When Do WOM Codes Improve the Erasure Factor in Flash Memories?

Flash memory is a write-once medium in which reprogramming cells requires first erasing the block that contains them. The lifetime of the flash is a function of the number of block erasures and can be as small as several thousands. To reduce the number of block erasures, pages, which are the smallest write unit, are rewritten out-of-place in the memory. A Write-once memory (WOM) code is a coding scheme which enables to write multiple times to the block before an erasure. However, these codes come with significant rate loss. For example, the rate for writing twice (with the same rate) is at most 0.77. In this paper, we study WOM codes and their tradeoff between rate loss and reduction in the number of block erasures, when pages are written uniformly at random. First, we introduce a new measure, called erasure factor, that reflects both the number of block erasures and the amount of data that can be written on each block. A key point in our analysis is that this tradeoff depends upon the specific implementation of WOM codes in the memory. We consider two systems that use WOM codes; a conventional scheme that was commonly used, and a new recent design that preserves the overall storage capacity. While the first system can improve the erasure factor only when the storage rate is at most 0.6442, we show that the second scheme always improves this figure of merit.Comment: to be presented at ISIT 201

Abstract Karma: Know-it-All Replacement for a Multilevel cAche ∗

Multilevel caching, common in many storage configurations, introduces new challenges to traditional cache management: data must be kept in the appropriate cache and replication avoided across the various cache levels. Some existing solutions focus on avoiding replication across the levels of the hierarchy, working well without information about temporal locality–information missing at all but the highest level of the hierarchy. Others use application hints to influence cache contents. We present Karma, a global non-centralized, dynamic and informed management policy for multiple levels of cache. Karma leverages application hints to make informed allocation and replacement decisions in all cache levels, preserving exclusive caching and adjusting to changes in access patterns. We show the superiority of Karma through comparison to existing solutions including LRU, 2Q, ARC, MultiQ, LRU-SP, and Demote, demonstrating better cache performance than all other solutions and up to 85 % better performance than LRU on representative workloads.

How to Best Share a Big Secret

When sensitive data is stored in the cloud, the only way to ensure its secrecy is by encrypting it before it is uploaded. The emerging multi-cloud model, in which data is stored redundantly in two or more independent clouds, provides an opportunity to protect sensitive data with secret-sharing schemes. Both data-protection approaches are considered computationally expensive, but recent advances reduce their costs considerably: (1) Hardware acceleration methods promise to eliminate the computational complexity of encryption, but leave clients with the challenge of securely managing encryption keys. (2) Secure RAID, a recently proposed scheme, minimizes the computational overheads of secret sharing, but requires non-negligible storage overhead and random data generation. Each data-protection approach offers different tradeoffs and security guarantees. However, when comparing them, it is difficult to determine which approach will provide the best application-perceived performance, because previous studies were performed before their recent advances were introduced. To bridge this gap, we present the first end-to-end comparison of state-of-the-art encryption-based and secret sharing data protection approaches. Our evaluation on a local cluster and on a multi-cloud prototype identifies the tipping point at which the bottleneck of data protection shifts from the computational overhead of encoding and random data generation to storage and network bandwidth and global availability

How to Best Share a Big Secret

When sensitive data is stored in the cloud, the only way to ensure its secrecy is by encrypting it before it is uploaded. The emerging multi-cloud model, in which data is stored redundantly in two or more independent clouds, provides an opportunity to protect sensitive data with secret-sharing schemes. Both data-protection approaches are considered computationally expensive, but recent advances reduce their costs considerably: (1) Hardware acceleration methods promise to eliminate the computational complexity of encryption, but leave clients with the challenge of securely managing encryption keys. (2) Secure RAID, a recently proposed scheme, minimizes the computational overheads of secret sharing, but requires non-negligible storage overhead and random data generation. Each data-protection approach offers different tradeoffs and security guarantees. However, when comparing them, it is difficult to determine which approach will provide the best application-perceived performance, because previous studies were performed before their recent advances were introduced. To bridge this gap, we present the first end-to-end comparison of state-of-the-art encryption-based and secret sharing data protection approaches. Our evaluation on a local cluster and on a multi-cloud prototype identifies the tipping point at which the bottleneck of data protection shifts from the computational overhead of encoding and random data generation to storage and network bandwidth and global availability

The Devil is in the Details: Implementing Flash Page Reuse with WOM Codes

Flash memory is prevalent in modern servers and devices. Coupled with the scaling down of flash technology, the popularity of flash memory motivates the search for methods to increase flash reliability and lifetime. Erasures are the dominant cause of flash cell wear, but reducing them is challenging because flash is a write-once medium-memory cells must be erased prior to writing. An approach that has recently received considerable attention relies on write-once memory (WOM) codes, designed to accommodate additional writes on write-once media. However, the techniques proposed for reusing flash pages with WOM codes are limited in their scope. Many focus on the coding theory alone, while others suggest FTL designs that are application specific, or not applicable due to their complexity, overheads, or specific constraints of MLC flash. This work is the first that addresses all aspects of page reuse within an end-to-end implementation of a general-purpose FTL on MLC flash. We use our hardware implementation to directly measure the short and long-term effects of page reuse on SSD durability, I/O performance and energy consumption, and show that FTL design must explicitly take them into account

An Analysis of Flash Page Reuse With WOM Codes

Flash memory is prevalent in modern servers and devices. Coupled with the scaling down of flash technology, the popularity of flash memory motivates the search for methods to increase flash reliability and lifetime. Erasures are the dominant cause of flash cell wear, but reducing them is challenging because flash is a write-once medium— memory cells must be erased prior to writing. An approach that has recently received considerable attention relies on write-once memory (WOM) codes, designed to accommodate additional writes on write-once media. However, the techniques proposed for reusing flash pages with WOM codes are limited in their scope. Many focus on the coding theory alone, whereas others suggest FTL designs that are application specific, or not applicable due to their complexity, overheads, or specific constraints of multilevel cell (MLC) flash. This work is the first that addresses all aspects of page reuse within an end-to-end analysis of a general-purpose FTL on MLC flash. We use a hardware evaluation setup to directly measure the short- and long-term effects of page reuse on SSD durability and energy consumption, and show that FTL design must explicitly take them into account. We then provide a detailed analytical model for deriving the optimal garbage collection policy for such FTL designs, and for predicting the benefit from reuse on realistic hardware and workload characteristics