Dynamic Replication Algorithm Research Articles

Enhancing replica management in a cloud environment using data mining based dynamic replication algorithm

Cloud computing has recently gained a lot of popularity and attention from the research community. One of the many on-demand services that large-scale applications provide to cloud customers is storage, which accumulates more generated data and subsequently leads to the need for storage. Despite the fact that users can use the cloud to store and provide the type of storage that desire, it still takes a significant amount of time to store and retrieve data due to the large accumulation of data. Due to the need to improve data availability, response time, reliability, and migration costs, the current storage engine needs to be replicated across multiple sites. When copies are properly distributed, data replication speeds up execution. The biggest challenges in data replication are choosing which data to replicate, where to put it, how to manage replication, and how many replicas it needs. Therefore, various studies have been carried out on some data mining-based data replication systems to evaluate replication issues and manage cloud storage. In most cases, data replication in a data mining environment is done using data mining along with a replication algorithm and a data grid policy. In addition, this paper addresses replica management issues and proposes affordable data replication in the cloud that satisfies all Quality of Service (QoS) requirements.

A replication strategy for mobile opportunistic networks based on utility clustering

Dynamic replication is a wide-spread multi-copy routing approach for efficiently coping with the intermittent connectivity in mobile opportunistic networks. According to it, a node forwards a message replica to an encountered node based on a utility value that captures the latter’s fitness for delivering the message to the destination. The popularity of the approach stems from its flexibility to effectively operate in networks with diverse characteristics without requiring special customization. Nonetheless, its drawback is the tendency to produce a high number of replicas that consume limited resources such as energy and storage. To tackle the problem we make the observation that network nodes can be grouped, based on their utility values, into clusters that portray different delivery capabilities. We exploit this finding to transform the basic forwarding strategy, which is to move a packet using nodes of increasing utility, and actually forward it through clusters of increasing delivery capability. The new strategy works in synergy with the basic dynamic replication algorithms and is fully configurable, in the sense that it can be used with virtually any utility function. We also extend our approach to work with two utility functions at the same time, a feature that is especially efficient in mobile networks that exhibit social characteristics. By conducting experiments in a wide set of real-life networks, we empirically show that our method is robust in reducing the overall number of replicas in networks with diverse connectivity characteristics without at the same time hindering delivery efficiency.

Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

The efficiency of data-intensive applications in distributed environments such as Cloud, Fog, and Grid is directly related to data access delay. Delays caused by queue workload and delays caused by failure can decrease data access efficiency. Data replication is a critical technique in reducing access latency. In this paper, a fuzzy-based replication algorithm is proposed, which avoids the mentioned imposed delays by considering a comprehensive set of significant parameters to improve performance. The proposed algorithm selects the appropriate replica using a hierarchical method, taking into account the transmission cost, queue delay, and failure probability. The algorithm determines the best place for replication using a fuzzy inference system considering the queue workload, number of accesses in the future, last access time, and communication capacity. It uses the Simple Exponential Smoothing method to predict future file popularity. The OptorSim simulator evaluates the proposed algorithm in different access patterns. The results show that the algorithm improves performance in terms of the number of replications, the percentage of storage filled, and the mean job execution time. The proposed algorithm has the highest efficiency in random access patterns, especially random Zipf access patterns. It also has good performance when the number of jobs and file size are increased.

Hierarchical data replication strategy to improve performance in cloud computing

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.

USING FOG AND REPLICATION TECHNIQUES INORDER TO ENHANCE CLOUD DATA SECURITY

In recent days with the tremendous increase of unstructured data, cloud storage technology gets more attention and better development. All the user’s data is totally stored in the centralized cloud servers rather than in their own location. Traditional privacy protection methods are usually based on encryption and decryption technology, but those methods don’t resist attack from the internal or external attacker of cloud server. In order to solve this problem, we try to apply fog computing and data replication technique for providing more security for the sensitive data. The proposed techniques can both take full advantage of cloud storage and protect the privacy of data. However, appropriate replica placement in a large-scale, dynamically scalable and totally virtualized data centers is much more complicated. In order to develop a cost-effective availability, minimize the response time of applications and make load balancing for cloud storage, a new replica placement like Efficient Dynamic Replication Algorithm (EDRA) is proposed. Here we divide data into different parts. And then place the data blocks in a replicated manner in local machine and fog server and cloud server in order to protect the privacy. By conducting various experiments on our current approach the theoretical and experimental evaluation has been validated, which is really a powerful supplement to existing cloud storage scheme.

Performance Evaluation of Novel Dynamic Data Replication Algorithm under Optical Burst Switching

Abstract The database replication mechanism ensures the duplication of data from one geographical location to another. In recent past, PDDRA (pre-fetching-based dynamic data replication algorithm) is investigated heavily under various classes of request arrivals. The request arriving at a particular server for replication depends on many factors such as bandwidth supported by link, its geographical location and routing algorithms. The number of served requests depends on the processing speed and buffering capacity of a particular server. If a request cannot be served at the server, it is dropped. If a bunch of requests arrive at a server, then traffic modelled as bursty traffic. The objective of this paper is to investigate the performance of dynamic replication algorithm under bursty request arrival. In a similar context, a mathematical model is detailed for the estimation of request burst length, such that both buffering capacity and transmission delay can be kept minimal. Finally, simulation results are present to evaluate the fraction of successful request under diverse conditions.

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.

Dynamic Replication Algorithm for Data Replication to Improve System Availability: A Performance Engineering Approach

ABSTRACTInformation technology systems deployed by enterprises should not only fulfil their business functionalities but also cater to Quality of Service concerns such as Availability, Scalability, and Performance. To enhance the system performance, the system availability is an important factor and to improve the system availability, one of the strategies is replicating the frequently accessed data to multiple suitable locations which is a practical choice as the users can access the data from a nearby site. This is, however, not the case for replicas which must have a preset number of copies on several locations. How to decide a sensible number and right location for replicas have become an important issue in cloud computing. In this paper, we show a dynamic data replication strategy to enhance the performance of software system. To identify the suitable file to replicate and to decide respective number of replicas, we calculate popularity degree and replica factor. We use the fuzzy logic system to identify the system to place the replicas and we use the round robin method to place the replicas in the identified systems. We compare the performance of our technique with the existing technique.

Enhancing Fuzzy Topsis for web service selection

With proliferation of web services, quality-of-service QoS capability is becoming a significant factor for distinguishing services. QoS includes various non-functional issues such as performance, reliability and availability, etc. The functional requirements are assured through Semantic Matchmaking algorithm whereas the non functional requirements are guaranteed though QoS factors. Selection of a web service has become very crucial due to the availability of multiple web service with similar functionality. To solve this issue there is a need to distinguish the services based on their non-functional criteria QoS. This paper proposes an enhanced QoS-based semantic web service selection and ranking solution with the application of Fuzzy Topsis method. Further, this paper proposes a new dynamic replication algorithm to increase the availability of the service and decrease network delays. This is demonstrated using a diabetes monitoring system.

A Dynamic Replication Algorithm Based on Access Popularity in Cloud Storage Environment

Replication technology is a very important data management in cloud storage. Traditional replication control methods cannot usefully solve the load imbalance caused by the hotspot access. This paper proposed a dynamic replication algorithm based on access popularity called APDRA. Replicas were created and deleted dynamically according to the popularity of data, situation of the system and the load of nodes, effectively solving the problem of local overload caused by hot spot access and the recovery of idle replicas. In the end, the superiority of APDRA was verified by experiment.

Management and Placement of Replicas in a Hierarchical Data Grid

In large systems, data management is only possible through the use of replication techniques. In order to improve availability and ensure fast and efficient access, we propose in this paper a dynamic replication algorithm which takes into account also the placement of data in a hierarchical data grid. Experimental results show the effectiveness of our approach in terms of response time and availability of data in the system.

A study on performance of dynamic file replication algorithms for real-time file access in Data Grids

Real-time Grid applications are emerging in many disciplines of science and engineering. In order to run these applications while meeting the associated real-time constraints with them, the Grid infrastructure should be designed to respect these constraints and allocate its computing, networking, storage, and the other resources accordingly. Furthermore, these applications involve a large number of data intensive jobs and require to access terabytes of data in real-time. On the other hand, a variety of dynamic file replication algorithms were proposed for the best-effort Data Grid environments in an attempt to decrease job completion times and save network bandwidth. Until now, there is no study in the literature which tries to elaborate on the real-time performance of these dynamic file replication algorithms. Based on this motivation, in this study, the performance of eight dynamic replication algorithms are evaluated under various Data Grid settings. For this evaluation, a process oriented and discrete-event driven simulator called DGridSim is developed. A detailed set of simulation studies are conducted using DGridSim and the results obtained are presented to reveal the real-time performance of the dynamic file replication algorithms.

An object replication algorithm for real-time distributed databases

A real-time distributed database system (RTDDBS) must maintain the consistency constraints of objects and must also guarantee the time constraints imposed by each request arriving at the system. Such a time constraint of a request is usually defined as a deadline period, which means that the request must be serviced on or before its time constraint. Servicing these requests may incur I/O costs, control-message transferring costs or data-message transferring costs. As a result, in our work, we first present a mathematical model that considers all these costs. Using this cost model, our objective is to service all the requests on or before their respective deadline periods and minimize the total servicing cost. To this end, from theoretical standpoint, we design a dynamic object replication algorithm, referred to as Real-time distributed dynamic Window Mechanism (RDDWM), that adapts to the random patterns of read-write requests. Using competitive analysis, from practical perspective, we study the performance of RDDWM algorithm under two different extreme conditions, i.e., when the deadline period of each request is sufficiently long and when the deadline period of each request is very short. Several illustrative examples are provided for the ease of understanding.

A Dynamic Object Allocation And Replication Algorithm For Distributed Systems With Centralized Control

The design of distributed systems can be based on exercising either centralized or decentralized control mechanisms as per the application requirements. In this paper, for a centralized controlled distributed system, we design a dynamic object allocation and replication algorithm that adapts to the arriving requests patterns. We propose a mathematical cost model that considers the costs involved in servicing a request, such as I/O cost and communication cost, and design a dynamic algorithm, referred to as dynamic window mechanism (DWM). Our objective is to minimize the total servicing cost of all the arriving requests. We use competitive analysis to quantify the performance of DWM algorithm in the stationary computing environment (SCE) and extend our analytical study to the mobile computing environment (MCE).

The impact of data replication on job scheduling performance in the Data Grid

In the Data Grid environment, the primary goal of data replication is to shorten the data access time experienced by the job and consequently reduce the job turnaround time. After introducing a Data Grid architecture that supports efficient data access for the Grid job, the dynamic data replication algorithms are put forward. Combined with different Grid scheduling heuristics, the performances of the data replication algorithms are evaluated with various simulations. The simulation results demonstrate that the dynamic replication algorithms can reduce the job turnaround time remarkably. In particular, the combination of shortest turnaround time scheduling heuristic (STT) and centralized dynamic replication with response-time oriented replica placement (CDR_RTPlace) exhibits remarkable performance in diverse system environments and job workloads.

Dynamic replication algorithms for the multi-tier Data Grid

Data replication is a common method used to improve the performance of data access in distributed systems. In this paper, two dynamic replication algorithms, Simple Bottom-Up (SBU) and Aggregate Bottom-Up (ABU), are proposed for the multi-tier Data Grid. A multi-tier Data Grid simulator called DRepSim is developed for studying the performances of the dynamic replication algorithms. The simulation results show that both algorithms can reduce the average response time of data access greatly compared to the static replication method. ABU can achieve great performance improvements for all access patterns even if the available storage size of the replication server is very small. Comparing the two algorithms to Fast Spread dynamic replication strategy, ABU proves to be superior. As for SBU, although the average response time of Fast Spread is better in most cases, Fast Spread’s replication frequency is too high to be applicable in the real world.

Cost-optimal dynamic data replication for distributed database systems: DYVO

In this paper, we present a dynamic replication algorithm, DYVO, which could provide an optimal cost for operating replicas according to a read/write pattern of transactions. Unlike previous algorithms for dynamic replication, in which read one and write all available approach is used for preserving consistency of replicas, DYVO basically uses quorum approach for managing replicas. The quorum approach is basically able to encompass a large class of failures. Moreover, the quorum approach simplifies considerably the task maintaining the consistency of replicated data. As a result, DYVO is fault-tolerant to all kinds of failures and gives a way that minimizes overload for managing a replication algorithm.