23,830 research outputs found
Coding for the Clouds: Coding Techniques for Enabling Security, Locality, and Availability in Distributed Storage Systems
Cloud systems have become the backbone of many applications such as multimedia
streaming, e-commerce, and cluster computing. At the foundation of any cloud architecture
lies a large-scale, distributed, data storage system. To accommodate the massive
amount of data being stored on the cloud, these distributed storage systems (DSS) have
been scaled to contain hundreds to thousands of nodes that are connected through a networking
infrastructure. Such data-centers are usually built out of commodity components,
which make failures the norm rather than the exception.
In order to combat node failures, data is typically stored in a redundant fashion. Due to
the exponential data growth rate, many DSS are beginning to resort to error control coding
over conventional replication methods, as coding offers high storage space efficiency. This
paradigm shift from replication to coding, along with the need to guarantee reliability, efficiency,
and security in DSS, has created a new set of challenges and opportunities, opening
up a new area of research. This thesis addresses several of these challenges and opportunities
by broadly making the following contributions. (i) We design practically amenable,
low-complexity coding schemes that guarantee security of cloud systems, ensure quick
recovery from failures, and provide high availability for retrieving partial information; and
(ii) We analyze fundamental performance limits and optimal trade-offs between the key
performance metrics of these coding schemes.
More specifically, we first consider the problem of achieving information-theoretic
security in DSS against an eavesdropper that can observe a limited number of nodes. We
present a framework that enables design of secure repair-efficient codes through a joint
construction of inner and outer codes. Then, we consider a practically appealing notion
of weakly secure coding, and construct coset codes that can weakly secure a wide class of regenerating codes that reduce the amount of data downloaded during node repair.
Second, we consider the problem of meeting repair locality constraints, which specify
the number of nodes participating in the repair process. We propose a notion of unequal
locality, which enables different locality values for different nodes, ensuring quick recovery
for nodes storing important data. We establish tight upper bounds on the minimum
distance of linear codes with unequal locality, and present optimal code constructions.
Next, we extend the notion of locality from the Hamming metric to the rank and subspace
metrics, with the goal of designing codes for efficient data recovery from special types of
correlated failures in DSS.We construct a family of locally recoverable rank-metric codes
with optimal data recovery properties.
Finally, we consider the problem of providing high availability, which is ensured by
enabling node repair from multiple disjoint subsets of nodes of small size. We study
codes with availability from a queuing-theoretical perspective by analyzing the average
time necessary to download a block of data under the Poisson request arrival model when
each node takes a random amount of time to fetch its contents. We compare the delay
performance of the availability codes with several alternatives such as conventional erasure
codes and replication schemes
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Comparing Compensation for Federal and Private-Sector Workers: An Overview
[Excerpt] In recent years, there has been significant congressional interest in compensation of the federal workforce. The increased interest has been driven at least in part by the federal fiscal situation and in part by the state of the economy since the recession began in 2007. Issues related to the compensation of federal employees often center on the pay differential between federal workers and their private sector counterparts. For years, the annual President’s Pay Agent (PPA) study, which is covered in greater detail later in this report, has shown a large wage penalty for federal workers compared to private sector workers in similar occupations. A spate of recent studies, which use a different analytical approach and data sources, has partially contradicted the findings of the PPA study by concluding that at least some federal workers enjoy a wage premium over comparable private sector workers. These studies and accompanying reporting on the comparison of compensation of federal workers to private sector workers provide an indication of the disparate findings, which makes it difficult to determine how compensation of federal employees actually compares to that of workers in the private sector. This report attempts to clarify why the recent studies have arrived at different conclusions and examines limitations of the approaches employed in the different studies
Locally Repairable Convolutional Codes With Sliding Window Repair
Locally repairable convolutional codes (LRCCs) for distributed storage systems (DSSs) are introduced in this work. They enable local repair, for a single node erasure (or more generally, ∂−1 erasures per local group), and sliding-window global repair, which can correct erasure patterns with up to dcj−1 erasures in every window of j+1 consecutive blocks of n nodes, where dcj−1 is the j th column distance of the code. The parameter j can be adjusted, for a fixed LRCC, according to different catastrophic erasure patterns, requiring only to contact n(j+1)−dcj+1 nodes, plus less than μn other nodes, in the storage system, where μ is the memory of the code. A Singleton-type bound is provided for dcj−1 . If it attains such a bound, an LRCC can correct the same number of catastrophic erasures in a window of length n(j+1) as an optimal locally repairable block code of the same rate and locality, and with block length n(j+1) . In addition, the LRCC is able to perform the flexible and somehow local sliding-window repair by adjusting j . Furthermore, by adjusting and/or sliding the window, the LRCC can potentially correct more erasures in the original window of n(j+1) nodes than an optimal locally repairable block code of the same rate and locality, and length n(j+1) . Finally, the concept of partial maximum distance profile (partial MDP) codes is introduced. Partial MDP codes can correct all information-theoretically correctable erasure patterns for a given locality, local distance and information rate. An explicit construction of partial MDP codes whose column distances attain the provided Singleton-type bound, up to certain parameter j=L , is obtained based on known maximum sum-rank distance convolutional codes.This work was supported in part by the Independent Research Fund Denmark under Grant DFF-7027-00053B, in part by the Generalitat Valenciana under Grant AICO/2017/128, and in part by the Universitat d’Alacant under Grant VIGROB-287
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