Dynamic Data Replication Based on Access Cost in Distributed Systems

File replication is widely used in structured P2P systems to avoid hot spots in servers and enhance file availability. The number of replicas and replication distance affect the file replication cost. These two elements and the replica update frequency determined in the file replication stage also affect the cost of subsequent consistency maintenance. However, most existing file replication protocols focus on improving file lookup efficiency without considering its cost and its subsequent influence on consistency maintenance. This paper studies the problem about how a server chooses files to replicate and where to replicate files to achieve low cost in both file replication and consistency maintenance stages without compromising the effectiveness of file replication. This paper presents a lightweight and Cooperative multifactOr considered file Replication Protocol (CORP) to achieve this goal. CORP simultaneously takes into account multiple factors including file popularity, update rate, node available capacity, file load, and node locality, aiming to minimize the number of replicas, update frequency, and replication distance. CORP also dynamically adjusts the number of replicas based on ever-changing file popularity and visit pattern. Extensive experimental results from simulation and PlanetLab real-world testbed demonstrate the efficiency and effectiveness of CORP in comparison with other file replication protocols. It dramatically reduces the overhead of both file replication and consistency maintenance. In addition, it exhibits high adaptiveness to skewed lookups and yields significant improvement in reducing overloaded nodes. Specifically, compared to the other replication protocols, CORP can reduce more than 71 percent of file replicas, 84 percent of overloaded nodes, 94 percent of consistency maintenance cost, and 72 percent of file replication and consistency maintenance latency.

File replication and file consistency maintenance are two important protocols for achieving overall high performance of peer-to-peer file sharing systems. In spite of the significant dependency of consistency maintenance on file replication, there has been little file replication research devoted to tackling the dependency issue. Most traditional file replication protocols focus on improving file lookup efficiency without considering the subsequent consistency maintenance. This paper presents a COoperative file Replication Protocol (CORP). CORP observes a set of factors including file update rate, file popularity, node available capacity, file load and node locality that affect the efficiency of consistency maintenance. It considers these factors to achieve high efficiency not only in file replication but also in consistency maintenance. CORP generates less replicas and optimally chooses infrequently-updated or popular files to replicate. In addition, it makes file replicas in physically close nodes. Further, CORP can dynamically adjust the number of replicas based on ever-changing file popularity and visit pattern. Simulation results demonstrate the efficiency and effectiveness of CORP in comparison with other file replication protocols. It dramatically reduces the overhead of both file replication and consistency maintenance. Moreover, it yields significant improvement in reducing overloaded nodes.

Most replication methods either monitor the popularity of files or use complicated functions to calculate the overall cost of whether or not a replication decision or a deletion decision should be issued. However, once the replication decision is issued, the popularity of the files is changed and may have already impacted access latency and resource usage. This article proposes a decision-tree-based predictive file replication strategy that forecasts files’ future popularity based on their characteristics on the Grids. The proposed strategy has shown superb performance in terms of mean job time and effective network usage compared with the other two replication strategies, LRU and Economic under OptorSim simulation environment.

Data replication can be used to reduce bandwidth consumption and access latency in the distributed system where users require remote access to large data objects. In this paper, according to the intrinsic characteristic of distributed storage system, the prediction-based parallel replication algorithm PPR is proposed. In the PPR, according to the characteristic of spatial data, the data that will be accessed is predicted, then the data is prefetched; during replication, according to the network state, several replicas of a data object are selected, which are of the least access cost; the different parts of the data object are transferred from these replicas, and they are used to make a new replica. The results of performance evaluation show that the PPR can utilize the network bandwidth efficiently, provide high data replication efficiency and substantially better access efficiency, and can avoid the interference between different replications efficiently.

Data grids deal with a huge amount of data regularly. It is a fundamental challenge to ensure efficient accesses to such widely distributed data sets. Creating replicas to a suitable site by data replication strategy can increase the system performance. It shortens the data access time and reduces bandwidth consumption. In this paper, a dynamic data replication mechanism called Latest Access Largest Weight (LALW) is proposed. LALW selects a popular file for replication and calculates a suitable number of copies and grid sites for replication. By associating a different weight to each historical data access record, the importance of each record is differentiated. A more recent data access record has a larger weight. It indicates that the record is more pertinent to the current situation of data access. A Grid simulator, OptorSim, is used to evaluate the performance of this dynamic replication strategy. The simulation results show that LALW successfully increases the effective network usage. It means that the LALW replication strategy can find out a popular file and replicates it to a suitable site without increasing the network burden too much.

The data grid is one of the infrastructures for data and storage resource management. Data replication can improve data access performance, fault tolerance, and reduce network transmission bandwidth. As the site storage space is limited in the grid, how to effectively use grid resources become an important challenge. This paper introduces a dynamic grid replication algorithm based on popularity support and confidence (BPSC). Through the algorithm, data and its associated copy can be placed on a suitable site, with reduced access latency. Through Optorsim, simulation results show that the algorithm can provide better performance compared with other algorithms Grid computing is an important branch of distributed computing. Grid computing constructs a virtual computer system through using a different location of the large number of heterogeneous resources (CPU and storage), provides the model to solve the problem of large scale computing and storage. The data grid is mainly the grid for data storage management. The data grid is defined as an effective combination of data and computing resources of distributed systems by Data Grid Group. The large-scale data- intensive applications, such as bioinformatics, high energy physics, space information generated vast amounts of data, these data may be TB scale, even up to the scale of PB. It is not practical to maintain large scale data at each site, and provide computing resources for visitors. At the same time, it will lead to increased access latency through a centralized way to storing and accessing these data from each site. Data replication technology is an effective method to reduce access latency in the data grid system. Data replication is one of the basic methods of distributed systems to improve the efficiency of system access, maintenance, system high availability. Data replication is to store data in the appropriate site for users to effective access to massive data. Data replication can reduce data access latency, reduce network bandwidth consumption, avoid congestion, adjust the load balancing in server-side, improve system reliability. Many data replication algorithm can reduce data access latency, improve system reliability, but which data will be copied, when the data will be copied at, where the data will be copied are the basic means of determining data replication algorithm. In this paper, we introduced a real-time dynamic data replication algorithm. The algorithm is based on the popularity of file dynamic to replicate data, and consider the storage space limitations of the site. Through optorsim simulation verified the algorithm. This paper is organized as follows: Section 2 is about related works on replication in data grids. In Section 3, system model and component in system are presented. The file

Cloud computing environment is getting more interesting as a new trend of data management. Data replication has been widely applied to improve data access in distributed systems such as Grid and Cloud. However, due to the finite storage capacity of each site, copies that are useful for future jobs can be wastefully deleted and replaced with less valuable ones. Therefore, it is considerable to have appropriate replication strategy that can dynamically store the replicas while satisfying quality of service (QoS) requirements and storage capacity constraints. In this paper, we present a dynamic replication algorithm, named hierarchical data replication strategy (HDRS). HDRS consists of the replica creation that can adaptively increase replicas based on exponential growth or decay rate, the replica placement according to the access load and labeling technique, and finally the replica replacement based on the value of file in the future. We evaluate different dynamic data replication methods using CloudSim simulation. Experiments demonstrate that HDRS can reduce response time and bandwidth usage compared with other algorithms. It means that the HDRS can determine a popular file and replicates it to the best site. This method avoids useless replications and decreases access latency by balancing the load of sites.

At present, the demand for information in distributed database systems is large and growing day by day. While this is happening, new challenges arise to improve the performance of the databases. Data fragmentation and replication methods have a leading role in distributed systems over the cloud, which is why this paper presents the design of a horizontal fragmentation, allocation and replication method in the cloud. This research proposes an algorithm for solving the problem of overlapping horizontal fragments in a data fragmentation and replication method in the cloud. The design of a Web application using the aforementioned method is also presented, this allows the fragmentation, assignment and replication scheme proposed by the method to be directly applied on a distributed database.

Data replication-based cloud data access control mechanism plays a major role in real-time cloud computing environment due to high computational cost and memory. Hybrid data replication models play a vital role in the cloud-based applications for data recovery and security. Machine learning tools and techniques play an essential role in the medical field and cloud computing applications. Most of the traditional machine learning models use static data partitioning and replication methods in order to recover patterns from multiple virtual machines in cloud computing environment. In this work, a hybrid data replication and multi-user data access mechanism is developed to provide strong data recovery and security in cloud computing environment. In this work, a machine learning based patterns are used for data partitioning and security in the cloud server for cloud data security. Experimental results show that the hybrid data replication model has better data replication time and storage space than the conventional data replication models in cloud computing environment.KeywordsData replicationData partitioningCloud computingData accessMedical patterns

i»? A. A. Helal, A. A. Heddaya, and B. B. Bhargrava Kluwer Academic Publishers, Boston, 1996, 176 pp. ISBN 0-7923-9800-9, $118.50 Computing in the 1990s has reached the state of distributed computing. A basis of this form of computing is a distributed computing system, which is built on the following three components: (a) personal computers, (b) local and fast wide area networks, and (c) system and application software. By amalgamating computers and networks into one single computing system and providing an appropriate system software, a distributed computing system has created the possibility of sharing information and peripheral resources. Furthermore, these systems improved performance of a computing system and individual users through parallel execution of programs, load balancing and sharing, and replication of programs and data. Distributed computing systems are also characterized by enhanced availability, and increased reliability. Replication is the key to providing high availability, fault tolerance, and enhanced performance in a distributed computing system. As companies move toward systems that are more open and distributed, replication is becoming increasingly important in the ability to provide data and services that are current, correct and available, which is a key factor in maintaining a competitive advantage over rivals. However, replication has also generated some serious challenges. For example, when a replica is updated, how do we propagate such an update to other replicas? If multiple replicas are updated simultaneously and some of the updates conflict one another, how do we resolve the conflict? How do we deal with the situation where a replica is down and then subsequently recovered? How do we deal with network partitions? Not surprisingly, considerable research efforts have been directed towards the solution of these challenges. However, most of the research results are scattered among many journals, conference proceedings, theses, and technical reports. The book by Helal, Heddaya and Bhargava has gathered the best material of these research results and formed a coherent collection that includes definitions, theoretical background, algorithms, annotated bibliographies of commercial and experimental prototype systems, and more than 200 references. This book classifies the entities that can be replicated on a distributed computing system into the following categories: (a) data, (b) process, (c) objects, and (d) messages. After the introduction on the goals and main approaches of replication techniques, the book uses individual chapters to describe major methods for each of the above replication categories. The main focus of this part, however, is on the replication of data. The book also contains two chapters on the replication issues in heterogeneous, mobile, and large-scale systems, and on the future of replication techniques. Apart from the normal chapters, the book contains a rich set of appendices that are very useful for further studies of replication techniques. These appendices include brief descriptions of two dozen commercial and experimental systems that use replication techniques, annotated bibliographies of selected literature on various topics of replication, and an introduction on serializability theory. Each annotated bibliography contains an overview of the specific topic and is written by an invited expert in that field. The combination of the normal chapters and appendices cover the entire spectrum of replication, from definitions, concepts, theories, algorithms, techniques, to systems. The book serves as an excellent introduction and roadmap to the issues of replication. Although it does not provide a detailed description of every replication techniques and systems, the book covers most of the fundamental work that allows the reader to understand the roots of these techniques and systems, and to find the ways to search for the details. This book is a valuable reference book for anyone studying replication techniques or implementing replication systems, specifically, it can be very useful to post-graduate students, practitioners, and beginning researchers of this exciting field. Wanlei Zhou, School of Computing and Mathematics, Deakin University

Data access latency is an important metric of system performance in data grid. By means of efficient replication strategy, the amount of data transferred in a wide area network will decrease, and the average access latency of data will decrease ultimately. The motivation of our research is to solve the optimized replica distribution problem in a data grid; that is, the system should utilize many replicas for every data with storage constraints to minimize the average access latency of data. This paper proposes a model of replication strategy in federated data grid and gives the optimized solution. The analysis results and simulation results show that the optimized replication strategy proposed in this paper is superior to LRU caching strategy, uniform replication strategy, proportional replication strategy and square root replication strategy in terms of wide area network bandwidth requirement and in the average access latency of data.

Effective data management is a crucial problem in distributed systems such as data grid and cloud. This can be achieved by replicating file in a wise manner, which reduces data access time, increases data availability, reliability and system load balancing. Determining a reasonable number and appropriate location of replicas is essential decision in cloud computing. In this paper, a new dynamic replication strategy called Data Mining-based Data Replication (DMDR) is proposed, which determines the correlation of the data files accessed using the file access history. We focus particularly on how extracted knowledge with maximal frequent correlated pattern mining improves data replication. We can group files with high dependency in the same replica set. Through the DMDR strategy, replicas can be stored in the suitable locations, with reduced access latency according to the centrality factor. In addition, due to the finite storage space of each node, replicas that are useful for future tasks can be wastefully deleted and replaced with less beneficial ones. Results of simulation using CloudSim indicate that DMDR strategy has a relative advantage in effective network usage, average response time, hit ratio in comparison with current methods. It can be concluded from this investigation that data mining technique is effective and helpful in the finding of users’ future access behavior in cloud environment.

In this paper, we propose an adaptive data replication algorithm, called the Popular File Replicate First algorithm (PFRF for short), which is developed on a star-topology data grid with limited storage space based on aggregated information on previous file accesses. The PFRF periodically calculates file access popularity to track the variation of usersi¦ access behaviour behaviors, and then replicates popular files to appropriate sites to adapt to the variation. We employ several types of file access behaviors, including Zipf-like, geometric, and uniform distributions, to evaluate PFRF. The simulation results show that PFRF can effectively improve average job turnaround time and data availability as compared with those of the tested algorithms.

This research work focus on distributed system based network issues. The networked servers and computers organize their actions only by passing the proper messages. This networked system having especially significant characteristics like concurrency components and independent failures components. While we share the files through by passing message, there will be security problems because of connecting the entire computers in network. For High performance in data accessing, data surviving and also secure storage designing, the data partitioning and replication techniques can be combined. In this work, two heuristic algorithms developed for two sub problem in which the Authentication of the data share can be done using network manager to improve security. The replicated data are authenticated and send dynamically in the distributed system. The newly developed algorithms provide maximum performances in data sharing through distributed system and in achieving maximum security.

<p><b>The client-centric multi-cloud has become a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud providers to achieve high-quality services with lower operation costs and higher application resilience. From the perspective of application providers, the location of cloud resources for application deployment significantly impacts the deployment cost and performance of applications, e.g., request response time. This gives rise to the problem of location-aware application deployment in multi-cloud to select suitable cloud resources from widely distributed multi-cloud data centers to balance the cost and performance. Existing research works did not pay full attention to the key impact of the location for application deployment. Therefore, it is urgent to study the problem both theoretically and in practice. In this thesis, innovative optimization methods and machine learning techniques are proposed for three common scenarios, namely composite application deployment, application replication and deployment, and elastic application deployment.</b></p> <p>First, this thesis studies the composite application deployment problem with the goal to minimize the average response time of composite applications subject to a budget constraint. We propose a Hybrid Genetic Algorithm (GA)-based approach, i.e., H-GA, for solving the problem with an extremely large search space. H-GA features a newly designed and domain-tailored service clustering algorithm, repair algorithm, solution representation, population initialization, and genetic operators. Experiments show that H-GA can outperform significantly several state-of-the-art approaches, achieving up to about 8% performance improvement in terms of response time, and 100% budget satisfaction in the meantime.</p> <p>Second, this thesis studies the application replication and deployment problem with the goal to minimize the total deployment cost of all application replicas subject to a stringent requirement on average response time. We propose two approaches under different optimization frameworks to solve the problem. With user requests dispatched to the closest application replicas, we develop an approach under a GA framework for Application Replication and Deployment (ARD), i.e., GA-ARD. GAARD features problem-specific solution representation, fitness measurement, and population initialization, which are effective to optimize the deployment of application replicas in multi-cloud. The experiments show that GA-ARD outperforms common application replication and placement strategies in the industry. With user requests flexibly dispatched among different application replicas, we develop another approach under a two-stage optimization framework, i.e., MCApp. MCApp can optimize both replica deployment and request dispatching by combining mixed-integer linear programming with domain-tailored large neighborhood search. Our experiments show that MCApp can achieve up to 25% reduction in total deployment cost compared with several recently developed approaches.</p> <p>Third, this thesis studies the elastic application deployment problem to minimize the deployment cost over a time span such as a billing day while satisfying the constraint on average response time. The goal of adapting resources for application deployment in response to dynamic and distributed workloads motivates us to adopt deep reinforcement learning (DRL) techniques. The proposed approach, namely DeepScale, applies a deep Q-network (DQN) to capture the optimal scaling policy that can perform online resource scaling. DeepScale also includes a long short-term memory-based prediction model to allow the DQN to consider predicted future requests while making cost-effective scaling decisions. Besides, we design a penalty-based reward function and a safety-aware action executor to ensure that any scaling decisions made by DRL can satisfy the response time constraint. The experiments show that DeepScale can significantly reduce the deployment cost of applications compared with the state-of-the-art baselines, including Amazon auto-scaling service and recently proposed RL-based algorithms. In the meanwhile, DeepScale can effectively satisfy the constraint on the average response time.</p> <p>In summary, this thesis studies three new problems for location-aware application deployment in multi-cloud. We propose four novel approaches under different optimization and machine learning frameworks, i.e., HGA, GA-ARD, MCApp, and DeepScale, for solving these problems. New constraint handling techniques are developed to satisfy the practical deployment requirements of enterprise applications.</p>