Replica maintenance strategy for data grid

Data Grid is an infrastructure that manages huge amount of data files, and provides intensive computational resources across geographically distributed collaboration.Increasing the performance of such system can be achieved by improving the overall resource usage, which includes network and storage resources.Improving network resource usage is achieved by good utilization of network bandwidth that is considered as an important factor affecting job execution time.Meanwhile, improving storage resource usage is achieved by good utilization of storage space usage. Data replication is one of the methods used to improve the performance of data access in distributed systems by replicating multiple copies of data files in the distributed sites.Having distributed the replicas to various locations, they need to be monitored.As a result of dynamic changes in the data grid environment, some of the replicas need to be relocated.In this paper we proposed a maintenance replica placement strategy termed as Unwanted Replica Deletion Strategy (URDS) as a part of Replica maintenance service.The main purpose of the proposed strategy is to find the placement of unwanted replicas to be deleted.OptorSim is used to evaluate the performance of the proposed strategy. The simulation results show that URDS requires less execution time and consumes less network usage and has a best utilization of storage space usage compared to existing approaches

Analyse the Performance of Mobile Peer to Peer Network using Ant Colony Optimization

A mobile peer-to-peer computer network is the one in which each computer in the network can act as a client or server for the other computers in the network. The communication process among the nodes in the mobile peer to peer network requires more no of messages. Due to this large number of messages passing, propose an interconnection structure called distributed Spanning Tree (DST) and it improves the efficiency of the mobile peer to peer network. The proposed method improves the data availability and consistency across the entire network and also reduces the data latency and the required number of message passes for any specific application in the network. Further to enhance the effectiveness of the proposed system, the DST network is optimized with the Ant Colony Optimization method. It gives the optimal solution of the DST method and increased availability, enhanced consistency and scalability of the network. The simulation results shows that reduces the number of message sent for any specific application and average delay and increases the packet delivery ratio in the network

Replica Creation Algorithm for Data Grids

Data grid system is a data management infrastructure that facilitates reliable access and sharing of large amount of data, storage resources, and data transfer services that can be scaled across distributed locations. This thesis presents a new replication algorithm that improves data access performance in data grids by distributing relevant data copies around the grid. The new Data Replica Creation Algorithm (DRCM) improves performance of data grid systems by reducing job execution time and making the best use of data grid resources (network bandwidth and storage space). Current algorithms focus on number of accesses in deciding which file to replicate and where to place them, which ignores resources’ capabilities. DRCM differs by considering both user and resource perspectives; strategically placing replicas at locations that provide the lowest transfer cost. The proposed algorithm uses three strategies: Replica Creation and Deletion Strategy (RCDS), Replica Placement Strategy (RPS), and Replica Replacement Strategy (RRS). DRCM was evaluated using network simulation (OptorSim) based on selected performance metrics (mean job execution time, efficient network usage, average storage usage, and computing element usage), scenarios, and topologies. Results revealed better job execution time with lower resource consumption than existing approaches. This research contributes replication strategies embodied in one algorithm that enhances data grid performance, capable of making a decision on creating or deleting more than one file during same decision. Furthermore, dependency-level-between-files criterion was utilized and integrated with the exponential growth/decay model to give an accurate file evaluation

Data Replication-Based Scheduling in Cloud Computing Environment

Abstract— High-performance computing and vast storage are two key factors required for executing data-intensive applications. In comparison with traditional distributed systems like data grid, cloud computing provides these factors in a more affordable, scalable and elastic platform. Furthermore, accessing data files is critical for performing such applications. Sometimes accessing data becomes a bottleneck for the whole cloud workflow system and decreases the performance of the system dramatically. Job scheduling and data replication are two important techniques which can enhance the performance of data-intensive applications. It is wise to integrate these techniques into one framework for achieving a single objective. In this paper, we integrate data replication and job scheduling with the aim of reducing response time by reduction of data access time in cloud computing environment. This is called data replication-based scheduling (DRBS). Simulation results show the effectiveness of our algorithm in comparison with well-known algorithms such as random and round-robin

A novel dynamic replica creation mechanism for Data Grids

The abstract Data Grid is an infrastructure that manages huge amount of data files, and provides intensive computational resources across geographically distributed collaboration.A key concept in Data Grids is replication of data, whereby multiple copies of data are stored at different geographical locations, making access to data faster and more reliable.However, replication is also bounded by two factors: the size of storage available at different sites within the Data Grid and the bandwidth between these sites. In this paper, we proposed a dynamic replication mechanism termed as Replica Number Mechanism (RNM) that determine the optimal number of replicas to be created or deleted with the aim of minimizing the overall resource usage (network bandwidth and storage usage).OptorSim is used to evaluate the performance of the proposed mechanism. The simulation results show that RNM requires less execution time and consumes less network usage and storage usage compared to existing approaches of Simple Optimizer and LFU (Least Frequently Used)

An enhanced dynamic replica creation and eviction mechanism in data grid federation environment

Data Grid Federation system is an infrastructure that connects several grid systems, which facilitates sharing of large amount of data, as well as storage and computing resources. The existing mechanisms on data replication focus on finding file values based on the number of files access in deciding which file to replicate, and place new replicas on locations that provide minimum read cost. DRCEM finds file values based on logical dependencies in deciding which file to replicate, and allocates new replicas on locations that provide minimum replica placement cost. This thesis presents an enhanced data replication strategy known as Dynamic Replica Creation and Eviction Mechanism (DRCEM) that utilizes the usage of data grid resources, by allocating appropriate replica sites around the federation. The proposed mechanism uses three schemes: 1) Dynamic Replica Evaluation and Creation Scheme, 2) Replica Placement Scheme, and 3) Dynamic Replica Eviction Scheme. DRCEM was evaluated using OptorSim network simulator based on four performance metrics: 1) Jobs Completion Times, 2) Effective Network Usage, 3) Storage Element Usage, and 4) Computing Element Usage. DRCEM outperforms ELALW and DRCM mechanisms by 30% and 26%, in terms of Jobs Completion Times. In addition, DRCEM consumes less storage compared to ELALW and DRCM by 42% and 40%. However, DRCEM shows lower performance compared to existing mechanisms regarding Computing Element Usage, due to additional computations of files logical dependencies. Results revealed better jobs completion times with lower resource consumption than existing approaches. This research produces three replication schemes embodied in one mechanism that enhances the performance of Data Grid Federation environment. This has contributed to the enhancement of the existing mechanism, which is capable of deciding to either create or evict more than one file during a particular time. Furthermore, files logical dependencies were integrated into the replica creation scheme to evaluate data files more accurately