Peter Hellinckx

Survey of the DASH7 Alliance Protocol for 433 MHz Wireless Sensor Communication

433 MHz is getting more attention for Machine-to-Machine communication. This paper presents the DASH7 Alliance Protocol, an active RFID alliance standard for 433 MHz wireless sensor communication based on the ISO/IEC 18000-7. First, the major differences of 433 MHz communication compared to more frequently used frequencies, such as 2.4 GHz and 868/920 MHz are explained. Subsequently, the general concepts of DASH7 Alliance Protocol are described, such as the BLAST networking topology and the different OSI layer implementations, in a top-down method. Basic DASH7 features such as the advertising protocol, ad-hoc synchronization and query based addressing are used to explain the different layers. Finally, the paper introduces a software stack implementation named OSS-7, which is an open source implementation of the DASH7 alliance protocol used for testing, rapid prototyping, and demonstrations.

DDoS defense system for web services in a cloud environment

Abstract Recently, a new kind of vulnerability has surfaced: application layer Denial-of-Service (DoS) attacks targeting web services. These attacks aim at consuming resources by sending Simple Object Access Protocol (SOAP) requests that contain malicious XML content. These requests cannot be detected on the network or transportation (TCP/IP) layer, as they appear as legitimate packets. Until now, there is no web service security specification that addresses this problem. Moreover, the current WS-Security standard induces crucial additional vulnerabilities threatening the availability of certain web service implementations. First, this paper introduces an attack-generating tool to test and confirm previously reported vulnerabilities. The results indicate that the attacks have a devastating impact on the web service availability, even whilst utilizing an absolute minimum of attack resources. Since these highly effective attacks can be mounted with relative ease, it is clear that defending against them is essential, looking at the growth of cloud and web services. Second, this paper proposes an intelligent, fast and adaptive system for detecting against XML and HTTP application layer attacks. The intelligent system works by extracting several features and using them to construct a model for typical requests. Finally, outlier detection can be used to detect malicious requests. Furthermore, the intelligent defense system is capable of detecting spoofing and regular flooding attacks. The system is designed to be inserted in a cloud environment where it can transparently protect the cloud broker and even cloud providers. For testing its effectiveness, the defense system was deployed to protect web services running on WSO2 with Axis2: the defacto standard for open source web service deployment. The proposed defense system demonstrates its capability to effectively filter out the malicious requests, whilst generating a minimal amount of overhead for the total response time.

TACLeBench : a benchmark collection to support worst-case execution time research

Engineering related research, such as research on worst-case execution time, uses experimentation to evaluate ideas. For these experiments we need example programs. Furthermore, to make the research experimentation repeatable those programs shall be made publicly available. We collected open-source programs, adapted them to a common coding style, and provide the collection in open-source. The benchmark collection is called TACLeBench and is available from GitHub in version 1.9 at the publication date of this paper. One of the main features of TACLeBench is that all programs are self-contained without any dependencies on standard libraries or an operating system.

Runtime Prediction Based Grid Scheduling of Parameter Sweep Jobs

This paper examines the problem of predicting job runtimes by exploiting the properties of parameter sweeps. A new parameter sweep prediction framework GIPSy (grid information prediction system) is introduced. Predictions are made based on prior runtime information and the parameters used to configure each job. The main objective is providing a tool combining development, simulation and application of prediction models within one framework. The different kinds of available sample selectors and models are discussed in detail. Results are presented for a quantum physics problem. A previously introduced scheduling technique and the implementation called PGS (prediction based grid scheduling) is improved and presented in combination with GIPSy to obtain a real-world grid implementation that optimizes the distribution of parameter sweeps.

The 5H resonance structure studied with a three-cluster J-matrix model

The resonance structure of 5H is investigated within a three-cluster microscopic model. Hyperspherical harmonics are used to characterize the channels of the three-cluster continuum and to implement the appropriate boundary conditions. The model reveals the energy and width of the 5H resonance states and allows for a detailed channel analysis. These results are verified against the available experimental data and compare qualitatively favorably with some other theoretical calculations.

Dynamic Grid Scheduling Using Job Runtime Requirements and Variable Resource Availability

We describe a scheduling technique in which estimated job runtimes and estimated resource availability are used to efficiently distribute workloads across a homogeneous grid of resources with variable availability. The objective is to increase efficiency by minimizing job failure caused by resources becoming unavailable. Optimal scheduling will be accomplished by mapping jobs onto resources with sufficient availability. Both the scheduling technique and the implementation called PGS (Prediction based Grid Scheduling) are described in detail. Results are presented for a set of sleep jobs, and compared with a first come, first serve scheduling approach.

User experiences with nuclear physics calculations on a h2o metacomputing system and on the BEgrid

We report on user experiences gathered with quantum nuclear scattering calculations on a H2O based metacomputing system and on the BEgrid, the Belgian EGEE Grid infrastructure. We compare quantifiable aspects such as Grid utilization but also ease-of-use and the experience encountered in porting the application to each of the Grid environments.

Context-Aware Optimization of Distributed Resources in Internet of Things Using Key Performance Indicators

The recent advancements in Internet of Things (IoT) show us a glimpse of a future in which all our devices are connected to the internet, providing users with services that make life easier, more comfortable and safer. Although this interconnectivity seems simple, in practice management of the IoT hardware and the enormous amounts of data it generates is challenging. To bring the connected future into reality and build advanced and useful services, better resource usage estimation (memory, bandwidth, storage etc.) and resource management is required. We propose a IoT optimization methodology, where resources are estimated at each level of the IoT architecture (i.e. nodes, edges and cloud). Using these estimates, the executed code is redistributed across the network in order to optimize the cost and efficiency of the IoT environment, while taking into a specific context (e.g. environment). Initially, we aim to apply this methodology for statically defined contexts. In our future research we aim to perform the optimization at runtime, redistributing tasks across the IoT network dynamically as the context of the nodes changes.

Modeling Resource Prices in Grid Markets

Economic principles have been proposed in the literature to underpin the algorithms for managing resources in complex distributed systems such as grids or peer-to-peer systems. Such principles represents a shift away from traditional system-centric management towards user-centric management. It allows each user to formulate an individual valuation of the requested resources by indicating an acceptable price level for their usage. in a computational commodity market, the price for CPU usage is set by equilibrating supply and demand. We investigate the possibility of modeling the price evolution in a computational market with number of techniques (support vector machines, back propagation neural networks and sequence patterns). If such models have predictive capability, they may be used to optimize resource usage. This study extends previous work. It now includes more differentiated resource model with several categories of CPUs differing in their performance levels and it also adds new modeling approaches for the CPU price evolution. Our study indicates no significant difference in the capabilities of the approaches.

Predicting Parameter Sweep Jobs: From Simulation to Grid Implementation

Efficiently using the computational power made available through desktop grids based distributed systems is a complicated and many-sided problem, caused by the intermittent resource availability. In this paper a novel solution is presented for predicting the runtimes of parameter sweep jobs. These jobs are characterized by their lack of inter-dependence and suitability for runtime prediction by modeling. This makes them ideal candidates for deployment on volatile grid configurations using prediction based techniques. The parameter sweep prediction framework used to make the predictions is referred to as GIPSy (Grid Information Prediction System). Previous research involving GIPSy has focused on results obtained during simulation were it is necessary to make some basic assumptions. By combining GIPSy with PGS (Prediction based Grid Scheduling), an actual grid implementation, real results can be obtained. A detailed comparison between the expected results, based on simulation analysis, and the final results is given. Discrepancies are highlighted and possible causes are identified, solutions are proposed and implemented. By comparing the results for different model building configurations an optimal configuration is found that produces reliable result independent of the chosen job type. Results are presented for a quantum physics problem and two simulated workloads represented by sleepjobs.