Automatic and scalable data replication manager in distributed computation and storage infrastructure of Cyber-Physical Systems

被引:5
|
作者
Yang Z. [1 ]
Bhimani J. [1 ]
Wang J. [2 ]
Evans D. [3 ]
Mi N. [1 ]
机构
[1] Dept. of Electrical and Computer Engineering, Northeastern University, 360 Huntington Ave, Boston, 02115, MA
[2] Dept. of Computer Science, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, MA
[3] Samsung Semiconductor Inc., Memory Solution Research Lab, Storage Software Group, San Diego, 92121, CA
来源
| 1600年 / West University of Timisoara卷 / 18期
基金
美国国家科学基金会;
关键词
Atomicity; Backup; Cache and replacement policy; Cluster migration; Consistency; Cyber Physical Systems infrastructure; Device failure recovery; Dis- tributed storage system; Fault tolerance; Parallel I/O; Replication; SLA; VM Crash;
D O I
10.12694/scpe.v18i4.1330
中图分类号
学科分类号
摘要
Cyber-Physical System (CPS) is a rising technology that utilizes computation and storage resources for sensing, processing, analysis, predicting, understanding of field-data, and then uses communication resources for interaction, intervene, and interface management, and finally provides control for systems so that they can inter-operate, evolve, and run in a stable evidence-based environment. There are two major demands when building the storage infrastructure for a CPS cluster to support above-mentioned functionalities: (1) high I/O and network throughput requirements during runtime, and (2) low latency demand for disaster recovery. To address challenges brought by these demands, in this paper, we propose a complete solution called "AutoReplica" - an automatic and scalable data replication manager in distributed computation and storage infrastructure of cyber-physical systems, using tiering storage with SSD (solid state disk) and HDD (hard disk drive). Specifically, AutoReplica uses SSD to absorb hot data and to maximize I/Os, and its intelligent replication scheme further helps to recovery from disaster. To effectively balance the trade-off between I/O performance and fault tolerance, AutoReplica utilizes the SSDs of remote CPS server nodes (which are connected by high speed fibers) to replicate hot datasets cached in the SSD tier of the local CPS server node. AutoReplica has three approaches to build the replica cluster in order to support multiple SLAs. AutoReplica automatically balances loads among nodes, and can conduct seamlessly online migration operation (i.e., migrate-on-write scheme), instead of pausing the subsystem and copying the entire dataset from one node to the other. Lastly, AutoReplica supports parallel prefetching from both primary node and replica node(s) with a new dynamic optimizing streaming technique to improve I/O performance. We implemented AutoReplica on a real CPS infrastructure, and experimental results show that AutoReplica can significantly reduce the total recovery time with slight overhead compared to the no replication cluster and traditional replication clusters. © 2017 SCPE.
引用
收藏
页码:291 / 311
页数:20
相关论文
共 50 条
  • [31] Automatic Model Generation and Data Assimilation Framework for Cyber-Physical Production Systems
    Tan, Wen Jun
    Seok, Moon Gi
    Cai, Wentong
    PROCEEDINGS OF THE 2023 ACM SIGSIM INTERNATIONAL CONFERENCE ON PRINCIPLES OF ADVANCED DISCRETE SIMULATION, ACMSIGSIM-PADS 2023, 2023, : 73 - 83
  • [32] State estimation for distributed cyber-physical power systems under data attacks
    Li, Yining
    Wu, Jing
    Li, Shaoyuan
    INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL, 2016, 26 (04) : 317 - 323
  • [33] Agent-Based Distributed Data Analysis in Industrial Cyber-Physical Systems
    Queiroz, Jonas
    Leitao, Paulo
    Barbosa, Jose
    Oliveira, Eugenio
    Garcia, Gisela
    IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 2022, 3 (01): : 5 - 12
  • [34] Automatic identification of integrity attacks in cyber-physical systems
    Ntalampiras, Stavros
    EXPERT SYSTEMS WITH APPLICATIONS, 2016, 58 : 164 - 173
  • [35] A modeling language to describe massive data storage management in cyber-physical systems
    Jing, Yuxin
    Wang, Hanpin
    Huang, Yu
    Zhang, Lei
    Xu, Jiang
    Cao, Yongzhi
    JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2017, 103 : 113 - 120
  • [36] The Cyber-Physical Marketplace: A Framework for Large-Scale Horizontal Integration in Distributed Cyber-Physical Systems
    Wolf, Tilman
    Zink, Michael
    Nagurney, Anna
    2013 33RD IEEE INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS WORKSHOPS (ICDCSW 2013), 2013, : 296 - 302
  • [37] Control Behavior Integrity for Distributed Cyber-Physical Systems
    Adepu, Sridhar
    Brasser, Ferdinand
    Garcia, Luis
    Rodler, Michael
    Davi, Lucas
    Sadeghi, Ahmad-Reza
    Zonouz, Saman
    2020 ACM/IEEE 11TH INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SYSTEMS (ICCPS 2020), 2020, : 30 - 40
  • [38] Risk and Mitigation of Nondeterminism in Distributed Cyber-Physical Systems
    Bateni, Soroush
    Lohstroh, Marten
    Wong, Hou Seng
    Kim, Hokeun
    Lin, Shaokai
    Menard, Christian
    Lee, Edward A.
    2023 21ST ACM-IEEE INTERNATIONAL SYMPOSIUM ON FORMAL METHODS AND MODELS FOR SYSTEM DESIGN, MEMOCODE, 2023, : 1 - 11
  • [39] Monitoring Mobile and Spatially Distributed Cyber-Physical Systems
    Bartocci, Ezio
    Bortolussi, Luca
    Loreti, Michele
    Nenzi, Laura
    MEMOCODE 2017: PROCEEDINGS OF THE 15TH ACM-IEEE INTERNATIONAL CONFERENCE ON FORMAL METHODS AND MODELS FOR SYSTEM DESIGN, 2017, : 147 - 156
  • [40] Which IT Governance for Distributed Intelligent Cyber-Physical Systems?
    Margaria, Tiziana
    39TH ANNUAL IEEE COMPUTERS, SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC 2015), VOL 2, 2015, : 46 - 47