Comparative Analysis of OSPFv3/IS-IS and RIPng/IS-IS Mixed Protocols for Real-Time Applications in IPv6 Communication Networks

In network designs, the decision made when implementing dynamic routing protocols is very paramount to the speed of the network. To make the best choice of protocol to deploy, several decisions has to be considered. Usually, these decisions are made based on the performance of the routing protocol with respect to some quantitative parameters. The protocol that performs better than other protocols involved in a research is selected for routing purposes. In this research paper, performance comparison of two mixed protocols namely OSPFv3/IS-IS and RIPng/IS-IS in IPv6 network has been made. Their performances have been measured and comparison made by simulation using Riverbed Modeller Academic Edition. The objective of this paper is mainly to determine which of the mixed protocols will be more suitable to route traffic in IPv6 network. The main motivation for this paper is to find out if the difference in the routing algorithms of RIPng and IS-IS will offset and produce a better performance than a combination of two routing protocols of the same routing algorithm (thus OSPFv3 and IS-IS). To achieve this paper’s objective, the simulation was divided into two scenarios. The first scenario was an OSPFv3/IS-IS configured IPv6 network topology. The second scenario is a copy of the first scenario but configured with RIPng/IS-IS. The two scenarios were simulated and the effect of using each of the scenarios to separately route the selected applications was measured and recorded. The performance comparison of the mixed protocols was based on the following quantitative parameters: database query response time, database query traffics received, email upload/download response time, ftp upload/download response time, ftp traffic received, http page response time, remote login response timeandIPv6 traffics dropped. The results obtained from the simulation indicated that RIPng/IS-IS scenario performed better in email download/upload response time, remote login response time, IPv6 traffics dropped and remote login response time while the mixture of OSPFv3/IS-IS performed better in database query response time, database query traffics received, ftp download/upload response time, ftp traffic received and http page response time. Hence OSPFv3/IS-IS is the better option when the choice is between RIPng/IS-IS and OSPFv3/IS-IS for most of the quantitative parameters involved in this paper. This is because the combination of RIPng and IS-IS took a longer time to converge, affecting the speed on the network scenario. The time the RIPng/IS-IS combination took to access most of the application servers is slower than that of OSPFv3/IS-IS network scenario. On the basis of database query and ftp traffics received, the simulation results showed that network configured with OSPFv3/IS-IS performs better than RIPng/IS-IS. This is because the OSPFv3/IS-IS received the highest database and ftp traffics. The mixture of OSPFv3/IS-IS sent and received more application packets because it had very high throughput values which had an effect on the total quantity of application traffics received. Although the OSPFv3/IS-IS network scenario recorded the highest database and ftp traffics, this could not affect its speed to become lower than the RIPng/IS-IS scenario.

Adaptive replication strategy based on popular content in cloud computing

The cloud is an infrastructure that provides decentralized on-demand services. It allows consumers to pay only for the services they use. The consumer is the important entity in the cloud. The violation of the SLA contract between the consumer and the provider often leads to consequences because the service provider has to pay penalties. Data replication is emerging as an ideal solution to meet the new challenges of the cloud. This paper proposes a new replication strategy based on the popularity of data. This strategy adaptively selects the files to be replicated to improve the overall availability of data in the system, minimize query response time, and achieve the required quality of service. In addition, it dynamically determines the number of replicas to add and the best locations to store them. Experimental results show the effectiveness of the proposed strategy.

Discovery and Application of Data Dependencies

This work makes contributions that reach central problems in connection with data dependencies. The first problem regards the discovery of dependencies of high expressive power. We introduce an efficient algorithm for the discovery of denial constraints: a type of dependency that has enough expressive power to generalize other important types of dependencies and to express complex business rules. The second problem concerns the application of dependencies for improving data consistency. We present a modification for traditional dependency discovery approaches that enables the dependency discovery algorithms to return reliable results even if they run on data containing some inconsistent records. Also, we present a system for detecting violations of dependencies efficiently. Our extensive experimental evaluation shows that our system is up to three orders-of-magnitude faster than state-of-the-art solutions, especially for larger datasets and massive numbers of dependency violations. The last contribution in this work regards the application of dependencies in query optimization. We present a system for the automatic discovery and selection of functional dependencies. Our experimental evaluation shows that our system selects relevant functional dependencies that help reducing the overall query response time for various types of query workloads.

An empirical study on the evaluation of the RDF storage systems

In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and vertical partitioning approaches. As important, we consider the issue of how to select the most relevant strategy for storing the RDF data depending on the dataset characteristics. For this, we investigate the balancing between two performance metrics, including load time and query response time. In this context, we provide an empirical comparative study between on one hand the three proposed methods, and on the other hand the proposed methods versus the existing ones by using various publicly available datasets. Finally, in order to further assess where the statistically significant differences appear between studied methods, we have performed a statistical analysis, based on the non-parametric Friedman test followed by a Nemenyi post-hoc test. The obtained results clearly show that the proposed RDFVP method achieves highly competitive computational performance against other state-of-the-art methods in terms of load time and query response time.

Multi-Objective Big Data View Materialization Using NSGA-II

Big data views, in the context of distributed file system (DFS), are defined over structured, semi-structured and unstructured data that are voluminous in nature with the purpose to reduce the response time of queries over Big data. As the size of semi-structured and unstructured data in Big data is very large compared to structured data, a framework based on query attributes on Big data can be used to identify Big data views. Materializing Big data views can enhance the query response time and facilitate efficient distribution of data over the DFS based application. Given all the Big data views cannot be materialized, therefore, a subset of Big data views should be selected for materialization. The purpose of view selection for materialization is to improve query response time subject to resource constraints. The Big data view materialization problem was defined as a bi-objective problem with the two objectives- minimization of query evaluation cost and minimization of the update processing cost, with a constraint on the total size of the materialized views. This problem is addressed in this paper using multi-objective genetic algorithm NSGA-II. The experimental results show that proposed NSGA-II based Big data view selection algorithm is able to select reasonably good quality views for materialization.

Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning

In cloud environments, hardware configurations, data usage, and workload allocations are continuously changing. These changes make it difficult for the query optimizer of a cloud database management system (DBMS) to select an optimal query execution plan (QEP). In order to optimize a query with a more accurate cost estimation, performing query re-optimizations during the query execution has been proposed in the literature. However, some of there-optimizations may not provide any performance gain in terms of query response time or monetary costs, which are the two optimization objectives for cloud databases, and may also have negative impacts on the performance due to their overheads. This raises the question of how to determine when are-optimization is beneficial. In this paper, we present a technique called ReOptML that uses machine learning to enable effective re-optimizations. This technique executes a query in stages, employs a machine learning model to predict whether a query re-optimization is beneficial after a stage is executed, and invokes the query optimizer to perform the re-optimization automatically. The experiments comparing ReOptML with existing query re-optimization algorithms show that ReOptML improves query response time from 13% to 35% for skew data and from 13% to 21% for uniform data, and improves monetary cost paid to cloud service providers from 17% to 35% on skewdata.

An Empirical Study on the Evaluation of the RDF Storage Systems

In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and vertical partitioning approaches. As important, we consider the issue of how to select the most relevant strategy for storing the RDF data depending on the dataset characteristics. For this, we investigate the balancing between two performance metrics, including load time and query response time. In this context, we provide an empirical comparative study between on one hand the three proposed methods, and on the other hand the proposed methods versus the existing ones by using various publicly available datasets. Finally, in order to further assess where the statistically significant differences appear between studied methods, we have performed a statistical analysis, based on the non-parametric Friedman test followed by a Nemenyi post-hoc test. The obtained results clearly show that the proposed RDFVP method achieve highly competitive computational performance against other state-of-the-art methods in terms of Load Time and Query Response Time.

Fire Equipment Information Traceability System based on Blockchain

To solve the problems of centralization, tampering, incomplete storage, and privacy of fire fighting equipment traceability system, this paper proposes a fire fighting equipment information traceability system based on blockchain. The system is developed on the Fabric blockchain platform of Hyperledger. The system environment is equipped with three organizations: manufacturer, dealer, and consumers, and the query request is initiated with the traceability function of fire fighting equipment in the chaincode. Finally, through the certificate authentication user account can realize the fire fighting equipment information inquiry, and the query response time average value is 19.5 ms. The characteristics of blockchain are difficult to tamper with, timestamp and transaction traceability, which can be well applied to the traceability system of fire fighting equipment, which makes the traceability function of the system more perfect, and consumers can get all traceability information, including production information, logistics information and usage information of fire fighting equipment.

Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is a huge adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ different mechanisms to implement key components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is key for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a key optimization component of the RDF storage solutions. Finally, query execution involving different join orders significantly affects the query response time. This paper provides a comprehensive review of centralized and distributed RDF engines in terms of storage, indexing, language support, and query execution.