).
process mining, event logs, ontologies, process model, Petri net.
Construction Of an Oral Cancer Auto-Classify system Based On Machine- Learning for Artificial Intelligence
Meng-Jia Lian1, Chih-Ling Huang2, Tzer-Min Lee1,3
1 School of Dentistry, Kaohsiung Medical University, Kaohsiung, Taiwan
2 Center for Fundamental Science, Kaohsiung Medical University, Kaohsiung, Taiwan
3Institute of Oral Medicine, National Cheng Kung University Medical College, Tainan
Oral cancer is one of the most prevalent tumors of the head and neck region. An earlier diagnosis can help dentist getting a better therapy plan, giving patients a better treatment and the reliable techniques for detecting oral cancer cells are urgently required. This study proposes an optic and automation method
using reflection images obtained with scanned laser pico-projection system, and Gray-Level Cooccurrence Matrix for sampling. Moreover, the artificial intelligence technology, Support Vector Machine, was used to classify samples. Normal Oral Keratinocyte and dysplastic oral keratinocyte were simulating the evolvement of cancer to be classified. The accuracy in distinguishing two cells has reached 85.22%. Compared to existing diagnosis methods, the proposed method possesses many advantages, including a lower cost, a larger sample size, an instant, a non-invasive, and a more reliable diagnostic performance. As a result, it provides a highly promising solution for the early diagnosis of oral squamous carcinoma.
Oral Cancer Cell, Normal Oral Keratinocyte (NOK), Dysplastic oral keratinocyte (DOK), Gray-Level Cooccurrence Matrix (GLCM), Scanned Laser Pico-Projection (SLPP), Support Vector Machine (SVM), Machine-Learning
Automatic Extraction of Feature Lines on 3D Surface
Zhihong Mao,Division of Intelligent Manufacturing, Wuyi University, Jiangmen, China
Many applications in mesh processing require the detection of feature lines. Feature lines convey the
inherent features of the shape. Existing techniques to find feature lines in discrete surfaces are relied on
user-specified thresholds and are inaccurate and time-consuming. We use an automatic approximation
technique to estimate the optimal threshold for detecting feature lines. Some examples are presented to
show our method is effective, which leads to improve the feature lines visualization.
Feature Lines; Extraction; Meshes .
HMM-Based Dari Named Entity Recognition for Information Extraction
Ghezal Ahmad Jan, Zia
Department of Models and Theory of Distributed Systems, TU Berlin
Straße des 17. Juni 135, 10623 Berlin, Germany
Named Entity Recognition (NER) is the fundamental subtask of information extraction systems that labels elements into categories such as persons, organizations or locations. The task of NER is to detect and classify words that are parts of sentences. This paper describes a statistical approach to modeling NER on the Dari language.Dari and Pashto are low resources languages, spoken as official languages in Afghanistan. Unlike other languages, named entity detection
approaches differ in Dari. Since in Dari language there is no capitalization for identifying named entities. We seek to bridge the gap between
Dari linguistic structure and supervised learning model that predict the
sequences of words paired with a sequence of tags as outputs. Dari corpus was developed from the collection of news, reports and articles based
on the original orthographic structure of the Dari language. The experimental result presents the named entity recognition performance 95%
Natural Language Processing (NLP), Hidden Markov Model
(HMM), Named Entity Recognition (NER), Part-of-Speech (POS) Tagging
Designing Dynamic Protocol for Real-Time IIoT-based Applications by Efficient Management of System Resources
Farzad Kiani, Sajjad Nematzadehmiandoab, Amir Seyyedabbasi
Computer Engineering Dept., Engineering and Natural Sciences Faculty at Istanbul Sabahattin Zaim
University, Kucukcekmece, 34303, Istanbul, Turkey
Due to increased applicability, wireless sensor networks have captured the attention of researchers from various fields. These
networks still suffer from various challenges and limitations regardless. These problems are even much more pronounced in some
areas of the field such as real time IoT based applications. Here, a dynamic protocol that efficiently utilizes the available resources
is proposed. The protocol employs five developed algorithms that aid the data transmission, neighbor and optimal path finding
processes. The protocol can be utilized in, but not limited to, real-time large data streaming applications.. In this paper is defined
a structure that enables the sensor devices to communicate with each other over their local network or internet as required in
order to preserve the available resources. Both theoretical and experimental result analysis of the entire protocol in general and
individual algorithms is also performed.
Big data wireless sensor networks, real-time systems, energy efficiency, routing protocol, IoT.
Interactive Mesh Cutout Using Graph Cuts
Zhihong Mao,Division of Intelligent Manufacturing, Wuyi University, Jiangmen529020, China
Mesh segmentation is a foundational operation for many computer graphics applications.
Although various automatic segmentation schemes have been proposed, to precisely obtain the
meaningful part of a mesh is a challenging issue. In this paper, we introduce an Interactive system
to efficiently extract meaningful objects from a triangular mesh. The algorithm proposed in this
paper extends min-cut based on 2D-image segmentation techniques to the domain of 3D mesh. We
also provide a screen-space user interface that allows the user to indicate the meaningful object
easily. In our system, quadric-based surface simplification is adopted for a large mesh, we use
min-cut in the simplified mesh, then graph cuts are used to refine the previous cuts in the original
mesh. The results show that our proposed method is relatively simple and effective as a powerful
tool for mesh cutout.
Mesh Segmentation, Mesh Cutout, Graph cuts.
Tough Random Symmetric 3-SAT Generator
Robert Amador1, Chang-Yu Hsieh2 , Chen-Fu Chiang3
1,3Department of Computer Science, State University of New York Polytechnic Institute, Utica, NY 13502,
USA,2Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
We designed and implemented an efficient tough random symmetric 3-SAT generator. We quantify the hardness in terms of CPU time, numbers of restarts, decisions,
propagations, conflicts and conflicted literals that occur when a solver tries to solve
3-SAT instances. In this experiment, the clause variable ratio was chosen to be around
the conventional critical phase transition number 4.24. The experiment shows that
instances generated by our generator are significantly harder than instances generated
by the Tough K-SAT generator. The difference in hardness between two SAT instance
generators exponentiates as the number of boolean variables used increases.
Data Analysis of Wireless Networks Using Classification Techniques
Daniel Rosa Canêdo1,2 and Alexandre Ricardo Soares Romariz1
1Department of Electrical Engineering, University of Brasília, Brasília, Brazil
,2Federal Institute of Goiás, Luziânia, Brazil
In the last decade, there has been a great technological advance in the infrastructure of mobile technologies. The increase in the use of wireless local area networks and the use of satellite services are also noticed. The high utilization rate of mobile devices for various purposes makes clear the need to monitor wireless networks to ensure the integrity and confidentiality of the information transmitted. Therefore, it is necessary to quickly and efficiently identify the normal and abnormal traffic of such networks, so that administrators can take action. This work aims to analyze classification techniques in relation to data from Wireless Networks, using some classes of anomalies pre-established according to some defined criteria of the MAC layer. For data analysis, WEKA Data Mining software (Waikato Environment for Knowledge Analysis) is used. The classification algorithms present a success rate in the classification of viable data, being indicated in the use of intrusion detection systems for wireless networks.
Wireless Networks, Classification Thecniques, Weka.
A Survey Of State-Of-The-Art GAN-Based Approaches To Image Synthesis
Shirin Nasr Esfahani and Shahram Latifi,University of Nevada, Las Vegas,USA.
In the past few years, Generative Adversarial Networks (GANs) have received immense attention by researchers in a variety of application domains. This new field of deep learning has been growing rapidly and has provided a way to learn deep representations without extensive use of annotated training data. Their achievements may be used in a variety of applications, including speech synthesis, image and video generation, semantic image editing, and style transfer. Image synthesis is an important component of expert systems and it attracted much attention since the introduction of GANs. However, GANs are known to be difficult to train especially when they try to generate high resolution images. This paper gives a through overview of the state-of-the-art GANs-based approaches in four applicable areas of image generation including Text-to-Image-Synthesis, Image- to- Image-Translation, Face Aging, and 3D Image Synthesis. Experimental results show state-of-the-art performance using GANs compared to traditional approaches in the fields of image processing and machine vision.
Conditional generative adversarial networks (cGANs), image synthesis, image-to-image translation, text-to-image synthesis, 3D GANs.
A Call Graph Reduction based Novel Storage Allocation for Smart City Applications
Prabhdeep Singh, Rajvir Kaur, Diljot Singh, Vivek Gupta,Punjabi University, India.
Today's world is going to be smart even smarter day by day. Smart cities play an important role to make the world
smart. Thousands of smart city applications are developing in every day. Every second very huge amount of data
is generated. The data need to be managed and stored properly so that information can be extracted using
various emerging technologies. The main aim of this paper is to propose a storage scheme for data generated by
smart city applications. A matrix is used which store the information of each adjacency node of each level as well
as the weight and frequency of call graph. It has been experimentally depicted that the applied algorithm reduces
the size of the call graph without changing the basic structure without any loss of information. Once the graph is
generated from the source code, it is stored in the matrix and reduced appropriately using the proposed
algorithm. The proposed algorithm is also compared to another call graph reduction techniques and it has been
experimentally evaluated that the proposed algorithm significantly reduces the graph and store the smart city
application data efficiently
Comparing String Similarity Measures In The Task Of Name Matching
Aleksandra Zaba,University of Utah, USA.
This pilot study reports recall, precision, and f-measures for three groups of string similarity algorithms
contained in the ‘stringdist’ package of R, the edit-based Levenshtein, full Levenshtein-Damerau,
Hamming, and longest common substring, the q-gram based Jaccard, q-gram, and cosine measures, and
the heuristic Jaro and Jaro-Winkler. The algorithms are to specify values for the similarity between a
base word, a female first name, and three of its variants, that same name, and two of the following: Its
foreign version (categorized by us as ‘same’), its male version (‘different’), and a different, also female,
version of the base name in American English (‘different’). We report f-measures, and these are
interpreted in the context of the given algorithm. For our data so far, a relatively low threshold (from
‘match’ to ‘not match’; assigned by us to an algorithm’s value for a given similarity) provides the highest
weighted average of recall and precision.
Artificial Intelligence, Natural Language Processing, String Similarity Algorithms, R, F-Measure.
Performance Comparison Of Web-Based Book Recommender Systems
Swathi S Bhat, Pranav P, Shashank K V and Arpitha Raghunandan, National Institute of Technology, India.
Recommendation systems are being widely used for personalization on the web today. E-Commerce
giants rely highly on their recommendation systems to improve their business. As a result, the quality of
recommendations can have a significant impact on their sales. Hence, proper evaluation of such
recommender systems is important. Traditional evaluation metrics are limited to error based and
accuracy metrics and do not take into consideration factors like diversity, novelty, informedness,
markedness etc. We aim to perform a comprehensive performance comparison of two web-based book
recommendation systems using lesser known but equally important metrics like diversity, informedness
Recommendation systems, diversity, metrics, informedness, markedness, precision, recall, ROC,