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.

In this paper, we present DKS(N, k, f), a family of infrastructures for building Peer-To-Peer applications. Each instance of DKS(N, k, f) is a fully decentralized overlay network characterized by three parameters: N the maximum number of nodes that can be in the network; k the search arity within the network and f the degree of fault-tolerance. Once these parameters are instantiated, the resulting network has several desirable properties. The first property, which is the main contribution of this paper, is that there is no separate procedure for maintaining routing tables; instead, any out-of-date or erroneous routing entry is eventually corrected on-the-fly thereby, eliminating unnecessary bandwidth consumption. The second property is that each lookup request is resolved in at most log/sub k/(N) overlay hops under normal operations. Third, each node maintains only (k-1) log/sub k/(N) + 1 addresses of other nodes for routing purposes. Fourth, new nodes can join and existing nodes can leave at will with a negligible disturbance to the ability to resolve lookups in logk(N) hops in average. Fifth, any pair key/value that is inserted into the system is guaranteed to be located even in the presence of concurrent joins. Sixth, even if f consecutive nodes fail simultaneously, correct lookup is still guaranteed.

Recent developments in the area of peer-to-peer computing show that structured overlay networks implementing distributed hash tables scale well and can serve as infrastructures for Internet scale applications. We are developing a family of infrastructures, DKS(N; k; f), for the construction of peer-to-peer applications. An instance of DKS(N; k; f) is an overlay network that implements a distributed hash table and which has a number of desirable properties: low cost of communication, scalability, logarithmic lookup length, fault-tolerance and strong guarantees of locating any data item that was inserted in the system. In this paper, we show how multicast is achieved in DKS(N, k, f) overlay networks. The design presented here is attractive in three main respects. First, members of a multicast group self-organize in an instance of DKS(N, k, f) in a way that allows co-existence of groups of different sizes, degree of fault-tolerance, and maintenance cost, thereby, providing flexibility. Second, each member of a group can multicast, rather than having single source multicast. Third, within a group, dissemination of a multicast message is optimal under normal system operation in the sense that there are no redundant messages despite the presence of outdated routing information.

Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This paper has two goals: to give a tutorial of logic programming in Oz; and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. We give examples that can be run interactively on the Mozart system, which implements Oz. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Furthermore, as consequences of its multiparadigm nature, the model supports new abilities such as first-class top levels, deep guards, active objects, and sophisticated control of the search process. Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We give a brief history of Oz that traces the development of its main ideas and we summarize the lessons learned from this work. Finally, we give many entry points into the Oz literature.

We define a practical algorithm for distrubuted rational tree unification and prove its correctness in both the off-line and on-line cases. We derive the distributed algorithm from a centralized one, showing clearly the trade-offs between local and distributed execution. The algorithm is used to realize logic variables in the Mozart Programming System, which implements the Oz language (see http://www/mozart-oz.org). Oz appears to the programmer as a concurrent object-oriented language with dataflow synchronization. Logic variables implement the dataflow behavior. We show that lohgic variables can easily be added to the more restricted models of Java and ML, thus providing an alternative way to do concurent programming in these languages. We present common distributed programming idioms in a network-transparent way using logic variables. We show that in common cases the algorithm maintains the same message latency as explicit message passing. In addition, it is able to handle uncommon cases that arise from the properties of latency tolerance and third-party independence. This is evidence that using logic variables in distributed computing is beneficial at both the system and language levels. At the system level, they improve latency tolerance and third-party independence. At the language level, they help make network-transparent distribution practical.

Some of the most difficult questions to answer when designing a distributed application are related to mobility: what information to transfer between sites and when and how to transfer it. Network-transparent distribution, the property that a program's behavior is independent of how it is partitioned among sites, does not directly address these questions. Therefore we propose to extend all language entities with a network behavior that enables efficient distributed programming by giving the programmer a simple and predictable control over network communication patterns. In particular, we show how to give objects an arbitrary mobility behavior that is independent of the objects definition. In this way, the syntax and semantics of objects are the same regardless of whether they are used as stationary servers, mobile agents, or simply as caches. These ideas have been implemented in Distributed Oz, a concurrent object-oriented language that is state aware and has dataflow synchronization. We prove that the implementation of objects in Distributed Oz is network transparent. To satisfy the predictability condition, the implementation avoids forwarding chains through intermediate sites. The implementation is an extension to the publicly available DFKI Oz 2.0 system.