A Recommendation System for Dynamic Replication and Securing Data Process

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 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

Operational services in MANETs, such as resource location and distribution of connectivity information, deal with node mobility and resource constraints to support applications. The reliability and availability of these services can be assured by data management approaches, as replication techniques using quorum systems. However, these systems are vulnerable to selfish and malicious nodes, that intentionally do not collaborate with replication operations or spread malicious data while participating in data replication. QS2, a bio-inspired scheme to tolerate selfish and malicious nodes in replication operation of quorum systems can effectively detect misbehaving nodes in quorum systems, increasing data reliability. This paper evaluates the performance of QS2 considering two realistic scenarios: the distribution of local market information in a downtown city and the distribution of traffic and schedule information in bus lines. Simulation results show that QS2 improves the reliability of replicated data in MANETs and the amount of data compromised by misbehaving nodes inside the replication system is much lower than outdated data due to network topology.

The Hadoop Distributed File System (HDFS) is designed to store, analysis, transfer larg datasets reliably, and stream it at high bandwidth to the user applications. HDFS is a variant of the Google File System (GFS). It handles fault tolerance by using data replication, where each data block is replicated and stored on multiple DataNodes. Therefore, the HDFS supports availability and reliability. The existed implementation of the HDFS in Hadoop performs replication in a pipelined manner that takes much time for replication. In this paper, an alternative technique for efficient replica placement, called Enhancement Lazy replication technique, has been proposed. The main principle of this technique is that, the client allows to write a block to two DataNodes in parallel, which store the packet. which will send acknowledgement directly to the client without waiting of receiving acknowledgement from other DataNodes. The experiment has been performed to evaluate the performance of the proposed HDFS replication technique with the default pipelined replication technique and the existed replication techniques; using TestDFSIO benchmark. According to the experimental results (i.e., the execution time and throughput), it is found that the HDFS availability has been improved in the proposed replication technique.

Data Replication Techniques in Mobile Adhoc Networks is presented in this paper and discussed about the Data Replication Technique for Real-Time Ad Hoc Mobile Databases (DREAM); Collaborative Allocation and Deallocation of Replicas with Efficiency (CADRE); Consistency and Load Based Efficicient Allocation of Replicas (CLEAR); Expanding Ring Replication; Distributed Hash Table Replication (DHTR); Hara's Data Replication Schemes; An Economic Model for Effcient Dynamic Replication in Mobile-P2P networks (EcoRep).

This paper describes a data replication service for large-scale, data intensive applications whose results can be shared among geographically distributed scientists. We introduce two kinds of data replication techniques, called owner-initiated data replication and client-initiated data replication, that are developed to support data replica consistency without requiring any special file system-level locking functions. Also, we present performance results on Linux clusters located at Sejong University.

Replication is a widely used method for achieving high availability in database systems. The data replication approach can be used for solutions such as sharing data among remote offices, sharing data among dispersed users, make server data more accessible, distribute solution updates, back up data, provide internet or intranet replication. The main aim of this research is to provide a better technique for solving the drawbacks that currently exist in the literary works of data replica environment. Here, we intend to propose data replication system based on data mining techniques. That is in proposed method FDDRA (data mining based dynamic replication algorithm), we will use a data mining with combination of replication algorithm and data grid policy for data replication in the data mining environment through monitoring all data process. The data replication will be done by identifying the frequently used data patterns in the large database of a node. This will be done by Apriorialgorithm. The system availability and replica will be measured and identifying the location in which the replicated data will be stored. The result shows the efficiency of our algorithm in terms latency time and shortest path of nodes.

Partition technology is the key step to realize the extensional architecture in the cloud and support the data placement on multiple nodes. This paper proposes a multi-tenant data partition model and algorithm for SaaS (Software as a Service) application. It solves the problem that data partitions would produce lots of distributed transactions caused by the existing cloud data management. The management is unconscious of SaaS tenants during the transformation from a single node to multiple nodes in the cloud to obtain the dynamic extension of the system’s scale. With the increase of tenants and data, the single node becomes the bottleneck of the whole system. Fortunately, the scale of the whole system can be expanded by data partition. This paper puts forward a multi-tenant data partition model with three-layer structure: Tenant layer, Relevance, Group layer and Tenant Partition layer. Furthermore, we propose the concepts of Relevance, Relevance Value and Relevance Matrix. The customized tables for one tenant accessed by the same transactions can form a minimum high-relevance granularity based on the Relevance Group algorithm. Then we construct an abstracted graph, where group is the basic unit and transaction accessing is weight. Through the Stoer–Wagner algorithm, the multi-tenant partition with group as granularity is obtained. The partition algorithm proposed in this paper enables the greatest reduction of distributed transactions between partitions while realizing the dynamic extension on multiple nodes for multi-tenant data based on shared storage. Experiments show that the number of distributed transactions is reduced dramatically compared with other data partition techniques. We also prove that the SaaS applications run at high efficiency.

Internet-scale services rely on data partitioning and replication to provide scalable performance and high availability. Moreover, to reduce user-perceived response times and tolerate disasters (i.e., the failure of a whole datacenter), services are increasingly becoming geographically distributed. Data partitioning and replication, combined with local and geographical distribution, introduce daunting challenges, including the need to carefully order requests among replicas and partitions. One way to tackle this problem is to use group communication primitives that encapsulate order requirements. This paper presents a detailed performance evaluation of Multi-Ring Paxos, a scalable group communication primitive. We focus our analysis on extreme conditions with deployments including high-end 10 Gbps networks, a large number of combined rings (i.e., independent Paxos instances), a large number of replicas in a ring, and a global deployment. We also report on the performance of recovery under peak load and present two novel extensions to boost Multi-Ring Paxos's performance.

Applications deployed in the Cloud usually come with dedicated performance and availability requirements. This can be achieved by replicating data across several sites and/or by partitioning data. Data replication allows to parallelize read requests and thus to decrease data access latency, but induces significant overhead for the synchronization of updates. Partitioning, in contrast, is highly beneficial if all the data accessed by an application is located at the same site, but again necessitates coordination if distributed transactions are needed to serve applications. In this paper, we analyze three protocols for distributed data management in the Cloud, namely Read-One Write-All-Available (ROWAA), Majority Quorum (MQ) and Data Partitioning (DP) - all in a configuration that guarantees strong consistency. We introduce Beowulf, a meta protocol based on a comprehensive cost model that integrates the three protocols and that dynamically selects the protocol with the lowest latency for a given workload. In the evaluation, we compare the prediction of the Beowulf cost model with a baseline evaluation. The results nicely show the effectiveness of the analytical model and the precision in selecting the best suited protocol for a given workload.

In a cloud distributed system, machine failure or region failure is a very common scenario. Data replication is a key technique for ensuring data availability. However, Objects are usually assumed independently by distributed systems, despite, a user-level task typically requests multiple data objects. This paper studies the effect of data placement on the availability of user-level tasks from a theoretical perspective, and finds the best and the worst placements which can provide the highest and the lowest availability for user-level tasks in a cloud distributed system. This paper also gives a novel algorithm called SPOverlap (S Parts Overlap), which provides a tradeoff between task availability and other system performance.

Data replication is a common method to reduce the bandwidth consumption and the access latency in distributed systems. The existing replication methods mostly deal with the read-only data, thus the replica management mechanism is relatively simple. Most replication methods replicate files in all nodes or two endpoints on a query path. Naturally, the larger number of replicas, the better performance it can obtain. However, these methods lead to high overhead for unnecessary file replications and consistency maintenance. This paper presents a dynamic minimum access cost based replication strategy (MAC replication strategy). MAC replication strategy takes into account the access frequency, the status of the network connection and average response time to perform optimal replication. It first selects the popular files. To each popular file, the average response time is calculated to determine which logical resource should be replicated. Then MAC replication strategy calculates an appropriate site to replicate for better shortening the response time of the data resource. Simulation results demonstrate the efficiency and effectiveness of MAC replication strategy in comparison with other approaches.

Recent technology which focus in cloud computing with ICT based service providers for better challenges in the field of online services. This provides the computing world as an utility based scenario of sharing the given resources of different data centeres with enormous opportunities. Though replicas of a data file has increased, the performance and availability of data also increases. In this paper the different aspects on dynamic, cost-aware with the data replication method through optimization is proposed which identifies the less amount of information of data replication which is required to make sure that the data availability increases with the increase in the replication process. The multi objective optimization strategy for the cost of replication from higher-cost data centers to lower-cost data centers being implemented by the concept of Improved knapsack algorithm and considering the availability and load balancing in the replication process. Data can be managed effectively by file replication, which reduces effectively the file service time and access latency, which increases the file availability and the system get improved through load balancing. An efficient and improved multi-objective optimized replication management (EIMORM) can solve these optimal solutions by balancing among these optimization objectives. Some experiments clearly shows that the EIMORM is much more energy efficiency and the performance in the replication system of the Hadoop Distributed File System (HDFS) with the multi-objective evolutionary stated algorithm with the performance, load balancing in cloud storage clustors. Now considering the energy efficiency also considering the bandwidth of system. Hence the results taken through simulator can support the energy efficiency as the guidance in the areas of data replication.

Distributed data processing is becoming a reality. Businesses want to do it for many reasons, and they often must do it in order to stay competitive. While much of the infrastructure for distributed data processing is already there (e.g., modern network technology), a number of issues make distributed data processing still a complex undertaking: (1) distributed systems can become very large, involving thousands of heterogeneous sites including PCs and mainframe server machines; (2) the state of a distributed system changes rapidly because the load of sites varies over time and new sites are added to the system; (3) legacy systems need to be integrated—such legacy systems usually have not been designed for distributed data processing and now need to interact with other (modern) systems in a distributed environment. This paper presents the state of the art of query processing for distributed database and information systems. The paper presents the “textbook” architecture for distributed query processing and a series of techniques that are particularly useful for distributed database systems. These techniques include special join techniques, techniques to exploit intraquery paralleli sm, techniques to reduce communication costs, and techniques to exploit caching and replication of data. Furthermore, the paper discusses different kinds of distributed systems such as client-server, middleware (multitier), and heterogeneous database systems, and shows how query processing works in these systems.

Large, distributed network systems such as the Cloud cater to the immense data storage needs of people around the world. This data stored upon the cloud may be sensitive, which makes the Security aspect of these Cloud systems extremely imperative. The Cloud nodes host this data. If these nodes are compromised by a malicious entity then consequences may be dire. In this paper we have proposed and put forth the results of a system which will implement a storage methodology that uses the Division and Replication strategy for storing the data. In this system, the file will be fragmented and these fragments will be replicated according to the replication factor before storing it upon the cloud. The fragments are dispersed such that consecutive nodes on the Cloud do not hold the fragments of a same file and so that even if a node is compromised no significant information is leaked to the attacker. This system will enhance cloud security using Division and Replication of data in Cloud for Optimal Performance and Security (DROPS) methodology. This additional security will come without any cryptographic techniques being applied. Lack of these computation heavy cryptographic techniques will mean that security will come without the compromise of increasing the computation load. The system will analyze the performance of our methodology by portraying it in a virtual environment comprising of virtual machines which form a Cloud network. We will depict the results of our system and how it handled storing data onto the cloud.