HIODS: hybrid inline and offline deduplication system

Dissertação de mestrado integrado em Engenharia InformáticaDeduplication is a technique that allows finding and removing duplicate data at storage systems. With the current exponential growth of digital information, this mechanism is becoming more and more desirable for reducing the infrastructural costs of persisting such data. Therefore, deduplication is now being widely applied to several storage appliances serving applications with different requirements (e.g., archival, backup, primary storage). However, deduplication requires additional processing logic for each storage request in order to detect and eliminate duplicate content. Traditionally, this processing is done in the I/O critical path (inline), thus introducing a performance penalty on the throughput and latency of requests being served by the storage appliance. An alternative solution is to do this process as a background task, thus outside of the I/O critical path (offline), at the cost of requiring additional storage space as duplicate content is not found and eliminated immediately. However, the choice of what type of strategy to use is typically done manually and does not take into consideration changes in the applications' workloads. This dissertation proposes HIODS, a hybrid deduplication solution capable of automati cally changing between inline and offline deduplication according to the requirements (e.g., desired storage I/O throughput goal) of applications and their dynamic workloads. The goal is to choose the best strategy that fulfills the targeted I/O performance objectives while optimizing deduplication space savings. Finally, a prototype of HIODS is implemented and evaluated extensively with different storage workloads. Results show that HIODS is able to change its deduplication mode dy namically, according to the storage workload being served, while balancing I/O performance and space savings requirements efficiently.A deduplicação é uma técnica que permite encontrar e remover dados duplicados guardados nos sistemas de armazenamento. Com o crescimento exponencial da informação digital que vivemos atualmente, este mecanismo está a tornar-se cada vez mais popular para reduzir os custos das infraestruturas onde esses dados se encontram alojados. De facto, a deduplicação é, hoje em dia, usada numa grande variedade de serviços de armazenamento que servem diferentes aplicações com requisitos particulares (ex.: arquivo, backup, armazenamento primário). No entanto, a deduplicação adiciona uma camada de processamento extra a cada pedido de armazenamento, de modo a conseguir detetar e eliminar o conteúdo redundante. Tradicionalmente, este processo é realizado durante o caminho crítico do I/O (inline), causando perdas de desempenho e aumentos na latência dos pedidos processados. Uma alternativa é alterar o processamento para segundo plano, aliviando assim os custos no caminho crítico do I/O (offline). Esta solução requer espaço de armazenamento adicional, visto que os duplicados não são encontrados nem eliminados imediatamente. No entanto, a estratégia a seguir é escolhida de forma manual, não tendo em consideração qualquer possível mudança na carga de trabalho das aplicações. Esta dissertação propõe assim o HIODS, um sistema de deduplicação híbrido capaz de alterar entre o modo inline e offline de forma automática considerando os requisitos (ex.: débito do sistema de armazenamento desejado) das aplicações e das suas cargas de trabalho dinâmicas. Por fim, um protótipo do HIODS é implementado e avaliado exaustivamente. Os resultados mostram que o HIODS é capaz de alterar o modo de deduplicação de forma dinâmica e de acordo com a carga de trabalho, considerando os requisitos de desempenho e a eliminação eficiente dos dados duplicados

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

Statistical characterization of storage system workloads for data deduplication and load placement in heterogeneous storage environments

University of Minnesota Ph.D. dissertation. November 2013. Major: Electrical Engineering. Advisor: David J. Lilja. 1 computer file (PDF); xi, 110 pages.The underlying technologies for storing digital bits have become more diverse in last decade.There is no fundamental differences in their functionality yet their behaviors can be quite different and no single management technique seems to fit them all.The differences can be categorized based on the metric of interest such as the performance profile, the reliability profile and the power profile.These profiles are a function of the system and the workload assuming that the systems are exposed only to a pre-specified environment. Near infinite workload space makes it infeasible to obtain the complete profiles for any storage systems unless the system enforces a discrete and finite profile internally. The thesis of this work is that an acceptable approximation of the profiles may be achieved by proper characterization of the workloads.A set of statistical tools as well as understanding of system behavior were used to evaluate and design such characterizations.The correctness of the characterization cannot be fully proved except by showing that the resulting profile can correctly predict any workload and storage system interactions. While this is not possible, we show that we can provide a reasonable confidence in our characterization by statistical evaluation of results.The characterizations of this work were applied to compression ratio for backup data deduplication and load balancing of heterogeneous storage systems in a virtualized environments.The validation of our characterization is validated through hundreds of real world test cases as well as reasonable deductions based on our understanding of the storage systems. In both cases, the goodness of characterizations were rigorously evaluated using statistical techniques.The findings along the validations were both confirming and contradicting of many previous beliefs