Currently there is significant and increasing demand for quick data storage and access any time. Storage is a fundamental need and Cloud environments accomplish this. The evolvement of the IT technologies offers the user to a large variety of useful services. In order for the user to have a guarantee for these services SLAs are signed. In this paper, we present an SLA Management mechanism for Cloud environments. SLA enforcement is based on rules that are updated in runtime in order to proactively detect possible SLA violations and handle them in an appropriate manner.

Future Internet applications raise the need for infrastructures that can facilitate real-time and interactivity without major modifications in the application domain. Such infrastructures should be able to efficiently adapt resource provisioning to the dynamic demands of the applications, the majority of which tends to be real-time and interactive, posing specific Quality of Service (QoS) requirements. In this context, we present initial experimentation outcomes of a set of mechanisms (namely QoS-oriented Service Management mechanisms) that aim at enabling the provision of QoS guarantees in cloud infrastructures. They incorporate an orchestration framework that allows for workflow management in the case of non-monolithic applications, an adaptable monitoring mechanism that collects and aggregates monitoring information from application and infrastructure levels, and an evaluator service enabling runtime adaptability through provisioning policies. We also demonstrate the operation of the implemented mechanisms and evaluate their effectiveness in a large-scale multi-site cloud facility.

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.

The emergence of Cloud technologies ultimately affected the service computing ecosystem introducing new roles and relationships as well as new architectural and business models. Along with the increase of the capabilities and potentials of the service providers came the increase of the information being available and the need to manage it in an efficient way. To this end, in this paper we present a management service architecture as well as the information model that the solution is based on. The model has been designed to serve a storage service provided by a Cloud infrastructure but the approach is implemented in a flexible and modular fashion in order to support different Cloud situations.

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 customer's charge.

The emergence of cloud environments has made feasible the delivery of Internet-scale services by addressing a number of challenges such as live migration, fault tolerance and quality of service. However, current approaches do not tackle key issues related to cloud storage, which are of increasing importance given the enormous amount of data being produced in today's rich digital environment (e.g. by smart phones, social networks, sensors, user generated content). In this paper we present the architecture of a scalable and flexible cloud environment addressing the challenge of providing data-intensive storage cloud services through raising the abstraction level of storage, enabling data mobility across providers, allowing computational and content-centric access to storage and deploying new data-oriented mechanisms for QoS and security guarantees. We also demonstrate the added value and effectiveness of the proposed architecture through two real-life application scenarios from the healthcare and media domains.

The emergence of new environments such as Cloud computing highlighted new challenges in traditional fields like performance estimation. Most of the current cloud environments follow the Software, Platform, Infrastructure service model in order to map discrete roles / providers according to the offering in each “layer”. However, the limited amount of information passed from one layer to the other has raised the level of difficulty in translating user-understandable application terms from the Software layer to resource specific attributes, which can be used to manage resources in the Platform and Infrastructure layers. In this paper, a generic black box approach, based on Artificial Neural Networks is used in order to perform the aforementioned translation. The efficiency of the approach is presented and validated through different application scenarios (namely FFMPEG encoding and real-time interactive e-Learning) that highlight its applicability even in cases where accurate performance estimation is critical, as in cloud environments aiming to facilitate real-time and interactivity.

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 multimedia applications. Common to these applications is the service-oriented approach being followed, thus applications consist of application service components that interact in order to provide the corresponding application functionality. In this paper we present an architectural design of a complete Workflow Management System (WfMS) for enacting application service components that have been deployed in a Virtualized Environment. The WfMS aims at synchronizing the application components, monitoring their execution and handling faults while adhering to the QoS requirements of the application.