81 research outputs found
On Minimizing Data-read and Download for Storage-Node Recovery
We consider the problem of efficient recovery of the data stored in any
individual node of a distributed storage system, from the rest of the nodes.
Applications include handling failures and degraded reads. We measure
efficiency in terms of the amount of data-read and the download required. To
minimize the download, we focus on the minimum bandwidth setting of the
'regenerating codes' model for distributed storage. Under this model, the
system has a total of n nodes, and the data stored in any node must be
(efficiently) recoverable from any d of the other (n-1) nodes. Lower bounds on
the two metrics under this model were derived previously; it has also been
shown that these bounds are achievable for the amount of data-read and download
when d=n-1, and for the amount of download alone when d<n-1.
In this paper, we complete this picture by proving the converse result, that
when d<n-1, these lower bounds are strictly loose with respect to the amount of
read required. The proof is information-theoretic, and hence applies to
non-linear codes as well. We also show that under two (practical) relaxations
of the problem setting, these lower bounds can be met for both read and
download simultaneously.Comment: IEEE Communications Letter
The Role of Author Identities in Peer Review
There is widespread debate on whether to anonymize author identities in peer
review. The key argument for anonymization is to mitigate bias, whereas
arguments against anonymization posit various uses of author identities in the
review process. The Innovations in Theoretical Computer Science (ITCS) 2023
conference adopted a middle ground by initially anonymizing the author
identities from reviewers, revealing them after the reviewer had submitted
their initial reviews, and allowing the reviewer to change their review
subsequently. We present an analysis of the reviews pertaining to the
identification and use of author identities. Our key findings are: (I) A
majority of reviewers self-report not knowing and being unable to guess the
authors' identities for the papers they were reviewing. (II) After the initial
submission of reviews, 7.1% of reviews changed their overall merit score and
3.8% changed their self-reported reviewer expertise. (III) There is a very weak
and statistically insignificant correlation of the rank of authors'
affiliations with the change in overall merit; there is a weak but
statistically significant correlation with respect to change in reviewer
expertise. We also conducted an anonymous survey to obtain opinions from
reviewers and authors. The main findings from the 200 survey responses are: (i)
A vast majority of participants favor anonymizing author identities in some
form. (ii) The "middle-ground" initiative of ITCS 2023 was appreciated. (iii)
Detecting conflicts of interest is a challenge that needs to be addressed if
author identities are anonymized. Overall, these findings support anonymization
of author identities in some form (e.g., as was done in ITCS 2023), as long as
there is a robust and efficient way to check conflicts of interest
When Do Redundant Requests Reduce Latency ?
Several systems possess the flexibility to serve requests in more than one
way. For instance, a distributed storage system storing multiple replicas of
the data can serve a request from any of the multiple servers that store the
requested data, or a computational task may be performed in a compute-cluster
by any one of multiple processors. In such systems, the latency of serving the
requests may potentially be reduced by sending "redundant requests": a request
may be sent to more servers than needed, and it is deemed served when the
requisite number of servers complete service. Such a mechanism trades off the
possibility of faster execution of at least one copy of the request with the
increase in the delay due to an increased load on the system. Due to this
tradeoff, it is unclear when redundant requests may actually help. Several
recent works empirically evaluate the latency performance of redundant requests
in diverse settings.
This work aims at an analytical study of the latency performance of redundant
requests, with the primary goals of characterizing under what scenarios sending
redundant requests will help (and under what scenarios they will not help), as
well as designing optimal redundant-requesting policies. We first present a
model that captures the key features of such systems. We show that when service
times are i.i.d. memoryless or "heavier", and when the additional copies of
already-completed jobs can be removed instantly, redundant requests reduce the
average latency. On the other hand, when service times are "lighter" or when
service times are memoryless and removal of jobs is not instantaneous, then not
having any redundancy in the requests is optimal under high loads. Our results
hold for arbitrary arrival processes.Comment: Extended version of paper presented at Allerton Conference 201
The MDS Queue: Analysing the Latency Performance of Erasure Codes
In order to scale economically, data centers are increasingly evolving their
data storage methods from the use of simple data replication to the use of more
powerful erasure codes, which provide the same level of reliability as
replication but at a significantly lower storage cost. In particular, it is
well known that Maximum-Distance-Separable (MDS) codes, such as Reed-Solomon
codes, provide the maximum storage efficiency. While the use of codes for
providing improved reliability in archival storage systems, where the data is
less frequently accessed (or so-called "cold data"), is well understood, the
role of codes in the storage of more frequently accessed and active "hot data",
where latency is the key metric, is less clear.
In this paper, we study data storage systems based on MDS codes through the
lens of queueing theory, and term this the "MDS queue." We analytically
characterize the (average) latency performance of MDS queues, for which we
present insightful scheduling policies that form upper and lower bounds to
performance, and are observed to be quite tight. Extensive simulations are also
provided and used to validate our theoretical analysis. We also employ the
framework of the MDS queue to analyse different methods of performing so-called
degraded reads (reading of partial data) in distributed data storage
Fundamental Limits on Communication for Oblivious Updates in Storage Networks
In distributed storage systems, storage nodes intermittently go offline for
numerous reasons. On coming back online, nodes need to update their contents to
reflect any modifications to the data in the interim. In this paper, we
consider a setting where no information regarding modified data needs to be
logged in the system. In such a setting, a 'stale' node needs to update its
contents by downloading data from already updated nodes, while neither the
stale node nor the updated nodes have any knowledge as to which data symbols
are modified and what their value is. We investigate the fundamental limits on
the amount of communication necessary for such an "oblivious" update process.
We first present a generic lower bound on the amount of communication that is
necessary under any storage code with a linear encoding (while allowing
non-linear update protocols). This lower bound is derived under a set of
extremely weak conditions, giving all updated nodes access to the entire
modified data and the stale node access to the entire stale data as side
information. We then present codes and update algorithms that are optimal in
that they meet this lower bound. Next, we present a lower bound for an
important subclass of codes, that of linear Maximum-Distance-Separable (MDS)
codes. We then present an MDS code construction and an associated update
algorithm that meets this lower bound. These results thus establish the
capacity of oblivious updates in terms of the communication requirements under
these settings.Comment: IEEE Global Communications Conference (GLOBECOM) 201
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