DeltaTree: A Practical Locality-aware Concurrent Search Tree

As other fundamental programming abstractions in energy-e cient computing, search trees are expected to support both high parallelism and data locality. However, existing highly-concurrent search trees such as red-black trees and AVL trees do not consider data locality while existing locality-aware search trees such as those based on the van Emde Boas layout (vEB-based trees), poorly support concurrent (update) operations. This paper presents DeltaTree, a practical locality-aware concurrent search tree that combines both locality-optimisation techniques from vEB-based trees and concurrency-optimisation techniques from non-blocking highly-concurrent search trees. DeltaTree is a k-ary leaf-oriented tree of DeltaNodes in which each DeltaNode is a size- xed tree-container with the van Emde Boas layout. The expected memory transfer costs of DeltaTree's Search, Insert and Delete operations are O(logBN), where N;B are the tree size and the unknown memory block size in the ideal cache model, respectively. DeltaTree's Search operation is wait-free, providing prioritised lanes for Search operations, the dominant operation in search trees. Its Insert and Delete operations are non-blocking to other Search, Insert and Delete operations, but they may be occasionally blocked by maintenance operations that are sometimes triggered to keep DeltaTree in good shape. Our experimental evaluation using the latest implementation of AVL, red-black, and speculation friendly trees from the Synchrobench benchmark has shown that DeltaTree is up to 5 times faster than all of the three concurrent search trees for searching operations and up to 1.6 times faster for update operations when the update contention is not too high

Evaluation of the power efficiency of UPC, OpenMP and MPI

In this study we compare the performance and power efficiency of Unified Parallel C (UPC), MPI and OpenMP by running a set of kernels from the NAS Benchmark. One of the goals of this study is to focus on the Partitioned Global Address Space (PGAS) model, in order to describe it and compare it to MPI and OpenMP. In particular we consider the power effi- ciency expressed in millions operations per second per watt as a criterion to evaluate the suitability of PGAS compared to MPI and OpenMP. Based on these measurements, we provide an analysis to explain the difference of performance between UPC, MPI, and OpenMP

HyperProv: Decentralized Resilient Data Provenance at the Edge with Blockchains

Data provenance and lineage are critical for ensuring integrity and reproducibility of information in research and application. This is particularly challenging for distributed scenarios, where data may be originating from decentralized sources without any central control by a single trusted entity. We present HyperProv, a general framework for data provenance based on the permissioned blockchain Hyperledger Fabric (HLF), and to the best of our knowledge, the first system that is ported to ARM based devices such as Raspberry Pi (RPi). HyperProv tracks the metadata, operation history and data lineage through a set of built-in queries using smart contracts, enabling lightweight retrieval of provenance data. HyperProv provides convenient integration through a NodeJS client library, and also includes off-chain storage through the SSH file system. We evaluate HyperProv's performance, throughput, resource consumption, and energy efficiency on x86-64 machines, as well as on RPi devices for IoT use cases at the edge

GridWatch: a smart network for smart grid

The adoption of decentralized energy market models facilitates the exchange of surplus power among local nodes in peer-to-peer settings. However, decentralized energy transactions within untrusted and non-transparent energy markets in modern Smart Grids expose vulnerabilities and are susceptible to attacks. One such attack is the False Data Injection Attack, where malicious entities intentionally inject misleading information into the system. To address this threat, this paper proposes GridWatch, an effective real-time in-network intelligent framework to detect false data injection attacks. Gridwatch operates in a hybrid model. It deploys inference model in the programmable network devices and also on the server to detect false data injection attacks. GridWatch was evaluated using a real-world dataset from Austin, Texas, and can detect false data injection attacks with 94.8% accuracy. GridWatch on average performs 4 billions transactions per second in less than 1.8 microsecond latency

Demo abstract: Towards IoT service deployments on edge community network microclouds

Internet of Things (IoT) services for personal devices and smart homes provided by commercial solutions are typically proprietary and closed. These services provide little control to the end users, for instance to take ownership of their data and enabling services, which hinders these solutions' wider acceptance. In this demo paper, we argue for an approach to deploy professional IoT services on user-controlled infrastructure at the network edge. The users would benefit from the ability to choose the most suitable service from different IoT service offerings, like the one which satisfies their privacy requirements, and third-party service providers could offer more tailored IoT services at customer premises. We conduct the demonstration on microclouds, which have been built with the Cloudy platform in the Guifi.net community network. The demonstration is conducted from the perspective of end users, who wish to deploy professional IoT data management and analytics services in volunteer microclouds.Peer ReviewedPostprint (author's final draft

Weather-aware Wake-up of Sleeping Cyber-Physical IoT Nodes

Cyber-physical IoT nodes located in environments which are resource-constrained and physically hard to access, like the Arctic tundra, must achieve long operational lifetimes from a single battery and report data over data networks. The nodes sleep most of the time, and only wake up to perform mission tasks, including reporting data. However, networks can become unavailable, or have low bandwidth and require many re-transmissions for multiple reasons, including a sparse network infrastructure and adverse weather. The state of the network can be quantified by the Received Signal Strength (RSS). If nodes wake up to report data when the signal strength is low they waste energy, because the reporting of data will require more energy or take more accumulated time. RSS decreases with increasing temperature and precipitation. Therefore, nodes should wake up when the temperature and precipitation are low. We explore four algorithms for picking a single time to wake up per 24-hr day over one year. For each wake-up-time, we compute the change in RSS as a function of the change in temperature and precipitation. We use historic weather forecasts and measurements from MET Norway. The data covers 37 locations in Northern Norway over one year. The weather-forecast-based algorithm is able to frequently select a timeslot near the highest expected RSS. It also avoids the large decrease in RSS caused by precipitation more often than the other algorithms presented