Haithem Mezni

AFAWS: An Agent based Framework for Autonomic Web Services

Autonomic computing is about systems that can manage themselves. Self-management includes self-configuration, self-healing, self-optimization, etc. self-* properties. Agent technology offers key advantages for the development of autonomic computing systems as it supports autonomy, adaptability, etc. Current Web service standards and technologies do not provide a suitable architecture in which all aspects of self-management can be designed. Moreover, traditional registries are passive entities and are not able to participate, in an autonomic manner, to the Web service adaptation process. In this paper, we present an Agent-based Framework for Autonomic Web Services AFAWS. This framework is based on two agent-based systems which collaborate to enrich Web services and registries with self-* capabilities.

Multi-cloud service composition using Formal Concept Analysis

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. 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.

An experimental survey on big data frameworks

Abstract 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 and iterative workloads. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.

AWS-Policy: An Extension for Autonomic Web Service Description

Abstract In the last years, the need for integrating QoS to service oriented architecture has become a significant factor for managing service-based systems. In addition, combining self-* and policy-based management has major advantages since it reduces complexity management and effectively drives self-adaptation of Web services. Current service oriented architecture does not support policy-based self-management. To allow this, the description of autonomic Web services (AWS) must not be limited to functional and non-functional data. Indeed, QoS data are not sufficient to effectively drive the self-adaptation process. In addition, providers must participate in the self-adaptation process as they are aware of the capabilities and requirements of their published services and the exceptions that may occur. In this paper, we propose a rich information model that allows describing autonomic Web services based not only on QoS data but also on additional information such as service specific adaptation actions. We extend WS-Policy to specify services related data. UDDI is also extended to support policy-based self-management.

An autonomic registry-based SOA model

In the last years, the need for integrating QoS to service oriented architecture (SOA) has become a significant factor for managing service-based systems. In addition, combining self-∗ and policy-based management has major advantages since it reduces complexity management and effectively drives self-adaptation of Web services. Current service oriented architecture does not support policy-based self-management. In this paper, we present a SOA model based on autonomic registries which are endowed with self-∗ capabilities. Autonomic registries are in charge of managing their content and collaborating in a federated manner with other autonomic actors in order to maintain service quality, and consequently to participate in the self-adaptation process of executing Web services. The proposed model is combined with a technology that has found popularity and acceptance namely WS-Policy. We extend WS-Policy to represent a rich Web service information model based on QoS data and additional information such as service specific adaptation actions. UDDI is also extended to support policy-based self-management.

Reusing process fragments for fast service composition: a clustering-based approach

ABSTRACT With the proliferation of Web services over the Internet and due to the increasing complexity of users’ needs, Web service composition has emerged as a powerful method of software reuse, allowing to deliver complete business processes as a set of interacting services. To guarantee a rapid and secure service composition, fragments of available business processes at different granularities may be considered as a composition unit and recombined to deliver effective compositions. Despite the benefits of this method, most of the existing works do not take into consideration the reuse of service process fragments (SPF). Reusing SPFs allows, not only to minimize the composition time, but also to improve the reliability of the composition process. In this paper, we propose a Web service composition approach that aims to combine service process fragments rather that atomic services. We adopt a powerful mathematical model called Formal Concept Analysis (FCA) to define the relationships between services and fragments. Moreover, we exploit the regrouping capabilities of FCA by proposing algorithms for the extraction of candidate fragments’ combinations. A scoring function is also defined to determine the quality level of each SPF and its ability to participate in a composition. The experimental studies proved the effectiveness of our FCA-based approach compared to existing state-of-the-art solutions.

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

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.

A cloud services recommendation system based on Fuzzy Formal Concept Analysis

Abstract 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. Thats 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 its 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.

A Prediction-based VM Consolidation Approach in IaaS Cloud Data Centers

Abstract 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.

Multicloud service composition: A survey of current approaches and issues

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.