2,149 research outputs found
Tortoise: Interactive System Configuration Repair
System configuration languages provide powerful abstractions that simplify
managing large-scale, networked systems. Thousands of organizations now use
configuration languages, such as Puppet. However, specifications written in
configuration languages can have bugs and the shell remains the simplest way to
debug a misconfigured system. Unfortunately, it is unsafe to use the shell to
fix problems when a system configuration language is in use: a fix applied from
the shell may cause the system to drift from the state specified by the
configuration language. Thus, despite their advantages, configuration languages
force system administrators to give up the simplicity and familiarity of the
shell.
This paper presents a synthesis-based technique that allows administrators to
use configuration languages and the shell in harmony. Administrators can fix
errors using the shell and the technique automatically repairs the higher-level
specification written in the configuration language. The approach (1) produces
repairs that are consistent with the fix made using the shell; (2) produces
repairs that are maintainable by minimizing edits made to the original
specification; (3) ranks and presents multiple repairs when relevant; and (4)
supports all shells the administrator may wish to use. We implement our
technique for Puppet, a widely used system configuration language, and evaluate
it on a suite of benchmarks under 42 repair scenarios. The top-ranked repair is
selected by humans 76% of the time and the human-equivalent repair is ranked
1.31 on average.Comment: Published version in proceedings of IEEE/ACM International Conference
on Automated Software Engineering (ASE) 201
The safety case and the lessons learned for the reliability and maintainability case
This paper examine the safety case and the lessons learned for the reliability and maintainability case
Alpha Entanglement Codes: Practical Erasure Codes to Archive Data in Unreliable Environments
Data centres that use consumer-grade disks drives and distributed
peer-to-peer systems are unreliable environments to archive data without enough
redundancy. Most redundancy schemes are not completely effective for providing
high availability, durability and integrity in the long-term. We propose alpha
entanglement codes, a mechanism that creates a virtual layer of highly
interconnected storage devices to propagate redundant information across a
large scale storage system. Our motivation is to design flexible and practical
erasure codes with high fault-tolerance to improve data durability and
availability even in catastrophic scenarios. By flexible and practical, we mean
code settings that can be adapted to future requirements and practical
implementations with reasonable trade-offs between security, resource usage and
performance. The codes have three parameters. Alpha increases storage overhead
linearly but increases the possible paths to recover data exponentially. Two
other parameters increase fault-tolerance even further without the need of
additional storage. As a result, an entangled storage system can provide high
availability, durability and offer additional integrity: it is more difficult
to modify data undetectably. We evaluate how several redundancy schemes perform
in unreliable environments and show that alpha entanglement codes are flexible
and practical codes. Remarkably, they excel at code locality, hence, they
reduce repair costs and become less dependent on storage locations with poor
availability. Our solution outperforms Reed-Solomon codes in many disaster
recovery scenarios.Comment: The publication has 12 pages and 13 figures. This work was partially
supported by Swiss National Science Foundation SNSF Doc.Mobility 162014, 2018
48th Annual IEEE/IFIP International Conference on Dependable Systems and
Networks (DSN
On Distributed Storage Codes
Distributed storage systems are studied. The interest in such system has become relatively wide due to the increasing amount of information needed to be stored in data centers or different kinds of cloud systems. There are many kinds of solutions for storing the information into distributed devices regarding the needs of the system designer. This thesis studies the questions of designing such storage systems and also fundamental limits of such systems. Namely, the subjects of interest of this thesis include heterogeneous distributed storage systems, distributed storage systems with the exact repair property, and locally repairable codes. For distributed storage systems with either functional or exact repair, capacity results are proved. In the case of locally repairable codes, the minimum distance is studied.
Constructions for exact-repairing codes between minimum bandwidth regeneration (MBR) and minimum storage regeneration (MSR) points are given. These codes exceed the time-sharing line of the extremal points in many cases. Other properties of exact-regenerating codes are also studied. For the heterogeneous setup, the main result is that the capacity of such systems is always smaller than or equal to the capacity of a homogeneous system with symmetric repair with average node size and average repair bandwidth. A randomized construction for a locally repairable code with good minimum distance is given. It is shown that a random linear code of certain natural type has a good minimum distance with high probability. Other properties of locally repairable codes are also studied.Siirretty Doriast
Finding Benefits of Utilizing RFID Technology in Skanska Maskin AB
In this paper, we discuss benefits of RFID
technology that Skanska Maskin AB at Linnarhult
Gothenburg in Sweden has planed to utilize in the
future. We used case study methodology in this
research to find benefits of RFID at Skanska Maskin
AB. We gathered qualitative data from the present
system by using mediating tools such as observation
and interviews. We compared present barcode system
with proposed future RFID system to see the benefits
and effects of the RFID technology. The store
environment of Skanska Maskin AB is more complex
and difficult to manage. We used Soft System
Methodology (SSM) to analyze and identify both
present barcode and future RFID systems in such a
complex and messy situation. SSM uses “systems
thinking” in learning and reflection to help understand
the various perceptions come from minds of different
people who involved in the problem situation. The SSM
considers the importance of cultural, social and
political attributes in the present system. We discussed
the results of system comparison illustrating the benefit
of future RFID system
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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