Leila Ismail

A Secure Mobile Agents Platform

Mobile Agents is a new paradigm for distributed computing where security is very essential to the acceptance of this paradigm in a large scale distributed environment. In this paper, we propose protection mechanisms for mobile agents. In these mechanisms, the authentication of mobile agents and the access control to the system resources are controlled by the mobile-agents platform. Each agent defines its own access control policy with regard to other agents using an Interface Definition Language (IDL), thus enforcing modularity and easing programming task. An evaluation of these mechanisms has been conducted. The measurements give the overhead involved by the proposed protection mechanisms to the performance of mobile agents. An important advantage of our protection mechanisms are transparency to agents and the portability of nonsecure applications onto a secure environment. A mobile agent system and the protection mechanisms have been implemented. Our experiments have shown the feasibility and the advantages of our mechanisms.

Implementation and performance evaluation of a scheduling algorithm for divisible load parallel applications in a cloud computing environment

Cloud computing is an emerging technology in which information technology resources are virtualized to users in a set of computing resources on a pay‐per‐use basis. It is seen as an effective infrastructure for high performance applications. Divisible load applications occur in many scientific and engineering applications. However, dividing an application and deploying it in a cloud computing environment face challenges to obtain an optimal performance due to the overheads introduced by the cloud virtualization and the supporting cloud middleware. Therefore, we provide results of series of extensive experiments in scheduling divisible load application in a Cloud environment to decrease the overall application execution time considering the cloud networking and computing capacities presented to the applications user. We experiment with real applications within the Amazon cloud computing environment. Our extensive experiments analyze the reasons of the discrepancies between a theoretical model and the reality and propose adequate solutions. These discrepancies are due to three factors: the network behavior, the application behavior and the cloud computing virtualization. Our results show that applying the algorithm result in a maximum ratio of 1.41 of the measured normalized makespan versus the ideal makespan for application in which the communication to computation ratio is big. They show that the algorithm is effective for those applications in a heterogeneous setting reaching a ratio of 1.28 for large data sets. For application following the ensemble clustering model in which the computation to communication ratio is big and variable, we obtained a maximum ratio of 4.7 for large data set and a ratio of 2.11 for small data set. Applying the algorithm also results in an important speedup. These results are revealing for the type of applications we consider under experiments. The experiments also reveal the impact of the choice of the platforms provided by Amazon on the performance of the applications under study. Considering the emergence of cloud computing for high performance applications, the results in this paper can be widely adopted by cloud computing developers. Copyright

Implementation and performance evaluation of a distributed conjugate gradient method in a cloud computing environment

Cloud computing is an emerging technology where information technology resources are provisioned to users in a set of a unified computing resources on a pay per use basis. The resources are dynamically chosen to satisfy a user service level agreement and a required level of performance. A cloud is seen as a computing platform for heavy load applications. Conjugate gradient (CG) method is an iterative linear solver that is used by many scientific and engineering applications to solve a linear system of algebraic equations. CG generates a heavy load of computation, and therefore, it slows the performance of the applications using it. Distributing CG is considered as a way to increase its performance. However, running a distributed CG, based on a standard API, such as Message Passing Interface, in a cloud face many challenges, such as the cloud processing and networking capabilities. In this work, we present an in‐depth analysis of the CG algorithm and its complexity to develop adequate distributed algorithms. The implementation of these algorithms and their evaluation in our cloud environment reveal the gains and losses achieved by distributing the CG. The performance results show that despite the complexity of the CG processing and communication, a speedup gain of at least 1157.7 is obtained using 128 cores compared with National Aeronautics and Space Administration Advanced Supercomputing sequential execution. Given the emergence of clouds, the results in this paper analyzes performance issues when a generic public cloud, along with a standard development library, such as Message Passing Interface, is used for high‐performance applications, without the need of some specialized hardware and software. Copyright

Evaluation of Authentication Mechanisms for Mobile Agents on top of Java

This paper presents an evaluation of authentication mechanisms for mobile agents on top of a Java environment. In these mechanisms, the authentication of mobile agents is controlled by the mobile-agents platform using digital signature and a Public Key Infrastructure. An evaluation of these mechanisms has been conducted. The measurements give the cost of the basic mechanisms involved in the implementation of authentication mechanisms integrated within a mobile-agent based platform. A prototype of a mobile agent system and the authentication mechanisms has been implemented. The implementation experiments have shown the feasibility and the advantage of these mechanisms. The measurements results show insignificant performance overhead of the mechanisms. This paper presents our implementation, a performance analysis and the lessons learned.

Communication issues in parallel Conjugate Gradient method using a star-based network

Conjugate Gradient (CG) method is an iterative linear solver which is used by many scientific and engineering applications to solve a linear system of algebraic equations. CG generates a heavy load of computation and therefore it slows the performance of the applications using it. Parallelizing CG is considered as a way to increase its performance. However, CG suffers from communication dependencies among its divisible loads. Most of the studies to parallelize CG concentrate on parallelizing its matrix-vector multiplication. In this paper, we answer the following questions: 1) what are the divisible loads in the CG, and 2) where is communication involved in the parallel CG. To answer 1), we highlight the different divisible data blocks in CG. To answer 2), we introduce a dependency graph among the different data blocks. We conduct experiments on a parallel CG implementation and evaluate communication cost.

Key Distribution Framework for a Mobile Agent Platform

Mobile agents have been proposed as a distributed programming model for large-scale distributed systems. The model is appealing as a response to many of the challenges in todays large scale distributed environments: efficiency, heterogeneity, autonomy, etc. Electronic commerce, data mining, and network management are examples of applications domains. Though the model is very well understood by industrial and research communities, aspects still need to be investigated. In this paper, we investigate a key distribution approach suitable for mobile agents. The approach takes into account the dynamic behavior of the system: in particular the dynamic agent itinerary and the dynamic platform topology where servers can join and disjoin for/from receiving mobile agents.

Empirical Study for Communication Cost of Parallel Conjugate Gradient on a Star-Based Network

Conjugate Gradient is an iterative linear solver that is used in many scientific and engineering applications to solve a system of linear equations. However, Conjugate Gradient generates a heavy load of computation and therefore it slows the performance of the applications using it. In this paper, we conduct an empirical cost study of a parallel CG on our star-based network. We evaluate the communication overhead involved by a parallel CG. In particular, we derive network parameters; the Maximum Transfer Unit (MTU), that can contribute to the optimization of communication cost and to the reduction of the waiting overhead of the parallel algorithm.

Semantic Grid-Based E-learning Architecture

This paper presents an architecture along with an implementation approach to build efficient e-learning applications. In this architecture, e-learning infrastructure uses the Grid computing technology which chooses the best available resources for e-learning objects execution. An e-learning object is defined as the unit for structuring e-learning applications/courses. Furthermore, the infrastructure uses the Semantic Web to specify the learning objects requirements as well as the Grid resources description. A broker entity is developed within the architecture as a mediator who receives performance requirements of the learning object specified using the E-learning standard LOM and the available resources on the Grid that are required to execute that object The proposed architecture is expected to guarantee the optimum allocation of available Grid resources to satisfy the performance requirements of E-learning applications.

Full and Autonomic Mobility Management for Mobile Agents

Cooperation among autonomous mobile agents requires mechanisms to efficiently support communication. The interaction between mobility and communication and the constant re-localization of mobile agents raise several mobility management issues. To address these issues, we develop mechanisms for a full and autonomic mobility management of agents within an underlying model. In addition of being efficient and transparent to agents, our mechanisms take into account the interaction between mobility and communication among agents.

Modeling and Performance Analysis to Predict the Behavior of a Divisible Load Application in a Cloud Computing Environment

Cloud computing is an emerging technology where IT resources are virtualized to users as a set of a unified computing resources on a pay per use basis. The resources are dynamically chosen to satisfy a user Service Level Agreement and a required level of performance. Divisible load applications occur in many scientific and engineering applications and can easily be mapped to a Cloud using a master-worker pattern. However, those applications pose challenges to obtain the required performance. We model divisible load applications tasks processing on a set of cloud resources. We derive a novel model and formulas for computing the blocking probability in the system. The formulas are useful to analyze and predict the behavior of a divisible load application on a chosen set of resources to satisfy a Service Level Agreement before the implementation phase, thus saving time and platform energy. They are also useful as a dynamic feedback to a cloud scheduler for optimal scheduling. We evaluate the model in a set of illustrative scenarios.