Several real-time applications rely on dynamic graphs to model and store data arriving from multiple streams. Providing both high ingestion rate and efficient analytics with transactional guarantees is challenging, even more so when updates may be received out-of-order at the database. In this work, we propose HAL, a novel in-memory dynamic graph database design, addressing these challenges. HAL outperforms comparable systems by a factor of up to 73× in terms of update processing throughput and up to 357× for analytics, while being the first to support out-of-order updates.

When graph database users explore unfamiliar graphs, potentially with heterogeneous structure, users may need to find how two or more groups of nodes are connected in a graph, even when users are not able to describe the connections. This is only partially supported by existing query languages, which allow searching for paths, but not for trees connecting three or more node groups.In this work, we formally show how to integrate connecting tree patterns (CTPs, in short) with a graph query language such as GPML [1], SPARQL or Cypher, leading to Extended Queries (or EQs, in short). We then study a set of algorithms for evaluating CTPs; we generalize prior keyword search work to be complete, most importantly by (i) considering bidirectional edge traversal, (ii) allowing users to select any score function for ranking CTP results and (iii) returning all results. To cope with very large search spaces, we propose efficient pruning techniques and formally establish a large set of cases where our best algorithm, MOLESP, is complete even with pruning. Our experiments validate the performance of our algorithms on many synthetic and real-world workloads.

Most modern in-memory online transaction processing (OLTP) engines rely on multi-version concurrency control (MVCC) to provide data consistency guarantees in the presence of conflicting data accesses. MVCC improves concurrency by generating a new version of a record on every write, thus increasing the storage requirements. Existing approaches rely on garbage collection and chain consolidation to reduce the length of version chains and reclaim space by freeing unreachable versions. However, finding unreachable versions requires the traversal of long version chains, which incurs random accesses right into the critical path of transaction execution, hence limiting scalability. This paper introduces OneShotGC, a new multi-version storage design that eliminates version traversal during garbage collection, with minimal discovery and memory management overheads. OneShotGC leverages the temporal correlations across versions to opportunistically cluster them into contiguous memory blocks that can be released in one shot. We implement OneShotGC in Proteus and use YCSB and TPC-C to experimentally evaluate its performance with respect to the state-of-the-art, where we observe an improvement of up to 2x in transactional throughput.

Investigative Journalism (IJ, in short) requires combining highly heterogeneous digital datasets coming from a wide variety of sources. We have developed ConnectionLens, a system that integrates such sources into a single heterogeneous graph and enables users to query the graph using keywords. The first iteration of the system [7] followed a mediator architecture which severely constrained its query scalability. Thus, we fully re-engineered the system, moving it to a warehouse architecture, and replacing its core components (information extraction, data querying, and interactive interfaces), which allowed us to handle uses cases orders of magnitude larger than the previous platform. In a consortium of computer scientists and investigative journalists, we propose to demonstrate ConnectionLens' capability to integrate arbitrary heterogeneous datasets and query them flexibly by means of keywords. Among several scenarios, our main focus will be on a real-world journalistic use case about situations which may lead to Conflicts of Interest between biomedical experts and various organizations, such as corporations, lobbies, etc. The demonstration will showcase the end-to-end data analysis pipeline, illustrate each system component, and the different parameters governing graph creation and querying.

Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an integrated data processing engine that performs analytics on fresh data, which are ingested from a transactional engine. HTAP systems typically consider data freshness at design time, and are optimized for a fixed range of freshness requirements, addressed at a performance cost for either OLTP or OLAP. The data freshness and the performance requirements of both engines, however, may vary with the workload. We approach HTAP as a scheduling problem, addressed at runtime through elastic resource management. We model an HTAP system as a set of three individual engines: an OLTP, an OLAP and a Resource and Data Exchange (RDE) engine. We devise a scheduling algorithm which traverses the HTAP design spectrum through elastic resource management, to meet the workload data freshness requirements. We propose an in-memory system design which is non-intrusive to the current state-of-art OLTP and OLAP engines, and we use it to evaluate the performance of our approach. Our evaluation shows that the performance benefit of our system for OLAP queries increases over time, reaching up to 50% compared to static schedules for 100 query sequences, while maintaining a small, and controlled, drop in the OLTP throughput.

Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of redesigning the engine, which has led to the introduction of several, new, cloud-based transaction processing systems mainly focusing on: (i) the transaction coordination protocol, (ii) the data partitioning strategy, and, (iii) the resource isolation across multiple tenants. As a result, standalone OLTP engines cannot be easily deployed with an elastic setting in the cloud and they need to migrate to another, specialized deployment. In this paper, we focus on workload variations that can be addressed by modern multi-socket, multi-core servers and we present a system design for providing fine-grained elasticity to multi-tenant, scale-up OLTP deployments. We introduce novel components to the virtualization software stack that enable on-demand addition and removal of computing and memory resources. We provide a bi-directional, low-overhead communication stack between the virtual machine and the hypervisor, which allows the former to adapt to variations coming both from the workload and the resource availability. We show that our system achieves NUMA-aware, millisecond-level, stateful and fine-grained elasticity, while it is not intrusive to the design of state-of-the-art, in-memory OLTP engines. We evaluate our system through novel use cases demonstrating that scale-up elasticity increases resource utilization, while allowing tenants to pay for actual use of resources and not just their reservation.

Wireless sensor networks (WSNs) are an integral part of an Internet of Things (IoT) ecosystem. In fact, a typical IoT service deployment considers an integrated network of switches and sensors (as well as actuators). In order to manage this heterogeneous network infrastructure, the use of software defined networking (SDN) technologies is anticipated. However, existing SDN-enabling technologies consider only isolated networks and there has been no concrete solution that treats the whole network in a unified way. In this paper, a network operating system is proposed as a way to address the above issue. Starting from the open network operating system, which currently supports only OpenFlow networks, the proposed solution leverages SDN-WISE to integrate WSNs and achieves unified system representation and treatment. The proposed system has been prototyped and is publicly available in open source. Experiments have been executed demonstrating that, by exploiting the proposed solution, it is also possible to improve efficiency of the overall network.

Main-memory OLTP engines are being increasingly deployed on multicore servers that provide abundant thread-level parallelism. However, recent research has shown that even the state-of-the-art OLTP engines are unable to exploit available parallelism for high contention workloads. While previous studies have shown the lack of scalability of all popular concurrency control protocols, they consider only one system architecture---a non-partitioned, shared everything one where transactions can be scheduled to run on any core and can access any data or metadata stored in shared memory. In this paper, we perform a thorough analysis of the impact of other architectural alternatives (Data-oriented transaction execution, Partitioned Serial Execution, and Delegation) on scalability under high contention scenarios. In doing so, we present Trireme, a main-memory OLTP engine testbed that implements four system architectures and several popular concurrency control protocols in a single code base. Using Trireme, we present an extensive experimental study to understand i) the impact of each system architecture on overall scalability, ii) the interaction between system architecture and concurrency control protocols, and iii) the pros and cons of new architectures that have been proposed recently to explicitly deal with high-contention workloads.

In this paper, we propose a solution to support big data processing with MapReduce inside a wireless sensor network (WSN). The proposed framework provides a simple MapReduce interface, whose implementation can be dynamically loaded into the appropriate nodes. The execution of map and reduce functions inside the network is achieved by leveraging software defined networking principles and in particular by enhancing the SDN-WISE protocol on both the Controller and the sensor node sides. On the Controller side, the proposed framework introduces a functionality, which evaluates the optimal deployment of the processing functions in the network taking the typical WSN requirements into account and enforces it by instructing the flow tables to forward traffic from the mappers to the appropriate reducers. Then, on the node side, SDN-WISE is integrated with Contiki, a widely popular operating system for sensor nodes, which has the capability to load and execute new functions without service disruption, i.e., on the fly. The proposed framework is evaluated considering different network topologies and approaches about the data processing location.

Abstract Power control has been a standard issue in modern wireless communications. As a result, several cooperative schemes have been developed towards reducing the downlink transmission power. One possible approach is the use of relays that forward information from the base station (BS) to the mobile stations (MSs). However, up to now, the proposed relaying schemes are focusing solely on channel characteristics. In this work, a novel, content‐aware relaying scheme is introduced, where the MSs can act as active relays based on their requested content and channel quality. Content awareness is achieved with a cross‐layer algorithm, which considers an information‐centric networking layer. The performance of the proposed approach has been evaluated considering a multiple input multiple output (MIMO) cellular configuration with one tier of cells around the central cell, where the downlink transmission power (in terms of mean values) has been compared to the typical non‐content‐aware case. For this reason, a hybrid system—link level simulator has been developed that executes independent Monte Carlo (MC) simulations in parallel. Moreover, the orthogonal frequency division multiple access (OFDMA) physical layer protocol has been employed as well, in order to comply with fourth generation standards. The derived results indicate that, compared to the non‐content‐aware case, the proposed relay transmission algorithm can provide up to 7/17% transmission power gain in medium or highly loaded networks (i.e., 10% and 40% blocking probabilities, respectively) in a symmetric 2 × 2 MIMO configuration (i.e., equal number of transmit and receive antennas). Moreover, compared to the case where no relay transmission is supported, then the aforementioned gain can reach up to 20%. Finally, the proposed algorithm can provide up to 25% reduction on the overall transmission delay from the relay node to the destination compared to the non‐content‐aware case. Copyright © 2014 John Wiley & Sons, Ltd.

The contribution of this position paper is twofold: the first one extends the use of social networking in the direction of allowing not only communications among persons but also human-machine interaction and machine to machine collaboration; the second contribution is the presentation of an experimental platform for large-scale testing of innovative mobile social networking applications, including the one described in the first part of the paper.