Mohamed Mohsen Gammoudi

Decentralized method for complex task allocation in massive MAS

Task allocation is still a fundamental problem in Multi‐Agents System (MAS). It allows the formation a coalition of agents to cooperate together in order to carry out a complex task. Generally, the process of task allocation requires calculating the value of all the possible allocations, then determining which optimal. In the context of Massive Multi‐Agent Systems (MMAS), one agent generates all the possible coalitions and then calculates the value of each, is inefficient. Moreover, the traditional approaches based on the negotiation between agents, are impractical because of the complexity of communications between agents. In this paper we propose a decentralized method, based on grouping agents using the Formal Concepts Analysis (FCA) approach for the task allocation in MMAS. In our model, agents are fully cooperative and each task is composed of several subtasks. The proposed solution is based on two steps: i) computing groups of agents having similar characteristics in the objective to distribute the ...

Cloud Services Orchestration: A Comparative Study of Existing Approaches

Cloud Computing is emerging today as a service model used to relocate locally-based data and applications to virtualized services available via Internet at a lower cost. A key to exploit the benefits of this model is orchestration which consists in coordinating effectively the deployment of a set of virtualized services in order to fulfill operational and quality objectives of end users and Cloud providers. Cloud orchestration can be carried out at two levels: hardware level orchestration and software level orchestration. In this paper, we highlight the main challenging points about the Cloud orchestration concept. Then, we carry on a comparative study of some existing research works involved with this concept at hardware and software levels.

Energy Efficient Partitioning and Scheduling Approach for Scientific Workflows in the Cloud

Energy consumption is emerging as a new crucial issue of the Cloud Computing environments such as data centers. The problem of power consumption is more challenging especially in the context of scientific workflows deployment in the Cloud as they trigger intensive computational tasks and data manipulation steps which begets excessive data movement operations over communication networks. For instance, it was revealed that network devices consume up to one-third of the total energy consumption of Cloud data centers. In this paper, we propose an energy-aware approach for scientific workflows scheduling in the Cloud. In the first step, we propose a Workflow Partitioning for Energy Minimization (WPEM) algorithm that allows reducing the network energy consumption of the workflow and the total amount of data communication while achieving a high degree of parallelism. In the second step, we use the heuristic of Cat Swarm Optimization to schedule the generated partitions in order to minimize the workflows overall energy consumption and execution time. We evaluated the proposed approach using three real cases of data intensive workflows and compare it with other algorithms from literature. The experimental results show that our proposal allows to reduce remarkably the network energy consumption of the tested workflows (up to 96% of network energy consumption saving for memory intensive workflows) and the overall energy consumption of the workflows while ensuring a reasonable execution time and using less Cloud resources.

Enhancing Decentralized MAS-Based Framework for Composite Web Services Orchestration and Exception Handling by Means of Mobile Agents Technology

Decentralized orchestration offers performance improvements in terms of increased throughput and scalability and lower response time. However, decentralized orchestration also brings additional complexity to the system, mainly, in terms of exception handling. The research presented in this paper is carried out on the basis of some previous work of the authors, including: decentralizing orchestration of composite Web services and exception handling. We focus also on current works expanding the previous one, exhibiting thus a higher performance degree which the integration of mobile agents performs by moving the applications functionality through the network.

QoS-Aware Scheduling of Workflows in Cloud Computing Environments

Cloud Computing has emerged as a service model that enables on-demand network access to a large number of available virtualized resources and applications with a minimal management effort and a minor price. The spread of Cloud Computing technologies allowed dealing with complex applications such as Scientific Workflows, which consists of a set of intensive computational and data manipulation operations. Cloud Computing helps such Workflows to dynamically provision compute and storage resources necessary for the execution of its tasks thanks to the elasticity asset of these resources. However, the dynamic nature of the Cloud incurs new challenges, as some allocated resources may be overloaded or out of access during the execution of the Workflow. Moreover, for data intensive tasks, the allocation strategy should consider the data placement constraints since data transmission time can increase notably in this case which implicates the increase of the overall completion time and cost of the Workflow. Likewise, for intensive computational tasks, the allocation strategy should consider the type of the allocated virtual machines, more specifically its CPU, memory and network capacities. Yet, a critical challenge is how to efficiently schedule the Workflow tasks on Cloud resources to optimize its overall quality of service. In this paper, we propose a QoS-aware algorithm for Scientific Workflows scheduling that aims to improve the overall quality of service (QoS) by considering the metrics of execution time, data transmission time, cost, resources availability and data placement constraints. We extended the Parallel Cat Swarm Optimization (PCSO) algorithm to implement our proposed approach. We tested our algorithm within two sample Workflows of different scales and we compared the results to those given by the standard PSO, the CSO and the PCSO algorithms. The results show that our proposed algorithm improves the overall quality of service of the tested Workflows.

QoS-aware Automatic Web Service Composition based on Cooperative Agents

Automatic Web Service Composition (AWSC) is the processes of combining a chain of connected atomic services together in order to create a more complex and value-added composite service. To differentiate web services which have the same functionalities, Quality of Service (QoS) has been mostly applied. Given a high dynamicity and a rapid growth in the number of similar functionally web services, finding an efficient web service composition in a reasonable time satisfying a user requirements has become a challenging task. Many approaches in literature address the problem of QoS-aware automatic Web service composition. However, the majority of the existing approaches are restricted to predefined workflows and have limitations in terms of accuracy, scalability as well as dynamicity. In this paper, we propose a novel approach that solves major limitations encountered in the studied approaches. The proposed approach which is a set of cooperative autonomous agents is based on two mains ideas: 1) Self-organization of agents into dependency graph named social network agent, and 2) distributed computing of the optimal web services composition by a cooperative protocol among agents. Our approach can generate an accurate composition in a dynamic environment and is scale with the number of web services.

Semantic shared space-based complex tasks allocation method for massive MAS

Task allocation is still a fundamental problem in Multi-Agents System (MAS). It allows coalition formation of agents in order to cooperate together to perform a complex task. In general, the task allocation process includes two steps: i) finding the set of agents that can, potentially, participate to task allocation process, ii) computing the optimal allocation to execute the given task. In this work further attention is given for the first step. Indeed, in the context of massive MAS, characterized by dynamic, heterogeneous and a large number of autonomous agents, an efficient model of communication is required. This implies a need for a scalable and semantic infrastructure which allows: i) agents to be able to easily find each other and ii) semantic interoperability that refers to a common understanding of information communicated between agents. In this work information refers to an announced task. Different models of communication have been proposed, including broadcasting, forwarding, central server and group communication. Most of these approaches do not scale well in the context of massive MAS; when the number of agents grows. In additional, agent communication languages (ACLs), such as the KQML or FIPA ACL divide messages into several layers, and provide a specific syntax and semantics only for the outer layer, but its content is still arbitrary. To deal with these limitations, this paper extends our last task allocation method for massive MAS to shared space mechanism. This mechanism allows agents to find each by providing a logical shared space with temporal and special decoupling properties. To ensure semantic interoperability, we use a Task Ontology language (OWL-T) as a tuple space and a FIPA content message. OWL-T is based on the OWL for formally and semantically defining task in a high-level abstraction.

Cooperative agents based-decentralized and scalable complex task allocation approach pro massive multi-agents system

A major challenge in the field of Multi-Agent Systems is to enable autonomous agents to allocate tasks efficiently. In previous work, we have developed a decentralized and scalable method for complex task allocation for Massive Multi-Agent System (MMAS). The method was based on two steps: 1) hierarchical organization of agent groups using Formal Concepts Analysis approach (FCA) and 2) computing the optimal allocation. The second step distributes the tasks allocation process among all agent groups as follows: i. Each local allocator proposes a local allocation, then ii. The global allocator computes the global allocation by resolution of eventual conflict situations. Nevertheless, a major boundary of the method used to compute the global allocation is its centralized aspect. Moreover, conflicts process is a greedy solution. In fact, if a conflict is detected steps i) and ii) are reiterated until a non conflict situation is attained. This paper extends our last approach by distributing the global allocation process among all agents. It provides a solution based on cooperation among agents. This solution prohibits generation of conflicts. Its based on the idea that each agent picks out its own sub-task.

PACT: A New Trust Prediction Method for Multi-agents Recommender Systems

In multi-agents recommender systems, agents interact with each other to provide an efficient recommendation to the end user. Trust is, therefore, important to make these interactions useful. In an open, heterogeneous and dynamic multi-agent environment, it is difficult for agents to assess and establish trusting relationships for cooperation. Thus, modeling and evaluating trust relationships between agents for a better recommendation, is a major challenge. In this paper, we propose a new approach that allows predicting trust relationships between agents. This approach is based on a sound mathematical basis, namely the Fuzzy Formal Concepts Analysis and the Theory of Belief Functions. To validate the efficiency of our work, we carried out a series of experiments using the Advogato dataset.

Data-intensive service composition in Cloud Computing: State-of-the-art

In recent years, the rapid proliferation of enormous sources of digital data and the development of Cloud Computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data-intensive services are all set to soar in the future. Therefore, to meet complicated requirements, multiple data-intensive services are assembled in a service composition. The data-intensive service composition presents a new set of different requirements and characteristics, which makes the composition more and more challenging. Issues of autonomy, scalability, adaptability, and robustess, become difficult to resolve. In this paper, we present a literary study of data-intensive service composition problem. Firstly, we present the problem statement. Secondly, we present the QoS sensitive attributes towards the data size growth. After that, we study the different existing approaches, in literature, to solve the data-intensive services composition problem and we propose a comparative study.