Jesús García-Galán

Automated configuration support for infrastructure migration to the cloud

With an increasing number of cloud computing offerings in the market, migrating an existing computational infrastructure to the cloud requires comparison of different offers in order to find the most suitable configuration. Cloud providers offer many configuration options, such as location, purchasing mode, redundancy, and extra storage. Often, the information about such options is not well organised. This leads to large and unstructured configuration spaces, and turns the comparison into a tedious, error-prone search problem for the customers. In this work we focus on supporting customer decision making for selecting the most suitable cloud configuration-in terms of infrastructural requirements and cost. We achieve this by means of variability modelling and analysis techniques. Firstly, we structure the configuration space of an IaaS using feature models, usually employed for the modelling of variability-intensive systems, and present the case study of the Amazon EC2. Secondly, we assist the configuration search process. Feature models enable the use of different analysis operations that, among others, automate the search of optimal configurations. Results of our analysis show how our approach, with a negligible analysis time, outperforms commercial approaches in terms of expressiveness and accuracy. We support the decision making in migration planning to the cloud.We use Feature Models to describe the configuration space of an IaaS.We automate the search of the most suitable IaaS configuration.Our approach improves the results of commercial applications on Amazon EC2.

Migrating to the Cloud - A Software Product Line based Analysis

Identifying which part of a local system should be migrated to a public Cloud environment is often a difficult and error prone process. With the significant (and increasing) number of commercial Cloud providers, choosing a provider whose capability best meets requirements is also often difficult. Most Cloud service providers offer large amounts of configurable resources, which can be combined in a number of different ways. In the case of small and medium companies, finding a suitable configuration with the minimum cost is often an essential requirement to migrate, or even to initiate the decision process for migration. We interpret this need as a problem associated with variability management and analysis. Variability techniques and models deal with large configuration spaces, and have been proposed previously to support configuration processes in industrial cases. Furthermore, this is a mature field which has a large catalog of analysis operations to extract valuable information in an automated way. Some of these operations can be used and tailored for Cloud environments. We focus in this work on Amazon Cloud services, primarily due to the large number of possible configurations available by this service provider and its popularity. Our approach can also be adapted to other providers offering similar capabilities.

User-centric adaptation of multi-tenant services: preference-based analysis for service reconfiguration

Multi-tenancy is a key pillar of cloud services. It allows different tenants to share computing resources transparently and, at the same time, guarantees substantial cost savings for the providers. However, from a user perspective, one of the major drawbacks of multi-tenancy is lack of configurability. Depending on the isolation degree, the same service instance and even the same service configuration may be shared among multiple tenants (i.e. shared multi-tenant service). Moreover tenants usually have different - and in most of the cases - conflicting configuration preferences. To overcome this limitation, this paper introduces a novel approach to support user-centric adaptation in shared multi-tenant services. The adaptation objective aims to maximise tenants’ satisfaction, even when tenants and their preferences change during the service life-time. This paper describes how to engineer the activities of the MAPE loop to support user-centric adaptation, and focuses on the analysis of tenants’ preferences. In particular, we use a game theoretic analysis to identify a service configuration that maximises tenants’ preferences satisfaction. We illustrate and motivate our approach by utilising a multi-tenant desktop scenario. Obtained experimental results demonstrate the feasibility of the proposed analysis.

Feed me, feed me: an exemplar for engineering adaptive software

The Internet of Things (IoT) promises to deliver improved quality of life for citizens, through pervasive connectivity and quantified monitoring of devices, people, and their environment. As such, the IoT presents a major new opportunity for research in adaptive software engineering. However, there are currently no shared exemplars that can support software engineering researchers to explore and potentially address the challenges of engineering adaptive software for the IoT, and to comparatively evaluate proposed solutions. In this paper, we present Feed me, Feed me, an exemplar that represents an IoT-based ecosystem to support food security at different levels of granularity: individuals, families, cities, and nations. We describe this exemplar using animated videos which highlight the requirements that have been informally observed to play a critical role in the success or failure of IoT-based software systems. These requirements are: security and privacy, interoperability, adaptation, and personalisation. To elicit a wide spectrum of user reactions, we created these animated videos based on the ContraVision empirical methodology, which specifically supports the elicitation of end-user requirements for controversial or futuristic technologies. Our deployment of ContraVision presented our pilot study subjects with an equal number of utopian and dystopian scenarios, derived from the food security domain, and described them at the different level of granularity. Our synthesis of the preliminary empirical findings suggests a number of key requirements and software engineering research challenges in this area. We offer these to the research community, together with a rich exemplar and associated scenarios available in both their textual form in the paper, and as a series of animated videos (http://sead1.open.ac.uk/fmfm/)

Are you ready? Towards the engineering of forensic-ready systems

As security incidents continue to impact organisations, there is a growing demand for systems to be ‘forensic-ready’ - to maximise the potential use of evidence whilst minimising the costs of an investigation. Researchers have supported organisational forensic readiness efforts by proposing the use of policies and processes, aligning systems with forensics objectives and training employees. However, recent work has also proposed an alternative strategy for implementing forensic readiness called forensic-by-design. This is an approach that involves integrating requirements for forensics into relevant phases of the systems development lifecycle with the aim of engineering forensic-ready systems. While this alternative forensic readiness strategy has been discussed in the literature, no previous research has examined the extent to which organisations actually use this approach for implementing forensic readiness. Hence, we investigate the extent to which organisations consider requirements for forensics during systems development. We first assessed existing research to identify the various perspectives of implementing forensic readiness, and then undertook an online survey to investigate the consideration of requirements for forensics during systems development lifecycles. Our findings provide an initial assessment of the extent to which requirements for forensics are considered within organisations. We then use our findings, coupled with the literature, to identify a number of research challenges regarding the engineering of forensic-ready systems.

Towards adaptive compliance

Mission critical software is often required to comply with multiple regulations, standards or policies. Recent paradigms, such as cloud computing, also require software to operate in heterogeneous, highly distributed, and changing environments. In these environments, compliance requirements can vary at runtime and traditional compliance management techniques, which are normally applied at design time, may no longer be sufficient. In this paper, we motivate the need for adaptive compliance by illustrating possible compliance concerns determined by runtime variability. We further motivate our work by means of a cloud computing scenario, and present two main contributions. First, we propose and justify a process to support adaptive compliance that extends the traditional compliance management lifecycle with the activities of the Monitor-Analyse-Plan-Execute (MAPE) loop, and enacts adaptation through re-configuration. Second, we explore the literature on software compliance and classify existing work in terms of the activities and concerns of adaptive compliance. In this way, we determine how the literature can support our proposal and what are the open research challenges that need to be addressed in order to fully support adaptive compliance.

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 multi-tenant 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.

Multi-user variability configuration: a game theoretic approach

Multi-user configuration is a neglected problem in variability-intensive systems area. The appearance of conflicts among user configurations is a main concern. Current approaches focus on avoiding such conflicts, applying the mutual exclusion principle. However, this perspective has a negative impact on users satisfaction, who cannot make any decision fairly. In this work, we propose an interpretation of multi-user configuration as a game theoretic problem. Game theory is a well-known discipline which analyzes conflicts and cooperation among intelligent rational decision-makers. We present a taxonomy of multi-user configuration approaches, and how they can be interpreted as different problems of game theory. We focus on cooperative game theory to propose and automate a tradeoff-based bargaining approach, as a way to solve the conflicts and maximize user satisfaction at the same time.

Modelling and Analysing Highly-Configurable Services

Since the emergence of XaaS and Cloud Computing paradigms, the number and complexity of available services have been increasing enormously. These services usually offer a plethora of configuration options, which can even include additional services provided as a bundled offer. In this scenario, usual tasks, such as description, discovery and selection, become increasingly complex due to the variability of the decision space. The notion of Highly-Configurable Service (HCS) has been coined to identify such group of services that can be configured and bundled together to perform demanding computing tasks. In this paper we characterize HCSs by means of an abstract model and a text-based, human-readable notation named SYNOPSIS that facilitates the execution of various service tasks. In particular, we validate the usefulness of our model when checking the validity of HCSs descriptions in SYNOPSIS, as well as selecting the optimal configuration with regards to user requirements and preferences by providing a prototype implementation.

Configurable Feature Models

Feature models represent all the products that can be built under a variability-intensive system such as a software product line, but they are not fully configurable. There exist no explicit effort in defining configuration models that enable making decisions on attributes and cardinalities in feature models that use these artefacts. In this paper we present configurable feature models as an evolution from feature models that integrate configuration models within, improving the configurability of variability-intensive systems.