Replica Selection Strategy Research Articles

A Prediction Based Replica Selection Strategy for Reducing Tail Latency in Geo-Distributed Systems

The deployment of modern applications in geo-distributed systems results in performance fluctuation which is a consequence of long-tail latency. To deliver high-quality services these applications always strive to adapt to the changing situation and an appropriate replica selection strategy is one efficient way to achieve this. Several replica selection strategies have already been developed but none of them are efficient enough to reduce tail latency and adapt to the dynamic environment of the geo-distributed systems. In this paper, we present the design and implementation of a prediction based replica selection strategy for reducing tail latency in geo-distributed systems. We have meticulously designed the proposed strategy to adapt to the dynamic behavior of the distributed system. For evaluating its effectiveness in reducing tail latency and adapting to the dynamic behavior of the geo-distributed systems we perform some extensive experiments in a 15 nodes Cassandra cluster that is deployed on Amazon EC2 over 5 geographical regions. For generating test datasets and workloads we use industry-standard Yahoo Cloud Serving Benchmark (YCSB). Our experimental results show that the proposed strategy not only reduces tail latency but also increases the overall throughput of the systems.

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

Abstract 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.

A combination replication strategy for data-intensive services in distributed geographic information system

Distributed geographic information system is a typical service-intensive application which has to store massive data in lots of storages and server for a large number of users. Due to the slow network input/output, replicas can be used to improve system performance. Since all data have the relationships of long-term stability as well as short-term bursty, a comprehensive method which considers not only static replicas and its placement strategy but also dynamic replicas and its selection strategy can achieve more significant improvements and are proposed in this article. First, a general dynamic correlation representation model of all data is designed and implemented. And then replica selection strategies for static copies and dynamic copies are proposed based on their relationships. Also, a comprehensive data placement strategy for all data and all replicas is defined to realize load balance. Finally, the performance of the proposed method has been proved through a series of comparative experiments, and ...

QDR: a QoS-aware data replication algorithm for Data Grids considering security factors

Data Grid integrates geographically distributed resources for solving data sensitive scientific applications. Dynamic data replication algorithms are becoming increasingly valuable in solving large-scale, realistic, difficult problems, and selecting replica with multiple selection criteria--availability, security and time- is one of these problems. The current algorithms do not offer balanced QoS levels and the mechanism of rating QoS parameters. In this paper, we propose a new replica selection strategy, which based on response time and security. However, replication should be used wisely because the storage size of each Data Grid site is limited. Thus, the site must keep only the important replicas. We also present a new replica replacement strategy based on the availability of the file, the last time the replica was requested, number of access, and size of replica. We evaluate our algorithm using the OptorSim simulator and find that it offers better performance in comparison with other algorithms in terms of mean job execution time, effective network usage, SE usage, replication frequency, and hit ratio.

A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids

Mining grid data is an interesting research field which aims at analyzing grid systems with data mining techniques in order to efficiently discover new meaningful knowledge to enhance grid management. In this paper, we focus particularly on how extracted knowledge enables enhancing data replication and replica selection strategies which are important data management techniques commonly used in data grids. Indeed, relevant knowledge such as file access patterns, file correlations, user or job access behavior, prediction of future behavior or network performance, and so on, can be efficiently discovered. These findings are then used to enhance both data replication and replica selection strategies. Various works in this respect are then discussed along with their merits and demerits. In addition, we propose a new guideline to data mining application in the context of data replication and replica selection strategies.

Security-aware data replica selection strategy for Bag-of-Tasks application in cloud computing

As a typical distributed network computing environment, cloud computing has been growing tremendously recently. Although it could provide a convenient environment for the execution of data-intensive application, the primary challenge for it is how to choose the security date replica for data-intensive application from different storage resources. Considering that the security has the characteristic of fuzziness and randomness, it is quantified using the cloud model in this paper. On this basis, taking the constraints of time, cost, and security into account, a scheduling algorithm of data-intensive application based on weighted set covering problem (WSCP) is proposed. Simulation results demonstrate that the security-aware data replica selection strategy performs better than traditional selection strategy on time, cost, security utility value and load balancing deviation (LBD).

A Replica Selection Strategy On Ant-algorithm in Data-intensive Applications

In data-intensive applications, multiple copies of the same data were created to improve reliability and reduce the bandwidth consumption. Replica selection is the crucial factor in accessing data quickly and effectively. It affects task scheduling, thereby affecting the efficiency and service quality of the application. Within this paper, a modified replica selection strategy based on ant-algorithm in data-intensive grid is proposed. The strategy is implemented and compared with other file replica selection algorithms on simulator OptorSim. The experiments results show that the strategy discussed in this thesis has certain advantages in balancing the load of sites and reducing the mean job time.

A dynamic replica management strategy in data grid

Data Grid provides scalable infrastructure for storage resource and data files management, which supports several large scale applications. Due to limitation of available resources in grid, efficient use of the grid resources becomes an important challenge. Replication is a technique used in data grid to improve fault tolerance and to reduce the bandwidth consumption. This paper proposes a Dynamic Hierarchical Replication (DHR) algorithm that places replicas in appropriate sites i.e. best site that has the highest number of access for that particular replica. It also minimizes access latency by selecting the best replica when various sites hold replicas. The proposed replica selection strategy selects the best replica location for the users' running jobs by considering the replica requests that waiting in the storage and data transfer time. The simulated results with OptorSim, i.e. European Data Grid simulator show that DHR strategy gives better performance compared to the other algorithms and prevents unnecessary creation of replica which leads to efficient storage usage.

New Replica Selection Technique for Binding Replica Sites in Data Grids

The objective in Data Grids is to reduce access and file (replica) transfer latencies, as well as to avoid single site congestion by the numerous requesters. To facilitate access and transfer of the data, the files of the Data Grid are distributed across the multiple sites. The effectiveness of a replica selection strategy in data grids depends on its ability to serve the requirement posed by the users' jobs. Most jobs are required to be executed at a specific execution time. To achieve the QoS perceived by the users, response time metrics should take into account a replica selection strategy. Total execution time needs to factor latencies due to network transfer rates and latencies due to search and location. Network resources affect the speed of moving the required data and searching methods can reduce scope for replica selection. This paper presents a replica selection strategy that adapts its criteria dynamically so as to best approximate application providers' and clients' requirements. We introduce a new selection technique (EST) that shows improved performance over the more common algorithms.

New Replica Selection Technique for Binding Replica Sites in Data Grids

The objective in Data Grids is to reduce access and file (replica) transfer latencies, as well as to avoid single site congestion by the numerous requesters. To facilitate access and transfer of the data, the files of the Data Grid are distributed across the multiple sites. The effectiveness of a replica selection strategy in data grids depends on its ability to serve the requirement posed by the users' jobs. Most jobs are required to be executed at a specific execution time. To achieve the QoS perceived by the users, response time metrics should take into account a replica selection strategy. Total execution time needs to factor latencies due to network transfer rates and latencies due to search and location. Network resources affect the speed of moving the required data and searching methods can reduce scope for replica selection. This paper presents a replica selection strategy that adapts its criteria dynamically so as to best approximate application providers’ and clients’ requirements. We introduce a new selection technique (EST) that shows improved performance over the more common algorithms.

Replica selection strategies in data grid

Replication in Data Grids reduces access latency and bandwidth consumption. When different sites hold replicas of datasets, there is a significant benefit realized by selecting the best replica. By selecting the best replica, the access latency can be minimized. In this research, we propose two different replica selection techniques. To select the best replica from information gathered locally, a simple technique called the k -Nearest Neighbor (KNN) rule is exploited. The KNN rule selects the best replica for a file by considering previous file transfer logs indicating the history of the file and those nearby. We also propose a predictive technique to estimate the transfer time between sites. The predicted transfer time can be used as an estimate of transfer bandwidth of different sites that hold replica currently, and help in selecting the best replica among different sites. Simulation results demonstrate that the k -nearest algorithm shows a significant performance improvement over the traditional replica catalog based model. Besides, the neural network predictive technique estimates the transfer time among sites more accurately than the multi-regression model.