Failures of any type are common in current datacenters, partly due to the higher scales of the data stored. As data scales up, its availability becomes more complex, while different availability levels per application or per data item may be required. In this paper, we propose a self-managed key-value store that dynamically allocates the resources of a data cloud to several applications in a costefficient and fair way. Our approach offers and dynamically maintains multiple differentiated availability guarantees to each different application despite failures. We employ a virtual economy, where each data partition (i.e. a key range in a consistent-hashing space) acts as an individual optimizer and chooses whether to migrate, replicate or remove itself based on net benefit maximization regarding the utility offered by the partition and its storage and maintenance cost. As proved by a game-theoretical model, no migrations or replications occur in the system at equilibrium, which is soon reached when the query load and the used storage are stable. Moreover, by means of extensive simulation experiments, we have proved that our approach dynamically finds the optimal resource allocation that balances the query processing overhead and satisfies the availability objectives in a cost-efficient way for different query rates and storage requirements. Finally, we have implemented a fully working prototype of our approach that clearly demonstrates its applicability in real settings.

No abstract

Abstract-A growing amount of data is produced daily resulting in a growing demand for storage solutions. While cloud storage providers offer a virtually infinite storage capacity, data owners seek geographical and provider diversity in data placement, in order to avoid vendor lock-in and to increase availability and durability. Moreover, depending on the customer data access pattern, a certain cloud provider may be cheaper than another. In this paper 1 , we introduce Scalia, a cloud storage brokerage solution that continuously adapts the placement of data based on its access pattern and subject to optimization objectives, such as storage costs. Scalia efficiently considers repositioning of only selected objects that may significantly lower the storage cost. By extensive simulation experiments, we prove the cost-effectiveness of Scalia against static placements and its proximity to the ideal data placement in various scenarios of data access patterns, of available cloud storage solutions and of failures.

Participatory sensing (PS) is becoming a popular data acquisition means for interesting emerging applications. However, as data queries from these applications increase, the sustainability of this platform for multiple concurrent applications is at stake. In this paper 1 , we consider the problem of efficient data acquisition in PS when queries of different types come from different applications. We effectively deal with the issues related to resource constraints, user privacy, data reliability, and uncontrolled mobility. We formulate the problem as multi-query optimization and propose efficient heuristics for its effective solution for the various query types and mixes that enable sustainable sensing. Based on simulations with real and artificial data traces, we found that our heuristic algorithms outperform baseline approaches in a multitude of settings considered.

Peer-to-peer are popular environments for exchanging services. A reputation mechanism is a proper means of discovering low-performing peers that fail to provide their services. In this paper, we present an in-depth and innovative study of how reputation can be exploited so that the right incentives for high performance are provided to peers. Such incentives do not arise if peers exploit reputation only when selecting the best providing peer; this approach may lead high-performing peers to receive unfairly low value from the system. We argue and justify experimentally that the calculation of reputation values has to be complemented by proper reputation-based policies that determine the pairs of peers eligible to interact with each other. We introduce two different dimensions of reputation-based policies, namely "provider selection" and "contention resolution", as well as specific policies for each dimension. We perform extensive comparative assessment of a wide variety of policy pairs and identify the most effective ones by means of simulations of dynamically varying peer-to-peer environments. We show that both dimensions have considerable impact on both the incentives for peers and the efficiency attained. In particular, when peers follow fixed strategies, certain policy pairs differentiate the value received by different types of peers in accordance to the value offered to the system per peer of each type. Moreover, when peers follow dynamic strategies, incentive compatibility applies under certain pairs of reputation-based policies: each peer is provided with the incentive to improve her performance in order to receive a higher value. Finally, we show experimentally that reputation values can be computed quickly and accurately by aggregating only a small randomly selected subset of the ratings' feedback provided by the peers, thus reducing the associated communications' overhead.

Abstract-The increasing use of sensor technology for various monitoring applications (e.g. air-pollution, traffic, climatechange, etc.) has led to an unprecedented volume of streaming data that has to be efficiently stored and retrieved. Real-time model-based data approximation and filtering is a common solution for reducing the storage (and communication) overhead. However, the selection of the most efficient model depends on the characteristics of the data stream, namely rate, burstiness, data range, etc., which cannot be always known a priori for mobile sensors and they can even be dynamic. In this paper, we investigate the innovative concept of efficiently combining multiple approximation models in real-time. Our approach dynamically adapts to the properties of the data stream and approximates each data segment with the most suitable model. As experimentally proved, our multi-model approximation approach always produces fewer or equal data segments than those of the best individual model. Finally, by employing both simple and more advanced indexing approaches for modeled data segments, we prove that multi-model data approximation is preferable to single model-based approximation in terms of response time for range-queries sent to big data tables.

Abstract-Significant achievements have been made for automated allocation of cloud resources. However, the performance of applications may be poor in peak load periods, unless their cloud resources are dynamically adjusted. Moreover, although cloud resources dedicated to different applications are virtually isolated, performance fluctuations do occur because of resource sharing, and software or hardware failures (e.g. unstable virtual machines, power outages, etc.). In this paper, we propose a decentralized economic approach for dynamically adapting the cloud resources of various applications, so as to statistically meet their SLA performance and availability goals in the presence of varying loads or failures. According to our approach, the dynamic economic fitness of a Web service determines whether it is replicated or migrated to another server, or deleted. The economic fitness of a Web service depends on its individual performance constraints, its load, and the utilization of the resources where it resides. Cascading performance objectives are dynamically calculated for individual tasks in the application workflow according to the user requirements. By fully implementing our framework, we experimentally proved that our adaptive approach statistically meets the performance objectives under peak load periods or failures, as opposed to static resource settings.

Abstract-As the online social networks (OSNs), such as Facebook, witness explosive growth, the privacy challenges gain critical consideration from governmental and law agencies due to concentration of vast amount of personal information within a single administrative domain. In this paper, we demonstrate a privacy-aware decentralized OSN called My3 12 , where users can exercise full access control on their data. Our system exploits trust relationships in the social network for providing the necessary decentralized storage infrastructure. By taking users' geographical locations and online time statistics into account, it also addresses availability and storage performance issues.