基于持久化内存的索引设计重新思考与优化

非易失性内存(non-volatile memory,NVM)是近几年来出现的一种新型存储介质.一方面,同传统的易失性内存一样,它有着低访问延迟、可字节寻址的特性;另一方面,与易失性内存不同的是,掉电后它存储的数据不会丢失,此外它还有着更高的密度以及更低的能耗开销这些特性使得非易失性内存有望被大规模应用在未来的计算机系...     

Reducing Transaction Processing Latency in Hardware Transactional Memory-based Database with Non-volatile Memory

The emergency of Hardware Transactional Memory (HTM) has greatly boosted the transaction processing performance in in-memory databases. However, the group commit protocol, aiming at reducing the impact from slow storage devices, leads to high transaction commit latency. Non-Volatile Memory (NVM) opens opportunities for reducing transaction commit latency. However, HTM cannot cooperate with NVM together: flushing data to NVM will always cause HTM to abort. In this paper, we propose a technique called parity version to decouple the process of HTM execution and NVM write. Thus, the transactions can correctly and efficiently use NVM to reduce their commit latency with HTM. We have integrated this technique into DBX, a state-of-the-art HTM-based database, and propose DBXN: a low-latency and high-throughput in-memory transaction processing system. Evaluations using typical OLTP workloads including TPC-C show that it has 99% lower latency and 2.1 times higher throughput than DBX.     

Fog computing for next-generation Internet of Things: Fundamental,state-of-the-art and research challenges

In recent times, the Internet of Things (IoT) applications, including smart transportation, smart healthcare, smart grid, smart city, etc. generate a large volume of real-time data for decision making. In the past decades, real-time sensory data have been offloaded to centralized cloud servers for data analysis through a reliable communication channel. However, due to the long communication distance between end-users and centralized cloud servers, the chances of increasing network congestion, data loss, latency, and energy consumption are getting significantly higher. To address the challenges mentioned above, fog computing emerges in a distributed environment that extends the computation and storage facilities at the edge of the network. Compared to centralized cloud infrastructure, a distributed fog framework can support delay-sensitive IoT applications with minimum latency and energy consumption while analyzing the data using a set of resource-constraint fog/edge devices. Thus our survey covers the layered IoT architecture, evaluation metrics, and applications aspects of fog computing and its progress in the last four years. Furthermore, the layered architecture of the standard fog framework and different state-of-the-art techniques for utilizing computing resources of fog networks have been covered in this study. Moreover, we included an IoT use case scenario to demonstrate the fog data offloading and resource provisioning example in heterogeneous vehicular fog networks. Finally, we examine various challenges and potential solutions to establish interoperable communication and computation for next-generation IoT applications in fog networks.     

基于NVM和HTM的低时延事务处理

硬件事务内存(hardware transactional memory,HTM)能够极大地提升多核内存事务处理的吞吐.然而,为了避免慢速持久化设备对事务吞吐的影响,现有系统以批量的方式提交事务,这使得事务提交有极高的延迟.低时延非易失性内存(non-volatile memory,NVM)的出现,给降低基于HTM的内...     

基于海云协同的物联网大数据管理

大数据不断地从复杂的应用系统中产生,并且将会以更多、更复杂、更多样化的方式持续增长。多样化的物联网传感设备不断地感知着海量的具有不同格式的数据。物联网系统中大数据的复杂化和格式多样化,决定了物联网系统中针对大数据的应用场景和服务类型的多样化,从而要求物联网大数据管理系统必须采用不同的新技术来应对具有不同格式的大数据,而现有的针对特定数据类型和业务的系统在架构上已经难以满足如此多样化的需求,因此,设计新的具有可扩展性的系统架构已经成为物联网大数据管理的研究热点。文章提出了一种物联网大数据管理的创新解决方案:面向物联网大数据管理的海云协同模型。首先讨论海云协同模型的整体架构和协同机制,然后分别讨论了海云协同模型中海端计算系统和云端计算系统的设计和实现方案,测试结果表明提出的解决方案性能良好、具有实践可行性。     

WOBTree: a write-optimized B+-tree for non-volatile memory

The emergence of non-volatile memory (NVM) has introduced new opportunities for performance optimizations in existing storage systems. To better utilize its byte-addressability and near-DRAM performance, NVM can be attached on the memory bus and accessed via load/store memory instructions rather than the conventional block interface. In this scenario, a cache line (usually 64 bytes) becomes the data transfer unit between volatile and non-volatile devices. However, the failureatomicity of write on NVM is the memory bit width (usually 8 bytes). This mismatch between the data transfer unit and the atomicity unit may introduce write amplification and compromise data consistency of node-based data structures such as B+-trees. In this paper, we propose WOBTree, a Write-Optimized B+-Tree for NVM to address the mismatch problem without expensive logging. WOBTree minimizes the update granularity from a tree node to a much smaller subnode and carefully arranges the write operations in it to ensure crash consistency and reduce write amplification. Experimental results show that compared with previous persistent B+-tree solutions, WOBTree reduces the write amplification by up to 86× and improves write performance by up to 61× while maintaining similar search performance.     

Exploring and Modelling IoT Offloading Policies in Edge Cloud Environments

The Internet of Things (IoT) has recently become a popular technology that can play increasingly important roles in every aspect of our daily life. For collaboration between IoT devices and edge cloud servers, edge server nodes provide the computation and storage capabilities for IoT devices through the task offloading process for accelerating tasks with large resource requests. However, the quantitative impact of different offloading architectures and policies on IoT applications’ performance remains far from clear, especially with a dynamic and unpredictable range of connected physical and virtual devices. To this end, this work models the performance impact by exploiting a potential latency that exhibits within the environment of edge cloud. Also, it investigates and compares the effects of loosely-coupled (LC) and orchestrator-enabled (OE) architecture. The LC scheme can smoothly address task redistribution with less time consumption for the offloading sceneries with small scale and small task requests. Moreover, the OE scheme not only outperforms the LC scheme in the large-scale tasks requests and offloading occurs but also reduces the overall time by 28.19%. Finally, to achieve optimized solutions for optimal offloading placement with different constraints, orchestration is important.     

Deadline-aware multi-objective IoT services placement optimization in fog environment using parallel FFD-genetic algorithm

Nowadays, Internet of things has become as an inevitable aspect of humans’ IT-based life. A huge number of geo-distributed IoT enabled devices such as smart phones, smart cameras, health care systems, vehicles, etc. are connected to the Internet and manage users’ applications. The IoT applications are generally time sensitive, so that giving them up to Cloud and receiving the response may violate their required deadline, due to distance between user device and centralized Cloud data center and consequently increasing network latency. Fog environment, as an intermediate layer between Cloud and IoT devices, brings a smaller scales of Cloud capabilities closer to user location. Processing real time applications in Fog layer helps more deadlines to be met. Although Fog computing enhances quality of service parameters, limited resources and power of Fog nodes is a challenge in processing applications. Furthermore, the network latency is still an issue for communications between applications’ services and between user device and Fog node, which seriously threatens deadline condition. Regarding to mentioned points, this paper proposes a 3-partite deadline-aware applications’ services placement optimization model in Fog environment which optimizes total power consumption, total resources wastage, and total network latency, simultaneously. The proposed model prioritizes applications in 3 levels based on their associated deadline, and then the model is solved using a parallel model of first fit decreasing and genetic algorithm combination. Simulations results indicates the superiority of proposed approach against counterpart algorithms in terms of reducing power consumption, resource wastage, network latency, and service rejection rate.     

Highly efficient key agreement for remote patient monitoring in MEC-enabled 5G networks

Remote patient monitoring is one of the cornerstones to enable Ambient Assisted Living. Here, a set of devices provide their corresponding input, which should be carefully aggregated and analysed to derive health-related conclusions. In the new Fifth-Generation (5G) networks, Internet of Things (IoT) devices communicate directly to the mobile network without any need of proxy devices. Moreover, 5G networks consist of Multi-access Edge Computing (MEC) nodes, which are taking the role of a mini-cloud, able to provide sufficient computation and storage capacity at the edge of the network. MEC IoT integration in 5G offers a lot of benefits such as high availability, high scalability, low backhaul bandwidth costs, low latency, local awareness and additional security and privacy. In this paper, we first detail the procedure on how to establish such remote monitoring in 5G networks. Next, we focus on the key agreement between IoT, MEC and registration center in order to guarantee mutual authentication, anonymity, and unlinkability properties. Taking into account the high heterogeneity of IoT devices that can contribute to an accurate image of the health status of a patient, it is of utmost importance to design a very lightweight scheme that allows even the smallest devices to participate. The proposed protocol is symmetric key based and thus highly efficient. Moreover, it is shown that the required security features are established and protection against the most of the well-known attacks is guaranteed.

     

雾计算中支持外包与撤销的属性基加密方案

雾计算将云计算的计算能力、数据分析应用等扩展到网络边缘,可满足物联网设备的低时延、移动性等要求,但同时也存在数据安全和隐私保护问题。传统云计算中的属性基加密技术不适用于雾环境中计算资源有限的物联网设备,并且难以管理属性变更。为此,提出一种支持加解密外包和撤销的属性基加密方案,构建“云-雾-终端”的三层系统模型,通过引入属性组密钥的技术,实现动态密钥更新,满足雾计算中属性即时撤销的要求。在此基础上,将终端设备中部分复杂的加解密运算外包给雾节点,以提高计算效率。实验结果表明,与KeyGen、Enc等方案相比,该方案具有更优的计算高效性和可靠性。     

Notes on ensembles of IoT,network functions and clouds for service-oriented computing and applications

Many advances have been introduced recently for service-oriented computing and applications (SOCA). The Internet of Things (IoT) has been pervasive in various application domains. Fog/Edge computing models have shown techniques that move computational and analytics capabilities from centralized data centers where most enterprise business services have been located to the edge where most customer’s Things and their data and actions reside. Network functions between the edge and the cloud can be dynamically provisioned and managed through service APIs. Microservice architectures are increasingly used to simplify engineering, deployment and management of distributed services in not only cloud-based powerful machines but also in light-weighted devices. Therefore, a key question for the research in SOCA is how do we leverage existing techniques and develop new ones for coping with and supporting the changes of data and computation resources as well as customer interactions arising in the era of IoT and Fog/Edge computing. In this editorial paper, we attempt to address this question by focusing on the concept of ensembles for IoT, network functions and clouds.     

Quality-enabled decentralized IoT architecture with efficient resources utilization

The Internet of Things (IoT) is a paradigm aimed at connecting everyday objects to the internet. IoT applications include smart cities, healthcare, agriculture, as well as the industry and manufacturing. The ability to monitor and control the physical world using information technology creates many opportunities. However, it also comes with some costs. The exponential growth of connected devices, the heterogeneity of IoT use cases, and the diversity of the network technologies yield a concern regarding IoT sustainability. With this work, we aim to contribute to this concern. In doing so, we introduce a novel representation model that is destined for (i) monitoring the IoT environment at runtime, (ii) expressing the overall quality of the system, and (iii) helping to utilize the available resources efficiently. We also define a feature set that describes the best the expectations of decentralized IoT platforms. Furthermore, we describe a quality-enabled decentralized IoT architecture too that incorporates the specified feature set as well as our representation model. Such solutions are necessary to improve and maintain IoT of the future and all its application domains, including the Industrial Internet of Things (IIoT). With the presented research, we aim to encourage the efficient utilization of resources and simplify the production of next-generation IoT solutions.     

面向物联网的基于智能合约的认证和授权方案

由于存在单点失效、规模受限等问题,传统中心化的解决方案很难满足物联网的安全需求。针对这种情况,提出一个面向IoT的基于智能合约的访问控制方案。通过引用IoT智能网关作为IoT设备的中心管理节点和公有区块链的全能节点,采用中心化与去中心化相结合、私有区块链和公有区块相结合、本地局部存储和外部公共存储相结合的方法加以实现。该方案实现IoT设备和IoT智能网关的相互认证,并实现用户对IoT设备中资源及存储在数据库中的数据的授权访问,具有去中心化、分布式优点,满足了规模性和安全性要求。     

A novel 3D NoC architecture based on De Bruijn graph

Networks on Chip (NoC) and 3-Dimensional Integrated Circuits (3D IC) have been proposed as the solutions to the ever-growing communication problem in System on Chip (SoC). Most of contemporary 3D architectures are based on Mesh topology, which fails to achieve small latency and power consumption due to its inherent large network diameter. Moreover, the conventional XY routing lacks the ability of fault tolerance. In this paper, we propose a new 3D NoC architecture, which adopts De Bruijn graph as the topology in physical horizontal planes by leveraging its advantage of small latency, simple routing, low power, and great scalability. We employ an enhanced pillar structure for vertical interconnection. We design two shifting based routing algorithms to meet separate performance requirements in latency and computing complexity. Also, we use fault tolerant routing to guarantee reliable data transmission. Our simulation results show that the proposed 3D NoC architecture achieves better network performance and power efficiency than 3D Mesh and XNoTs topologies.     

油田物联网系统边缘计算研究与实践

随着智能传感器和无线通信技术的发展,油田物联网系统提高了现场生产数据采集的频率和生产过程控制的效率,然而现有物联网系统仍然通过位于远程数据中心的计算资源进行数据处理和控制,网络带宽和通信延迟成为严重的瓶颈。通过对物联网系统的边缘层设备应用边缘计算技术,充分利用边缘网关的计算和存储能力,使用孤立森林算法实现异常数据检测和报警规则学习,同时对温度和阀门开关进行逻辑控制,将之前在云端的处理功能下沉在边缘端实现,降低对网络的要求,满足偏远地区油田生产需要。     

EnergIoT: A solution to improve network lifetime of IoT devices

Internet of Things (IoT) is a novel paradigm attracting significant attention in the modern wireless telecommunications field. However, in some scenarios, the performance of IoT network is limited by energy-constrained devices. In order to improve the energy efficiency of such IoT devices, researchers have proposed several approaches based on duty cycle operation (switching devices between sleeping and active mode). However, current solutions adopting duty cycle (i.e., the fraction of time in which a node is active) have three issues: (i) they assign the same duty cycle ratio to all the nodes without balancing energy consumption; or (ii) they distribute different duty cycle ratios without considering the energy consumption during network construction phase; or (iii) their network structure models are based on concentric corona, instead of clustering structure.In this paper, we propose EnergIoT, a hierarchical clustering approach based on duty cycle ratio to maximize network lifetime of battery-powered IoT devices. In particular, we assign different duty cycle ratios to devices according to their distance from the sink, since different duty cycle ratios balance the energy consumption among devices at different layers. Furthermore, we calculate the energy consumption of IoT devices, considering both network construction phase and data processing phase. We evaluate EnergIoT through extensive simulation analyses on the OMNet++ platform. The result shows that EnergIoT is not only feasible but also efficient. Moreover, EnergIoT improves the network lifetime by 32%, compared to the uniform duty cycle approach, without sacrificing the network performance (i.e., end-to-end delay).     

MacroTrend: A Write-Efficient Cache Algorithm for NVM-Based Read Cache

The future storage systems are expected to contain a wide variety of storage media and layers due to the rapid development of NVM(non-volatile memory)techniques.For NVM-based read caches,many kinds of NVM devices cannot stand frequent data updates due to limited write endurance or high energy consumption of writing.However,traditional cache algorithms have to update cached blocks frequently because it is difficult for them to predict long-term popularity according to such limited information about data blocks,such as only a single value or a queue that reflects frequency or recency.In this paper,we propose a new MacroTrend(macroscopic trend)prediction method to discover long-term hot blocks through blocks'macro trends illustrated by their access count histograms.And then a new cache replacement algorithm is designed based on the MacroTrend prediction to greatly reduce the write amount while improving the hit ratio.We conduct extensive experiments driven by a series of real-world traces and find that compared with LRU,MacroTrend can reduce the write amounts of NVM cache devices significantly with similar hit ratios,leading to longer NVM lifetime or less energy consumption.     

面向非易失内存的异构索引

非易失内存(non-volatile memory,NVM)为数据存储与管理带来新的机遇,但同时也要求已有的索引结构针对NVM的特性进行重新设计.围绕NVM的存取特性,重点研究了树形索引在NVM上的访问、持久化、范围查询等操作的性能优化,并提出了一种上下两层结构的异构索引HART.该索引结合了B+树与Radix树的特点...     

基于DICE的证明存储方案

信息技术的不断发展和智能终端设备的普及导致全球数据存储总量持续增长,数据面临的威胁挑战也随着其重要性的凸显而日益增加,但目前部分计算设备和存储设备仍存在缺乏数据保护模块或数据保护能力较弱的问题.现有数据安全存储技术一般通过加密的方式实现对数据的保护,但是数据的加解密操作即数据保护过程通常都在应用设备上执行,导致应用设备遭受各类攻击时会对存储数据的安全造成威胁.针对以上问题,本文提出了一种基于DICE的物联网设备证明存储方案,利用基于轻量级信任根DICE构建的可信物联网设备为通用计算设备(统称为主机)提供安全存储服务,将数据的加解密操作移至可信物联网设备上执行,消除因主机遭受内存攻击等风险对存储数据造成的威胁.本文工作主要包括以下3方面:(1)利用信任根DICE构建可信物联网设备,为提供可信服务提供安全前提.(2)建立基于信任根DICE的远程证明机制和访问控制机制实现安全认证和安全通信信道的建立.(3)最终利用可信物联网设备为合法主机用户提供可信的安全存储服务,在实现数据安全存储的同时,兼顾隔离性和使用过程的灵活性.实验结果表明,本方案提供的安全存储服务具有较高的文件传输速率,并具备较高...