DDEAS: Distributed Deduplication System with Efficient Access in Cloud Data Storage

Cloud storage service is one of the vital function of cloud computing that helps cloud users to outsource a massive volume of data without upgrading their devices. However, cloud data storage offered by Cloud Service Providers (CSPs) faces data redundancy problems. The data de-duplication technique aims to eliminate redundant data segments and keeps a single instance of the data set, even if similar data set is owned by any number of users. Since data blocks are distributed among the multiple individual servers, the user needs to download each block of the file before reconstructing the file, which reduces the system efficiency. We propose a server level data recover module in the cloud storage system to improve file access efficiency and reduce network bandwidth utilization time. In the proposed method, erasure coding is used to store blocks in distributed cloud storage and The MD5 (Message Digest 5) is used for data integrity. Executing recover algorithm helps user to directly fetch the file without downloading each block from the cloud servers. The proposed scheme improves the time efficiency of the system and quick access ability to the stored data. Thus consumes less network bandwidth and

Computing at massive scale: Scalability and dependability challenges

Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications. However, with the increase of data volume, together with the heterogeneity of workloads and resources, and the dynamic nature of massive user requests, the uncertainties and complexity of resource management and service provisioning increase dramatically, often resulting in poor resource utilization, vulnerable system dependability, and user-perceived performance degradations. In this paper we report our latest understanding of the current and future challenges in this particular area, and discuss both existing and potential solutions to the problems, especially those concerned with system efficiency, scalability and dependability. We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment. We then examine and analyze several fundamental challenges and the solutions we are developing to tackle them, including for example incremental but decentralized resource scheduling, incremental messaging communication, rapid system failover, and request handling parallelism. We integrate these solutions with our data analysis methodology in order to establish an engineering approach that facilitates the optimization, tuning and verification of massive-scale distributed systems. We aim to develop and offer innovative methods and mechanisms for future computing platforms that will provide strong support for new big data and IoE (Internet of Everything) applications

A survey and classification of storage deduplication systems

The automatic elimination of duplicate data in a storage system commonly known as deduplication is increasingly accepted as an effective technique to reduce storage costs. Thus, it has been applied to different storage types, including archives and backups, primary storage, within solid state disks, and even to random access memory. Although the general approach to deduplication is shared by all storage types, each poses specific challenges and leads to different trade-offs and solutions. This diversity is often misunderstood, thus underestimating the relevance of new research and development. The first contribution of this paper is a classification of deduplication systems according to six criteria that correspond to key design decisions: granularity, locality, timing, indexing, technique, and scope. This classification identifies and describes the different approaches used for each of them. As a second contribution, we describe which combinations of these design decisions have been proposed and found more useful for challenges in each storage type. Finally, outstanding research challenges and unexplored design points are identified and discussed.This work is funded by the European Regional Development Fund (EDRF) through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the Fundacao para a Ciencia e a Tecnologia (FCT; Portuguese Foundation for Science and Technology) within project RED FCOMP-01-0124-FEDER-010156 and the FCT by PhD scholarship SFRH-BD-71372-2010

Data De-Duplication in NoSQL Databases

With the popularity and expansion of Cloud Computing, NoSQL databases (DBs) are becoming the preferred choice of storing data in the Cloud. Because they are highly de-normalized, these DBs tend to store significant amounts of redundant data. Data de-duplication (DD) has an important role in reducing storage consumption to make it affordable to manage in today’s explosive data growth. Numerous DD methodologies like chunking and, delta encoding are available today to optimize the use of storage. These technologies approach DD at file and/or sub-file level but this approach has never been optimal for NoSQL DBs. This research proposes data De-Duplication in NoSQL Databases (DDNSDB) which makes use of a DD approach at a higher level of abstraction, namely at the DB level. It makes use of the structural information about the data (metadata) exploiting its granularity to identify and remove duplicates. The main goals of this research are: to maximally reduce the amount of duplicates in one type of NoSQL DBs, namely the key-value store, to maximally increase the process performance such that the backup window is marginally affected, and to design with horizontal scaling in mind such that it would run on a Cloud Platform competitively. Additionally, this research presents an analysis of the various types of NoSQL DBs (such as key-value, tabular/columnar, and document DBs) to understand their data model required for the design and implementation of DDNSDB. Primary experiments have demonstrated that DDNSDB can further reduce the NoSQL DB storage space compared with current archiving methods (from 17% to near 69% as more structural information is available). Also, by following an optimized adapted MapReduce architecture, DDNSDB proves to have competitive performance advantage in a horizontal scaling cloud environment compared with a vertical scaling environment (from 28.8 milliseconds to 34.9 milliseconds as the number of parallel Virtual Machines grows)

Dependency-Based Decomposition of Systems Involving Rare Events

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryIBM Ph.D. Fellowshi

Using Rollback Avoidance to Mitigate Failures in Next-Generation Extreme-Scale Systems

High-performance computing (HPC) systems enable scientists to numerically model complex phenomena in many important physical systems. The next major milestone in the development of HPC systems is the construction of the first supercomputer capable executing more than an exaflop, 10^18 floating point operations per second. On systems of this scale, failures will occur much more frequently than on current systems. As a result, resilience is a key obstacle to building next-generation extreme-scale systems. Coordinated checkpointing is currently the most widely-used mechanism for handling failures on HPC systems. Although coordinated checkpointing remains effective on current systems, increasing the scale of today\u27s systems to build next-generation systems will increase the cost of fault tolerance as more and more time is taken away from the application to protect against or recover from failure. Rollback avoidance techniques seek to mitigate the cost of checkpoint/restart by allowing an application to continue its execution rather than rolling back to an earlier checkpoint when failures occur. These techniques include failure prediction and preventive migration, replicated computation, fault-tolerant algorithms, and software-based memory fault correction. In this thesis, I examine how rollback avoidance techniques can be used to address failures on extreme-scale systems. Using a combination of analytic modeling and simulation, I evaluate the potential impact of rollback avoidance on these systems. I then present a novel rollback avoidance technique that exploits similarities in application memory. Finally, I examine the feasibility of using this technique to protect against memory faults in kernel memory

Fragmentation in storage systems with duplicate elimination

Deduplication inevitably results in data fragmentation, because logically continuous data is scattered across many disk locations. Even though this significantly increases restore time from backup, the problem is still not well examined. In this work I close this gap by designing algorithms that reduce negative impact of fragmentation on restore time for two major types of fragmentation: internal and inter-version.Internal stream fragmentation is caused by the blocks appearing many times within a single backup. Such phenomenon happens surprisingly often and can result in even three times lower restore bandwidth. With an algorithm utilizing available forward knowledge to enable efficient caching I managed to improve this result on average by 62%-88% with only about 5% extra memory used. Although these results are achieved with limited forward knowledge, they are very close to the ones measured with no such limitation.Inter-version fragmentation is caused by duplicates from previous backups of the same backup set. Since such duplicates are very common due to repeated full backups containing a lot of unchanged data, this type of fragmentation may double the restore time after even a few backups. The context-based rewriting algorithm minimizes this effect by selectively rewriting a small percentage of duplicates during backup, limiting the bandwidth drop from 21.3% to 2.48% on average with only small increase in writing time and temporary space overhead.The two algorithms combined end up in a very effective symbiosis resulting in an average 142% restore bandwidth increase with standard 256MB of per-stream cache memory. In many cases such setup achieves results close to the theoretical maximum achievable with unlimited cache size. Moreover, all the above experiments where performed assuming only one spindle, even though in majority of today’s systems many spindles are used. In a sample setup with ten spindles, the restore bandwidth results are on average 5 times higher than in standard LRU case.Fragmentacja jest nieuniknioną konsekwencją deduplikacji, ponieważ pojedynczy strumień danych rozrzucany jest pomiędzy wiele lokalizacji na dysku. Fakt ten powoduje znaczące wydłużenie czasu odzyskiwania danych z kopii zapasowych. Mimo to, problem wciąż nie jest dobrze zbadany. Niniejsza praca wypełnia tę lukę poprzez propozycje algorytmów, które redukują negatywny wpływ fragmentacji na czas odczytu dla dwóch najważniejszych jej rodzajów: wewnętrznej fragmentacji strumienia oraz fragmentacji pomiędzy różnymi wersjami danych.Wewnętrzna fragmentacja strumienia jest spowodowana blokami powtarzającymi się wielokrotnie w pojedynczym strumieniu danych. To zjawisko zdarza się zaskakująco często i powoduje nawet trzykrotnie niższą wydaj-ność odczytu. Proponowany w tej pracy algorytm efektywnego zarządzania pamięcią, wykorzystujący dostępną wiedzę o danych, jest w stanie podnieść wydajność odczytu o 62-88%, używając przy tym tylko 5% dodatkowej pamięci.Fragmentacja pomiędzy różnymi wersjami danych jest spowodowana duplikatami pochodzącymi z wcześniejszych zapisów tego samego zbioru danych. Ponieważ pełne kopie zapasowe tworzone są regularnie i zawierają duże ilości powtarzających się danych, takie duplikaty występują bardzo często. W przypadku późniejszego odczytu, ich obecność może powodować nawet podwojenie czasu potrzebnego na odzyskanie danych, po utworzeniu zaledwie kilku kopii zapasowych. Algorytm przepisywania kontekstowego minimalizuje ten efekt przez selektywne przepisywanie małej ilości duplikatów podczas zapisu. Takie postępowanie jest w stanie ograniczyć średni spadek wydajności odczytu z 21,3% do 2,48%, kosztem minimalnego zwiększenia czasu zapisudanych i wymagania niewielkiej przestrzeni dyskowej na pamięć tymczasową.Obydwa algorytmy użyte razem działają jeszcze wydajniej, poprawiając przepustowość odczytu przeciętnie o 142% przy standardowej ilości 256MB pamięci cache dla każdego strumienia. Dodatkowo, ponieważ powyższe wyniki zakładają odczyt z jednego dysku, przeprowadzone zostały testy symulujące korzystanie z przepustowości wielu dysków, gdyż takie konfiguracje są bardzo częste w dzisiejszych systemach. Dla przykładu, używając dziecięciu dysków i proponowanych algorytmów, można osiągnąć średnio pięciokrotnie wyższą wydajność niż w standardowym podejściu z algorytmem typu LRU