Adil Yousif

Content-based Information Retrieval Techniques Based on Grid Computing: A Review

Abstract Distributed information retrieval methods are growing rapidly because of the rising need to access and search distributed digital documents. However, the content-based information retrieval (CBIR) is concentrated to extract and retrieve the information from massive digital libraries, which require a huge amount of computing and storage resources. The grid computing provides the reliable infrastructure for effective and efficient retrieval on these large collections. In order to build an effective and efficient CBIR technique, varieties of architectures were developed based on grid technologies. The goal of such architecture is to solve interoperability and heterogeneous resource issues, and increase the efficiency and effectiveness of information retrieval (IR) techniques by harnessing the grid computing capabilities. This paper reviews and analyzes latest research carried out in the domain of large-scale dataset IR based on a grid. The evaluation is based on scalability, response time, scope, data type, search technique, middleware, and query type. The contribution is to illustrate the features, capabilities, and shortages of current solutions that can guide the researchers in this evolving area.

A Discrete Firefly Algorithm for Scheduling Jobs on Computational Grid

Computational grid emerged as a large scale distributed system to offer dynamic coordinated resources sharing and high performance computing. Due to the heterogeneity of grid resources scheduling jobs on computational grids is identified as NP-hard problem. This chapter introduces a job scheduling mechanism based on Discrete Firefly Algorithm (DFA) to map the grid jobs to available resources in order to finish the submitted jobs within a minimum makespan time. The proposed scheduling mechanism uses population based candidate solutions rather than single path solution as in traditional scheduling mechanism such as tabu search and hill climbing, which help avoids trapping in local optimum. We used simulation and real workload traces to evaluate the proposed scheduling mechanism. The simulation results of the proposed DFA scheduling mechanism are compared with Genetic Algorithm and Tabu Search scheduling mechanisms. The obtained results demonstrated that, the proposed DFA can avoid trapping in local optimal solutions and it could be efficiently utilized for scheduling jobs on computational grids. Furthermore, the results have shown that DFA outperforms the other scheduling mechanisms in the case of typical and heavy loads.

A Cloud Based Framework for Platform as a Service

Cloud computing systems assist applications by offering virtualized resources that can be provisioned on demand basis. Computing resources are delivered by Virtual Machines (VMs). Traditional development models have many limitations such as cost and delay. Platform as services is an effective solution for most of problems on traditional development application model that faced by programmers as Platform as a Service provides them the same traditional features with a faster speed, reducing the cost and increasing collaboration. The aim of this paper is to develop cloud based framework based on Platform as a Services to assist in developing, testing and deploying application without the need to purchase of hardware, operating systems, and tools that is obtainable as services via the Internet. To evaluate the proposed framework, this study conducted an empirical study by setting up an experiment for programmers and IT specialists. The achieved results revealed that the proposed framework can overcome the tradition framework limitations.

A survey of grid-based searching techniques for large scale distributed data

The large-scale distributed dataset searching faced dynamicity, heterogeneity, and latency issues that emphasize the importance of approach to orchestrate the search operations. The growth of grid computing gives a reliable environment for the effective usage of this distributed huge dataset. In order to build effective and efficient searching technique, varieties of researches were developed based on grid technology. The goal of such researches is to solve interoperability and heterogeneous of the data sets, and increase the efficiency of search techniques by harnessing the grid computing capabilities. This paper reviews and analyzes the search techniques carried out in the domain of large-scale dataset search based on a grid. Several searching approaches are studied to identify which attributes are ignored and which are considered. The techniques are evaluated depending on the aforementioned attributes and are presented in a table. The contribution of this study is to illustrate the characteristics, capabilities and limitations of existing solutions that can guide the researchers in this evolving area. HighlightsIssues involved in Grid-Based massive data search techniques are being discussed.Criteria such as real time search, online data, and search technique are explained.Features such as research Field, UI, automate grid Tasks, architecture are discussed.Explanation table to Identify Open issues and help future data search techniques.

Scheduling jobs on grid computing using firefly algorithm

Scheduling jobs on computational grids is identified as NP-complete problem due to the heterogeneity of resources; the resources belong to different administrative domains and apply different management policies. This paper presents a novel metaheuristics method based on Firefly Algorithm (FA) for scheduling jobs on grid computing. The proposed method is to dynamically create an optimal schedule to complete the jobs within minimum makespan. The proposed method is compared with other heuristic methods using simple and different simulation scenarios. Each firefly represents a candidate solution of the grid scheduling problem in a vector form, with n elements; where n is the number of jobs to be scheduled. Firefly[i] specifies the resource to which the job number i is allocated. Therefore, the vector values are natural numbers. Also we note that the vector values are the resource IDs and hence the resource ID may appear more than one time in the firefly vector. This comes about because more than one job may be allocated to the same resource. To evaluate the effectiveness and the efficiency of job scheduling algorithms on computational grid, it is difficult and impractical to achieve performance assessment experimentally in such large scale heterogeneous system and to repeat and control the experiments to perform different scenarios. To encounter this limitation this research used mathematical modeling and simulation to model and evaluate the proposed mechanism. The results demonstrated that, the firefly scheduling mechanism achieved less makespan time than Min-Min and Max- Min heuristics in several scheduling scenarios. The results in this paper showed that the FA is promising method that can be used to optimize scheduling jobs on grid computing.

An Efficient and Secured Grid-Enabler Interface for Large-Scale Systems

Grid-based Massive dataset search systems integrate many processes to search for a large number of shared datasets distributed over several locations. The implementation of the search mechanism to deal with these distributed dataset greatly affected the system performance. However, the execution of the search over the grid requires someone with grid knowledge and expertise. Consequently, this issue leads this research to propose an interface that hides the complexities of the grid operations from the end users. Additionally, the interface provides single sign-on mechanism to allow users to access several Virtual Organizations (VO) with a single user name and a password. The experiment was conducted to evaluate the user interface overhead and the efficiency of the interface. The result shows that the client user interface produces less overhead and is reasonably efficient in dealing with several VOs.

Grid-based architecture for sharing distributed massive datasets

In large-scale distributed dataset sharing systems, challenges of dynamicity, heterogeneity, and latency emphasise the importance of infrastructural support in enhancing the system performance. The growth of grid computing gives a reliable environment for the effective usage of this distributed huge dataset. In order to build efficient sharing architecture, varieties of architectures were developed based on grid technology. The goal of such architecture is to solve interoperability and heterogeneous resource issues, and increase the efficiency and effectiveness of sharing techniques by harnessing the grid computing capabilities. This study proposes grid-based sharing architecture for distributed massive datasets. The architecture is considering a mediator between the users and the data providers. The experiments conducted to evaluate the performance of the architecture by measuring the grid overhead, throughput, and scalability of the architecture. The result shows that the architecture has reasonable performance and can be scalable with the growing data and computing nodes.