Roie Melamed

SpiderCast: a scalable interest-aware overlay for topic-based pub/sub communication

We introduce SpiderCast, a distributed protocol for constructing scalable churn-resistant overlay topologies for supporting decentralized topic-based pub/sub communication. SpiderCast is designed to effectively tread the balance between average overlay degree and communication cost of event dissemination. It employs a novel coverage-optimizing heuristic in which the nodes utilize partial subscription views (provided by a decentralized membership service) to reduce the average node degree while guaranteeing (with high probability) that the events posted on each topic can be routed solely through the nodes interested in this topic (in other words, the overlay is topic-connected). SpiderCast is unique in maintaining an overlay topology that scales well with the average number of topics a node is subscribed to, assuming the subscriptions are correlated insofar as found in most typical workloads. Furthermore, the degree grows logarithmically in the total number of topics, and slowly decreases as the number of nodes increases. We show experimentally that, for many practical work-loads, the SpiderCast overlays are both topic-connected and have a low per-topic diameter while requiring each node to maintain a low average number of connections. These properties are satisfied even in very large settings involving up to 10,000 nodes, 1,000 topics, and 70 subscriptions per-node, and under high churn rates. In addition, our results demonstrate that, in a large setting, the average node degree in SpiderCast is at least 45% smaller than in other overlays typically used to support decentralized pub/sub communication (such as e.g., similarity-based, rings-based, and random overlays).

Araneola: a scalable reliable multicast system for dynamic environments

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 nodes degree is either k or k + 1, and roughly 90% of the nodes have degree k. Empirical evaluation shows that Araneolas 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 overlays 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 overlays robustness.

Magnet: practical subscription clustering for Internet-scale publish/subscribe

An effective means for building Internet-scale distributed applications, and in particular those involving group-based information sharing, is to deploy peer-to-peer overlay networks. The key pre-requisite for supporting these types of applications on top of the overlays is efficient distribution of messages to multiple subscribers dispersed across numerous multicast groups. In this paper, we introduce Magnet: a peer-to-peer publish/subscribe system which achieves efficient message distribution by dynamically organizing peers with similar subscriptions into dissemination structures which preserve locality in the subscription space. Magnet is able to significantly reduce the message propagation costs by taking advantage of subscription correlations present in many large-scale group-based applications. We evaluate Magnet by comparing its performance against a strawman pub/sub system which does not cluster similar subscriptions by simulation. We find that Magnet outperforms the strawman by a substantial margin on clustered subscription workloads produced using both generative models and real application traces.

Octopus: a fault-tolerant and efficient ad-hoc routing protocol

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.

Evaluating unstructured peer-to-peer lookup overlays

Unstructured peer-to-peer lookup systems incur small constant overhead per single join or leave operation, and can easily support keyword searches. Hence, they are suitable for dynamic failure-prone environments. In this paper, we define metrics for evaluating unstructured overlays for peer-to-peer lookup systems. These metrics capture the search dependability and efficiency, and the granularity at which one can control the tradeoff between the two, as well as fairness. According to these metrics, we evaluate different graphs and overlays, including a Gnutella graph, a power law random graph, normal random graphs, a 3-regular random graph, and a 3-Araneola overlay. Our study shows that, according to our metrics, a 3-Araneola overlay achieves the best results, and hence it is an excellent solution for flooding-based peer-to-peer lookup system.

Location and Context-Based Microservices for Mobile and Internet of Things Workloads

Research institutes such as Gartner and Forrester claim that the future of mobile will focus around the users context. Most of the future mobile applications will leverage user context to provide a richer user experience and deeper engagement, and consequently higher customer value. We present three cloud micro services that can substantially accelerate the development and evolvement of location and context-based applications. These include a contextual triggering micro service used to derive the users context in the moment of interaction, and visualization and analytics micro services to distill business and operational insights from application data. These micro services are described alongside mobile and Internet of Things usage examples.

Adaptive and dynamic funnel replication in clouds

We consider the problem of strongly consistent replication in a multi data center cloud setting. This environment is characterized by high latency communication between data centers, significant fluctuations in the performance of seemingly identical virtual machines (VMs) and temporary disconnects of data centers from the rest of the cloud. In this paper we introduce the adaptive and dynamic Funnel Replication (FR) protocol that is designed to achieve high throughout and low latency for reads, to accommodate arbitrary latency/throughput tradeoffs for writes, to maximize performance in the face of VM performance variations and to provide high availability for read requests in the presence of network partitions. FR is based on the idea of flexible write dissemination topologies which enables it to achieve, per message, the desired tradeoff between latency and throughput, depending on the message size, the observed network conditions, and the importance of latency as indicated by the client. We demonstrate the benefits of flexible dissemination topologies and show that in a cloud setting with N identical replicas FR can improve the write latency up to a factor of N/2 for N ≥ 2 compared to the notable chain replication (CR) protocol at the expense of a slight decrease in the write throughput. In a setting with potentially high variability in the performance of replicas, e.g., as in Amazon EC2, FR can achieve throughput up to a factor of 16 higher than CR while also improving the latency. FR does this by adopting a topology that consists of concurrent disjoint data replication paths so that load on high throughput paths is adaptively increased while load on congested replicas is reduced.

Indoor localization: challenges and opportunities

Accurate indoor positioning can transform the retail, travel and transportation, and sports industries. For example, think of getting a mobile coupon for a shirt when you are standing near to it in a clothing store and turn-by-turn indoor navigation to the food booth with the shortest queue in airports and stadiums. While the Indoor Location market includes many new business opportunities (estimated around

Enabling Location-Based Services 2.0: Challenges and Opportunities

4.5B by 2019), this emerging area suffers from shortcomings such as limited accuracy, complex maintenance of indoor sensing platforms and lack of data quality assessment tools. This talk will review existing indoor positioning technologies and will discuss their aforementioned limitations. We will present new directions for mitigating these limitations, and we will focus on novel data smoothing algorithms for cleansing noisy indoor data. These algorithms open market opportunities supporting new indoor use cases such detection of common customer paths, targeted/wanderer customers and queues length. Finally, we will discuss future trends in indoor localization and how these technologies will be able to pin point you to a small grocery product in a large supermarket.

Smoothing indoor trajectories

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.