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Wissenschaftliche Vorträge

Automation of the SLA Life Cycle in Cloud Computing

Verteidigung im Promotionsverfahren von M. Sc. Waheed Aslam Ghumman (Institut für Systemarchitektur, Lehrstuhl Rechnernetze)

30.1.2017, 9:00 Uhr, APB 1004 (Ratssaal)

Cloud computing has become a prominent paradigm to offer on-demand services for softwares, infrastructures and platforms. Cloud services are contracted by a service level agreement (SLA) between a cloud service provider (CSP) and a cloud service user (CSU) which contains service definitions, quality of service (QoS) parameters, guarantees and obligations. Cloud service providers mostly offer SLAs in descriptive format which is not directly consumable by a machine or a system. The SLA written in natural language may impede the utility of rapid elasticity in a cloud service. Manual management of SLAs with growing usage of cloud services can be a challenging, erroneous and tedious task especially for the CSUs acquiring multiple cloud services. The necessity of automating the complete SLA life cycle (which includes SLA description in machine readable format, negotiation, monitoring and management) becomes imminent due to complex requirements for the precise measurement of QoS parameters. Current approaches toward automating the complete SLA life cycle, lack in standardization, completeness and applicability to cloud services. Automation of different phases of the SLA life cycle (e.g. negotiation, monitoring and management) is dependent on the availability of a machine readable SLA. In this work, a structural specification for the SLAs in cloud computing (S3LACC in short) is presented which is designed specifically for cloud services, covers complete SLA life cycle and conforms with the available standards. A time efficient SLA negotiation technique is accomplished (based on the S3LACC) for concurrently negotiating with multiple CSPs. After successful negotiation process, next leading task in the SLA life cycle is to monitor the cloud services for ensuring the quality of service according to the agreed SLA. A distributed monitoring approach for the cloud SLAs is presented, in this work, which is suitable for services being used at single or multiple locations. The proposed approach reduces the number of communications of SLA violations to a monitoring coordinator by eliminating the unnecessary communications. The presented work on the complete SLA life cycle automation is evaluated and validated with the help of use cases, experiments and simulations.


Toward Resilience in HPC: A Prototype to Analyze and Predict System Behavior

Vortrag im Promotionsverfahren (alle Studiengänge) von Siavash Ghiasvand (ZIH)

2.2.2017, 11:00 Uhr, APB 1004 (Ratssaal)

Nowadays, failures in high performance computers (HPC) became the norm rather than the exception. In the near future, the mean time between failures (MTBF) of HPC systems is expected to be too short, and that current failure recovery mechanisms e.g., checkpoint-restart, will no longer be able to recover the systems from failures. Early failure detection is a new class of failure recovery methods that can be also beneficial for HPC systems with short MTBF. Detecting failures in their early stage can reduce their negative effects by preventing their propagation to other parts of the system. Analyzing system behavior, may even enable us to predict certain types of failures, and proactively employ protection mechanisms against them. Preventing failures and their propagation within the HPC system, besides extending the system uptime, reduces the energy consumption, and subsequently the cost of system resilience. We use 'Taurus' HPC cluster as our test bed.
This presentation, gives an overview about the state-of-the-art in failure detection and prediction on HPC systems, and current HPC system behavioral analysis methods.
Furthermore, correlation of failures, importance of system logs, challenges toward a generic approach, and current status of the project will be discussed.

Diese Veranstaltung wird unterstützt von ZIH.


Graph Processing in Main-Memory Column Stores

Verteidigung im Promotionsverfahren von Dipl.-Inf. Marcus Paradies (Institut für Systemarchitektur, Professur Datenbanken)

3.2.2017, 15:00 Uhr, APB 1004 (Ratssaal)

Evermore, novel and traditional business applications leverage the advantages of a graph data model, such as the offered schema flexibility and an explicit representation of relationships between entities. As a consequence, companies are confronted with the challenge of storing, manipulating, and querying terabytes of graph data for enterprise-critical applications. Although these business applications operate on graph-structured data, they still require direct access to the relational data and typically rely on an RDBMS to keep a single source of truth and access. Existing solutions performing graph operations on business-critical data either use a combination of SQL and application logic or employ a graph data management system. For the first approach, relying solely on SQL results in poor execution performance caused by the functional mismatch between typical graph operations and the relational algebra. To the worse, graph algorithms expose a tremendous variety in structure and functionality caused by their often domain-specific implementations and therefore can be hardly integrated into a database management system other than with custom coding. Since the majority of these enterprise-critical applications exclusively run on relational DBMSs, employing a specialized system for storing and processing graph data is typically not sensible. Besides the maintenance overhead for keeping the systems in sync, combining graph and relational operations is hard to realize as it requires data transfer across system boundaries. A basic ingredient of graph queries and algorithms are traversal operations and are a fundamental component of any database management system that aims at storing, manipulating, and querying graph data. Well-established graph traversal algorithms are standalone implementations relying on optimized data structures. The integration of graph traversals as an operator into a database management system requires a tight integration into the existing database environment and a development of new components, such as a graph topology-aware optimizer and accompanying graph statistics, graph-specific secondary index structures to speedup traversals, and an accompanying graph query language. In this thesis, we introduce and describe Graphite, a hybrid graph-relational data management system. Graphite is a performance-oriented graph data management system as part of an RDBMS allowing to seamlessly combine processing of graph data with relational data in the same system. We propose a columnar storage representation for graph data to leverage the already existing and mature data management and query processing infrastructure of relational database management systems. At the core of Graphite we propose an execution engine solely based on set operations and graph traversals. Our design is driven by the observation that different graph topologies expose different algorithmic requirements to the design of a graph traversal operator. We derive two graph traversal implementations targeting the most common graph topologies and demonstrate how graph-specific statistics can be leveraged to select the optimal physical traversal operator. To accelerate graph traversals, we devise a set of graph-specific, updateable secondary index structures to improve the performance of vertex neighborhood expansion. Finally, we introduce a domain-specific language with an intuitive programming model to extend graph traversals with custom application logic at runtime. We use the LLVM compiler framework to generate efficient code that tightly integrates the user-specified application logic with our highly optimized built-in graph traversal operators. Our experimental evaluation shows that Graphite can outperform native graph management systems by several orders of magnitude while providing all the features of an RDBMS, such as transaction support, backup and recovery, security and user management, effectively providing a promising alternative to specialized graph management systems that lack many of these features and require expensive data replication and maintenance processes.



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