George Kousiouris

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).

The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks

The aim of this paper is to study and predict the effect of a number of critical parameters on the performance of virtual machines (VMs). These parameters include allocation percentages, real-time scheduling decisions and co-placement of VMs when these are deployed concurrently on the same physical node, as dictated by the server consolidation trend and the recent advances in the Cloud computing systems. Different combinations of VM workload types are investigated in relation to the aforementioned factors in order to find the optimal allocation strategies. What is more, different levels of memory sharing are applied, based on the coupling of VMs to cores on a multi-core architecture. For all the aforementioned cases, the effect on the score of specific benchmarks running inside the VMs is measured. Finally, a black box method based on genetically optimized artificial neural networks is inserted in order to investigate the degradation prediction ability a priori of the execution and is compared to the linear regression method.

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.

Migrating Legacy Software to the Cloud with ARTIST

As cloud computing allows improving the quality of software and aims at reducing costs of operating software, more and more software is delivered as a service. However, moving from a software as a product strategy to delivering software as a service hosted in cloud environments is very ambitious. This is due to the fact that managing software modernization is still a major challenge, especially when paradigm shifts, such as moving to cloud environments, are targeted that imply fundamental changes to how software is modernized, delivered, and sold. Thus, in addition to technical aspects, business aspects need also to be considered. ARTIST proposes a comprehensive software modernization approach covering business and technical aspects. In particular, ARTIST employs Model-Driven Engineering (MDE) techniques to automate the reverse engineering of legacy software and forward engineering of cloud-based software in a way that modernized software truly benefits from targeted cloud environments. Therewith, ARTIST aims at reducing the risks, time, and costs of software modernization and lowers the barriers to exploit cloud computing capabilities and new business models.

A Real-time Service Oriented Infrastructure

The advancements in distributed computing have driven the emergence of service-based 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 and streaming applications. In this paper we propose an approach for providing real-time QoS guarantees by enhancing service oriented infrastructures with coherent and consistent real-time attributes at various levels (application, network, storage, processing). The approach considers the full lifecycle of service-based systems including service engineering, Service Level Agreement (SLA) negotiation and management, service provisioning and monitoring. QoS parameters at application, platform and infrastructure levels are given specific attention as the basis for provisioning policies in the context of temporal constraints

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.

Virtualised e-Learning with real-time guarantees on the IRMOS platform

In this paper we focus on how Quality of Service guarantees are provided to virtualised applications in the Cloud Computing infrastructure that is being developed in the context of the IRMOS1 European Project. Provisioning of proper timeliness guarantees to distributed real-time applications involves the careful use of real-time scheduling mechanisms at the virtual-machine hypervisor level, of QoS-aware networking protocols and of proper design methodologies and tools for stochastic modelling of the application. The paper focuses on how we applied these techniques to a case-study involving a real e-Learning mobile content delivery application that has been integrated into the IRMOS platform and its achieved performance.

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 Front-end, Hadoop-based Data Management Service for Efficient Federated Clouds

In the recent years, cloud computing has emerged as the new IT paradigm that promises elastic resources on a pay-per-use basis. The challenges of cloud computing are focused around massive data storage and efficient large scale distributed computation. Hadoop, a community driven Apache project has provided an efficient and cost effective platform for large scale computation using the map-reduce methodology, pioneered by Google. In this paper, the design of a Hadoop-based data management system as the front-end service for Cloud data management is investigated. This framework is enriched with Restful APIs in front of Hadoop and a series of components that aim to extend Hadoops functionality beyond its well known back-end, heavy data processing scope. These components are used to enrich security, logging and data analysis features and also data access compatibility between different but interconnected Cloud providers (federated Clouds). Hadoop capabilities are also extended in a quest for intelligent decision making regarding the choice of the fittest services for federation in a federated cloud scenario, in addition to legally compliant behaviour regarding the geographical location of data storage.

ARTIST Methodology and Framework: A Novel Approach for the Migration of Legacy Software on the Cloud

Nowadays Cloud Computing is considered as the ideal environment for engineering, hosting and provisioning applications. A continuously increasing set of cloud-based solutions is available to application owners and developers to tailor their applications exploiting the advanced features of this paradigm for elasticity, high availability and performance. Although these offerings provide many benefits to new applications, they also incorporate constrains to the modernization and migration of legacy applications by obliging the use of specific technologies and explicit architectural design approaches. The modernization and adaptation of legacy applications to cloud environments is a great challenge for all involved stakeholders, not only from the technical perspective, but also in business level with the need to adapt the business processes and models of the modernized application that will be offered from now on, as a service. In this paper we present a novel model-driven approach for the migration of legacy applications in modern cloud environments which covers all aspects and phases of the migration process, as well as an integrated framework that supports all migration process.