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Haithem Mezni

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PhD in Computer Science.Assistant prodessor, University of Jendouba.

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University of Zagreb

Armando Marques-Guedes

UNL - New University of Lisbon

Dublin Institute of Technology

Roshan Chitrakar

Nepal College of Information Technology

Sajadin Sembiring

Universitas Sumatera Utara

Dr. Shadab Alam

Jazan University

Journal of Computer Science IJCSIS

Ashok Paranjothi

Sri Sairam Institute Of Technology

PES University

Kwame Nkrumah University of Science and Technology

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Papers by Haithem Mezni

Context-aware Service Recommendation based on Knowledge Graph Embedding (Extended Abstract)

An Evidence Theory Based Embedding Model for the Management of Smart Water Environments

Sensors

Having access to safe water and using it properly is crucial for human well-being, sustainable de... more Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty hand...

Time-aware service recommendation: Taxonomy, review, and challenges

Software: Practice and Experience, 2018

Nowadays, a huge number of available Web services offer the same functionalities and a high quali... more Nowadays, a huge number of available Web services offer the same functionalities and a high quality of service, which makes the selection of suitable services a difficult task. In such situation, the services must be differentiated by additional criteria such as users' ratings. To meet this goal, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, whereas in the real world, the perception and popularity of Web services may continually change, and users' preferences and habits also shift frequently. Time is becoming an increasingly important factor in recommender systems, since time effects influence users' preferences to a large extent. In addition, quality-of-service performance of Web services is strongly linked to the service status and network environments, which are variable against time. Recently, a wide range of service recommendation approaches, dealing with the time dimension in user modeling and recommendation strategies, have been proposed. Thus, the purpose of this survey is to present a comprehensive study and analysis of the state-of-the-art on time-aware service recommendation. We identify the techniques used in recommender systems to provide the best services. Moreover, we present a classification of time-aware recommender systems based on the target recommendation time, the type of relationship between users, and the type of feedback. Besides, we present a comparison between time-aware recommendation approaches, and we discuss their advantages and disadvantages. Finally, challenges and requirements of time-aware service recommendation as well as the future directions are identified according to the studied approaches.

Web service adaptation: A decade’s overview

Computer Science Review

A Novel Scalable Clustering Method for Distributed Networks

Graph clustering is one of the key techniques to understand structures that are present in networ... more Graph clustering is one of the key techniques to understand structures that are present in networks. In addition to clusters, bridges and outliers detection is also a critical task as it plays an important role in the analysis of networks. Recently, several graph clustering methods are developed and used in multiple application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on the structural clustering algorithm. Yet, this kind of algorithm is based on the structural similarity, this later requires to parse all graph ' edges in order to compute the structural similarity. However, the height needs of similarity computing make this algorithm more adequate for small graphs, without significant support to deal with large-scale networks. In this paper, we propose a novel distributed graph clustering algorithm based on structural graph clustering. The experimental results show the efficiency in terms of r...

A Comparative Study on Streaming Frameworks for Big Data

Recently, increasingly large amounts of data are generated from a variety of sources. Existing da... more Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on streaming in Big Data, a task referring to the processing of massive volumes of structured/unstructured streaming data. Recently proposed streaming frameworks for Big Data applications help to store, analyze and process the continuously captured data. In this paper, we discuss the challenges of Big Data and we survey existing streaming frameworks for Big Data. We also present an experimental evaluation and a comparative study of the most popular streaming platforms.

Multi-cloud service composition using Formal Concept Analysis

Journal of Systems and Software, 2017

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  Formal Concept Analysis is adapted to the multi-cloud service composition problem.  Multi-cloud environment is modelled using lattice theory.  Algorithms for optimal cloud combination are proposed.  Experiments proved the ability of FCA to reduce the number of clouds and inter-cloud communication cost.

Semantic QoS synchronization of Web services

2016 International Symposium on Networks, Computers and Communications (ISNCC), 2016

Due to their promise to transform the way business is conducted, Web services increased tremendou... more Due to their promise to transform the way business is conducted, Web services increased tremendously in number. Accordingly, service providers are competing to make their services more visible to get closer to service consumers. This fact leads to having service description (including the quality of service, for short, QoS) of same Web services published in many service registries but with different vocabularies and terminologies according to the service publication requirements of each service publisher. Consequently, the challenge is how to synchronize QoS values over these service registries, whenever they are updated. The solution to this problem should be based on the semantics of QoS to be able to synchronize their values regardless of their different representations.

Towards trustworthy service adaptation: An ontology-based cross-layer approach

2014 IEEE 5th International Conference on Software Engineering and Service Science, 2014

Although several approaches have been proposed towards self-adaptation of Web services, most of t... more Although several approaches have been proposed towards self-adaptation of Web services, most of them work in isolation and few of them deal with cross-layer and trust issues. Indeed, the complex layered nature of service-based systems frequently leads to service failure and conflicting adaptation. To tackle this problem, we propose an ontology-based categorization of service behavior across all the functional layers. The proposed ontology provides support for cross-layer self-adaptation by facilitating reasoning about events to identify the real source of service failure, and reasoning about self-adaptation actions to check integrity and compatibility of self-adaptation with constraints imposed by each layer.

Multicloud service composition: A survey of current approaches and issues

Journal of Software: Evolution and Process, 2018

During the last decade, cloud computing became a natural choice to host and provide various compu... more During the last decade, cloud computing became a natural choice to host and provide various computing resources as on-demand services. To better satisfy user requirements, cloud services may be combined while considering the constraints of the virtualized environment, including security policies, resources availability, and interoperability. Extensive surveys have been conducted to study the major issues related to the cloud service composition problem. However, very few works have studied such issues in a multicloud setting. To fill this gap, we provide in this paper a systematic literature review on multicloud service composition. We start with a background on service composition in single clouds. Then, we present the multicloud taxonomy, and we study how service composition was tackled by researchers in multicloud environments. Finally, we identify the challenges and the requirements of multicloud service composition, as well as the future directions.

A distributed and incremental algorithm for large-scale graph clustering

by Haithem Mezni and Wissem Inoubli

Future Generation Computer Systems

Graph clustering is one of the key techniques to understand the structures present in the graph d... more Graph clustering is one of the key techniques to understand the structures present in the graph data. In addition to cluster detection, the identification of hubs and outliers is also a critical task as it plays an important role in the analysis of graph data. Recently, several graph clustering algorithms have been proposed and used in many application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on the structural clustering algorithm SCAN. Yet, SCAN algorithm has been designed for small graphs, without significant support to deal with big and dynamic graphs. In this paper, we propose DIS-CAN, a novel distributed and incremental graph clustering algorithm based on SCAN. We present an implementation of DISCAN on top of BLADYG framework, and experimentally show the efficiency of DISCAN in both large and dynamic networks.

Predictive service placement in cloud using deep learning and frequent subgraph mining

Journal of Ambient Intelligence and Humanized Computing, 2022

Predictive BPaaS management with quantum and neural computing

Journal of Software: Evolution and Process, 2022

With the increasing adoption of cloud computing, the deployment and management of business proces... more With the increasing adoption of cloud computing, the deployment and management of business processes over cloud environments have become an essential operation for most enterprises, leading to the emergence of BPaaS (Business Process as a Service) as a new cloud service model. This SaaS‐like service, like its ancestors, should be strategically distributed and managed over multiple cloud zones, while taking into account several constraints and conditions (e.g., sensitivity of BPaaS fragments, insecure and untrusted cloud zones, lack of resources, and workload changes). However, current BPaaS approaches are static, which means that they are no longer suitable to manage such enterprise‐oriented cloud service model and to deal with the uncertain and dynamic nature of cloud availability zones. To fill this gap, we adopt a predictive BPaaS management strategy by proposing a model that forecasts the next‐short time overload of cloud zones. These latter, as hosting environments for the managed BPaaS, are categorized as overloaded or underloaded, which triggers the migration of BPaaS fragments to high‐performance cloud zones. The proposed neural network prediction model (called QGA‐NN) is enhanced with a quantum genetic algorithm to optimize the prediction of cloud zones' overload. QGA‐NN is evaluated using a BPaaS placement algorithm, which we defined as a triggered management operation. Experimental results have proved the accuracy and effectiveness of our predictive approach, compared with state‐of‐the‐art solutions.

A Multi-Recommenders System for Service Provisioning in Multi-Cloud Environment

2017 28th International Workshop on Database and Expert Systems Applications (DEXA), 2017

Cloud service recommendation has become an important technique that helps users decide whether a ... more Cloud service recommendation has become an important technique that helps users decide whether a service satisfies their requirements or not. However, the few existing recommendation systems are not suitable for real world environments and only deal with services hosted in a single cloud, which is simply unrealistic. In addition, a same service may be hosted on more than one cloud and, hence, may have different user ratings that depend on specific conditions of their cloud availability zones. This uncertainty regarding the real quality of the cloud service and users' satisfaction levels raises a question about how to trust the different users' ratings in order to recommend the adequate cloud service. Unlike existing solutions, the goal of this work is to propose a cooperative recommender system that aims to resolve two major issues: recommendation of cloud services in multiple clouds and recommendation under uncertainty of users' ratings. The proposed system will take advantage from a set of powerful techniques and paradigms in order to offer an overlay of cloud recommender entities that cooperate to deliver top-rated services to the user.

Security-aware multi-cloud service composition by exploiting rough sets and fuzzy FCA

Soft Computing, 2021

With the emergence of cloud computing and to better satisfy users' complex requirements in front ... more With the emergence of cloud computing and to better satisfy users' complex requirements in front of the huge number of cloud services, these latter may be combined while considering the virtualized environment's constraints, including quality of services, security policies, resources availability, interoperability, etc. As composing services from multiple clouds was proved to be more beneficial than relying on services from one single cloud, the new approaches are now spanning multiple clouds. Despite the advantages of multi-cloud environments, there are always some security risks that mostly threaten the cloud consumers' data, which makes the identification of suitable services a challenging task. In this work, we propose a security-aware multi-cloud service composition approach using fuzzy formal concept analysis (fuzzy FCA) and rough set theory (RS), which are two techniques with a strong mathematical background. To guarantee a high security level of the hosting clouds and the selected services, we exploit the fuzzy relations of fuzzy FCA and the approximation of RS. These techniques will help reducing the search space, by eliminating the disqualified clouds and insecure services. The experimental results proved the performance and the effectiveness of our approach.

An experimental survey on big data frameworks (Highlight paper)

Recently, increasingly large amounts of data are generated from a variety of sources. Existing da... more Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch.

Temporal Knowledge Graph Embedding for Effective Service Recommendation

IEEE Transactions on Services Computing, 2021

Over the last decade, service selection and recommendation had been two strongly related service ... more Over the last decade, service selection and recommendation had been two strongly related service filtering steps. The ever changing services environment, users tastes, as well as the perception and popularity of available services, rise a question regarding the appropriate means to capture and analyze such changes over time. Most service recommendation solutions are static and do not offer a multi-relational modeling of user-service interactions over time. Time is a contextual dimension that has, recently, received a lot of attention, leading to a new class of recommender systems, called time-aware recommender systems. In this work, we propose a service recommendation method that takes advantage of temporal knowledge graphs. As a de facto standard to model multiple and complex interactions between heterogeneous entities, knowledge graphs will serve as a historical knowledge base for our TASR system. We, first, model the user-service interactions over time, by constructing a temporal service knowledge graph (TSKG) that will be later enriched through a completion step. Second, to explore the TSKG and extract top-rated services, we use Convolutional Neural Networks (CNN) to embed the TSKG into a low-dimensional vector space, facilitating then its mining.

Un algorithme distribué pour le clustering de grands graphes

Le clustering de graphes est l'une des techniques cles qui permet de comprendre les structure... more Le clustering de graphes est l'une des techniques cles qui permet de comprendre les structures presentes dans les donnees de graphe. La detection des clusters et l'identification des ponts et des bruit sont egalement des tâches critiques car elles jouent un role important dans l'analyse des graphes. Recem-ment, plusieurs algorithmes de clustering de graphes ont ete proposes et utilises dans de nombreux domaines d'application. La plupart de ces algorithmes sont bases sur les algorithmes de clustering structurel. Neanmoins, ces derniers ont ete concus pour le traitement des petits graphes. D'ou, leur performance peut se degrader dans le cas des graphes larges qui imposent des defis supplementaires. Dans cet article, nous proposons DSCAN, un algorithme distribue de clustering de graphes qui est base sur le clustering structurel. Notre algorithme est im-plimente sur la base de framework de traitement de grands graphes BLADYG. L'evaluation experimentale de DSCAN a...

A Distributed Algorithm for Large-Scale Graph Clustering

Graph clustering is one of the key techniques to understand the structures present in the graph d... more Graph clustering is one of the key techniques to understand the structures present in the graph data. In addition to cluster detection, the identification of hubs and outliers is also a critical task as it plays an important role in the understanding of graph data. Recently, several graph clustering algorithms have been proposed and used in many application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on structural clustering. Yet, these algorithms have been evaluated on small graph database. In this paper, we propose DSCAN, a novel distributed structural graph clustering algorithm. We present an implementation of DSCAN on top of BLADYG, a distributed graph processing framework. We experimentally show that DSCAN significantly outperforms existing clustering algorithm in terms of scalability and performance in the case of large graphs.

Context-aware Service Recommendation based on Knowledge Graph Embedding

IEEE Transactions on Knowledge and Data Engineering, 2021

As a class of context-aware systems, context-aware service recommendation aims to bind high-quali... more As a class of context-aware systems, context-aware service recommendation aims to bind high-quality services to users while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructed context-aware service knowledge graph (C-SKG) is, then, transformed into a low-dimentional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user's context. Experiments have proved the accuracy and scalability of our CASR approach.

Context-aware Service Recommendation based on Knowledge Graph Embedding (Extended Abstract)

An Evidence Theory Based Embedding Model for the Management of Smart Water Environments

Sensors

Having access to safe water and using it properly is crucial for human well-being, sustainable de... more Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty hand...

Time-aware service recommendation: Taxonomy, review, and challenges

Software: Practice and Experience, 2018

Nowadays, a huge number of available Web services offer the same functionalities and a high quali... more Nowadays, a huge number of available Web services offer the same functionalities and a high quality of service, which makes the selection of suitable services a difficult task. In such situation, the services must be differentiated by additional criteria such as users' ratings. To meet this goal, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, whereas in the real world, the perception and popularity of Web services may continually change, and users' preferences and habits also shift frequently. Time is becoming an increasingly important factor in recommender systems, since time effects influence users' preferences to a large extent. In addition, quality-of-service performance of Web services is strongly linked to the service status and network environments, which are variable against time. Recently, a wide range of service recommendation approaches, dealing with the time dimension in user modeling and recommendation strategies, have been proposed. Thus, the purpose of this survey is to present a comprehensive study and analysis of the state-of-the-art on time-aware service recommendation. We identify the techniques used in recommender systems to provide the best services. Moreover, we present a classification of time-aware recommender systems based on the target recommendation time, the type of relationship between users, and the type of feedback. Besides, we present a comparison between time-aware recommendation approaches, and we discuss their advantages and disadvantages. Finally, challenges and requirements of time-aware service recommendation as well as the future directions are identified according to the studied approaches.

Web service adaptation: A decade’s overview

Computer Science Review

A Novel Scalable Clustering Method for Distributed Networks

Graph clustering is one of the key techniques to understand structures that are present in networ... more Graph clustering is one of the key techniques to understand structures that are present in networks. In addition to clusters, bridges and outliers detection is also a critical task as it plays an important role in the analysis of networks. Recently, several graph clustering methods are developed and used in multiple application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on the structural clustering algorithm. Yet, this kind of algorithm is based on the structural similarity, this later requires to parse all graph ' edges in order to compute the structural similarity. However, the height needs of similarity computing make this algorithm more adequate for small graphs, without significant support to deal with large-scale networks. In this paper, we propose a novel distributed graph clustering algorithm based on structural graph clustering. The experimental results show the efficiency in terms of r...

A Comparative Study on Streaming Frameworks for Big Data

Recently, increasingly large amounts of data are generated from a variety of sources. Existing da... more Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on streaming in Big Data, a task referring to the processing of massive volumes of structured/unstructured streaming data. Recently proposed streaming frameworks for Big Data applications help to store, analyze and process the continuously captured data. In this paper, we discuss the challenges of Big Data and we survey existing streaming frameworks for Big Data. We also present an experimental evaluation and a comparative study of the most popular streaming platforms.

Multi-cloud service composition using Formal Concept Analysis

Journal of Systems and Software, 2017

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  Formal Concept Analysis is adapted to the multi-cloud service composition problem.  Multi-cloud environment is modelled using lattice theory.  Algorithms for optimal cloud combination are proposed.  Experiments proved the ability of FCA to reduce the number of clouds and inter-cloud communication cost.

Semantic QoS synchronization of Web services

2016 International Symposium on Networks, Computers and Communications (ISNCC), 2016

Due to their promise to transform the way business is conducted, Web services increased tremendou... more Due to their promise to transform the way business is conducted, Web services increased tremendously in number. Accordingly, service providers are competing to make their services more visible to get closer to service consumers. This fact leads to having service description (including the quality of service, for short, QoS) of same Web services published in many service registries but with different vocabularies and terminologies according to the service publication requirements of each service publisher. Consequently, the challenge is how to synchronize QoS values over these service registries, whenever they are updated. The solution to this problem should be based on the semantics of QoS to be able to synchronize their values regardless of their different representations.

Towards trustworthy service adaptation: An ontology-based cross-layer approach

2014 IEEE 5th International Conference on Software Engineering and Service Science, 2014

Although several approaches have been proposed towards self-adaptation of Web services, most of t... more Although several approaches have been proposed towards self-adaptation of Web services, most of them work in isolation and few of them deal with cross-layer and trust issues. Indeed, the complex layered nature of service-based systems frequently leads to service failure and conflicting adaptation. To tackle this problem, we propose an ontology-based categorization of service behavior across all the functional layers. The proposed ontology provides support for cross-layer self-adaptation by facilitating reasoning about events to identify the real source of service failure, and reasoning about self-adaptation actions to check integrity and compatibility of self-adaptation with constraints imposed by each layer.

Multicloud service composition: A survey of current approaches and issues

Journal of Software: Evolution and Process, 2018

During the last decade, cloud computing became a natural choice to host and provide various compu... more During the last decade, cloud computing became a natural choice to host and provide various computing resources as on-demand services. To better satisfy user requirements, cloud services may be combined while considering the constraints of the virtualized environment, including security policies, resources availability, and interoperability. Extensive surveys have been conducted to study the major issues related to the cloud service composition problem. However, very few works have studied such issues in a multicloud setting. To fill this gap, we provide in this paper a systematic literature review on multicloud service composition. We start with a background on service composition in single clouds. Then, we present the multicloud taxonomy, and we study how service composition was tackled by researchers in multicloud environments. Finally, we identify the challenges and the requirements of multicloud service composition, as well as the future directions.

A distributed and incremental algorithm for large-scale graph clustering

by Haithem Mezni and Wissem Inoubli

Future Generation Computer Systems

Graph clustering is one of the key techniques to understand the structures present in the graph d... more Graph clustering is one of the key techniques to understand the structures present in the graph data. In addition to cluster detection, the identification of hubs and outliers is also a critical task as it plays an important role in the analysis of graph data. Recently, several graph clustering algorithms have been proposed and used in many application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on the structural clustering algorithm SCAN. Yet, SCAN algorithm has been designed for small graphs, without significant support to deal with big and dynamic graphs. In this paper, we propose DIS-CAN, a novel distributed and incremental graph clustering algorithm based on SCAN. We present an implementation of DISCAN on top of BLADYG framework, and experimentally show the efficiency of DISCAN in both large and dynamic networks.

Predictive service placement in cloud using deep learning and frequent subgraph mining

Journal of Ambient Intelligence and Humanized Computing, 2022

Predictive BPaaS management with quantum and neural computing

Journal of Software: Evolution and Process, 2022

With the increasing adoption of cloud computing, the deployment and management of business proces... more With the increasing adoption of cloud computing, the deployment and management of business processes over cloud environments have become an essential operation for most enterprises, leading to the emergence of BPaaS (Business Process as a Service) as a new cloud service model. This SaaS‐like service, like its ancestors, should be strategically distributed and managed over multiple cloud zones, while taking into account several constraints and conditions (e.g., sensitivity of BPaaS fragments, insecure and untrusted cloud zones, lack of resources, and workload changes). However, current BPaaS approaches are static, which means that they are no longer suitable to manage such enterprise‐oriented cloud service model and to deal with the uncertain and dynamic nature of cloud availability zones. To fill this gap, we adopt a predictive BPaaS management strategy by proposing a model that forecasts the next‐short time overload of cloud zones. These latter, as hosting environments for the managed BPaaS, are categorized as overloaded or underloaded, which triggers the migration of BPaaS fragments to high‐performance cloud zones. The proposed neural network prediction model (called QGA‐NN) is enhanced with a quantum genetic algorithm to optimize the prediction of cloud zones' overload. QGA‐NN is evaluated using a BPaaS placement algorithm, which we defined as a triggered management operation. Experimental results have proved the accuracy and effectiveness of our predictive approach, compared with state‐of‐the‐art solutions.

A Multi-Recommenders System for Service Provisioning in Multi-Cloud Environment

2017 28th International Workshop on Database and Expert Systems Applications (DEXA), 2017

Cloud service recommendation has become an important technique that helps users decide whether a ... more Cloud service recommendation has become an important technique that helps users decide whether a service satisfies their requirements or not. However, the few existing recommendation systems are not suitable for real world environments and only deal with services hosted in a single cloud, which is simply unrealistic. In addition, a same service may be hosted on more than one cloud and, hence, may have different user ratings that depend on specific conditions of their cloud availability zones. This uncertainty regarding the real quality of the cloud service and users' satisfaction levels raises a question about how to trust the different users' ratings in order to recommend the adequate cloud service. Unlike existing solutions, the goal of this work is to propose a cooperative recommender system that aims to resolve two major issues: recommendation of cloud services in multiple clouds and recommendation under uncertainty of users' ratings. The proposed system will take advantage from a set of powerful techniques and paradigms in order to offer an overlay of cloud recommender entities that cooperate to deliver top-rated services to the user.

Security-aware multi-cloud service composition by exploiting rough sets and fuzzy FCA

Soft Computing, 2021

With the emergence of cloud computing and to better satisfy users' complex requirements in front ... more With the emergence of cloud computing and to better satisfy users' complex requirements in front of the huge number of cloud services, these latter may be combined while considering the virtualized environment's constraints, including quality of services, security policies, resources availability, interoperability, etc. As composing services from multiple clouds was proved to be more beneficial than relying on services from one single cloud, the new approaches are now spanning multiple clouds. Despite the advantages of multi-cloud environments, there are always some security risks that mostly threaten the cloud consumers' data, which makes the identification of suitable services a challenging task. In this work, we propose a security-aware multi-cloud service composition approach using fuzzy formal concept analysis (fuzzy FCA) and rough set theory (RS), which are two techniques with a strong mathematical background. To guarantee a high security level of the hosting clouds and the selected services, we exploit the fuzzy relations of fuzzy FCA and the approximation of RS. These techniques will help reducing the search space, by eliminating the disqualified clouds and insecure services. The experimental results proved the performance and the effectiveness of our approach.

An experimental survey on big data frameworks (Highlight paper)

Recently, increasingly large amounts of data are generated from a variety of sources. Existing da... more Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch.

Temporal Knowledge Graph Embedding for Effective Service Recommendation

IEEE Transactions on Services Computing, 2021

Over the last decade, service selection and recommendation had been two strongly related service ... more Over the last decade, service selection and recommendation had been two strongly related service filtering steps. The ever changing services environment, users tastes, as well as the perception and popularity of available services, rise a question regarding the appropriate means to capture and analyze such changes over time. Most service recommendation solutions are static and do not offer a multi-relational modeling of user-service interactions over time. Time is a contextual dimension that has, recently, received a lot of attention, leading to a new class of recommender systems, called time-aware recommender systems. In this work, we propose a service recommendation method that takes advantage of temporal knowledge graphs. As a de facto standard to model multiple and complex interactions between heterogeneous entities, knowledge graphs will serve as a historical knowledge base for our TASR system. We, first, model the user-service interactions over time, by constructing a temporal service knowledge graph (TSKG) that will be later enriched through a completion step. Second, to explore the TSKG and extract top-rated services, we use Convolutional Neural Networks (CNN) to embed the TSKG into a low-dimensional vector space, facilitating then its mining.

Un algorithme distribué pour le clustering de grands graphes

Le clustering de graphes est l'une des techniques cles qui permet de comprendre les structure... more Le clustering de graphes est l'une des techniques cles qui permet de comprendre les structures presentes dans les donnees de graphe. La detection des clusters et l'identification des ponts et des bruit sont egalement des tâches critiques car elles jouent un role important dans l'analyse des graphes. Recem-ment, plusieurs algorithmes de clustering de graphes ont ete proposes et utilises dans de nombreux domaines d'application. La plupart de ces algorithmes sont bases sur les algorithmes de clustering structurel. Neanmoins, ces derniers ont ete concus pour le traitement des petits graphes. D'ou, leur performance peut se degrader dans le cas des graphes larges qui imposent des defis supplementaires. Dans cet article, nous proposons DSCAN, un algorithme distribue de clustering de graphes qui est base sur le clustering structurel. Notre algorithme est im-plimente sur la base de framework de traitement de grands graphes BLADYG. L'evaluation experimentale de DSCAN a...

A Distributed Algorithm for Large-Scale Graph Clustering

Graph clustering is one of the key techniques to understand the structures present in the graph d... more Graph clustering is one of the key techniques to understand the structures present in the graph data. In addition to cluster detection, the identification of hubs and outliers is also a critical task as it plays an important role in the understanding of graph data. Recently, several graph clustering algorithms have been proposed and used in many application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on structural clustering. Yet, these algorithms have been evaluated on small graph database. In this paper, we propose DSCAN, a novel distributed structural graph clustering algorithm. We present an implementation of DSCAN on top of BLADYG, a distributed graph processing framework. We experimentally show that DSCAN significantly outperforms existing clustering algorithm in terms of scalability and performance in the case of large graphs.

Context-aware Service Recommendation based on Knowledge Graph Embedding

IEEE Transactions on Knowledge and Data Engineering, 2021

As a class of context-aware systems, context-aware service recommendation aims to bind high-quali... more As a class of context-aware systems, context-aware service recommendation aims to bind high-quality services to users while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructed context-aware service knowledge graph (C-SKG) is, then, transformed into a low-dimentional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user's context. Experiments have proved the accuracy and scalability of our CASR approach.