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A dynamic replica management strategy in data grid

--- Journal of Network and Computer Applications expired, propose, indicate, profitable, boost, claim, present, congestion, deficiency, moderately, metric, turnaround, assume,specify, display, illustrate, issue,

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Replication is a technique used in data grid to improve fault tolerance and to reduce the bandwidth consumption.

Managing this huge amount of data in a centralized way is ineffective due to extensive access latency and load on the central server.

Data Grids aggregate a collection of distributed resources placed in different parts of the world to enable users to share data and resources.

Data replication is an important technique to manage large data in a distributed manner.

There are three key issues in all the data replication algorithms which are replica placement, replica management and replica selection.

Meanwhile, even though the memory and storage size of new computers are ever increasing, they are still not keeping up with the request of storing large number of data.

each node along its path to the requester.

Enhanced Dynamic Hierarchical Replication and Weighted Scheduling

Strategy in Data Grid

--- Journal of Parallel and Distributed Computing duration, manually, appropriate, critical, therefore, hybrid, essential, respectively, candidate, typically, advantage, significantly, thereby, adopt, demonstrate, superiority, scenario, empirically, feasibility, duplicate, insufficient, interpret, beneficial, obviously, whilst, idle, considerably, notably, consequently, apparently,

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Best replica placement plays an important role for obtaining maximum benefit from replication as well as reducing storage cost and mean job execution time.

Data replication is a key optimization technique for reducing access latency and managing large data by storing data in a wise manner.

Effective scheduling in the Grid can reduce the amount of data transferred among nodes by submitting a job to a node where most of the requested data files are available.

Effective scheduling of jobs is necessary in such a system to use available resources such as computational, storage and network efficiently.

Storing replicas close to the users or grid computation nodes improves response time, fault tolerance and decreases bandwidth consumption.

The files of Grid environments that can be changed by Grid users might bring an important problem of maintaining data consistency among the various replicas distributed in different machines.

So the sum of them along with the proper weight (w1,w2) for each factor yields the combined cost (CCi,j) of executing job i in site j.

A classification of file placement and replication methods on grids

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striping files in blocks

Production approaches are slightly different than works evaluated in simulation or in controlled conditions....

File replication is a common solution to improve the reliability and performance of data transfers.

Many file management strategies were proposed but none was adopted in large-scale production infrastructures.

Clairvoyant models assume that resource characteristics of interest are entirely known to the file placement algorithm.

Cooperation between data placement and job scheduling can improve the overall transfer time and have a significant impact on the application makespan as shown in.

We conclude that replication policies should rely on a-priori information about file accesses, such as file type or workflow relation.

Dynamic replica placement and selection strategies in data grids----A

comprehensive survey

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Data replication techniques are used in data grid to reduce makespan, storage consumption, access latency and network bandwidth.

Data replication enhances data availability and thereby increases the system reliability.

Managing dynamic architecture of the grid, decision making of replica placement, storage space, cost of replication and selection are some of the issues that impact the performance of the grid.

Benefits of data replication strategies include availability, reliability, scalability, adaptability and improved performance.

As the name indicates, in dynamic grid, nodes can join and leave the grid anytime.

Any replica placement and selection strategy tries to improve one or more of the following parameters: makespan, quality assurance, file missing rate, byte missing rate, communication cost, response time, bandwidth consumption, access latency, load balancing, maintenance cost, job execution time, fault tolerance and strategic replica placement.

Identifying Dynamic Replication Strategies for a High-Performance

Data Grid

--- Grid Computing 2001 identify, comparative, alternative, preliminary, envision, hierarchical, tier, above-mentioned, interpret, exhibit, defer, methodology, pending, scale, solely, churn out

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in the order of petabytes

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Dynamic replication can be used to reduce bandwidth consumption and access latency in high performance “data grids” where users require remote access to large files.

A data grid connects a collection of geographically distributed computer and storage resources that may be located in different parts of a country or even in different countries, and enables users to share data and other resources.

The main aims of using replication are to reduce access latency and bandwidth consumption. Replication can also help in load balancing and can improve reliability by creating multiple copies of the same data.

Group-Based Management of Distributed File Caches

--- Distributed Computing Systems, 2002 mechanism, exploit, inherent, detrimental, preempt, incur, mask, fetch, likelihood, overlapping, subtle,

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Dynamic file grouping is an effective mechanism for exploiting the predictability of file access patterns and improving the caching performance of distributed file systems.

With our grouping mechanism we establish relationships by observing file access behavior, without relying on inference from file location or content.

We group files to reduce access latency. By fetching groups of files, instead of individual files, we increase cache hit rates when groups contain files that are likely to be accessed together.

Further experimentation against the same workloads demonstrated that recency was a better estimator of per-file succession likelihood than frequency counts.

Job scheduling and data replication on data grids

--- Future Generation Computer Systems throttle, hierarchical, authorized, indicate, dispatch, assign, exhaustive, revenue, aggregate, trade-off, mechanism, kaleidoscopic, approximately, plentiful, inexact, anticipated, mimic, depict, exhaust, demonstrate, superiority, namely, consume,

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A cluster represents an organization unit which is a group of sites that are geographically close.

Network bandwidth between sites within a cluster will be larger than across clusters.

Scheduling jobs to suitable grid sites is necessary because data movement between different grid sites is time consuming.

If a job is scheduled to a site where the required data are present, the job can process data in this site without any transmission delay for getting data from a remote site.

RADPA: Reliability-aware Data Placement Algorithm for large-scale network storage systems

--- High Performance Computing and Communications, 2009 ever-going, oblivious, exponentially, confront,

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it doesn't make sense to do

Most of the replica data placement algorithms concern about the following two objectives, fairness and adaptability.

In large-scale network storage systems, the reliabilities of devices are different relevant to device manufacturers and types.

It can fairly distributed data among devices and reorganize near-minimum amount of data to preserve the balanced distribution with the changes of devices.

Partitioning Functions for Stateful Data Parallelism in Stream Processing

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Accordingly, there is an increasing need to gather and analyze data streams in near real-time to extract insights and detect emerging patterns and outliers.

The increased affordability of distributed and parallel computing, thanks to advances in cloud computing and multi-core chip design, has made this problem tractable.

However, in the presence of skew in the distribution of the partitioning key, the balance properties cannot be maintained by the consistent hash.

MORM: A Multi-objective Optimized Replication Management strategy

for cloud storage cluster

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The volume of data is measured in terabytes and some time in petabytes in many fields.

Data replication allows speeding up data access, reducing access latency and increasing data availability.

How many suitable replicas of each data should be created in the cloud to meet a reasonable system requirement is an important issue for further research.

Where should these replicas be placed to meet the system task fast execution rate and load balancing requirements is another important issue to be thoroughly investigated.

As the system maintenance cost will significantly increase with the number of replicas increasing, keeping too many or fixed replicas are not a good choice.

Where should these replicas be placed to meet the system task fast execution rate and load balancing requirements is another important issue to be thoroughly investigated.

We build up five objectives for optimization which provides us with the advantage that we can search for solutions that yield close to optimal values for these objectives.

The shortcoming of them is that they only consider a restricted set of parameters affecting the replication decision. Further, they only focus on the improvement of the system performance and they do not address the energy efficiency issue in data centers.

Data node load variance is the standard deviation of data node load of all data nodes in the cloud storage cluster which can be used to represent the degree of load balancing of the system.

The advantage of using simulation is that we can easily vary parameters to understand their individual impact on system performance.

Throughout the simulation, we assumed "write-once, read-many" data and did not include the consistency or write and update propagations costs in the study.

Distributed replica placement algorithms for correlated data

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With the advances in Internet technologies, applications are all moving toward serving widely distributed users.

Replication techniques have been commonly used to minimize the communication latency by bringing the data close to the clients and improve data availability.

Thus, data needs to be carefully placed to avoid unnecessary overhead.

These correlations have significant impact on data access patterns.

For structured data, data correlated due to the structural relations may be frequently accessed together.

Assume that data objects can be clustered into different classes due to user accesses, and whenever a client issues an access request, it will only access data in a single class.

One challenge for using centralized replica placement algorithms in a widely distributed system is that a server site has to know the (logical) network topology and the resident set of all structured data sets to make replication decisions.

We assume that the data objects accessed by most of the transactions follow certain patterns, which will be stable for some time periods.

Locality-aware allocation of multi-dimensional correlated files on the

cloud platform

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The effective management of enormous data volumes on the Cloud platform has attracted devoting research efforts.

Currently, most prevailing Cloud file systems allocate data following the principles of fault tolerance and availability, while inter-file correlations, i.e. files correlated with each other, are often neglected.

There is a trade-off between data locality and the scale of job parallelism.

Although distributing data randomly is expected to achieve the best parallelism, however, such a method may lead to degraded user experiences for introducing extra costs on large volume of remote accesses, especially for many applications that are featured with data locality, e.g., context-aware search, subspace oriented aggregation queries, and etc.

However, there must be several application-dependent hot subspaces, under which files are frequently being processed.

The problem is how to find a compromised partition solution to well serve the file correlations of different feature subspaces as much as possible.

If too many files are grouped together, the imbalance cost would raise and degrade the scale of job parallelism;if files are partitioned into too many small groups, data copying traffic across storage nodes would increase.

Instead, our solution is to start from a sub-optimal solution and employ some heuristics to derive a near optimal partition with as less cost as possible.

By allocating correlated files together, significant I/O savings can be achieved on reducing the huge cost of random data access over the entire distributed storage network.

Big Data Analytics framework for Peer-to-Peer Botnet detection using

Random Forests

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Network traffic monitoring and analysis-related research has struggled to scale for massive amounts of data in real time.

In this paper the authors build up on the progress of open source tools like Hadoop, Hive and Mahout to provide a scalable implementation of quasi-real-time intrusion detection system.

As per the current knowledge of the authors, the area of network security analytics severely lacks prior research in addressing the issue of Big Data.

Improving pattern recognition accuracy of partial discharges by new

data preprocessing methods

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Thus, analyzing the huge amount of data is not feasible unless data pre-processing is manipulated.

As mentioned, PD is completely a random and nonlinear phenomenon. Since ANNs are the best classifiers to model such nonlinear systems, PD patterns can be recognized suitably by ANNs.

In other words, when classifier is trained after initial sophistications based on the PRPD patterns extracted from some objects including artificial defects, it can be efficiently used in practical fields to identify the exactly same PD sources by new test data without any iterative process.

In pulse shape characterization, some signal processing methods such as Wavelet or Fourier transforms are usually used to extract some features from PD waveshape. These methods are affected by noise and so it is necessary to incorporate some de-noising methods into the pattern recognition process.

PD identification is usually performed using PRPD recognition which is not influenced by changing the experimental set up.

Partial Discharge Pattern Recognition of Cast Resin Current

Transformers Using Radial Basis Function Neural Network

--- Journal of Electrical Engineering & Technology propose, novel, vital, demonstrate, conduct, significant,

This paper proposes a novel pattern recognition approach based on the radial basis function (RBF) neural network for identifying insulation defects of high-voltage electrical apparatus arising from partial discharge (PD).

PD measurement and pattern recognition are important tools for improving the reliability of the high-voltage insulation system.