Core Server Research Articles

EarthRanger: An open‐source platform for ecosystem monitoring, research and management

Abstract Effective approaches are needed to conserve the planet's remaining wildlife and wilderness landscapes, especially concerning global biodiversity conservation targets. Here, we present a new software system called EarthRanger: an open‐source platform built to help monitor, research and manage ecosystems. EarthRanger consists of seven main components (Core Server, API, Storage, Gundi, Web App, Mobile App, Ecoscope) that provide functionality for data (i) aggregation & collection, (ii) storage & management, (iii) real‐time and post hoc analysis, (iv) visualisation and (v) dissemination. The mobile application provides field‐based data recording and visualisation tools. EarthRanger may be deployed for single project use or can aggregate across multiple geographies as a centralised hub. EarthRanger can be used to collect standardised tracking data (e.g. from wildlife collars, vehicles and ranger patrols) and configurable event information (e.g. a singular recording with associated user‐defined attribute information such as a wildlife sighting or encounter with a poacher). Since development began in 2015, the platform has (at the time of writing) been deployed at over 500 sites across 70 countries and with myriad configurations and objectives. EarthRanger has improved the ability to monitor data feeds and manage conservation‐related operations in real time. For instance, the deployment of EarthRanger by African Parks has led to the removal of over 50,000 snares, steady population growth of key species of concern and near cessation of poaching. In Liwonde's protected area, enhanced mitigation efforts supported by EarthRanger reduced the number of deaths from wildlife conflict by more than 91%. EarthRanger is also providing a platform to enhance standardisation, aggregation, transfer and long‐term storage of ecological information and promote collaboration between groups conducting protected area management and ecology and biodiversity research.

SBM-SA: A Safety Beacon Message Separation Algorithm for Privacy Protection in Internet of Vehicles

A safety beacon message (SBM) plays a pivotal role in the Internet of Vehicles (IoV), broadcasting crucial events and road conditions to nearby vehicles. Given the sensitive nature of the data, such as vehicle identity and location, ensuring privacy is paramount. The significance of this research lies in addressing the pressing need for comprehensive privacy protection in the IoV, especially as most existing schemes focus on safeguarding either vehicle identity or location data during exchanges with core servers. The primary objective of this article is to introduce an SBM separation algorithm, termed SBM-SA, designed to holistically protect both identity and location data. Utilising correlation analysis, the SBM-SA stands as an innovative anonymisation privacy algorithm. Through a simulated IoV environment, the accuracy and efficacy of an SBM-SA are meticulously analysed and juxtaposed against prevailing privacy protection schemes. The findings underscore the SBM-SA’s potential to significantly enhance privacy measures in the IoV. Implications of this research extend to shaping future privacy protection strategies of the SBM, emphasising the need for holistic and robust solutions in an increasingly interconnected vehicular landscape.

Parallel Peeling of Bipartite Networks for Hierarchical Dense Subgraph Discovery

Wing and Tip decomposition are motif-based analytics for bipartite graphs that construct a hierarchy of butterfly (2,2-biclique) dense edge and vertex induced subgraphs, respectively. They have applications in several domains, including e-commerce, recommendation systems, document analysis, and others. Existing decomposition algorithms use a bottom-up approach that constructs the hierarchy in an increasing order of the subgraph density. They iteratively select the edges or vertices with minimum butterfly count peel , i.e., remove them along with their butterflies. The amount of butterflies in real-world bipartite graphs makes bottom-up peeling computationally demanding. Furthermore, the strict order of peeling entities results in a large number of sequentially dependent iterations. Consequently, parallel algorithms based on bottom up peeling incur heavy synchronization and poor scalability. In this article, we propose a novel Parallel Bipartite Network peelinG (PBNG) framework that adopts a two-phased peeling approach to relax the order of peeling, and in turn, dramatically reduce synchronization. The first phase divides the decomposition hierarchy into few partitions and requires little synchronization. The second phase concurrently processes all partitions to generate individual levels of the hierarchy and requires no global synchronization. The two-phased peeling further enables batching optimizations that dramatically improve the computational efficiency of PBNG. We empirically evaluate PBNG using several real-world bipartite graphs and demonstrate radical improvements over the existing approaches. On a shared-memory 36 core server, PBNG achieves up to 19.7× self-relative parallel speedup. Compared to the state-of-the-art parallel framework P ar B utterfly , PBNG reduces synchronization by up to 15,260× and execution time by up to 295×. Furthermore, it achieves up to 38.5× speedup over state-of-the-art algorithms specifically tuned for wing decomposition. Our source code is made available at https://github.com/kartiklakhotia/RECEIPT .

Optimal and Effective Resource Management in Edge Computing

Edge computing is a cloud computing extension where physical computers are installed closer to the device to minimize latency. The task of edge data centers is to include a growing abundance of applications with a small capability in comparison to conventional data centers. Under this framework, Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence (AI) model without actually revealing the underlying data, which is significantly enhanced in terms of privacy. Federated learning (FL) is a recently developed decentralized profound learning methodology, where customers train their localized neural network models independently using private data, and then combine a global model on the core server together. The models on the edge server use very little time since the edge server is highly calculated. But the amount of time it takes to download data from smartphone users on the edge server has a significant impact on the time it takes to complete a single cycle of FL operations. A machine learning strategic planning system that uses FL in conjunction to minimise model training time and total time utilisation, while recognising mobile appliance energy restrictions, is the focus of this study. To further speed up integration and reduce the amount of data, it implements an optimization agent for the establishment of optimal aggregation policy and asylum architecture with several employees’ shared learners. The main solutions and lessons learnt along with the prospects are discussed. Experiments show that our method is superior in terms of the effective and elastic use of resources.

Detecting Collusive Shill Bidding in Commercial Online Auctions

Online auctions are increasingly used as a smart and efficient way to optimise the consumers’ and sellers’ utility. A recently active field of research is the detection of fraud in online auctions. One of the most difficult types of fraud to detect is collusive shill bidding, where multiple user accounts jointly drive up the bids in an auction. This paper revises the Collusive Shill Bidding Algorithm(CSBD) proposed by Majadi et al. (2019) to develop an algorithm that is applied to a data set from an online auction platform (TBAuctions). We find that our algorithm converges, that computation time can be significantly reduced by appropriate choice of parameters, and we identify Shill Bidding for this data set, although the accuracy of the algorithm cannot be tested because of lack of ground truth values for the data. The paper further discusses steps needed for application of the algorithm to (very) large data sets, using a multiple core server, which despite substantial reduction in computation time would still require too much time to foresee a rapid implementation in real-time.

Transmission Failure Analysis of Multi-Protection Routing in Data Center Networks With Heterogeneous Edge-Core Servers

The recently proposed RCube network is a cube-based server-centric data center network (DCN), including two types of heterogeneous servers, called core and edge servers. Remarkably, it takes the latter as backup servers to deal with server failures and thus achieve high availability. This paper first points out that RCube is suitable as a candidate topology of DCNs for edge computing. Three transmission types are among core and edge servers based on the demand for applications' computation and instant response. We then employ protection routing to analyze the transmission failure of RCube DCNs. Unlike traditional protection routing, which only tolerates a single link or node failure, we use the multi-protection routing scheme to improve fault-tolerance capability. To configure a protection routing in a network, according to Tapolcai's suggestion, we need to construct two completely independent spanning trees (CISTs). A logic graph of RCube, denoted by $L$-$RCube(n,m,k)$, is a network with a recursive structure. Each basic building element consists of $n$ core servers and $m$ edge servers, where the order $k$ is the number of recursions applied in the structure. In this paper, we provide algorithms to construct $\min\{n,\lfloor(n+m)/2\rfloor\}$ CISTs in $L$-$RCube(n,m,k)$ for $n+m\geqslant 4$ and $n>1$. From a combination of the multiple CISTs, we can configure the desired multi-protection routing. In our simulation, we configure up to 10 protection routings for RCube DCNs. As far as we know, in past research, there were at most three protection routings developed in other network structures. Finally, we summarize some crucial analysis viewpoints about the transmission efficiency of DCNs with heterogeneous edge-core servers from the simulation results.

Fine Grained Access Control Based on Smart Contract for Edge Computing

Traditional centralized access control faces data security and privacy problems. The core server is the main target to attack. Single point of failure risk and load bottleneck are difficult to solve effectively. And the third-party data center cannot protect data owners. Traditional distributed access control faces the problem of how to effectively solve the scalability and diversified requirements of IoT (Internet of Things) applications. SCAC (Smart Contract-based Access Control) is based on ABAC (Attributes Based Access Control) and RBAC (Role Based Access Control). It can be applied to various types of nodes in different application scenarios that attributes are used as basic decision elements and authorized by role. The research objective is to combine the efficiency of service orchestration in edge computing with the security of consensus mechanism in blockchain, making full use of smart contract programmability to explore fine grained access control mode on the basis of traditional access control paradigm. By designing SSH-based interface for edge computing and blockchain access, SCAC parameters can be found and set to adjust ACLs (Access Control List) and their policies. The blockchain-edge computing combination is powerful in causing significant transformations across several industries, paving the way for new business models and novel decentralized applications. The rationality on typical process behavior of management services and data access control be verified through CPN (Color Petri Net) tools 4.0, and then data statistics on fine grained access control, decentralized scalability, and lightweight deployment can be obtained by instance running in this study. The results show that authorization takes into account both security and efficiency with the “blockchain-edge computing” combination.

A Proxy Signature-Based Swarm Drone Authentication With Leader Selection in 5G Networks

Drones are imperative for the 5G architecture as a mobile source to expand network coverage and support seamless services, particularly through enabling device-to-device (D2D) communication. Such deployment of drones in D2D settings raises various security threats in drone communication. While the existing D2D communication security standard within the 4G cellular architecture may address some of these issues, the standard includes heavy traffic toward the network core servers. If this security standard is to be adopted in the 5G D2D security services with the same traffic load, it may negatively impact the 5G network performance. Therefore, this paper proposes a lightweight proxy signature-based authentication mechanism for a swarm of drones compatible with the 5G D2D standard mechanisms. This paper proposes a distributed delegation-based authentication mechanism to reduce the traffic overhead toward the 5G core network. In this scheme, the legitimate drones are authorized as proxy delegated signers to perform authentication on behalf of the core network. Furthermore, we propose a mechanism to elect and relocate a new leader relay drone from the existing drone swarm. We implemented the proposed authentication algorithm in the 5G D2D-based communication package over NS-3 while performing the computational calculations on a RaspberryPi3 device to mimic the drone calculation process and delays. The performance of the proposed authentication shows a promising reduction in the authentication time and shows lightweight and reliable compatibility.

Design and implementation of MAP offloading technology based on P4 switch

According to the <italic>China IPv</italic>6 <italic>Development Report,</italic> with the exception of some large internet companies, private small and medium enterprises (SMEs) are very slow to transform into IPv6, and more and more SMEs are migrating their business to the cloud. For cloud service providers, high-throughput IPv4/IPv6 conversion should be completed. In 2014, Nick MCKEOWN proposed programmable protocol-independent packet processors (P4). Implementation of mapping of address and port with encapsulation (MAP-E) offloading technology on P4 switches, that is, offloading the IPv4/IPv6 conversion task to P4 switches, improves the flow forwarding rate and reduces the pressure on the core server. At the same time, the P4 language supports custom switch processing logic, so that there is no need to purchase new equipment when the protocol version is updated and iterated, which reduces future IPv4/IPv6 upgrade and operating costs.

TeksDB

Key-value stores (KVS) are now an integral part of modern dataintensive systems. thanks to its simplicity, scalability, and efficiency over traditional database systems. Databases such as MySQL employ KVS (in this case, RocksDB as their backend storage instead of conventional database engines because KVS is less affected by data fragmentation. OLAP and big data analytics applications often use KVS as the backend store to make use of the efficient range_query operations of LSM(Log Structured Merge Tree)-based KVS. Furthermore, the increasing use of KVS as backend stores in distributed systems diversifies the workload for each local KVS, making it necessary to design a highly-concurrent data store that simultaneously processes mixed operations. The underlying technology trends also put immense pressure on the design of KVS. Nowadays, many core servers with hundred GBs of memory are common in production environments, and this enhanced density of hardware technologies (both in terms of the number of cores and memory capacity) leads to more data items residing in memory. This phenomenon places greater emphasis on the efficiency of the in-memory data structure in KVS, as more data accesses are serviced from its memory component, which is often called the memtable. The current memtable structure of KVS, however, generally assumes a limited memory capacity that only serves as a temporary write buffer, and the use of single-threaded writes or unscalable data structures for the memtable fails to fulfill this external demand.

Research and Design of Self-service Car Washer Remote Monitoring System Based on NB-IoT

In order to solve the problem of increasing maintenance cost of self-washing machine operators, this paper designs a self-service car washing machine remote monitoring system based on NB-IoT. In this system, MSP430F5438A chip is used as the controller and NB-IoT is used as the communication module to realize the remote communication between the car washer and the server. Based on ASP.NET Core, React and SQL SERVER database, the monitoring centre saves the running state information of the car washing machine in the cloud for management. The test results displaying on the browser show that the system is stable and reliable, and has the advantages of low deployment cost and great real-time performance.

High-Performance Time Server Core for FPGA System-on-Chip

This paper presents the complete design and implementation of a low-cost, low-footprint, network time protocol server core for field programmable gate arrays. The core uses a carefully designed modular architecture, which is fully implemented in hardware using digital circuits and systems. Most remarkable novelties introduced are a hardware-optimized timekeeping algorithm implementation, and a full-hardware protocol stack and automatic network configuration. As a result, the core is able to achieve similar accuracy and performance to typical high-performance network time protocol server equipment. The core uses a standard global positioning system receiver as time reference, has a small footprint and can easily fit in a low-range field-programmable chip, greatly scaling down from previous system-on-chip time synchronization systems. Accuracy and performance results show that the core can serve hundreds of thousands of network time clients with negligible accuracy degradation, in contrast to state-of-the-art high-performance time server equipment. Therefore, this core provides a valuable time server solution for a wide range of emerging embedded and distributed network applications such as the Internet of Things and the smart grid, at a fraction of the cost and footprint of current discrete and embedded solutions.

IoTsafe, Decoupling Security From Applications for a Safer IoT

The use of robust security solutions is a must for the Internet of Things (IoT) devices and their applications: regulators in different countries are creating frameworks for certifying those devices with an acceptable security level. However, even for already certified devices, security protocols have to be updated when a breach is found or a certain version becomes obsolete. Many approaches for securing IoT applications are nowadays based on the integration of a security layer [e.g., using transport layer security, (TLS)], but this may result in difficulties when upgrading the security algorithms, as the whole application has to be updated. This fact may shorten the life of IoT devices. As a way to overcome these difficulties, this paper presents IoTsafe, a novel approach relying on secure socket shell (SSH), a feasible alternative to secure communications in IoT applications based on hypertext transfer protocol (HTTP and HTTP/2). In order to illustrate its advantages, a comparison between the traditional approach (HTTP with TLS) and our scheme (HTTP with SSH) is performed over low-power wireless personal area networks (6loWPAN) through 802.15.4 interfaces. The results show that the proposed approach not only provides a more robust and easy-to-update solution, but it also brings an improvement to the overall performance in terms of goodput and energy consumption. Core server stress tests are also presented, and the server performance is also analyzed in terms of RAM consumption and escalation strategies.

Cache-Enabled HetNets With Millimeter Wave Small Cells

In this paper, we consider a novel cache-enabled heterogeneous network (HetNet), where macro base stations (BSs) with traditional sub-6 GHz are overlaid by dense millimeter wave (mmWave) pico BSs. These two-tier BSs, which are modeled as two independent homogeneous Poisson Point Processes, cache multimedia contents following the popularity rank. High-capacity backhauls are utilized between macro BSs and the core server. In contrast to the simplified flat-top antenna pattern analyzed in previous articles, we employ an actual antenna model with the uniform linear array at all mmWave BSs. To evaluate the performance of our system, we introduce two distinctive user association strategies: 1) maximum received power (Max-RP) scheme; and 2) maximum rate (Max-Rate) scheme. With the aid of these two schemes, we deduce new theoretical equations for success probabilities and area spectral efficiencies (ASEs). Considering a special case with practical path loss laws, several closed-form expressions for coverage probabilities are derived to gain several insights. Monte Carlo simulations are presented to verify the analytical conclusions. We show that: 1) the proposed HetNet is an interference-limited system and it outperforms the traditional HetNets in terms of the success probability; 2) there exists an optimal pre-decided rate threshold that contributes to the maximum ASE; and 3) Max-Rate achieves higher success probability and ASE than Max-RP but it needs the extra information of the interference effect.

System and Architecture Level Characterization of Big Data Applications on Big and Little Core Server Architectures

The rapid growth in data yields challenges to process data efficiently using current high-performance server architectures such as big Xeon cores. Furthermore, physical design constraints, such as power and density, have become the dominant limiting factor for scaling out servers. Low-power embedded cores in servers such as little Atom have emerged as a promising solution to enhance energy-efficiency to address these challenges. Therefore, the question of whether to process the big data applications on big Xeon- or Little Atom-based servers becomes important. In this work, through methodical investigation of power and performance measurements, and comprehensive application-level, system-level, and micro-architectural level analysis, we characterize dominant big data applications on big Xeon- and little Atom-based server architectures. The characterization results across a wide range of real-world big data applications, and various software stacks demonstrate how the choice of big- versus little-core-based server for energy-efficiency is significantly influenced by the size of data, performance constraints, and presence of accelerator. In addition, we analyze processor resource utilization of this important class of applications, such as memory footprints, CPU utilization, and disk bandwidth, to understand their run-time behavior. Furthermore, we perform micro-architecture-level analysis to highlight where improvement is needed in big- and little-core microarchitectures to address their performance bottlenecks.

Big vs little core for energy-efficient Hadoop computing

Emerging big data applications require a significant amount of server computational power. However, the rapid growth in the data yields challenges to process them efficiently using current high-performance server architectures. Furthermore, physical design constraints, such as power and density, have become the dominant limiting factor for scaling out servers. Heterogeneous architectures that combine big Xeon cores with little Atom cores have emerged as a promising solution to enhance energy-efficiency by allowing each application to run on an architecture that matches resource needs more closely than a one-size-fits-all architecture. Therefore, the question of whether to map the application to big Xeon or little Atom in heterogeneous server architecture becomes important. In this paper, through a comprehensive system level analysis, we first characterize Hadoop-based MapReduce applications on big Xeon and little Atom-based server architectures to understand how the choice of big vs little cores is affected by various parameters at application, system and architecture levels and the interplay among these parameters. Second, we study how the choice between big and little core changes across various phases of MapReduce tasks. Furthermore, we show how the choice of most efficient core for a particular MapReduce phase changes in the presence of accelerators. The characterization analysis helps guiding scheduling decisions in future cloud-computing environment equipped with heterogeneous multicore architectures and accelerators. We have also evaluated the operational and the capital cost to understand how performance, power and area constraints for big data analytics affect the choice of big vs little core server as a more cost and energy efficient architecture.

Remote Transaction Commit: Centralizing Software Transactional Memory Commits

Software Transactional Memory (STM) has recently emerged as a promising synchronization abstraction for multicore architectures. State-of-the-art STM algorithms, however, suffer from performance challenges due to contention and spinning on locks during the transaction commit phase. In this paper, we introduce Remote Transaction Commit (or RTC), a mechanism for executing commit phases of STM transactions. RTC dedicates server cores to execute transactional commit phases on behalf of application threads. This approach has two major benefits. First, it decreases the overheads of spinning on locks during commit, such as the number of cache misses, blocking of lock holders, and CAS operations. Second, it enables exploiting the benefits of coarse-grained locking algorithms (simple and fast lock acquisition, reduced false conflicts) and bloom filter-based algorithms (concurrent execution of independent transactions). Our experimental study on a 64-core machine with four sockets shows that RTC solves the problem of performance degradation due to spin locking on both micro-benchmarks (red-black trees), and macro-benchmarks (STAMP), especially when the commit phase is relatively long and when thread contention increases.

Leveraging Hardware Message Passing for Efficient Thread Synchronization

As the level of parallelism in manycore processors keeps increasing, providing efficient mechanisms for thread synchronization in concurrent programs is becoming a major concern. On cache-coherent shared-memory processors, synchronization efficiency is ultimately limited by the performance of the underlying cache coherence protocol. This article studies how hardware support for message passing can improve synchronization performance. Considering the ubiquitous problem of mutual exclusion, we devise novel algorithms for (i) classic locking, where application threads obtain exclusive access to a shared resource prior to executing their critical sections (CSes), and (ii) delegation, where CSes are executed by special threads. For classic locking, our HybLock algorithm uses a mix of shared memory and hardware message passing, which introduces the idea of hybrid synchronization algorithms. For delegation, we propose mp-server and HybComb : the former is a straightforward adaptation of the server approach to hardware message passing, whereas the latter is a novel hybrid combining algorithm. Evaluation on Tilera's TILE-Gx processor shows that HybLock outperforms the best known classic locks. Furthermore, mp-server can execute contended CSes with unprecedented throughput, as stalls related to cache coherence are removed from the critical path. HybComb can achieve comparable performance while avoiding the need to dedicate server cores. Consequently, our queue and stack implementations, based on the new synchronization algorithms, largely outperform their most efficient shared-memory-only counterparts.

Равномерное распределение нагрузки аппаратно-программного ядра в UNIX-системах

In this article we consider the problem of maximizing the capacity of the network stack to the interaction of hardware and software core to ensure the stability of the physical server. The algorithms and program codes are proposed to optimize the load capacity of the CPU by core parallelization. The paper also considers statistics of improved power of distributed attacks affecting the network infrastructure. It proved the impact of any application with access to the external global network to the production of the physical server presented in the form of physical resources. With the help of the developed and implemented the algorithm (in the language of «BASH»), produced by the distribution of the load capacity of the physical server cores, to further reduce the load capacity on the processing power of the CPU is provided. Showcased flowcharts, as well as the final test results of each stage of development, are discussed. Implemented network optimization mode «AF_PACKET», which has given the opportunity to accept external network packets without any locks that, in turn, increases the efficiency of achievement of the set goals (upon request from the server to the client). The possibility of taking up to ten million incoming network packets by software physical server, which allows for stable processing of information for the smooth operation under DDoS-attacks «SYN-flood who realized the possibility of overload multimillion network packets. A similar number of incoming network packets provides an opportunity to fill the external network channel, with a consequent increase in the load capacity of the network TCP / IP stack that covers the remote control area physical server as soon as possible. Also adversely affect the performance of the working environment.

ATLAS TDAQ System Administration: evolution and re-design

The ATLAS Trigger and Data Acquisition system is responsible for the online processing of live data, streaming from the ATLAS experiment at the Large Hadron Collider at CERN. The online farm is composed of ∼3000 servers, processing the data read out from ∼100 million detector channels through multiple trigger levels. During the two years of the first Long Shutdown there has been a tremendous amount of work done by the ATLAS Trigger and Data Acquisition System Administrators, implementing numerous new software applications, upgrading the OS and the hardware, changing some design philosophies and exploiting the High- Level Trigger farm with different purposes. The OS version has been upgraded to SLC6; for the largest part of the farm, which is composed of net booted nodes, this required a completely new design of the net booting system. In parallel, the migration to Puppet of the Configuration Management systems has been completed for both net booted and local booted hosts; the Post-Boot Scripts system and Quattor have been consequently dismissed. Virtual Machine usage has been investigated and tested and many of the core servers are now running on Virtual Machines. Virtualisation has also been used to adapt the High-Level Trigger farm as a batch system, which has been used for running Monte Carlo production jobs that are mostly CPU and not I/O bound. Finally, monitoring the health and the status of ∼3000 machines in the experimental area is obviously of the utmost importance, so the obsolete Nagios v2 has been replaced with Icinga, complemented by Ganglia as a performance data provider. This paper serves for reporting of the actions taken by the Systems Administrators in order to improve and produce a system capable of performing for the next three years of ATLAS data taking.