MGR HASH FUNCTION

GOST-R is a Russian Standard Cryptographic Hash function which was first introduced in 1994 by Russian Federal for information processing, information security and digital signature. In 2012, it was updated to GOST-R 34.11-2012 and replaced older one for all its applications from January 2013. GOST-R is based on modified Merkle-Damg\aa rd construction. Here, we present a modified version of GOST-R (MGR-hash). The modified design is based on wide pipe construction which is also a modified Merkle-Damg\aa rd construction. MGR is much more secure as well as three times faster than GOST-R. AES like block cipher has been used in designing the compression function of MGR because AES is one of the most efficient and secure block cipher and it has been evaluated for more than 12 years. We will also analyze the MGR hash function with respect to its security and efficiency

Balancing the Migration of Virtual Network Functions with Replications in Data Centers

The Network Function Virtualization (NFV) paradigm is enabling flexibility, programmability and implementation of traditional network functions into generic hardware, in form of the so-called Virtual Network Functions (VNFs). Today, cloud service providers use Virtual Machines (VMs) for the instantiation of VNFs in the data center (DC) networks. To instantiate multiple VNFs in a typical scenario of Service Function Chains (SFCs), many important objectives need to be met simultaneously, such as server load balancing, energy efficiency and service execution time. The well-known \emph{VNF placement} problem requires solutions that often consider \emph{migration} of virtual machines (VMs) to meet this objectives. Ongoing efforts, for instance, are making a strong case for migrations to minimize energy consumption, while showing that attention needs to be paid to the Quality of Service (QoS) due to service interruptions caused by migrations. To balance the server allocation strategies and QoS, we propose using \emph{replications} of VNFs to reduce migrations in DC networks. We propose a Linear Programming (LP) model to study a trade-off between replications, which while beneficial to QoS require additional server resources, and migrations, which while beneficial to server load management can adversely impact the QoS. The results show that, for a given objective, the replications can reduce the number of migrations and can also enable a better server and data center network load balancing

SAFIUS - A secure and accountable filesystem over untrusted storage

We describe SAFIUS, a secure accountable file system that resides over an untrusted storage. SAFIUS provides strong security guarantees like confidentiality, integrity, prevention from rollback attacks, and accountability. SAFIUS also enables read/write sharing of data and provides the standard UNIX-like interface for applications. To achieve accountability with good performance, it uses asynchronous signatures; to reduce the space required for storing these signatures, a novel signature pruning mechanism is used. SAFIUS has been implemented on a GNU/Linux based system modifying OpenGFS. Preliminary performance studies show that SAFIUS has a tolerable overhead for providing secure storage: while it has an overhead of about 50% of OpenGFS in data intensive workloads (due to the overhead of performing encryption/decryption in software), it is comparable (or better in some cases) to OpenGFS in metadata intensive workloads.Comment: 11pt, 12 pages, 16 figure

A BDD Library

Binární rozhodovací diagram je datová struktura využívaná v mnoha oblastech informatiky. Tato práce popisuje BDD ako matematický formalizmus a navrhuje možnou reprezentaci BDD v počítači. Návrh je zaměřen především na rychlost snížením počtu alokací paměti a na jednoduchost a intuitivnost využívaní knihovny. V práci je několik jednoduchých příkladů užití knihovny a výstrahy, kterým by se měl programátor při používaní knihovny vyvarovat. Navržená reprezentace byla implementována v jazyce C.Binary decision program is a data structure used in many areas of information technology. This thesis describes BDD as a mathematical formalism and proposes possible representation of BDD in a computer. The propose is focused mainly on a reduction speed of number of memory allocation and on a simplicity and an intuitive system of using a library. There are several examples of a library usage and warnings which programmer should avoid of in the thesis. Proposed representation was implemented in C language.

Meeting the Memory Challenges of Brain-Scale Network Simulation

The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity, and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 105 neurons with up to 109 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been investigated in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Blue Gene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of neuronal simulators as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place. As a consequence, development cycles can be shorter and less expensive. Applying the model to our freely available Neural Simulation Tool (NEST), we identify the software components dominant at different scales, and develop general strategies for reducing the memory consumption, in particular by using data structures that exploit the sparseness of the local representation of the network. We show that these adaptations enable our simulation software to scale up to the order of 10,000 processors and beyond. As memory consumption issues are likely to be relevant for any software dealing with complex connectome data on such architectures, our approach and our findings should be useful for researchers developing novel neuroinformatics solutions to the challenges posed by the connectome project

Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval

The goal of this work is the computation of very compact binary hashes for image instance retrieval. Our approach has two novel contributions. The first one is Nested Invariance Pooling (NIP), a method inspired from i-theory, a mathematical theory for computing group invariant transformations with feed-forward neural networks. NIP is able to produce compact and well-performing descriptors with visual representations extracted from convolutional neural networks. We specifically incorporate scale, translation and rotation invariances but the scheme can be extended to any arbitrary sets of transformations. We also show that using moments of increasing order throughout nesting is important. The NIP descriptors are then hashed to the target code size (32-256 bits) with a Restricted Boltzmann Machine with a novel batch-level reg-ularization scheme specifically designed for the purpose of hashing (RBMH). A thorough empirical evaluation with state-of-the-art shows that the results obtained both with the NIP descriptors and the NIP+RBMH hashes are consistently outstanding across a wide range of datasets

An Automated XPATH to SQL Transformation Methodology for XML Data

In this thesis we present an automated system that allows users to execute XPATH queries against an XML data source. The system exploits the shared-inlining mapping from XML to Relational data. At the core of the system is an XPATH to SQL transformation algorithm that produces corresponding SQL queries for a subset of XPATH. This approach allows one to utilize standard relational databases to store XML data. Given a DTD, the system creates appropriate relational tables based on the shared-inlining method. The system is capable of transforming an XML data source that conforms to the DTD into relational data. The main component of the system is the XPATH interpreter that parses an XPATH expression for the XML data source and transforms it into an equivalent SQL query. The SQL query is then executed against the relational database and results are packaged into XML and returned as the answer to the XPATH query. The use of the relational database to store and query the XML data is transparent to the user as they interact only with the XPATH interpreter. This methodology provides a novel technique to provide an XML database system implementation. Index Words: XML SQL transformation, XPATH to SQL queries, XSU, Data mapping

EbbRT: Elastic Building Block Runtime - overview

EbbRT provides a lightweight runtime that enables the construction of reusable, low-level system software which can integrate with existing, general purpose systems. It achieves this by providing a library that can be linked into a process on an existing OS, and as a small library OS that can be booted directly on an IaaS node