Exploiting Rateless Codes in Cloud Storage Systems

Abstract

Abstract-Block-Level Cloud Storage (BLCS) offers to users and applications the access to persistent block storage devices (virtual disks) that can be directly accessed and used as if they were raw physical disks. In this paper we devise ENIGMA, an architecture for the back-end of BLCS systems able to provide adequate levels of access and transfer performance, availability, integrity, and confidentiality, for the data it stores. ENIGMA exploits LT rateless codes to store fragments of sectors on storage nodes organized in clusters. We quantitatively evaluate how the various ENIGMA system parameters affect the performance, availability, integrity, and confidentiality of virtual disks. These evaluations are carried out by using both analytical modeling (for availability, integrity, and confidentiality) and discrete event simulation (for performance), and by considering a set of realistic operational scenarios. Our results indicate that it is possible to simultaneously achieve all the objectives set forth for BLCS systems by using ENIGMA, and that a careful choice of the various system parameters is crucial to achieve a good compromise among them. Moreover, they also show that LT coding-based BLCS systems outperform traditional BLCS systems in all the aspects mentioned before.

Securing Coding-Based Cloud Storage Against Pollution Attacks

“…Moreover, we show that the average number of sector reads required to identify all polluters is very low and decreases as the coding redundancy increases. We use the architecture of ENIGMA (defined in [2]) as a blueprint for the model of a typical cloud storage system based on LT codes, and we exploit some results reported [2] to set the values of various system parameters in the experimental evaluation. We would like to point out that in [2] we limited ourselves to quantify the ability of ENIGMA of merely tolerate the presence of polluters in the system, i.e.…”

Joint latency and cost optimization for erasurecoded data center storage

“…Related design can also be found in data access scheduling [14,16,19,20], access collision avoidance [17,18], and encoding/decoding time optimization [32,33] and there are also some work using the LT erasure codes to adjust the system to meet user requirements such as availability, integrity and confidentiality [6]. Restricting to the special case of a single file or homogeneous files, service delay bounds of erasure-coded storage have been recently studied in [40,45,49,50].…”

“…To demonstrate the effectiveness of our joint optimization, we vary file size in the experiment from 50MB to 200MB and plot the average download latency of the 1000 individual files, out of which each quarter is using a distinct erasure code (11,6), (10,7), (10,6) and (9,4), and our analytical latency upper bound in Figure 11 . We see that latency increases superlinearly as file size grows, since it generates higher load on the storage system, causing larger queuing latency (which is super-linear according to our analysis).…”

“…Figure 12 shows that to achieve a minimization of the latency-plus-cost objective, the optimal solution from Algorithm JLCM allows higher storage cost for larger arrival rates, resulting in a nearly-linear growth of average latency as the request arrival rates increase. For instance, Algorithm JLCM chooses (12,6), (12,7), (11,6) and (11,4) erasure codes at the largest arrival rates, while (10,6), (10,7), (8,6) and (8,4) codes are selected at the smallest arrival rates in this experiment. We believe that this ability to autonomously manage latency and storage cost for latencyplus-cost minimization under different workload is crucial for practical distributed storage systems relying on erasure coding.…”

“…When θ = 0.5, the optimal solution from Algorithm JLCM chooses three sets of erasure codes (12,6), (12,7), and (12,4) erasure codes, which is the maximum erasure code length in our framework and leads to highest storage cost (i.e., 12 dollars for each user), yet lowest latency (i.e., 110 sec). On the other hand, θ = 200 results in the choice of (6,6), (8,7), and (6,4) erasure code, which is almost the minimum possible cost for storing the three file, with the highest latency of 128 seconds. Further, the theoretical tradeoff calculated by our analytical bound and Algorithm JLCM is very close to the actual measurement on our testbed.…”