A Case for a New IT Ecosystem: On-The-Fly Computing

The complexity of development and deployment in today’s IT world is enormous. Despite the existence of so many pre-fabricated components, frameworks, cloud providers, etc., building IT systems still remains a major challenge and most likely overtaxes even a single ambi- tious developer. This results in spreading such develop- ment and deployment tasks over different team members with their own specialization. Nevertheless, not even highly competent IT personnel can easily succeed in developing and deploying a nontrivial application that comprises a multitude of different components running on different platforms (from frontend to backend). Current industry trends such as DevOps strive to keep development and deployment tasks tightly integrated. This, however, only partially addresses the underlying complexity of either of these two tasks. But would it not be desirable to simplify these tasks in the first place, enabling one person – maybe even a non-expert – to deal with all of them? Today’s approaches to the development and deployment of complex IT applications are not up to this challenge. ‘‘On-The-Fly Computing’’ offers an approach to tackle this challenge by providing complex IT services through largely automated configuration and execution. The configuration of such services is based on simple, flexibly combinable services that are provided by different software providers and traded in a market. This constitutes a highly relevant challenge for research in many branches of computer science, informa- tion systems, business administration, and economics. In this research note, it is analyzed which pieces of this new ‘‘On-The-Fly Computing’’ ecosystem already exist and where additional, often significant research efforts are necessary

Improving Semantic Web Services Discovery Using SPARQL-Based Repository Filtering

Semantic Web Services discovery is commonly a heavyweight task, which has scalability issues when the number of services or the ontology complexity increase, because most approaches are based on Description Logics reasoning. As a higher number of services becomes available, there is a need for solutions that improve discovery performance. Our proposal tackles this scalability problem by adding a preprocessing stage based on two SPARQL queries that filter service repositories, discarding service descriptions that do not refer to any functionality or non-functional aspect requested by the user before the actual discovery takes place. This approach fairly reduces the search space for discovery mechanisms, consequently improving the overall performance of this task. Furthermore, this particular solution does not provide yet another discovery mechanism, but it is easily applicable to any of the existing ones, as our prototype evaluation shows. Moreover, proposed queries are automatically generated from service requests, transparently to the user. In order to validate our proposal, this article showcases an application to the OWL-S ontology, in addition to a comprehensive performance analysis that we carried out in order to test and compare the results obtained from proposed filters and current discovery approaches, discussing the benefits of our proposal

User-centric Adaptation Analysis of Multi-tenant Services

Multi-tenancy is a key pillar of cloud services. It allows different users to share computing and virtual resources transparently, meanwhile guaranteeing substantial cost savings. Due to the tradeoff between scalability and customization, one of the major drawbacks of multi-tenancy is limited configurability. Since users may often have conflicting configuration preferences, offering the best user experience is an open challenge for service providers. In addition, the users, their preferences, and the operational environment may change during the service operation, thus jeopardizing the satisfaction of user preferences. In this article, we present an approach to support user-centric adaptation of multi-tenant services. We describe how to engineer the activities of the Monitoring, Analysis, Planning, Execution (MAPE) loop to support user-centric adaptation, and we focus on adaptation analysis. Our analysis computes a service configuration that optimizes user satisfaction, complies with infrastructural constraints, and minimizes reconfiguration obtrusiveness when user- or service-related changes take place. To support our analysis, we model multitenant services and user preferences by using feature and preference models, respectively. We illustrate our approach by utilizing different cases of virtual desktops. Our results demonstrate the effectiveness of the analysis in improving user preferences satisfaction in negligible time.Ministerio de Economía y Competitividad TIN2012-32273Junta de Andalucía P12--TIC--1867Junta de Andalucía TIC-590

Integrating Semantic Web Services Ranking Mechanisms Using a Common Preference Model

Service ranking has been long-acknowledged to play a fundamental role in helping users to select the best o erings among services retrieved from a search request. There exist many ranking mechanisms, each one providing ad hoc preference models that o er di erent levels of expressiveness. Consequently, applying a single mechanism to a particular scenario constrains the user to de ne preferences based on that mechanism's facilities. Furthermore, a more exible solution that uses several independent mechanisms will face interoperability issues because of the di erences between preference models provided by each ranking mechanism. In order to overcome these issues, we propose a Preference- based Universal Ranking Integration (PURI) framework that enables the combination of several ranking mechanisms using a common, holistic preference model. Using PURI, di erent ranking mechanisms are seamlessly and transparently integrated, o ering a single fa cade to de ne preferences using highly expressive facilities that are not only decoupled from the concrete mechanisms that perform the ranking process, but also allow to exploit synergies from the combination of integrated mechanisms. We also thoroughly present a particular application scenario in the SOA4All EU project and evaluate the bene ts and applicability of PURI in further domains

An architecture for user preference-based IoT service selection in cloud computing using mobile devices for smart campus

The Internet of things refers to the set of objects that have identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social environments and user context. Interconnected devices communicating to each other or to other machines on the network have increased the number of services. The concepts of discovery, brokerage, selection and reliability are important in dynamic environments. These concepts have emerged as an important field distinguished from conventional distributed computing by its focus on large-scale resource sharing, delivery and innovative applications. The usage of Internet of Things technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although a set of terms can be used to express requirements for the desired service, a more detailed and specific user interface would make it easy for the users to express their requirements using high-level constructs. In order to address the challenge of service selection and discovery, we developed an architecture that enables a representation of user preferences and manipulates relevant descriptions of available services. To ensure that the key components of the architecture work, algorithms (content-based and collaborative filtering) derived from the architecture were proposed. The architecture was tested by selecting services using content-based as well as collaborative algorithms. The performances of the algorithms were evaluated using response time. Their effectiveness was evaluated using recall and precision. The results showed that the content-based recommender system is more effective than the collaborative filtering recommender system. Furthermore, the results showed that the content-based technique is more time-efficient than the collaborative filtering technique

Partitioning workflow applications over federated clouds to meet non-functional requirements

PhD ThesisWith cloud computing, users can acquire computer resources when they need them on a pay-as-you-go business model. Because of this, many applications are now being deployed in the cloud, and there are many di erent cloud providers worldwide. Importantly, all these various infrastructure providers o er services with di erent levels of quality. For example, cloud data centres are governed by the privacy and security policies of the country where the centre is located, while many organisations have created their own internal \private cloud" to meet security needs. With all this varieties and uncertainties, application developers who decide to host their system in the cloud face the issue of which cloud to choose to get the best operational conditions in terms of price, reliability and security. And the decision becomes even more complicated if their application consists of a number of distributed components, each with slightly di erent requirements. Rather than trying to identify the single best cloud for an application, this thesis considers an alternative approach, that is, combining di erent clouds to meet users' non-functional requirements. Cloud federation o ers the ability to distribute a single application across two or more clouds, so that the application can bene t from the advantages of each one of them. The key challenge for this approach is how to nd the distribution (or deployment) of application components, which can yield the greatest bene ts. In this thesis, we tackle this problem and propose a set of algorithms, and a framework, to partition a work ow-based application over federated clouds in order to exploit the strengths of each cloud. The speci c goal is to split a distributed application structured as a work ow such that the security and reliability requirements of each component are met, whilst the overall cost of execution is minimised. To achieve this, we propose and evaluate a cloud broker for partitioning a work ow application over federated clouds. The broker integrates with the e-Science Central cloud platform to automatically deploy a work ow over public and private clouds. We developed a deployment planning algorithm to partition a large work ow appli- - i - cation across federated clouds so as to meet security requirements and minimise the monetary cost. A more generic framework is then proposed to model, quantify and guide the partitioning and deployment of work ows over federated clouds. This framework considers the situation where changes in cloud availability (including cloud failure) arise during work ow execution

A service broker for Intercloud computing

This thesis aims at assisting users in finding the most suitable Cloud resources taking into account their functional and non-functional SLA requirements. A key feature of the work is a Cloud service broker acting as mediator between consumers and Clouds. The research involves the implementation and evaluation of two SLA-aware match-making algorithms by use of a simulation environment. The work investigates also the optimal deployment of Multi-Cloud workflows on Intercloud environments