Kadda Beghdad Bey

CPU load prediction using neuro-fuzzy and Bayesian inferences

Ensuring adequate use of the computing resources for highly fluctuating availability in multi-user computational environments requires effective prediction models, which play a key role in achieving application performance for large-scale distributed applications. Predicting the processor availability for scheduling a new process or task in a distributed environment is a basic problem that arises in many important contexts. The present paper aims at developing a model for single-step-ahead CPU load prediction that can be used to predict the future CPU load in a dynamic environment. Our prediction model is based on the control of multiple Local Adaptive Network-based Fuzzy Inference Systems Predictors (LAPs) via the Naive Bayesian Network inference between clusters states of CPU load time points obtained by the C-means clustering process. Experimental results show that our model performs better and has less overhead than other approaches reported in the literature.

Password hardened fuzzy vault for fingerprint authentication system

The present work attempts to build a bio-cryptographic system that combines transformed minutiae pairwise feature and user-generated password fuzzy vault. The fingerprint fuzzy vault is based on a new minutiae pairwise structure, which overcomes the fingerprint feature publication while the secret binary vault code is generated according to the fingerprint fuzzy vault result. The authentication process involves two stages: fuzzy vault matching and secret vault code validation. Our minutiae pairwise transformation produces different templates thus resolving the problem of cross matching attacks in fingerprint fuzzy vault. So, the original fingerprint template cannot be recreated because it is protected by the key generated from the user password. In addition, the proposed bio-cryptographic system ensures an acceptable security level for user authentication.

New tasks scheduling strategy for resources allocation in Cloud computing Environment

Scientific applications are very complex and need massive computing power and storage space. Distributed computing environment has become a new technology to execute large-scale applications and Cloud computing is one of these technologies. Resource allocation is one of the most important challenges in the Cloud Computing. The optimally assigning of the available resources to the needed cloud applications is known to be a NP complete problem. In this paper, we propose a new task scheduling strategy for resource allocation for maximizing profit in cloud computing environment. We focus on minimizing the total executing time (makespan) of task scheduling and maximizing the resources exploitation. To show the interest of the proposed solution, Experiments results are conducted on a simulation data set.

Balancing heuristic for independent task scheduling in cloud computing

Distributed computing environment has become a new technology to execute large-scale applications and Cloud computing is one of these technologies. Resource allocation is one of the most important challenges in the Cloud Computing. The optimally assigning of the available resources to the needed cloud applications is known to be a NP complete problem. In this paper, we propose a new task scheduling strategy based on the total order for resource allocation to improve the Min-Min algorithm. We focus on minimizing the total executing time (makespan) of task scheduling and maximizing the use of resources. Experimental results demonstrate that the proposed approach permits more adaptive resources allocation for independent jobs scheduling in the cloud computing environment.

EMBEDDED FINGERPRINT MATCHING ON SMART CARD

This paper describes an embedded minutia-based matching algorithm using the reference point neighborhoods minutiae. The proposed matching algorithm is implemented in restricted environments such as smart card devices requiring careful monitoring of both memory and processing time usage. The proposed algorithm uses a circular tessellation to encode fingerprint features in neighborhood minutia localization binary codes. The objective of the present study is the development of a new matching approach which reduces both computing time and required space memory for fingerprint matching on Java Card. The main advantage of our approach is avoiding the implicit alignment of fingerprint images during the matching process while improving the fingerprint verification accuracy. Tests carried out on the public fingerprint databases DB1-a and DB2-a of FVC2002 have shown the effectiveness of our approach.

Mixture of ANFIS systems for CPU load prediction in metacomputing environment

The metacomputing environments are becoming real distributed running platforms for compute-intensive services. One of the most difficult problems to be solved by metacomputing systems is ensuring accurate and fast prediction of available performance on each resource. The main objective of the present study is to develop a new prediction model that can be used to predict the future CPU load in a distributed computing environment. This prediction model is based on a mixture of Adaptive Network based Fuzzy Inference Systems (ANFIS) via the naive Bayes assumption. Experimental results for different load time series confirm that the new prediction model performs better than other CPU load prediction methods. In addition, a comparison with previous prediction methods to evaluate their accuracy is presented.

Independent task scheduling in heterogeneous environment via makespan refinery approach

Tasks scheduling in heterogeneous computing environments is one of the most challenging problems in distributed computing. The optimally mapping of independent tasks onto heterogeneous distributed computing systems is known to be NP complete problem. This paper addresses a two-stage methodology for solving the independent task scheduling problems in heterogeneous distributed computing. The scheduler aims to minimize the total completion time using the task reassignment strategy. This later uses a new Makespan Refinery Approach (MRA) to improve our initial task scheduling solution by reducing the maximum completion time. The effectiveness of the proposed scheduling method has been tested and evaluated using simulations. The experiment results show the behaviour of the scheduling method for the short completion time of a set of tasks.

Load Balancing Heuristic for Tasks Scheduling in Cloud Environment.

Distributed systems, a priori intended for applications by connecting distributed entities, have evolved into supercomputing to run a single application. Currently, Cloud Computing has arisen as a new trend in the world of IT (Information Technology). Cloud computing is an architecture in full development and has become a new computing model for running scientific applications. In this context, resource allocation is one of the most challenging problems. Indeed, assigning optimally the available resources to the needed cloud applications is known to be an NP complete problem. In this paper, we propose a new task scheduling strategy for resource allocation that minimizes the completion time (makespan) in cloud computing environment. To show the interest of the proposed solution, experiments are conducted on a simulated

Unsupervised Segmentation of Nonstationary Data using Triplet Markov Chains.

An important issue in statistical image and signal segmentation consists in estimating the hidden variables of interest. For this purpose, various Bayesian estimation algorithms have been developed, particularly in the framework of hidden Markov chains, thanks to their efficient theory that allows one to recover the hidden variables from the observed ones even for large data. However, such models fail to handle nonstationary data in the unsupervised context. In this paper, we show how the recent triplet Markov chains, which are strictly more general models with comparable computational complexity, can be used to overcome this limit through two different ways: (i) in a Bayesian context by considering the switches of the hidden variables regime depending on an additional Markov process; and, (ii) by introducing Dempster-Shafer theory to model the lack of precision of the hidden process prior distributions, which is the origin of data nonstationarity. Furthermore, this study analyzes both approaches in order to determine which one is better-suited for nonstationary data. Experimental results are shown for sampled data and noised images.

CSP Formulation for Scheduling Independent Jobs in Cloud Computing

This paper investigates the use of Constraint Satisfaction Problem formulation to schedule independent jobs in heterogeneous cloud environment. Our formulation provides a basis for computing an optimal Makespan using job and machine reordering heuristics based on Min-min algorithm result. The combination of these heuristics with the weighted constraints allows improving the efficiency of the tree search algorithm to schedule jobs with considerable space search reduction. The proposed CSP model is validated through simulation experiments against clusters of 10 virtual machines. The results demonstrate that our model is able to efficiently allocate resources for jobs with significant performance gains between 18% 40% compared to the Min-Min heuristic results to optimize the Makespan.