Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan

User centered neuro-fuzzy energy management through semantic-based optimization

This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system’s intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings

Join query enhancement processing (jqpro) with big rdf data on a distributed system using hashing-merge join technique

Semantic web technologies have emerged in the last few years across different fields of study and their data are still growing rapidly. Specifically, the increased data storage and publishing capabilities in standard open web formats have made the technology much more successful. So, the data have become readable by humans, and they can be processed on a computer. The demand for complex multiple RDF queries is becoming significant with the increasing number of RDF triples. Such complex queries occasionally produce many common subexpressions. It is therefore extremely challenging to reduce the amount of RDF queries and transmission time for a vast number of related RDF data. Moreover, Recent literature shows that join query processing of Big RDF data has introduced many problems with respect to execution time and throughput. The hash-based encoding induces low execution time, which takes a long time to load and hence does not load all graphs. This is because the Resource Description Framework (RDF) collects and analyses large data in swarms, thereby having to deal with the inherent challenge of efficient swarm storage. The effective storage and data retrieval, which could be applied to high amounts of possible schema-less data, has also proven exceedingly difficult for RDF data storage. For instance, it is particularly difficult to view semantic and SPARQL query languages, as well as huge and complex graph patterns. To address this problem, a Join Query Processing Model (JQPro) is introduced for Big RDF data. The objectives of this research are: (i) formulate plan generator algorithms for join query processing on the basis of the previous research. (ii) develop an enhancement model of Join Query Processing (JQPro) based on SPARQL and Hadoop MapReduce using hashing-merge join technique to process Big RDF Data. (iii) evaluate and compare the performance based on the execution time, throughput, and CPU utilization of the JQPro model with existing models. On the other hand, the throughput was employed to measure the units of information that a system can process in each time frame. In addition, the CPU utilization was used in the big join query processing as an important resource element particularly during the map, to reduce phases. Furthermore, the hash-join and Sort-Merge algorithms were used to generate the join query processing, and this was employed due to their capacity to allow for more data sets to be joined. Both processes were sorted by algorithms on join attributes and the sorted relations was merged. Therefore, the join column sorted the groups of datasets with the same value. The sort–merge–join algorithm sorts the datasets on the joining attribute and then searches for tuples by merging the two datasets. Then, a processing framework for RDF queries was introduced and the benchmark was used for performance evaluation. Finally, the validation was conducted by standard statistical analysis to validate and compare the performance of the JQPro model with current models. In addition, the synthetic benchmarks Lehigh University Benchmark (LUBM) and Waterloo SPARQL Diversity Test Suite (WatDiv) v06 were used for measurement. The experiment was carried out on three datasets ranging from 10 million to 1 billion RDF triples produced by the generator of WatDiv data with a scale factor of 10, 100 and 1000, respectively. A selective dataset for each experimental query was also used for the processing of RDFs with a LUBM benchmark in sizes 500, 1000 and 2000 million triples. The result revealed that there is a strong correlation between execution time and throughput with a strength of 99.9% percent as confirmed by the Pearson correlation coefficient. Furthermore, the findings show that the JQPro solution was comparable to gStore RDF-3X, RDFox and PARJ and the percentage of improved performance was 87.77% in terms of execution time. The CPU utilization was significantly increased by extensive mapping and reduced code computing. It is therefore inferred that the JQPro solution is timely and innovative, as it provides an efficient execution time and CPU utilization where users could perform better queries for Big RDF data processing in a seamless manne

Tools for enterprises collaboration in virtual enterprises

Virtual Enterprise (VE) is an organizational collaboration concept which provides a competitive edge in the globalized business environment. The life cycle of a VE consists of four stages i.e. opportunity identification (Pre-Creation), partner selection (Creation), operation and dissolution. The success of VEs depends upon the efficient execution of their VE-lifecycles along with knowledge enhancement for the partner enterprises to facilitate the future formation of efficient VEs. This research aims to study the different issues which occur in the VE lifecycle and provides a platform for the formation of high performance enterprises and VEs. In the pre-creation stage, enterprises look for suitable partners to create their VE and to exploit a market opportunity. This phase requires explicit and implicit information extraction from enterprise data bases (ECOS-ontology) for the identification of suitable partners. A description logic (DL) based query system is developed to extract explicit and implicit information and to identify potential partners for the creation of the VE. In the creation phase, the identified partners are analysed using different risks paradigms and a cooperative game theoretic approach is used to develop a revenue sharing mechanism based on enterprises inputs and risk minimization for optimal partner selection. In the operation phases, interoperability remains a key issue for seamless transfer of knowledge information and data. DL-based ontology mapping is applied in this research to provide interoperability in the VE between enterprises with different domains of expertise. In the dissolution stage, knowledge acquired in the VE lifecycle needs to be disseminated among the enterprises to enhance their competitiveness. A DL-based ontology merging approach is provided to accommodate new knowledge with existing data bases with logical consistency. Finally, the proposed methodologies are validated using the case study. The results obtained in the case study illustrate the applicability and effectiveness of proposed methodologies in each stage of the VE life cycle

JQPro : Join query processing in a distributed system for big RDF data using the hash-merge join technique

In the last decade, the volume of semantic data has increased exponentially, with the number of Resource Description Framework (RDF) datasets exceeding trillions of triples in RDF repositories. Hence, the size of RDF datasets continues to grow. However, with the increasing number of RDF triples, complex multiple RDF queries are becoming a significant demand. Sometimes, such complex queries produce many common sub-expressions in a single query or over multiple queries running as a batch. In addition, it is also difficult to minimize the number of RDF queries and processing time for a large amount of related data in a typical distributed environment encounter. To address this complication, we introduce a join query processing model for big RDF data, called JQPro. By adopting a MapReduce framework in JQPro, we developed three new algorithms, which are hash-join, sort-merge, and enhanced MapReduce-join for join query processing of RDF data. Based on an experiment conducted, the result showed that the JQPro model outperformed the two popular algorithms, gStore and RDF-3X, with respect to the average execution time. Furthermore, the JQPro model was also tested against RDF-3X, RDFox, and PARJs using the LUBM benchmark. The result showed that the JQPro model had better performance in comparison with the other models. In conclusion, the findings showed that JQPro achieved improved performance with 87.77% in terms of execution time. Hence, in comparison with the selected models, JQPro performs better

Big Data Optimization : Algorithmic Framework for Data Analysis Guided by Semantics

Fecha de Lectura de Tesis: 9 noviembre 2018.Over the past decade the rapid rise of creating data in all domains of knowledge such as traffic, medicine, social network, industry, etc., has highlighted the need for enhancing the process of analyzing large data volumes, in order to be able to manage them with more easiness and in addition, discover new relationships which are hidden in them Optimization problems, which are commonly found in current industry, are not unrelated to this trend, therefore Multi-Objective Optimization Algorithms (MOA) should bear in mind this new scenario. This means that, MOAs have to deal with problems, which have either various data sources (typically streaming) of huge amount of data. Indeed these features, in particular, are found in Dynamic Multi-Objective Problems (DMOPs), which are related to Big Data optimization problems. Mostly with regards to velocity and variability. When dealing with DMOPs, whenever there exist changes in the environment that affect the solutions of the problem (i.e., the Pareto set, the Pareto front, or both), therefore in the fitness landscape, the optimization algorithm must react to adapt the search to the new features of the problem. Big Data analytics are long and complex processes therefore, with the aim of simplify them, a series of steps are carried out through. A typical analysis is composed of data collection, data manipulation, data analysis and finally result visualization. In the process of creating a Big Data workflow the analyst should bear in mind the semantics involving the problem domain knowledge and its data. Ontology is the standard way for describing the knowledge about a domain. As a global target of this PhD Thesis, we are interested in investigating the use of the semantic in the process of Big Data analysis, not only focused on machine learning analysis, but also in optimization

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method