Cloud Process Execution Engine - Evaluation of the Core Concepts

In this technical report we describe describe the Domain Specific Language (DSL) of the Workflow Execution Execution (WEE). Instead of interpreting an XML based workflow description language like BPEL, the WEE uses a minimized but expressive set of statements that runs directly on to of a virtual machine that supports the Ruby language.Frameworks/Virtual Machines supporting supporting this language include Java, .NET and there exists also a standalone Virtual Machine. Using a DSL gives us the advantage of maintaining a very compact code base of under 400 lines of code, as the host programming language implements all the concepts like parallelism, threads, checking for syntactic correctness. The implementation just hooks into existing statements to keep track of the workflow and deliver information about current existing context variables and state to the environment that embeds WEE

Parallel Neural Networks in Golang

This paper describes the design and implementation of parallel neural networks (PNNs) with the novel programming language Golang. We follow in our approach the classical Single-Program Multiple-Data (SPMD) model where a PNN is composed of several sequential neural networks, which are trained with a proportional share of the training dataset. We used for this purpose the MNIST dataset, which contains binary images of handwritten digits. Our analysis focusses on different activation functions and optimizations in the form of stochastic gradients and initialization of weights and biases. We conduct a thorough performance analysis, where network configurations and different performance factors are analyzed and interpreted. Golang and its inherent parallelization support proved very well for parallel neural network simulation by considerable decreased processing times compared to sequential variants.Comment: Extended version of paper Daniela Kalwarowskyj and Erich Schikuta, SPMD-based Neural Network Simulation with Golang, published at International Conference on Computational Science (ICCS) 202

A Survey on Property-Preserving Database Encryption Techniques in the Cloud

Outsourcing a relational database to the cloud offers several benefits, including scalability, availability, and cost-effectiveness. However, there are concerns about the security and confidentiality of the outsourced data. A general approach here would be to encrypt the data with a standardized encryption algorithm and then store the data only encrypted in the cloud. The problem with this approach, however, is that with encryption, important properties of the data such as sorting, format or comparability, which are essential for the functioning of database queries, are lost. One solution to this problem is the use of encryption algorithms, which also preserve these properties in the encrypted data, thus enabling queries to encrypted data. These algorithms range from simple algorithms like Caesar encryption to secure algorithms like mOPE. The report at hand presents a survey on common encryption techniques used for storing data in relation Cloud database services. It presents the applied methods and identifies their characteristics.Comment: 34 pages, 10 figure

A Structured Marketplace for Arbitrary Services

Abstract Creating simple marketplaces with common rules, that enable the dynamic selection and consumption of functionality, is the missing link to allow small businesses to enter the cloud, not only as consumers, but also as vendors. In this paper we present the concepts behind a hybrid service and process repository that can act as the foundation for such a marketplace, as well as a prototype that allowed us to test various real-world scenarios. The advantage of a hybrid service and process repository is, that it not only holds a flat list of services, but exposes a generic set of use cases, information how specific services can be used to implement the use cases as well as information to select services at run-time according to customers goal functions