MetaCat - metadata catalog for data management systems

Metadata management is one of three major areas of scientific data management along with replica management and workflow management. Metadata is the information describing the data stored in a data item, a file or an object. It includes the data item provenance, recording conditions, format and other attributes. MetaCat is a metadata management database designed and developed for High Energy Physics experiments. As a component of a data management system, it’s main objectives are to provide efficient metadata storage and management and fast data selection functionality. MetaCat is required to work on the scale of 100 million files (or objects) and beyond. The article will discuss the functionality of MetaCat and technological solutions used to implement the product.

An Efficient Multi-Objective Model for Data Replication in Cloud Computing Environment

The main aim of the proposed methodology is to design a multi-objective function for replica management system using oppositional gravitational search algorithm (OGSA), in which we analyze the various factors influencing replication decisions such as mean service time, mean file availability, energy consumption, load variance, and mean access latency. The OGSA algorithm is hybridization of oppositional-based learning (OBL) and gravitational search algorithm (GSA), which is change existing solution, and to adopt a new good solution based on objective function. Here, firstly we create a set of files and data node to generate a population by assigning the file to data node randomly and evaluate the fitness which is minimizing the objective function. Secondly, we regenerate the population to produce optimal or suboptimal population using OGSA. The experimental results show that the performance of the proposed methods is better than the other methods of data replication problem.

Adaptive Replica Creation and Selection Strategies for Latency-Aware Application in Collaborative Edge-Cloud System

There are many research problems in cloud replica management such as low data reliability, unbalanced node load and large resource consumption. The strategy and status of replica creation, replica placement and replica selection are analyzed. The replica creation based on access tendency (DRC-AT), the replica placement based on user request response time and storage capacity (DRP-RS) and the replica selection based on response time (DRS-RT) are proposed. The DRC-AT algorithm introduces the two parameters of file popularity and period value of file popularity, calculates the file access tendency periodically and decides the creation and deletion of the replica of the file according to the size of the file access tendency. The DRP-RS algorithm evaluates the user’s request response time and storage capacity to select the best node set to place the replica. The DRS-RT algorithm returns to the user the node with the strongest service capability that contains the user’s requested data. Experiments show that the algorithm can improve the speed of data reading by the client, improve the resource utilization, balance the load of the node and improve the overall performance of the system.