Simplex Queues for Hot-Data Download

Abstract

In cloud storage systems, hot data is usually replicated over multiple disks/servers in order to accommodate simultaneous access by multiple users as well as increase the fault tolerance of the system. Recent cloud storage research has proposed using availability codes, which is a special class of erasure codes, as a more storagee cient way to store hot data. ese codes enable data recovery from multiple, small disjoint groups of servers. e number of the recovery groups is referred to as the availability and the size of each group as the locality of the code. Up till now, we have very limited knowledge on how code locality and availability a ect data access time. Data download from these systems involves multiple fork-join queues operating in-parallel, making the analysis of access time a very challenging problem.In this paper, we present an analysis of average data access time in storage systems employing simplex codes, which are an important, in certain sense optimal, class of availability codes. We generalize the analysis for codes with locality 2 and any degree of availability. Speci cally, using a queueing theoretic approach, we derive bounds and approximations on the average response time for two di erent Poisson request arrival models. We also compare two scheduling strategies for reduced access time and load balancing.

On the service capacity region of accessing erasure coded content

“…In addition to reliability, redundancy has been shown to be effective in enhancing availability by reducing download latency for retrieving entire data in a number of recent research papers, see e.g., [5]- [8]. On the other hand, for downloading hot data, wherein users are interested in downloading individual files with different popularities, the role of erasure codes in reducing latency is not yet well understood, and is a topic of active research, see [9]- [11].…”

Service Rate Region: A New Aspect of Coded Distributed System Design

“…Simple replication of objects, that is, storing identical copies, is allowed and, as we will see later, often to a certain extent desirable. It is worth to note here that the assumptions we make in our storage model are common in the prior work, see, e.g., [13], [14], [24], [25]. We denote the coded objects as c 1 , c 2 , .…”

A Geometric View of the Service Rates of Codes Problem and its Application to the Service Rate of the First Order Reed-Muller Codes

“…By appearing several delay-sensitive applications such as video streaming, particular codes have been developed that provide fast content download by minimizing the download latency (see e.g., [13]- [20]). Although they consider a finite service rate for each server, analyzing the download latency in general has shown to be quite challenging, and the optimal strategies are known only in some special cases.…”