Feature models are widely used to encode the configurations of a software product line in terms of mandatory, optional and exclusive features as well as propositional constraints over the features. Numerous computationally expensive procedures have been developed to model check, test, configure, debug, or compute relevant information of feature models. In this paper we explore the possible improvement of relying on the enumeration of all configurations when performing automated analysis operations. We tackle the challenge of how to scale the existing enumeration techniques by relying on distributed computing. We show that the use of distributed computing techniques might offer practical solutions to previously unsolvable problems and opens new perspectives for the automated analysis of software product lines.

A growing number of organizations are seeking to analyze extra large graphs in a timely and resource-efficient manner. With some graphs containing well over a billion elements, these organizations are turning to distributed graph-computing platforms that can scale out easily in existing data-centers and clouds. Unfortunately such platforms usually impose programming models that can be ill suited to typical graph computations, fundamentally undermining their potential benefits.

Applications running in today's data centers show high workload variability. While seasonal patterns, trends and expected events may help building proactive resource allocation policies, this approach has to be complemented with adaptive strategies which should address unexpected events such as flash crowds and volume spikes. Additionally, the limitations of current I/O infrastructures in the face of dramatic increase of data generation require, the ability to build novel abstractions and models for robust decision making regarding data layout and data locality. In this work, we present CONDESA (CONtrolling Data distribution on Elastic Server Architectures), a framework for exploring adaptive data distribution strategies for elastic server architectures. To the best of our knowledge CONDESA is the first platform that permits to systematically study the interplay between five data related strategies: workload prediction, adaptive control of data distribution and server provisioning, adaptive data grouping, adaptive data placement, and adaptive system sizing. We demonstrate how CONDESA can be used for browsing the design space of adaptive data distribution policies. We show how prediction models can be compared in terms of overhead and accuracy. We evaluate the impact of change detection on prediction accuracy and how CONDESA can be used for choosing an adequate prediction horizon. We demonstrate how adaptive prediction can be used for sizing a server system. Finally, we show how prediction models, change detection strategies, and data placement policies can be combined and compared based on server utilization, load balance, data locality, over- and underprovisioning.

Geolocated social networks, combining traditional social networking features with geolocation information, have grown tremendously over the last few years. Yet, very few works have looked at implementing geolocated social networks in a fully distributed manner, a promising avenue to handle the growing scalability challenges of these systems. In this paper, we focus on georecommendation, and show that existing decentralized recommendation mechanisms perform in fact poorly on geodata. We propose a set of novel gossip-based mechanisms to address this problem, in a modular similarity framework called GEOLOGY. The resulting platform is lightweight, efficient, and scalable, and we demonstrate its advantages in terms of recommendation quality and communication overhead on a real dataset of 15,694 users from Foursquare, a leading geolocated social network.

The cloud has recently surged as a promising paradigm for hosting scalable Web systems serving a large number of users with large workload variations. It makes possible to dynamically add and remove resources to horizontally scalable architectures in order to save costs, while maintaining the quality of service. However, in order to achieve these goals the resource management of a platform must include policies and mechanisms for dynamically resizing the system, redistributing content and redirecting user requests. In this work, we address the problem of reconciling dynamic system sizing and content locality. There are three main contributions of our study. First, we address the problem of determining the system size by employing a hierarchical prediction framework that proactively provisions resources based on statistical models of the incoming workload. Second, we show how to employ the hierarchical prediction framework for designing a dispatching mechanism which can be used with any request distribution policy. Third, we propose two novel prediction-based locality-aware request distribution policies: Oblivious Locality-Aware Request Distribution (OLARD) and Affinity-Based Locality-Aware Request Distribution (ABLARD). We demonstrate the advantages of using our hierarchical prediction framework and how our approach achieves a high content locality, while adapting to unexpected workload changes.

This paper presents a methodology for efficiently solving the file transfer scheduling problem in a distributed environment. Our solution is based on the relaxation of an objective-based time-indexed formulation of a linear programming problem. The main contributions of this paper are the following. First, we introduce a novel approach to the relaxation of the time-indexed formulation of the transfer scheduling problem in multi-server and multi-user environments. Our solution consists of reducing the complexity of the optimization by transforming it into an approximation problem, whose proximity to the optimal solution can be controlled depending on practical and computational needs. Second, we present a distributed deployment of our methodology, which leverages the inherent parallelism of the divide-and-conquer approach in order to speed-up the solving process. Third, we demonstrate that our methodology is able to considerably reduce the schedule length and idle time in a computationally tractable way.

Workload variations on Internet platforms such as YouTube, Flickr, LastFM require novel approaches to dynamic resource provisioning in order to meet QoS requirements, while reducing the Total Cost of Ownership (TCO) of the infrastructures. The economy of scale promise of cloud computing is a great opportunity to approach this problem, by developing elastic large scale server infrastructures. However, a proactive approach to dynamic resource provisioning requires prediction models forecasting future load patterns. On the other hand, unexpected volume and data spikes require reactive provisioning for serving unexpected surges in workloads. When workload can not be predicted, adequate data grouping and placement algorithms may facilitate agile scaling up and down of an infrastructure. In this paper, we analyze a dynamic workload of an on-line music portal and present an elastic Web infrastructure that adapts to workload variations by dynamically scaling up and down servers. The workload is predicted by an autoregressive model capturing trends and seasonal patterns. Further, for enhancing data locality, we propose a predictive data grouping based on the history of content access of a user community. Finally, in order to facilitate agile elasticity, we present a data placement based on workload and access pattern prediction. The experimental results demonstrate that our forecasting model predicts workload with a high precision. Further, the predictive data grouping and placement methods provide high locality, load balance and high utilization of resources, allowing a server infrastructure to scale up and down depending on workload.

Infrastructures serving on-line applications experience dynamic workload variations depending on diverse factors such as popularity, marketing, periodic patterns, fads, trends, events, etc. Some predictable factors such as trends, periodicity or scheduled events allow for proactive resource provisioning in order to meet fluctuations in workloads. However, proactive resource provisioning requires prediction models forecasting future workload patterns. This paper proposes a multi-model prediction approach, in which data are grouped into bins based on content locality, and an autoregressive prediction model is assigned to each locality-preserving bin. The prediction models are shown to be identified and fitted in a computationally efficient way. We demonstrate experimentally that our multi-model approach improves locality over the uni-model approach, while achieving efficient resource provisioning and preserving a high resource utilization and load balance.