Refine
Document Type
- Doctoral Thesis (5)
- diplomthesis (1)
Language
- English (6) (remove)
Has Fulltext
- yes (6)
Is part of the Bibliography
- no (6)
Keywords
- Big Data (1)
- Big Data Benchmarks (1)
- Caché (1)
- Cognitive (1)
- Conjoint Analysis (1)
- Data Analytics (1)
- Explicit Feedback (1)
- Heuristics (1)
- Implicit Feedback (1)
- InterSystems (1)
Institute
- Informatik (4)
- Informatik und Mathematik (2)
The main contribution of the thesis is in helping to understand which software system parameters mostly affect the performance of Big Data Platforms under realistic workloads. In detail, the main research contributions of the thesis are:
1. Definition of the new concept of heterogeneity for Big Data Architectures (Chapter 2);
2. Investigation of the performance of Big Data systems (e.g. Hadoop) in virtualized environments (Section 3.1);
3. Investigation of the performance of NoSQL databases versus Hadoop distributions (Section 3.2);
4. Execution and evaluation of the TPCx-HS benchmark (Section 3.3);
5. Evaluation and comparison of Hive and Spark SQL engines using benchmark queries (Section 3.4);
6. Evaluation of the impact of compression techniques on SQL-on-Hadoop engine performance (Section 3.5);
7. Extensions of the standardized Big Data benchmark BigBench (TPCx-BB)(Section 4.1 and 4.3);
8. Definition of a new benchmark, called ABench (Big Data Architecture Stack Benchmark), that takes into account the heterogeneity of Big Data architectures (Section 4.5).
The thesis is an attempt to re-define system benchmarking taking into account the new requirements posed by the Big Data applications. With the explosion of Artificial Intelligence (AI) and new hardware computing power, this is a first step towards a more holistic approach to benchmarking.
This thesis combines behavioral and cognitive approaches regarding the Web for analyzing users' behavior and supposed interests.
The work is placed in a new field of research called Web Science, which includes, but is not restricted to, the analysis of the World Wide Web. The term Web Science is affected by Tim Berners-Lee et al., who invited the researchers to "create a science of the web" [BLHH+06a]. The thesis is structured in two parts, reflecting the intersection of disciplines that is required for Web Science.
The first part is related to computer science and information systems. This part defines the Gugubarra concepts and algorithms for web user profiling and builds upon the results by Mushtaq et al. [MWTZ04]. This profiling aims at understanding the behavior and supposed interests of users. Based on these concepts, a framework was implemented to support the needs of web site owners. The core technologies used are Java, Spring, Hibernate, and content management systems. The design principles, architecture, implementation, and tests of the prototype are reported.
The second part is directly related to behavioral economics and is connected to the areas of economics, mathematics, and psychology. This part contributes to behavior models, as was claimed by Tim Berners-Lee et al.: "Though individual users may or may not be rational, it has long been noted that en masse people behave as utility maximisers. In that case, understanding the incentives that are available to web users should provide methods for generating models of behaviour..."[BLHH+06b]. The focus here is on studies that investigate the user's choice of online information services in a multi-attribute context. The introduced research framework takes into account background and local context effects and builds upon theoretical foundations by Tversky and Kahneman [TK86]. The findings provide useful insights to behavioral scientists and to practitioners on how to use framing strategies to alter the user's choice.
Time-critical applications process a continuous stream of input data and have to meet specific timing constraints. A common approach to ensure that such an application satisfies its constraints is over-provisioning: The application is deployed in a dedicated cluster environment with enough processing power to achieve the target performance for every specified data input rate. This approach comes with a drawback: At times of decreased data input rates, the cluster resources are not fully utilized. A typical use case is the HLT-Chain application that processes physics data at runtime of the ALICE experiment at CERN. From a perspective of cost and efficiency it is desirable to exploit temporarily unused cluster resources. Existing approaches aim for that goal by running additional applications. These approaches, however, a) lack in flexibility to dynamically grant the time-critical application the resources it needs, b) are insufficient for isolating the time-critical application from harmful side-effects introduced by additional applications or c) are not general because application-specific interfaces are used. In this thesis, a software framework is presented that allows to exploit unused resources in a dedicated cluster without harming a time-critical application. Additional applications are hosted in Virtual Machines (VMs) and unused cluster resources are allocated to these VMs at runtime. In order to avoid resource bottlenecks, the resource usage of VMs is dynamically modified according to the needs of the time-critical application. For this purpose, a number of previously not combined methods is used. On a global level, appropriate VM manipulations like hot migration, suspend/resume and start/stop are determined by an informed search heuristic and applied at runtime. Locally on cluster nodes, a feedback-controlled adaption of VM resource usage is carried out in a decentralized manner. The employment of this framework allows to increase a cluster’s usage by running additional applications, while at the same time preventing negative impact towards a time-critical application. This capability of the framework is shown for the HLT-Chain application: In an empirical evaluation the cluster CPU usage is increased from 49% to 79%, additional results are computed and no negative effect towards the HLT-Chain application are observed.
Deep learning and isolation based security for intrusion detection and prevention in grid computing
(2018)
The use of distributed computational resources for the solution of scientific problems, which require highly intensive data processing is a fundamental mechanism for modern scientific collaborations. The Worldwide Large Hadron Collider Computing Grid (WLCG) is one of the most important examples of a distributed infrastructure for scientific projects and is one of the pioneering examples of grid computing. The WLCG is the global grid that analyzes data from the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN), with 170 sites in 40 countries and more than 600,000 processing cores. The grid service providers grant users access to resources that they can utilize on demand for the execution of custom software applications used for the analysis of data. The code that the users can execute is completely flexible, and commonly there are no significant restrictions. This flexibility and the availability of immense computing power increases the security challenges of these environments. Attackers are a concern for grid administrators. These attackers may request the execution of software with a malicious code that gives them the possibility of compromising the underlying institutions’ infrastructure. Grid systems need security countermeasures to keep the user code running, without allowing access to critical components but whilst still retaining flexibility. The administrators of grid systems also need to be continuously monitoring the activities that the applications are carrying out. An analysis of these activities is necessary to detect possible security issues, to identify ongoing incidents and to perform autonomous responses. The size and complexity of grid systems make manual security monitoring and response expensive and complicated for human analysts. Legacy intrusion detection and prevention systems (IDPS) such as Snort and OSSEC are traditionally used for security incident monitoring in the grid, cloud, clusters and standalone systems. However, IDPS are limited due to the use of hardcoded fixed rules that need to be updated continuously to cope with different threats.
This thesis introduces an architecture for improving security in grid computing. The architecture integrates the use of security by isolation, behavior monitoring and deep learning (DL) for the classification of real-time traces of the running user payloads also known as grid jobs. The first component of the proposal, the Linux containers (LCs), are used to provide isolation between grid jobs and to gather specific traceable information about the behavior of individual jobs. LCs offer a safe environment for the execution of arbitrary user scripts or binaries, protecting the sensitive components of the grid member organizations. The containers consist of a software sandboxing technique and form a lightweight alternative to other technologies such as virtual machines (VMs) that usually implement a full machine-level emulation and can, therefore, significantly affect the performance. This performance loss is commonly unacceptable in high-throughput computing scenarios. Containers enable the collection of monitoring information from the processes running inside them. The data collected via the LCs monitoring is employed to feed a DL-based IDPS.
DL methods can acquire knowledge from experience, which eliminates the need for operators to formally specify all the knowledge that a system requires. These methods can improve IDPS by building models that are utilized to detect security incidents automatically, having the ability to generalize to new classes of issues. DL can produce lower false positive rates for intrusion detection, but also provides a measure of false negatives, which can be improved with new training data. Convolutional neural networks (CNNs) are utilized for the distinction between regular and malicious job classes. A set of samples is collected from regular production grid jobs from the grid infrastructure of “A Large Ion Collider Experiment” (ALICE) and malicious Linux binaries from a malware research website. The features extracted from these samples are utilized for the training and validation of the machine learning (ML) models. The utilization of a generative approach to enhance the required training data is also proposed. Recurrent neural networks (RNN) are used as generative models for the simulation of training data that complements and improves the real collected dataset. This data augmentation strategy is useful to supplement the lack of training data in ML processes.
...
The economic success of the World Wide Web makes it a highly competitive environment for web businesses. For this reason, it is crucial for web business owners to learn what their customers want. This thesis provides a conceptual framework and an implementation of a system that helps to better understand the behavior and potential interests of web site visitors by accounting for both explicit and implicit feedback. This thesis is divided into two parts.
The first part is rooted in computer science and information systems and uses graph theory and an extended click-stream analysis to define a framework and a system tool that is useful for analyzing web user behavior by calculating the interests of the users.
The second part is rooted in behavioral economics, mathematics, and psychology and is investigating influencing factors on different types of web user choices. In detail, a model for the cognitive process of rating products on the Web is defined and an importance hierarchy of the influencing factors is discovered.
Both parts make use of techniques from a variety of research fields and, therefore, contribute to the area of Web Science.
RDF is widely used in order to catalogue the chaos of data across the internet. But these descriptions must be stored, evaluated, analyzed and verified. This creates the need to search for an environment to realize these aspects and strengthen RDFs influence. InterSystems postrelational database Caché exposes many features that are similar to RDF and provide persistence with semantic part. Some models for relational databases exist but these lack features like object-oriented data-structures and multidimensional variables. The aim of this thesis is to develop an RDF model for Caché that saves RDF data in an object-oriented form. Furthermore an interface for importing RDF data will be presented and implemented.