In recent years, NoSQL databases have emerged as an answer to Internet scale datasets. However, these solutions do not support out of the box the necessary backup, restore and disaster recovery capabilities required by many big data scenarios. In this paper we address this gap: we present a novel disaster recovery approach designed for big data NoSQL workloads. This approach can support low recovery point and time objectives at low costs. Our approach is highly scalable and elastic, based on open source components and suitable for any document-based NoSQL solution, which makes it highly attractive for diverse big data workloads.

The next-generation mobile devices include smart watches, wristbands, wearables (e.g., Google Glass), etc. In the future such devices will constitute a large fraction of the total devices available in the market [1]. Latest studies confirm that location-based services are the most requested feature by developers with a market share of 13B in 2013 and have expected exponential growth [2]. Future location-based applications/services will use the data generated by the new mobile devices for providing enhanced user experience. This paper presents a vision of such next-generation location-based services, which we call LBS 2.0. We present the challenges and opportunities that LBS 2.0 will pose for mobile data management.

We investigate the problem of designing a scalable overlay network to support decentralized topic-based pub/sub communication. We introduce a new optimization problem, called Minimum Topic-Connected Overlay (Min-TCO), that captures the tradeoff between the scalability of the overlay (in terms of the nodes' fanout) and the message forwarding overhead incurred by the communicating parties. Roughly, the Min-TCO problem is as follows: Given a collection of nodes and their subscriptions, connect the nodes using the minimum possible number of edges so that for each topic t, a message published on t could reach all the nodes interested in t by being forwarded by onlythe nodes interested in t.

Peer-to-peer (P2P) networks suffer from the problem of "free-loaders", i.e., users who consume resources without contributing anything in return. In this paper, we tackle this problem taking a game theoretic perspective by modeling the system as a non-cooperative game. We introduce Equi-Cast, a wide-area P2P multicast protocol for large groups of selfish nodes. EquiCast is the first P2P multicast protocol that is formally proven to enforce cooperation in selfish environments. Additionally, we prove that EquiCast incurs a low constant load on each user.

Mobile ad-hoc networks (MANETs) are failure-prone environments; it is common for mobile wireless nodes to intermittently disconnect from the network, e.g., due to signal blockage. This paper focuses on withstanding such failures in large MANETs: we present Octopus, a fault-tolerant and efficient position-based routing protocol. Fault-tolerance is achieved by employing redundancy, i.e., storing the location of each node at many other nodes, and by keeping frequently refreshed soft state. At the same time, Octopus achieves a low location update overhead by employing a novel aggregation technique, whereby a single packet updates the location of many nodes at many other nodes. Octopus is highly scalable: for a fixed node density, the number of location update packets sent does not grow with the network size. And when the density increases, the overhead drops. Thorough empirical evaluation using the ns2 simulator with up to 675 mobile nodes shows that Octopus achieves excellent fault-tolerance at a modest overhead: when all nodes intermittently disconnect and reconnect, Octopus achieves the same high reliability as when all nodes are constantly up.

We present Araneola, a scalable reliable application-level multicast system for highly dynamic wide-area environments. Araneola supports multi-point to multi-point reliable communication in a fully distributed manner while incurring constant load on each node. For a tunable parameter k /spl ges/ 3, Araneola constructs and dynamically maintains an overlay structure in which each node's degree is either k or k + 1, and roughly 90% of the nodes have degree k. Empirical evaluation shows that Araneola's overlay structure achieves three important mathematical properties of k-regular random graphs (i.e., random graphs in which each node has exactly k neighbors) with N nodes: (i) its diameter grows logarithmically with N; (ii) it is generally k-connected; and (iii) it remains highly connected following random removal of linear-size subsets of edges or nodes. The overlay is constructed at a very low cost: each join, leave, or failure is handled locally, and entails the sending of only about 3k messages in total. Given this overlay, Araneola disseminates multicast messages by gossiping over the overlay's links. We show that compared to a standard gossip-based multicast protocol, Araneola achieves substantial improvements in load, reliability, and latency. Finally, we present an extension to Araneola in which the basic overlay is enhanced with additional links chosen according to geographic proximity and available bandwidth. We show that this approach reduces the number of physical hops messages traverse without hurting the overlay's robustness.