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Cloud computing

Description

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Membres

Ahmed Yosreddin Samti Ahmed Yosreddin Samti
Ines Ben Jaafar Ines Ben Jaafar
Meriem Ennigrou Meriem Ennigrou
Mouna Karaja Mouna Karaja
Mohamed Amine Jbili Mohamed Amine Jbili
Ameni Hedhli Ameni Hedhli
Haithem Mezni Haithem Mezni
Mondher Dhiflaoui Mondher Dhiflaoui

Publications

  • 2025
    Ahmed Yosreddin Samti, Ines Ben Jaafar, Issam Nouaouri, Patrick Hirsh

    A Novel NSGA-III-GKM++ Framework for Multi-Objective Cloud Resource Brokerage Optimization

    June 2025 Mathematics 13(13):2042, 2025

    Résumé

    Cloud resource brokerage is a fundamental challenge in cloud computing, requiring the efficient selection and allocation of services from multiple providers to optimize performance, sustainability, and cost-effectiveness. Traditional approaches often struggle with balancing conflicting objectives, such as minimizing the response time, reducing energy consumption, and maximizing broker profits. This paper presents NSGA-III-GKM++, an advanced multi-objective optimization model that integrates the NSGA-III evolutionary algorithm with an enhanced K-means++ clustering technique to improve the convergence speed, solution diversity, and computational efficiency. The proposed framework is extensively evaluated using Deb–Thiele–Laumanns–Zitzler (DTLZ) and Unconstrained Function (UF) benchmark problems and real-world cloud brokerage scenarios. Comparative analysis against NSGA-II, MOPSO, and NSGA-III-GKM demonstrates the superiority of NSGA-III-GKM++ in achieving high-quality tradeoffs between performance and cost. The results indicate a 20% reduction in the response time, 15% lower energy consumption, and a 25% increase in the broker’s profit, validating its effectiveness in real-world deployments. Statistical significance tests further confirm the robustness of the proposed model, particularly in terms of hypervolume and Inverted Generational Distance (IGD) metrics. By leveraging intelligent clustering and evolutionary computation, NSGA-III-GKM++ serves as a powerful decision support tool for cloud brokerage, facilitating optimal service selection while ensuring sustainability and economic feasibility.

  • Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Dynamic bag-of-tasks scheduling problem in a heterogeneous multi-cloud environment: a taxonomy and a new bi-level multi-follower modeling

    Journal of Supercomputing,1-38,, 2023

    Résumé

    Since more and more organizations deploy their applications through the cloud, an increasing demand for using inter-cloud solutions is noticed. Such demands could inherently result in overutilization of resources, which leads to resource starvation that is vital for time-intensive and life-critical applications. In this paper, we are interested in the scheduling problem in such environments. On the one hand, a new taxonomy of criteria to classify task scheduling problems and resolution approaches in inter-cloud environments is introduced. On the other hand, a bi-level multi-follower model is proposed to solve the budget-constrained dynamic Bag-of-Tasks (BoT) scheduling problem in heterogeneous multi-cloud environments. In the proposed model, the upper-level decision maker aims to minimize the BoT’ makespan under budget constraints. While each lower-level decision maker minimizes the completion time of tasks it received. Experimental results demonstrated the outperformance of the proposed bi-level algorithm and revealed the advantages of using a bi-level scheme with an improvement rate of 32%, 29%, and 21% in terms of makespan for the small, medium, and big size instances, respectively.

    Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Dynamic bag-of-tasks scheduling problem in a heterogeneous multi-cloud environment: a taxonomy and a new bi-level multi-follower modeling

    J Supercomput 79, 17716–17753 (2023), 2023

    Résumé

    Since more and more organizations deploy their applications through the cloud, an increasing demand for using inter-cloud solutions is noticed. Such demands could inherently result in overutilization of resources, which leads to resource starvation that is vital for time-intensive and life-critical applications. In this paper, we are interested in the scheduling problem in such environments. On the one hand, a new taxonomy of criteria to classify task scheduling problems and resolution approaches in inter-cloud environments is introduced. On the other hand, a bi-level multi-follower model is proposed to solve the budget-constrained dynamic Bag-of-Tasks (BoT) scheduling problem in heterogeneous multi-cloud environments. In the proposed model, the upper-level decision maker aims to minimize the BoT’ makespan under budget constraints. While each lower-level decision maker minimizes the completion time of tasks it received. Experimental results demonstrated the outperformance of the proposed bi-level algorithm and revealed the advantages of using a bi-level scheme with an improvement rate of 32%, 29%, and 21% in terms of makespan for the small, medium, and big size instances, respectively.

  • Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Efficient bilevel multi-objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment

    Applied Intelligence, 1-29, 2022

    Résumé

    Bag-of-Tasks is a well-known model that processes big-data applications supporting embarrassingly parallel jobs with independent tasks. Scheduling Bag-of-Tasks in a dynamic multi-cloud environment is an NP-hard problem that has attracted a lot of attention in the last years. Such a problem can be modeled using a bi-level optimization framework due to its hierarchical scheme. Motivated by this issue, in this paper, an efficient bi-level multi-follower algorithm, based on hybrid metaheuristics, is proposed to solve the multi-objective budget-constrained dynamic Bag-of-Tasks scheduling problem in a heterogeneous multi-cloud environment. In our proposed model, the objective function differs depending on the scheduling level: The upper level aims to minimize the makespan of the whole Bag-of-Tasks under budget constraints; while each follower aims to minimize the makespan and the execution cost of tasks belonging to the Bag-of-Tasks. Since multiple conflicting objectives exist in the lower level, we propose an improved variant of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) called Efficient NSGA-II (E-NSGA-II), applying a recently proposed quick non-dominated sorting algorithm (QNDSA) with quasi-linear average time complexity. By performing experiments on proposed synthetic datasets, our algorithm demonstrates high performance in terms of makespan and execution cost while respecting budget constraints. Statistical analysis validates the outperformance of our proposal regarding the considering metrics.

    Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Efficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment.

    Appl Intell 53, 9009–9037 (2023), 2022

    Résumé

    Bag-of-Tasks is a well-known model that processes big-data applications supporting embarrassingly parallel jobs with independent tasks. Scheduling Bag-of-Tasks in a dynamic multi-cloud environment is an NP-hard problem that has attracted a lot of attention in the last years. Such a problem can be modeled using a bi-level optimization framework due to its hierarchical scheme. Motivated by this issue, in this paper, an efficient bi-level multi-follower algorithm, based on hybrid metaheuristics, is proposed to solve the multi-objective budget-constrained dynamic Bag-of-Tasks scheduling problem in a heterogeneous multi-cloud environment. In our proposed model, the objective function differs depending on the scheduling level: The upper level aims to minimize the makespan of the whole Bag-of-Tasks under budget constraints; while each follower aims to minimize the makespan and the execution cost of tasks belonging to the Bag-of-Tasks. Since multiple conflicting objectives exist in the lower level, we propose an improved variant of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) called Efficient NSGA-II (E-NSGA-II), applying a recently proposed quick non-dominated sorting algorithm (QNDSA) with quasi-linear average time complexity. By performing experiments on proposed synthetic datasets, our algorithm demonstrates high performance in terms of makespan and execution cost while respecting budget constraints. Statistical analysis validates the outperformance of our proposal regarding the considering metrics.

    Haithem Mezni, Fatimetou Sidi Hamoud, Faouzi Ben Charrada

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

    Ambient Intelligence and Humanized Computing, 2022

    Résumé

    Over the last few years, service placement has become a strategic and fundamental management operation that allows cloud providers to deploy and arrange their services on the high-performance computation/storage servers, while taking various constraints (e.g., resource usage, security levels, data transfer time, SLA) into consideration. Despite the huge number of service placement schemes, most of them are static and do not take the cloud changes into account. To cope with this issue, predicting the cloud zones’ performance and availability should precede the placement task. For this purpose, we adopt gated recurrent neural network as a deep learning variant that allows forecasting the next short-term resource consumption on cloud servers and predicting the future service migration traffic between them. Also, to place cloud services’ application/data components on the optimum cloud zones, the frequently used high-performance servers are selected by mining the graph-like placement history, i.e. previous placement plans. To do so, we propose a Frequent Subgraph Mining algorithm that is reinforced with a tuning method to increase the probability of executing the past placement schemes. Experimental results have proved that our predictive approach outperforms state-of-the-art placement schemes in terms of performance and prediction quality.

    Ameni Hedhli, Haithem Mezni, Lamjed Ben Said

    Predictive BPaaS management with quantum and neural computing

    Software: Evolution and Process, 2022

    Résumé

    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.

  • Mouna Karaja, Meriem Ennigrou, Lamjed Ben Said

    Solving Dynamic Bag-of-Tasks Scheduling Problem in Heterogeneous Multi-cloud Environment Using Hybrid Bi-Level Optimization Model.

    In: Abraham A., Hanne T., Castillo O., Gandhi N., Nogueira Rios T., Hong TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham., 2021

    Résumé

    Task scheduling problem has attracted a lot of attention since it plays a key role to improve the performance of any distributed system. This is again more challenging, especially for multi-cloud computing environment, mainly based on the nature of the multi-cloud to scale dynamically and due to heterogeneity of resources which add more complexity to the scheduling problem. In this paper, we propose, for the first time, a new Hybrid Bi-level optimization model named HB-DBoTSP to solve the Dynamic Bag-of-Tasks Scheduling Problem (DBoTSP) in heterogeneous multi-cloud environment. The proposed model aims to minimize the makespan and the execution cost while taking into consideration budget constraints and guaranteeing load balancing between Cloud’s Virtual Machines. By performing experiments on synthetic data sets that we propose, we demonstrate the effectiveness of the algorithm.

    Ameni Hedhli, Haithem Mezni

    A Survey of Service Placement in Cloud Environments

    Grid Computing, 2021

    Résumé

    Cloud computing is largely adopted by the current computing industry. Not only users can benefit from cloud scalability, but also businesses are more and more attracted by its flexibility. In addition, the number of offered cloud services (e.g., SaaS, BPaaS, mobile services, etc.) is continuously growing. This raises a question about how to effectively arrange and place them in the cloud, in order to offer high-performance services. Indeed, companies’ and providers’ benefits are strongly related to the optimal placement and management of cloud services, together with their related data. This produces various challenges, including the heterogeneity and dynamicity of hosting cloud zones, the cloud/service -specific placement constraints, etc. Recent cloud service placement approaches have dealt with these issues through different techniques, and by fixing various criteria to optimize. Moreover, researchers have considered other specificities, like the cloud environment type, the deployment model and the placement mode. This paper provides a comprehensive survey on service placement schemes in the cloud. We also identify the current challenges for different cloud service models and environments, and we provide our future directions.

    Mokhtar Haithem, Haithem Mezni, Mohand Said Hacid, Mohamed Mohsen Gammoudi

    Clustering-based data placement in cloud computing: a predictive approach

    Cluster Computing, 2021

    Résumé

    Nowadays, cloud computing environments have become a natural choice to host and process a huge volume of data. The combination of cloud computing and big data frameworks is an effective way to run data-intensive applications and tasks. Also, an optimal arrangement of data partitions can improve the tasks executions, which is not the case in most big data frameworks. For example, the default distribution of data partitions in Hadoop-based clouds causes several problems, which are mainly related to the load balancing and the resource usage. In addition, most existing data placement solutions are static and lack precision in the placement of data partitions. To overcome these issues, we propose a data placement approach based on the prediction of the future resources usage. We exploit Kernel Density Estimation (KDE) and Fuzzy FCA techniques to, first, forecast the workers’ and tasks’ future resource consumption and, second, cluster data partitions and intensive jobs according to the estimated resource usage. Fuzzy FCA is also used to exclude partitions and jobs that require less resources, which will reduce the needless migrations. To allow monitoring and predicting the workers’ states and the data partitions’ consumption, we modeled the big data cluster as an autonomic service-based system. The obtained results have shown that our solution outperformed existing approaches in terms of migrations rate and resource consumption.

    Fatma Lahmar, Haithem Mezni

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

    Soft Computing, 2021

    Résumé

    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.

    Ameni Hedhli, Haithem Mezni, Lamjed Ben Said

    A Quantum-Inspired Neural Network Model for Predictive BPaaS Management

    International Conference on Database and Expert Systems Applications (DEXA), 2021

    Résumé

    Nowadays, companies are more and more adopting cloud technologies in the management of their business processes rising, then, the Business Process as a Service (BPaaS) model. In order to guarantee the consistency of the provisioned BPaaS, cloud providers should ensure a strategical management (e.g., allocation, migration, etc.) of their available resources (e.g., computation, storage, etc.) according to services requirements. Existing researches do not prevent resource provision problems before they occur. Rather, they conduct a real-time allocation of cloud resources. This paper makes use of historical resource usage information for providing enterprises and BPaaS providers with predictions of cloud availability zones’ states. For that, we first propose a Neural Network-based prediction model that exploits the superposition power of Quantum Computing and the evolutionary nature of the Genetic Algorithm, in order to optimize the accuracy of the predicted resource utilization. Second, we define a placement algorithm that, based on the prediction results, chooses the optimal cloud availability zones for each BPaaS fragment, i.e. under-loaded servers. We evaluated our approach using real cloud workload data-sets. The obtained results confirmed the effectiveness and the performance of our NNQGA approach, compared to traditional techniques.

  • Mouna Karaja, Meriem Ennigrou, Lamjed Ben Said

    Budget-constrained dynamic Bag-of-Tasks scheduling algorithm for heterogeneous multi-cloud environment

    2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA), Tunis, Tunisia, pp. 1-6., 2020

    Résumé

    Cloud computing has reached huge popularity for delivering on-demand services on a pay-per-use basis over the internet. However, since the number of cloud users evolves, multi-cloud environment has been introduced where clouds are interconnected in order to satisfy customers’ requirements. Task scheduling in such environments is very challenging mainly due to the heterogeneity of resources. In this paper, a budget-constrained dynamic Bag-of-Tasks scheduling algorithm for heterogeneous multi-cloud environment is proposed. By performing experiments on synthetic data sets that we propose, we demonstrate the effectiveness of the algorithm in terms of makespan.

    Souad Ghazouani, Haithem Mezni, Yahya Slimani

    Bringing semantics to multi-cloud service compositions

    Software: Practice and Experience, 2020

    Résumé

    Over the last decade, cloud computing has emerged as a new paradigm for delivering various on-demand virtualized resources as services. Cloud services have inherited not only the major characteristics of web services but also their classical issues, in particular, the interoperability issues and the heterogeneous nature of their hosting environments. This latter problem must be taken into account when composing various cloud services, in order to answer users' complex requirements. Moreover, leading cloud providers started to offer their services across multiple clouds. This adds a new factor of heterogeneity, as composition engines must take into consideration the heterogeneity not only at the service level (eg, service descriptions) but also at the cloud level (eg, pricing models, security policies). In this context, the semantics of multicloud actors must be incorporated into the multicloud service composition (MCSC) process. However, most existing approaches have treated the semantic service composition in traditional single-cloud environments. The few works in multicloud settings have ignored the semantics of cloud zones and resources. Moreover, they often focus on the general aspect of MCSC (eg, horizontal or vertical compositions). Even the few researchers who have addressed both vertical and horizontal service compositions, conducted their research studies in the context of single- cloud environments, which were proven to be unrealistic and offer limited quality of service (QoS) and security support. To ensure a high interoperability when composing services from multiple heterogeneous clouds and to enable a horizontal/vertical semantic service compositions, we take advantage of a standardized and semantically enriched generic service description, including all aspects (technical, operational, business, semantic, contextual) and supporting different cloud service models (SaaS, PaaS, IaaS, etc). We also incorporate Semantic Web Rule Language into the MCSC process to enable not only rule-based reasoning about various composition constraints (eg, QoS constraints, cloud zones constraints) but also to provide accurate semantic matching of cloud services' capabilities. Conducted experiments have proven the ability of our approach to combine high-quality services from the optimal number of clouds.

  • Allel Hadjali, Haithem Mezni, Sabeur Aridhi, Andrei Tchernykh

    Special issue on “Uncertainty in Cloud Computing: Concepts, Challenges and Current Solutions”

    International Journal of Approximate Reasoning, 2019

    Résumé

    This IJAR special issue on “Uncertainty in Cloud Computing: Concepts, Challenges and Current Solutions” is a follow-up to the first international workshop on Uncertainty in Cloud Computing (UCC'17), which was successfully held in Lyon, France, on August 29, 2017. This workshop collected researchers' insights and contributions on various cloud computing topics under uncertainty.

  • Ameni Hedhli, Haithem Mezni

    A DFA-based approach for the deployment of BPaaS fragments in the cloud

    Concurrency and Computation: Practice and Experience, 2018

    Résumé

    Cloud computing is an emerging technology that is largely adopted by the current computing industry. With the growing number of Cloud services, Cloud providers' main focus is how to best offer efficient services (eg, SaaS, BPaaS, mobile services, etc) in order to hook the eventual customers. To meet this goal, services arrangement and placement in the cloud is becoming a serious problem because an optimal placement of these applications and their related data in accordance with the available resources can increase companies' benefits. Since there is a widespread deployment of business processes in the cloud, the hereinafter conducted research works aim to enhance the business processes' outsourcing by providing an optimized placement scheme that would attract cloud customers. In the light of these facts, the purpose of this paper is to deal with the BPaaS placement problem while optimizing both the total execution time and cloud resources' usage. To do so, we first determine the redundant BPaaS fragments using a DNA Fragment Assembly technique. We apply a variant of the Genetic Algorithm to resolve it. Then, we propose a placement algorithm, which produces an optimized placement scheme on the basis of the determined fragments relations. We follow that by an implementation of the whole placement process and a set of experimental results that have shown the feasibility and efficiency of the proposed approach.

    Tarek Mahdhi, Haithem Mezni

    A prediction-Based VM consolidation approach in IaaS Cloud Data Centers

    Journal of Systems and Software, 2018

    Résumé

    Recent years have witnessed a rapid growth in exploiting Cloud environments to host and deliver various types of virtualized resources as on-demand services. In order to optimally use Cloud resources, the arrangement of virtual machines (VMs) in physical machines (PMs) must be performed strategically, because the placement of VMs in accordance with the available resources can reduce energy consumption, improve resource utilization and, consequently, can increase companies benefits. However, VMs could have time varying workloads, which leads to degradation of performance and power consumption. Thus, re-configuring the VMs placement is essential. Virtual machine consolidation aims to optimally use the available resources by allocating several virtual machines on a set of physical ones (PMs). To determine the PMs capacities to reallocate VMs, it is important to predict their states based on resource utilization history within each VM, and the past VMs migration traffic. However, a common limitation between existing VM consolidation approaches is the lack of information about the history of (and the future) VM migration traffic. Through this paper, we aim to propose a virtual machine consolidation approach based on the estimation of requested resources and the future VM migration traffic. We exploit the strength of Kernel Density Estimation technique (KDE) as a powerful mean to forecast the future resource usage of each VM, and AKKA toolkit as an actor-based model that allows exchanging useful information about the host’s states. We adopt a weighted-graph representation to model the history of migration traffic between PMs and to design the actor-based topology of the data center. The obtained results show the effectiveness of our approach in terms of total number of migrations and energy consumption.

    Haithem Mezni, Sabeur Aridhi, Allel Hadjali

    The uncertain cloud: State of the art and research challenges

    International Journal of Approximate Reasoning, 2018

    Résumé

    During the last decade, cloud computing became a natural choice to host and provide various computing resources as on-demand services. The correct characterization and management of cloud environment objects (clouds, data centers, providers, services, data, users, etc.) is the first step towards effective provisioning and integration of cloud services. However, cloud computing environment is often subject to uncertainty. This could be attributed to the incompleteness and imprecision of cloud available information, as well as the highly changing conditions. The purpose of this survey is to study, criticize and classify the already existing works that deal with uncertainty in the cloud. We present a taxonomy on the uncertainty in the cloud and we study how such concept was tackled by researchers in cloud environments. Finally, we identify the challenges and the requirements to deal with uncertain data in the cloud, as well as the future directions.

    Haithem Mezni, Tarek Abdeljaouad

    A cloud services recommendation system based on Fuzzy Formal Concept Analysis

    Data & Knowledge Engineering, 2018

    Résumé

    Cloud computing is an attractive paradigm which offers variant services on demand. Many available cloud services offer the same or similar functionalities, which made it challenging for cloud users to choose a suitable service that meets with their preferences. Existing service selection approaches were not enough to solve this challenge. That's why researchers went for recommendation approaches trying to find a solution. Cloud service recommendation has become an important technique for cloud services. It helps users decide whether a service satisfies their requirements or not. However, two main recommendation problems remain unsolved yet, data sparsity and cold start. In addition, existing solutions mostly tried to adapt techniques inherited from Web service and e-commerce domains. This approach is not always adequate due to many reasons such as the cloud architecture, the various service models, etc. To address the problems stated above, we propose a Collaborative Filtering based recommendation system for cloud services using Fuzzy Formal Concept Analysis (Fuzzy FCA). Fuzzy FCA has a solid mathematical foundation and it's based on the lattice theory. The lattice representation will give an explicit description of our cloud environment (users, services, ratings, etc.) and, then, extract the pertinent information from it (similar users to an active user, ratings of each similar user, top services, etc.) which will make the recommendations more suitable. Experimental results confirmed our expectations and proved the efficiency of such an approach.

    Fatma Lahmar, Haithem Mezni

    Multicloud service composition: A survey of current approaches and issues

    Software: Evolution and Process, 2018

    Résumé

    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.

    Haithem Mezni, Mokhtar Sellami, Jaber Kouki

    Security-aware SaaS placement using swarm intelligence

    Software: Evolution and Process, 2018

    Résumé

    Cloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer various on-demand services (eg, software, storage, network, etc.). Software as a Service (SaaS) is one of the most popular service models. To meet the increasing demands of users, SaaS can be offered in a composite form. Although this approach offers some advantages like flexibility and reusability, it raises a question about how to manage composite SaaS in the distributed and the highly dynamic cloud environment. In this paper, we address one of the major SaaS resource management issues referred to as SaaS placement problem. As existing efforts only focus on SaaS placement problem from the perspective of resources utilization to optimize SaaS performance and minimize resource usage, in this paper, we also incorporate security concerns in SaaS placement strategy. In fact, security risk is one of the major factors influencing the efficiency of the composite SaaS. We adopt a multi-swarm variant of particle swarm optimization to propose a security-aware SaaS placement method. Also, a cooperative learning strategy is hybridized to the placement algorithm, which makes information of best candidate servers be used more effectively to generate better placement plan. Experiments show that our solution outperforms existing SaaS placement approaches.

    Haithem Mezni, Mokhtar Sellami

    Multi-cloud service composition using Formal Concept Analysis

    Journal of Systems and Software, 2018

    Résumé

    Recent years have witnessed a rapid growth in exploiting Cloud environments to deliver various types of resources as services. To improve the efficiency of software development, service reuse and composition is viewed as a powerful means. However, effectively composing services from multiple clouds has not been solved yet. Indeed, existing solutions assume that the services participating to a composition come from a single cloud. This approach is unrealistic since the other existing clouds may host more suitable services. In order to deliver high quality service compositions, the user request must be checked against the services in the multi-cloud environment (MCE) or at least clouds in the availability zone of the user. In this paper, we propose a multi-cloud service composition (MCSC) approach based on Formal Concept Analysis (FCA). We use FCA to represent and combine information of multiple clouds. FCA is based on the concept lattice which is a powerful mean to classify clouds and services information. We first model the multi-cloud environment as a set of formal contexts. Then, we extract and combine candidate clouds from formal concepts. Finally, the optimal cloud combination is selected and the MCSC is transformed into a classical service composition problem. Conducted experiments proved the effectiveness and the ability of FCA based method to regroup and find cloud combinations with a minimal number of clouds and a low communication cost. Also, the comparison with two well-known combinatorial optimization approaches showed that the adopted top-down strategy allowed to rapidly select services hosted on the same and closest clouds, which directly reduced the inter-cloud communication cost, compared to existing approaches.

  • Mohamed Amin Hajji, Haithem Mezni

    A composite particle swarm optimization approach for the composite SaaS placement in cloud environment

    Soft Computing, 2017

    Résumé

    Cloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer on-demand various types of services (e.g., software, storage, network). One of the most popular service models is Software as a Service (SaaS). To allow flexibility and reusability, SaaS can be offered in a composite form, where a set of interacting application and data components cooperate to form a higher-level functional SaaS. However, this approach introduces new challenges to resource management in the cloud, especially finding the optimal placement for SaaS components to have the best possible SaaS performance. SaaS Placement Problem (SPP) refers to this challenge of determining which servers in the cloud’s data center can host which components without violating SaaS constraints. Most existing SPP approaches only addressed homogenous SaaS components placement and only considered one type of constraints (i.e., resource constraint). In addition, none of them has considered the objective of maintaining a good machine performance by minimizing the resource usage for the hosting machines. To allow finding the optimal placement of a composite SaaS, we adopt a new variation of PSO called ’Particle Swarm Optimization with Composite Particle (PSO-CP).’ In the proposed PSO-CP-based approach, each composite particle in the swarm represents a candidate SaaS placement scheme. Composite particles adopt a collective behavior to explore and evaluate the search space (i.e., data center) and adjust their structures by collaborating with other composite or independent particles (i.e., servers). The implementation and experimental results show the feasibility and efficiency of the proposed approach.

    Haithem Mezni, Jaber Kouki

    A multi-swarm based approach with cooperative learning strategy for composite SaaS placement

    ACM Symposium on Applied Computing, 2017

    Résumé

    This paper explores one of the critical issues, SaaS placement in cloud data centers, for reducing execution time of composite SaaS applications. We adopt a multi-swarm variant of Particle Swarm Optimization (PSO) to propose a service placement method. Also, a cooperative learning strategy is hybridized to the placement algorithm, which makes information of best candidate servers be used more effectively to generate better placement plan. In the proposed method, for each sub-swarm of servers, the worst placement learns from the best servers, so that worst servers can have more excellent exemplars to learn and can find the optimal placement for SaaS components more easily. Experiments show that our solution is efficient in comparison with existing SaaS placement approaches.