Ivona Brandic

A survey on service quality description

Quality of service (QoS) can be a critical element for achieving the business goals of a service provider, for the acceptance of a service by the user, or for guaranteeing service characteristics in a composition of services, where a service is defined as either a software or a software-support (i.e., infrastructural) service which is available on any type of network or electronic channel. The goal of this article is to compare the approaches to QoS description in the literature, where several models and metamodels are included. consider a large spectrum of models and metamodels to describe service quality, ranging from ontological approaches to define quality measures, metrics, and dimensions, to metamodels enabling the specification of quality-based service requirements and capabilities as well as of SLAs (Service-Level Agreements) and SLA templates for service provisioning. Our survey is performed by inspecting the characteristics of the available approaches to reveal which are the consolidated ones and which are the ones specific to given aspects and to analyze where the need for further research and investigation lies. The approaches here illustrated have been selected based on a systematic review of conference proceedings and journals spanning various research areas in computer science and engineering, including: distributed, information, and telecommunication systems, networks and security, and service-oriented and grid computing.

Low level Metrics to High level SLAs - LoM2HiS framework: Bridging the gap between monitored metrics and SLA parameters in cloud environments

Cloud computing represents a novel on-demand computing approach where resources are provided in compliance to a set of predefined non-functional properties specified and negotiated by means of Service Level Agreements (SLAs). In order to avoid costly SLA violations and to timely react to failures and environmental changes, advanced SLA enactment strategies are necessary, which include appropriate resource-monitoring concepts. Currently, Cloud providers tend to adopt existing monitoring tools, as for example those from Grid environments. However, those tools are usually restricted to locality and homogeneity of monitored objects, are not scalable, and do not support mapping of low-level resource metrics e.g., system up and down time to high-level application specific SLA parameters e.g., system availability. In this paper we present a novel framework for managing the mappings of the Low-level resource Metrics to High-level SLAs (LoM2HiS framework). The LoM2HiS framework is embedded into FoSII infrastructure, which facilitates autonomic SLA management and enforcement. Thus, the LoM2HiS framework detects future SLA violation threats and can notify the enactor component to act so as to avert the threats. We discuss the conceptual model of the LoM2HiS framework, followed by the implementation details. Finally, we present the first experimental results and a proof of concept of the LoM2HiS framework.

Cloud Computing: Survey on Energy Efficiency

Cloud computing is today’s most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such great significance comes with the support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. Their share in power consumption generates between 1.1% and 1.5% of the total electricity use worldwide and is projected to rise even more. Such alarming numbers demand rethinking the energy efficiency of such infrastructures. However, before making any changes to infrastructure, an analysis of the current status is required. In this article, we perform a comprehensive analysis of an infrastructure supporting the cloud computing paradigm with regards to energy efficiency. First, we define a systematic approach for analyzing the energy efficiency of most important data center domains, including server and network equipment, as well as cloud management systems and appliances consisting of a software utilized by end users. Second, we utilize this approach for analyzing available scientific and industrial literature on state-of-the-art practices in data centers and their equipment. Finally, we extract existing challenges and highlight future research directions.

Monitoring and Analyzing Influential Factors of Business Process Performance

Business activity monitoring enables continuous observation of key performance indicators (KPIs). However, if things go wrong, a deeper analysis of process performance becomes necessary. Business analysts want to learn about the factors that influence the performance of business processes and most often contribute to the violation of KPI target values, and how they relate to each other. We provide a framework for performance monitoring and analysis of WS-BPEL processes, which consolidates process events and Quality of Service measurements. The framework uses machine learning techniques in order to construct tree structures, which represent the dependencies of a KPI on process and QoS metrics. These dependency trees allow business analysts to analyze how the process KPIs depend on lower-level process metrics and QoS characterisitics of the IT infrastructure. Deeper knowledge about the structure of dependencies can be gained by drill-down analysis of single factors of influence.

Adaptive resource configuration for Cloud infrastructure management

To guarantee the vision of Cloud Computing QoS goals between the Cloud provider and the customer have to be dynamically met. This so-called Service Level Agreement (SLA) enactment should involve little human-based interaction in order to guarantee the scalability and efficient resource utilization of the system. To achieve this we start from Autonomic Computing, examine the autonomic control loop and adapt it to govern Cloud Computing infrastructures. We first hierarchically structure all possible adaptation actions into so-called escalation levels. We then focus on one of these levels by analyzing monitored data from virtual machines and making decisions on their resource configuration with the help of knowledge management (KM). The monitored data stems both from synthetically generated workload categorized in different workload volatility classes and from a real-world scenario: scientific workflow applications in bioinformatics. As KM techniques, we investigate two methods, Case-Based Reasoning and a rule-based approach. We design and implement both of them and evaluate them with the help of a simulation engine. Simulation reveals the feasibility of the CBR approach and major improvements by the rule-based approach considering SLA violations, resource utilization, the number of necessary reconfigurations and time performance for both, synthetically generated and real-world data. Highlights? We apply knowledge management to guarantee SLAs and low resource wastage in Clouds. ? Escalation levels provide a hierarchical model to structure possible reconfiguration actions. ? Case-Based Reasoning and rule-based approach prove feasibility as KM techniques. ? In-depth evaluation of rule-based approach shows major improvements towards CBR. ? KM is applied to real-world data gathered from scientific bioinformatic workflows.

Towards Self-Manageable Cloud Services

Cloud computing represents a promising computing paradigm, where computational power is provided as a utility. An important characteristic of Cloud computing, other than in similar paradigms like Grid or HPC computing, is the provision of non-functional guarantees to users. Thereby, applications can be executed considering predefined execution time, price, security or privacy standards, which are guaranteed in real time in form of Service Level Agreements (SLAs). However, due to changing components, workload, external conditions, hardware, and software failures, established SLAs may be violated. Thus, frequent user interactions with the system, which are usually necessary in case of failures, might turn out to be an obstacle for the success of Cloud computing. In this paper we discuss self-manageable Cloud services. In case of failures, environmental changes, and similar, services manage themselves automatically following the principles of autonomic computing. Based on the life cycle of a self-manageable Cloud service we derive a resource submission taxonomy. Furthermore, we present an architecture for the implementation of self-manageable Cloud services. Finally, we discuss the application of autonomic computing to Cloud services based on service mediation and negotiation bootstrapping case study.

QoS support for time-critical grid workflow applications

Time critical grid applications as for example simulations for medical surgery or disaster recovery have special quality of service requirements. The Vienna Grid Environment, developed and evaluated in the context of the EU Project GEMSS, facilitates the provision of HPC applications as QoS-aware grid services by providing support for dynamic negotiation of various QoS guarantees like required execution time and price. In this paper, we extend the QoS mechanisms offered by the Vienna Grid Environment to workflow applications. We describe QoS extensions of the business process execution language and present a first prototype of a corresponding QoS-aware workflow engine which implements different strategies in order to bind the tasks of a workflow to adequate grid services subject to user-specified QoS constraints. We present different grid workflow planning approaches as well as first experimental results

Compliant Cloud Computing (C3): Architecture and Language Support for User-Driven Compliance Management in Clouds

Cloud computing represents a promising computing paradigm, where computational power is provided similar to utilities like water, electricity or gas. While most of the Cloud providers can guarantee some measurable non-functional performance metrics e.g., service availability or throughput, there is lack of adequate mechanisms for guaranteeing certifiable and auditable security, trust, and privacy of the applications and the data they process. This lack represents an obstacle for moving most business relevant applications into the Cloud. In this paper we devise a novel approach for compliance management in Clouds, which we termed Compliant Cloud Computing (C3). On one hand, we propose novel languages for specifying compliance requirements concerning security, privacy, and trust by leveraging domain specific languages and compliance level agreements. On the other hand, we propose the C3 middleware responsible for the deployment of certifiable and auditable applications, for provider selection in compliance with the user requirements, and for enactment and enforcement of compliance level agreements. We underpin our approach with a use case discussing various techniques necessary for achieving security, privacy, and trust in Clouds as for example data fragmentation among different protection domains or among different geographical regions.

An SLA-based resource virtualization approach for on-demand service provision

Cloud computing is a newly emerged research infrastructure that builds on the latest achievements of diverse research areas, such as Grid computing, Service-oriented computing, business processes and virtualization. In this paper we present an architecture for SLA-based resource virtualization that provides an extensive solution for executing user applications in Clouds. This work represents the first attempt to combine SLA-based resource negotiations with virtualized resources in terms of on-demand service provision resulting in a holistic virtualization approach. The architecture description focuses on three topics: agreement negotiation, service brokering and deployment using virtualization. The contribution is also demonstrated with a real-world case study.

VGE - a service-oriented grid environment for on-demand supercomputing

In this paper, we present the Vienna Grid Environment, a service-oriented grid infrastructure based on standard Web services technologies. VGE automates the provision of HPC applications as grid services for on-demand super-computing and simplifies the construction of client-side applications. As a key distinguishing feature, VGE supports a flexible QoS negotiation model which enables clients to negotiate dynamically, and on a case-by-case basis, QoS guarantees on execution time and price with potential service providers. The VGE service provision framework is currently utilized in the context of the EU Project GEMSS, which focuses on the provision of advanced medical simulation services by means of a grid infrastructure.