Distributed File Systems

Catalogs play an important role in distributed systems and especially in Grids where they store necessary user, middleware and system information. In the (first) EU-funded Enabling Grids for E-SciencE (EGEE) project's Grid middleware stack (called gLite), the Grid catalogs follow a Service Oriented Architecture, exposing a set of Web Service interfaces that may be combined and reused by higher-level services or applications. The functional decomposition of the interfaces was driven by the requirements on scalability, robustness, wide-area distribution and security. It is especially important to be able to evolve and extend the interfaces. We describe the requirements of the EGEE applications with respect to catalogs, the architecture of the interfaces and the performance of the gLite implementation.

Pretty much every part of life now results in the generation of data. Logs are documentation of events or records of system activities and are created automatically through IT systems. Log data analysis is a process of making sense of these records. Log data often grows quickly and the conventional database solutions run short for dealing with a large volume of log files. Hadoop, having a wide area of applications for Big Data analysis, provides a solution for this problem. In this study, Hadoop was installed on two virtual machines. Log files generated by a Python script were analyzed in order to evaluate the system activities. The aim was to validate the importance of Hadoop in meeting the challenge of dealing with Big Data. The performed experiments show that analyzing logs with Hadoop MapReduce makes the data processing and detection of malfunctions and defects faster and simpler. Keywords— Hadoop, MapReduce, Big Data, log analysis, distributed file systems.

Load balancing functionalities are crucial for best Grid performance and utilization. Accordingly,this paper presents a new meta-scheduling method called TunSys. It is inspired from the natural phenomenon of heat propagation and thermal equilibrium. TunSys is based on a Grid polyhedron model with a spherical like structure used to ensure load balancing through a local neighborhood propagation strategy. Furthermore, experimental results compared to FCFS, DGA and HGA show encouraging results in terms of system performance and scalability and in terms of load balancing efficiency.

We present a virtualized setup of a Hadoop cluster that provides greater computing capacity with lesser resources, since a virtualized cluster requires fewer physical machines. The master
node of the cluster is set up on a physical machine, and slave nodes are set up on virtual machines (VMs) that may be on a common physical machine. Hadoop configured VM images are created by cloning of VMs, which facilitates fast addition and deletion of nodes in the cluster without much overhead. Also, we have configured the Hadoop virtualized cluster to use capacity scheduler instead of the default FIFO scheduler. The capacity scheduler schedules tasks based on the availability of RAM and virtual memory (VMEM) in slave nodes before allocating any job. So instead of queuing up the jobs, they are efficiently allocated on the VMs based on the memory avail- able. Various configuration parameters of Hadoop are analyzed and the virtualized cluster is fine-tuned to ensure best performance and maximum scalability.

Smart metering, prevalent in the power grids, is a billing model in which consumers are charged variable rates for a service consumed based on the load conditions of the system providing the service. In this paper we present a similar billing model for cloud services. We develop a model where the tariff is varied based on the load prevailing on the cloud infrastructure. We obtain a mapping between the load condition for a particular time period and the pricing. The pricing for every time period is published and it is the consumer’s discretion to continue using the service or to suspend usage. Based on historical data, the load on the cloud infrastructure is predicted using the autoregressive integrated moving average (ARIMA) statistical model. Monitoring the cloud infrastructure is an essential component of any form of cloud metering and we rely on tools available off-the-shelf for the purpose. The pricing information obtained is used for billing the consumers. The bill amount is a function of pricing for a time interval and the corresponding utilization of compute resource. The calculated bill is produced to the consumer.

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Pretty much every part of life now results in the generation of data. Logs are documentation of events or records of system activities and are created automatically through IT systems. Log data analysis is a process of making sense of these records. Log data often grows quickly and the conventional database solutions run short for dealing with a large volume of log files. Hadoop, having a wide area of applications for Big Data analysis, provides a solution for this problem. In this study, Hadoop was installed on two virtual machines. Log files generated by a Python script were analyzed in order to evaluate the system activities. The aim was to validate the importance of Hadoop in meeting the challenge of dealing with Big Data. The performed experiments show that analyzing logs with Hadoop MapReduce makes the data processing and detection of malfunctions and defects faster and simpler.

Sommario Questo documento descrive i cluster file system: nel primo capitolo vengono illustrate le motivazioni che hanno portato al loro sviluppo ed utilizzo nel settore dell'High Performance Computing (HPC), partendo da un'introduzione alle varie tipologie di file system. Viene, inoltre, descritta nel dettaglio la terminologia utilizzata per descrivere le funzionalità di questa tipologia di file system.

Pada pengalokasian media penyimpanan skala jaringan (network storage), diperlukan konsep yang baru untuk meningkatkan efektivitas komunikasi antar storage server dan client. Pada network storage konvensional, setiap node berkomunikasi dengan storage server menggunakan single file image yang terdapat pada server sehingga memungkinkan terjadinya kegagalan node tunggal (single point failure) yang mengakibatkan node server untuk layanan tertentu tidak dapat diakses sama sekali.
Proses transfer file pada server dapat dimodifikasi dengan mengubah mekanisme menjadi end user-initiator-storage server dengan  konsep redundant server initiator sebagaimana konsep yang terdapat pada Storage Area Network. Dengan demikian hal tersebut membuat setiap initiator node yang ingin mengakses storage server harus memiliki file system yang sama dan mampu berkoordinasi satu sama lain. Untuk mengatasi hal tersebut diimplementasikan Cluster File System yang terpasang di setiap initiator node yang akan mengakses storage server. Mekanisme ini mendukung High Availability Clustering, setiap cluster node dapat mengakses dalam bentuk block-data pada storage server, dan file system pada setiap node merupakan Cluster File System yang dapat melakukan interkoneksi antar server dengan mekanisme fencing dan locking. 
Pada Storage Area Network (SAN), Cluster File System yang diuji pada tugas akhir ini, memiliki failover delay yang bervariasi antara 6-12 detik yang berpotensi menjadikan proses transfer file menjadi terputus, memiliki kemampuan IOPS yang seragam dengan nilai bervariasi bergantung kepada spesifikasi hardware yang terdapat pada node server dan mengakibatkan penggunaan resource memory dan CPU yang lebih besar pada saat menggunakan block data dengan ukuran yang lebih kecil.

Hadoop is an open-source framework to storing and processing of Big data in a distributed environment. Big data is collection of complex and large volume of structured and un-structured data. Hadoop stores data throughout clusters located in geographically different machines and distribute workload using parallel computing. MapReduce is software framework derived on Java, to analyze the large scale data. MapReduce uses Distributed Data processing model. HDFS is another component in Hadoop, storing large volume of data. Google File system supports immense amount of data stored into distributed data nodes, each node has redundant data storage maintained to avoid lost. This paper explains the HDFS, details of jobs node cluster environment, stack layered component on Hadoop framework, various Application development on Hadoop.

Distributed file systems are key building blocks for cloud computing applications based on the MapReduce programming paradigm. In such file systems, nodes simultaneously serve computing and storage functions; a file is partitioned into a number of chunks allocated in distinct nodes so that MapReduce tasks can be performed in parallel over the nodes. However, in a cloud computing environment, failure is the norm, and nodes may be upgraded, replaced, and added in the system. Files can also be dynamically created, deleted, and appended. This results in load imbalance in a distributed file system; that is, the file chunks are not distributed as uniformly as possible among the nodes. Emerging distributed file systems in production systems strongly depend on a central node for chunk reallocation. This dependence is clearly inadequate in a large-scale, failure-prone environment because the central load balancer is put under considerable workload that is linearly scaled with the system size, and may thus become the performance bottleneck and the single point of failure. In this paper, a fully distributed load rebalancing algorithm is presented to cope with the load imbalance problem. Our algorithm is compared against a centralized approach in a production system and a competing distributed solution presented in the literature. The simulation results indicate that our proposal is comparable with the existing centralized approach and considerably outperforms the prior distributed algorithm in terms of load imbalance factor, movement cost, and algorithmic overhead. The performance of our proposal implemented in the Hadoop distributed file system is further investigated in a cluster environment.

Cloud computing is recently a booming area and has been emerging as a commercial reality in the information technology domain. Cloud computing represents supplement, consumption and delivery model for IT services that are based on internet on pay as per usage basis. The scheduling of the cloud services to the consumers by service providers influences the cost benefit of this computing paradigm. In such a scenario, Tasks should be scheduled efficiently such that the execution cost and time can be reduced. In this paper, we proposed a meta-heuristic based scheduling, which minimizes execution time and execution cost as well. An improved genetic algorithm is developed by merging two existing scheduling algorithms for scheduling tasks taking into consideration their computational complexity and computing capacity of processing elements. Experimental results show that, under the heavy loads, the proposed algorithm exhibits a good performance.

Data deduplication is not only becoming an essential backup system component, because it reduces storage space requirements, but also a critical one, since the performance of all the backup operation depends on storage throughput. This paper is focused on the backup data volume ratio of deduplication when stored in file systems with this feature, and how a newly developed volume format can improve it. Bacula backup software is currently the third most popular backup software worldwide and it's also open source, therefore selected to this performance analysis study. Corresponding to the results, it is possible to verify that the proposed Bacula aligned backup format brings gains of 82%, even for a workload that emulates a relatively small backup policy and with minimum impact on backup or restore times.

This paper presents a virtual machine scheduling algorithm for Cloud Computing based on Green Computing concepts. The goal of this algorithm is the minimization of energy consumption in task executions in a cloud computing environment. This algorithm uses some features like shutdown of underutilized hosts, migration of loads of hosts that are operating below a certain threshold, and DVFS. It also applies the concept of active cooling control in order to minimize power consumption. The choice of which host will receive load is based on the concept of higher energy efficiency from the hosts, which is given by the ratio of MIPS by the energy consumed of each host. Results from simulation of this algorithm confronted with other surveyed algorithms have shown that it can improve the power consumption in clouds composed of heterogeneous datacenters while being equivalent to the best algorithms in homogeneous datacenters.

The AIRCC's International Journal of Computer Science and Engineering Survey (IJCSES) is devoted to fields of Computer Science and Engineering surveys, tutorials and overviews. The IJCSES is a peer-reviewed, open access scientific journal published in electronic form as well as print form. The journal will publish research surveys, tutorials and expository overviews in computer science and engineering. Articles from supplementary fields are welcome, as long as they are relevant to computer science and engineering.

PENDAHULUAN CERI : " A distributed DB is a collection of data which belong logically to the same system but are spread over the sites of a computer network " OZSU : " A distributed DB is a collection of multiple, logically interrelated DB distributed over a computer network " KORTH : " A DDB system consists of a collection of sites, each of which maintains a local database system " Dalam sebuah database terdistribusi, database disimpan pada beberapa komputer. Komputer-komputer dalam sebuah sistem terdistribusi berhubungan satu sama lain melalui bermacam-macam media komunikasi seperti high-speed buses atau telephone line. Sebuah sistem database terdistribusi berisikan sekumpulan site, di mana tiap-tiap site dapat berpartisipasi dalam pengeksekusian transaksi-transaksi yang mengakses data pada satu site atau beberapa site. Tiap-tiap site dapat memproses transaksi lokal yaitu sebuah transaksi yang mengakses data pada satu site di mana transaksi telah ditentukan. Sebuah site juga dapat mengambil bagian dalam mengeksekusi transaksi global yaitu transaksi yang mengakses data pada site yang berbeda di mana transaksi telah ditentukan, atau transaksi yang mengakses data pada beberapa site yang berbeda. Ada 2 aspek penting dari DDB : 1. Distribusi : data tidak disimpan pada tempat (prosesor) yang sama, sehingga DDB dapat dibedakan dari database tunggal, sentralisasi 2. Korelasi logika : data memiliki property yang berhubungan sehingga DDB dapat dibedakan dari sekumpulan database local atau file yang disimpan pada tempat yang berbeda pada jaringan komputer.

Pendahuluan File Sistem Terdistribusi (Distributed File System , disingkat DFS-penulis akan menggunakan kata ini selanjutnya) adalah file sistem yang mendukung sharing files dan resources dalam bentuk penyimpanan persistent di sebuah network. File server pertama kali didevelop pada tahun 1970 dan Sun NFS (Network File System) menjadi DFS pertama yang banyak digunakan setelah awal pemunculannya di tahun 1985. DFS yang terkenal selain NFS adalah AFS (Andrew File System) dan CIFS (Common Internet File System). Gambar 1. Service terdistribusi Sebuah file server menyediakan file service ke client. Dari sisi client terdapat interface untuk file service dalam hal operasi primitif file, seperti membuat file (create), menghapus (delete) dan read / write file. Komponen perangkat keras utama yang mana file server mengontrolnya adalah sebuah local storage (umumnya disk drive / HDD). Ditempat itulah file-file tersimpan dan dari tempat tersebut request client meretrive file. Pada DFS client, server dan juga perangkat penyimpanan merupakan mesin terpisah dalam sebuah lingkungan terdistribusi (Intranet). Jadi, aktifitas layanan (service) dibawa melewati jaringan (network), jadi selain sistem memiliki satu data terpusat, sistem memiliki beberapa perangkat penyimpanan independent. Konfigurasi konkret dan juga implementasi dari sebuah DFS dapat beragam bentuknya. Dalam beberapa konfigurasi, server berjalan sebagai dedicated machine (layanan terpusat) atau juga menjadi server dan client. DFS dapat diimplementasikan sebagai bagian dari Sistem Operasi Terdistribusi dengan sebuah layer software yang tugasnya mengatur komunikasi antara sistem operasi konvensional dan file system. Parameter untuk mancapai DFS adalah transparansi. Secara ideal, DFS terlihat sebagai bentuk file system terpusat, faktor keseragaman (multiplicity) dan penyebaran server serta perangkat penyimpanan tidak terlihat oleh user. Oleh karena itu interface client yang digunakan program tidak akan membedakan antara file local dan remote. Semuanya tergantung dari implementator DFS untuk mengalokasikan file-file dan menyusun transportasi

The increasing use of computers and a large amount of data generated by several applications, make necessary the storage improvement of such data.
This is a challenge when is considered a processors higher growth speed regarding input and output storage devices.
Therefore, the Distributed File Systems are an alternative to minimize this problem. This paper proposes a review of the most common Gluster Filesystem volumes about
the workloads that are generated by the IOzone benchmark tool. Thus, is identified the behavior of the GlusterFS volumes in environments with the submitted workloads.

Com o crescimento da utilização de computadores e a gigante massa de dados gerada por diversas aplicações, aumenta a necessidade na melhoria do armazenamento desses dados.
Isso é um desafio levando em consideração o maior crescimento da velocidade dos processadores em relação a velocidade de
entrada e saída dos dispositivos de armazenamento. Neste contexto os Sistemas de Arquivos Distribuídos aparecem como alternativa para minimizar este problema.
O presente trabalho propõe uma avaliação dos volumes mais comuns do Gluster Filesystem em relação à cargas geradas por meio da ferramenta de \textit{benchmark} IOzone, identificando assim o comportamento dos volumes do GlusterFS nos ambientes propostos em relação às cargas submetidas.

3rd International Conference on Data Science and Machine Learning (DSML 2022) will act as a
major forum for the presentation of innovative ideas, approaches, developments, and research projects in
the areas of Data Science and Machine Learning. It will also serve to facilitate the exchange of
information between researchers and industry professionals to discuss the latest issues and advancement
in the area of Data Science and Machine Learning.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results,
projects, surveying works and industrial experiences that describe significant advances in Data Science
and Machine Learning.

Although there is no lack of number and variety of file systems, new file systems must be written to keep up to date with changing software and hardware requirements. One such project is the HydrOS project, developed at the University of Kent, which is a multikernel OS, written entirely in Erlang, designed to withstand complete software failure. Much like any other OS, it requires a file system in order for it to store and retrieve data. This report details the background, design, development and functionality of HydroFS, a file system written entirely in Erlang for HydrOS, and how it can be extended in the future to take full advantage of storage space.

Pretty much every part of life now results in the generation of data. Logs are documentation of events or records of system activities and are created automatically through IT systems. Log data analysis is a process of making sense of these records. Log data often grows quickly and the conventional database solutions run short for dealing with a large volume of log files. Hadoop, having a wide area of applications for Big Data analysis, provides a solution for this problem. In this study, Hadoop was installed on two virtual machines. Log files generated by a Python script were analyzed in order to evaluate the system activities. The aim was to validate the importance of Hadoop in meeting the challenge of dealing with Big Data. The performed experiments show that analyzing logs with Hadoop MapReduce makes the data processing and detection of malfunctions and defects faster and simpler. Keywords— Hadoop, MapReduce, Big Data, log analysis, distributed file systems.

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New memory technologies are blurring the previously distinctive performance characteristics of adjacent layers in the memory hierarchy. No longer are such layers orders of magnitude different in request latency or capacity. Beyond the traditional single-layer view of caching, we now must re-cast the problem as a data placement challenge: which data should be cached in faster memory if it could instead be served directly from slower memory? We present Chopt, an offline algorithm for data placement across multiple tiers of memory with asymmetric read and write costs. We show that Chopt is optimal and can therefore serve as the upper bound of performance gain for any data placement algorithm. We also demonstrate an approximation of Chopt which makes its execution time for long traces practical using spatial sampling of requests incurring a small 0.2% average error on representative workloads at a sampling ratio of 1%. Our evaluation of Chopt on more than 30 production traces and benchmarks shows that optimal data placement decisions could improve average request latency by 8.2%-44.8% when compared with the long-established gold standard: Belady and Mattson's offline, evict-farthest-in-the-future optimal algorithms. Our results identify substantial improvement opportunities for future online memory management research.

Peer-to-peer (P2P) networks are gaining increased attention from both the scientific community and the larger Internet user community. Data retrieval algorithms lie at the center of P2P networks, and this paper addresses the problem of efficiently searching for files in unstructured P2P systems. We propose an Improved Ada pt ive Proba bilistic Search (IAPS) algorithm that is fully distributed and bandwidth effi-cient. IAPS uses ant-colony optimization and takes file types into consideration in order to search for file container nodes with a high probability of success. We have performed exten-sive simulations to study the performance of IAPS, and we compare it with the Random Walk and Adaptive Probabilistic Search algorithms. Our experimental results show that IAPS achieves high success rates, high response rates, and signifi-cant message reduction.

This is a brief introduction to Java Remote Method Invocation (RMI). Java RMI is a mechanism that allows one to invoke a method on an object that exists in another address space. The other address space could be on the same machine or a different one. The RMI mechanism is basically an object-oriented RPC mechanism. CORBA is another objectoriented RPC mechanism. CORBA differs from Java RMI in a number of ways:

MapReduce is a parallel programming model and an associated implementation introduced by Google. In the programming model, a user specifies the computation by two functions, Map and Reduce. The underlying MapReduce library automatically parallelizes the computation, and handles complicated issues like data distribution, load balancing and fault tolerance. Massive input, spread across many machines, need to parallelize. Moves the data, and provides scheduling, fault tolerance. The original MapReduce implementation by Google, as well as its open-source counterpart, Hadoop, is aimed for parallelizing computing in large clusters of commodity machines. Map Reduce has gained a great
popularity as it gracefully and automatically achieves fault tolerance. It automatically handles the gathering of results across the multiple nodes and returns a single result or set.

This paper gives an overview of MapReduce programming model and its applications. The author has described here the workflow of MapReduce process. Some important issues, like fault tolerance, are studied in more detail. Even the illustration of working of Map Reduce is given.

The data locality issue in heterogeneous environments can noticeably reduce the Map Reduce performance. In this paper, the author has addressed the illustration of data across nodes in a way that each node has a balanced data processing load stored in a parallel manner. Given a data intensive
application running on a Hadoop Map Reduce cluster, the auhor has exemplified how data placement is done in Hadoop architecture and the role of Map Reduce in the Hadoop Architecture. The amount of data stored in each node to achieve improved data-processing performance is explained here.

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Cloud Computing has dominated the vocabulary of Information Technologists the last years. But even though everyone is talking about it, not many utilize it. Cloud Computing has significant advantages over the traditional computing models. But at the same it faces its own infantile disorders. As a result, these weaknesses need to be analyzed and possible solutions need to be proposed in order to assist Cloud Computing to expand and allow a wider audience to take advantage of it.

Distributed File Systems have enabled the efficient and scalable sharing of data across networks. These systems were designed to handle some technical problems associated with network data. For instance reliability and availability of data, scalability of infrastructure supporting storage, high cost of gaining access to data, maintenance cost and expansion. In this paper, we attempt to make a comparison of the key technical blocks that are referred to as the mainstay of Distributed File Systems such as Hadoop FS, Google FS, Luster FS, Ceph FS, Gluster FS, Oracle Cluster FS and TidyFS. This paper aims at elucidating the basic concepts and techniques employed in the above mentioned File Systems. We explained the different architectures and applications of these Distributed File Systems.

In this paper, the authors introduce and describe the concept of File System as a Services (FSaaS), an highly configurable cloud service that enables cooperating, distributed applications to define their own rules and policies for managing sets of files shared. The FSaaS aims to create a logical virtual space, containing references to shared files, whose management layer supports the same functionalities of a file system (basic file operations) but where each single file can have different policies for consistency, synchronization and replication. This work explains the idea at the base of FSaaS, describes in details its main components and their interactions and illustrates two use cases for better explaining the provided functionalities.

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Rak Besi Siku lubang sangat cocok untuk menyimpan barang-barang dalam jumlah banyak seperti barang-barang di Pabrik, Kantor, Gudang, Toko, Laundry dan Rumah.Yang akan membantu ruangan jadi lebih tetata rapi.
Rak Besi Siku lubang sangat praktis dan efisien karena tidak membutuhkan tempat yang luas.

Rak Besi Siku lubang yang kami sediakan adalah produk BARU bukan Rekondisi.

Pemesanan hubungi :
Telp : 087886876617 / 081293221677 (Zaenal)
Whatsapp : 081293221677
                   
Home Produksi :
Jl.Komarudin No:64
Kel.Pulogebang Kec.Cakung Jakarta Timur 13950

Jual Rak Besi Siku Lubang, Rak Besi, Rak Besi Siku

Pretty much every part of life now results in the generation of data. Logs are documentation of events or records of system activities and are created automatically through IT systems. Log data analysis is a process of making sense of these records. Log data often grows quickly and the conventional database solutions run short for dealing with a large volume of log files. Hadoop, having a wide area of applications for Big Data analysis, provides a solution for this problem. In this study, Hadoop was installed on two virtual machines. Log files generated by a Python script were analyzed in order to evaluate the system activities. The aim was to validate the importance of Hadoop in meeting the challenge of dealing with Big Data. The performed experiments show that analyzing logs with Hadoop MapReduce makes the data processing and detection of malfunctions and defects faster and simpler. Keywords— Hadoop, MapReduce, Big Data, log analysis, distributed file systems.

In today's era there has been a huge demand in the automation space where most of the human tasks could be operated remotely. IoT (Internet enabled devices) makes it possible with the usage of sensors and gateways. The success of mission critical systems depends upon how quickly such devices respond. Edge computing can boost up the responsiveness of these devices by offloading certain computational tasks closer to these devices. Merging these technologies with blockchain makes this distributed system more secure and reliable. Our paper focuses on these technologies, their working and how well they complement each other when integrated. The main focus is to shed some light on the key metrics for measuring responsiveness of the system, the challenges encountered while performance testing such applications and to provide the possible solutions for the same. We have referred published literatures around these topics and collated insights under the hood. The main purpose of this paper is to maximize the ROI made on the testing activities of such complicated integrated system.