Gregory Katsaros

Platform-as-a-Service Architecture for Real-Time Quality of Service Management in Clouds

Cloud computing offers the potential to dramatically reduce the cost of software services through the commoditization of information technology assets and on-demand usage patterns. However, the complexity of determining resource provision policies for applications in such complex environments introduces significant inefficiencies and has driven the emergence of a new class of infrastructure called Platform-as-a-Service (PaaS). In this paper, we present a novel PaaS architecture being developed in the EU IST IRMOS project targeting real-time Quality of Service (QoS) guarantees for online interactive multimedia applications. The architecture considers the full service lifecycle including service engineering, service level agreement design, provisioning and monitoring. QoS parameters at both application and infrastructure levels are given specific attention as the basis for provisioning policies in the context of temporal constraints. The generic applicability of the architecture is being verified and validated through implemented scenarios from three important application sectors (film post-production, virtual augmented reality for engineering design, collaborative e-Learning in virtual worlds).

A Self-adaptive hierarchical monitoring mechanism for Clouds

While Cloud computing offers the potential to dramatically reduce the cost of software services through the commoditization of IT assets and on-demand usage patterns, one has to consider that Future Internet applications raise the need for environments that can facilitate real-time and interactivity and thus pose specific requirements to the underlying infrastructure. The latter, should be able to efficiently adapt resource provisioning to the dynamic Quality of Service (QoS) demands of such applications. To this direction, in this paper we present a monitoring system that facilitates on-the-fly self-configuration in terms of both the monitoring time intervals and the monitoring parameters. The proposed approach forms a multi-layered monitoring framework for measuring QoS at both application and infrastructure levels targeting trigger events for runtime adaptability of resource provisioning estimation and decision making. Besides, we demonstrate the operation of the implemented mechanism and evaluate its effectiveness using a real-world application scenario, namely Film Post Production.

A service framework for energy-aware monitoring and VM management in Clouds

The monitoring of QoS parameters in Services Computing as well as in Clouds has been a functionality provided by all contemporary systems. As the optimization of energy consumption becomes a major concern for system designers and administrators, it can be considered as another QoS metric to be monitored. In this paper, we present a service framework that allows us to monitor the energy consumption of a Cloud infrastructure, calculate its energy efficiency, and evaluate the gathered data in order to put in place an effective virtual machine (VM) management. In that context, a simulation scenario of an eco-driven VM placement policy resulted in a 14% improvement of the infrastructures energy efficiency. In total, the proposed approaches and implementations have been validated against a testbed, producing very promising results regarding the prospect of energy efficiency as an important quality factor in Clouds.

Building a Service-Oriented Monitoring Framework with REST and Nagios

Over the past years services computing has become an emerging science that is highly regarded as a necessary technology not only by research but by industry as well. In the same context, the advent of cloud computing gave to services and web applications a whole new perspective and potential. Regardless of the rapid evolution in the fields of services and web technologies, ensuring the QoS of computing resources still remains an important topic. To this end, monitoring computing resources and application execution is an integral part of the services computing value chain. In this paper we present the architectural design and implementation of a service framework that monitors the resources of a physical as well as virtual infrastructure. Our solution extends Nagios, a widely used monitoring toolkit, through the implementation of NEB2REST, a Restful Event Brokering module.

Workflow management for soft real-time interactive applications in virtualized environments

Many applications, especially the ones implementing multi-user collaborative environments, fall within the context of soft real-time systems in which only small deviations from timing constraints are allowed. The advancements in distributed computing have made it possible to follow a service oriented approach, taking advantage of the benefits this provides. In this context, applications consist of soft real-time critical application service components that interact with each other to provide the corresponding application functionality, forming application workflows. In this paper we present a new architectural design and implementation of a Workflow Management approach. This approach covers enacting soft real-time application service components according to a workflow description language, synchronizing the application components, monitoring the execution and reacting to events within a distributed virtualized environment. We also demonstrate the operation of the implemented mechanism and evaluate its effectiveness using an application scenario with soft real-time interactivity characteristics, namely Film post-production, under realistic settings.

A Service-Oriented Framework for GNU Octave-Based Performance Prediction

Cloud/Grid environments are characterized by a diverse set of technologies used for communication, execution and management. Service Providers, in this context, need to be equipped with an automated process in order to optimize service provisioning through advanced performance prediction methods. Furthermore, existing software solutions such as GNU Octave offer a wide range of possibilities for implementing these methods. However, their automated use as services in the distributed computing paradigm includes a number of challenges from a design and implementation point of view. In this paper, a loosely coupled service-oriented implementation is presented, for taking advantage of software like Octave in the process of creating and using prediction models during the service lifecycle of a SOI. In this framework, every method is applied as an Octave script in a plug-in fashion. The design and implementation of the approach is validated through a case study application which involves the transcoding of raw video to MPEG4.

A service oriented monitoring framework for soft real-time applications

The advancements in distributed computing have driven the emergence of Service Oriented infrastructures that allow for on-demand provision of ICT assets. Taking into consideration the complexity of distributed environments, significant challenges exist in providing and managing the offered on-demand resources with the required level of Quality of Service (QoS), especially for real-time interactive multimedia applications. Monitoring mechanisms are a fundamental part in service-based platforms that support realtime QoS guarantees by providing coherent and consistent real-time attributes at various levels of the infrastructure (application, network, storage, processing). In this paper we present an architectural design and implementation of a complete monitoring framework for measuring QoS at both application and infrastructure levels targeting trigger events for runtime adaptability of resource provisioning estimation and decision-making. We also demonstrate the operation of the implemented mechanism and evaluate its effectiveness using an application scenario, namely Film Postproduction.

Parametric Design and Performance Analysis of a Decoupled Service-Oriented Prediction Framework Based on Embedded Numerical Software

In modern utility computing infrastructures, like grids and clouds, one of the significant actions of a service provider is to predict the resources needed by the services included in its platform in an automated fashion for service provisioning optimization. Furthermore, a variety of software toolkits exist that implement an extended set of algorithms applicable to workload forecasting. However, their automated use as services in the distributed computing paradigm includes a number of design and implementation challenges. In this paper, a decoupled framework is presented, for taking advantage of software like GNU Octave in the process of creating and using prediction models during the service life cycle of a SOI. A performance analysis of the framework is also conducted. In this context, a methodology for creating parametric or gearbox services with multiple modes of operations based on the execution conditions is portrayed and is applied to transform the aforementioned service framework to optimize service performance. A new estimation algorithm is introduced, that creates performance rules of applications as black boxes, through the creation and usage of genetically optimized artificial neural networks. Through this combination, the critical parameters of the networks are decided through an evolutionary iterative process.

An Integrated Monitoring Infrastructure for Cloud Environments

As Cloud Computing evolves to becoming a major technological paradigm of our times, topics like Quality of Service (QoS) assurance and resource monitoring will remain active fields of investigation and research. Mechanisms that will allow the Consumers as well as the Providers to monitor the operation of the application into the Cloud or the Cloud itself are of great importance for the long-term adoption of this technology. In this paper we propose an architectural solution of an integrated monitoring infrastructure for Cloud environments. The mechanism is based on a multi-level design of a collection as well as a management layer. The collection functionality supports information coming from the physical, virtual and service infrastructures. By exploiting open source APIs combined with custom components we have come up with a generic yet efficient solution, applicable to public, private and hybrid Cloud scenarios.

Content Based SLAs in Cloud Computing Environments

In this paper, we address the problem of managing SLAs in cloud computing environments. The idea is to take advantage of the content terms that concern the objects and support more efficient capabilities, such as quicker search and retrieval of the objects. As a result, the operational cost is reduced and consequently this fact lessens the customers charge.