DOCSLIB.ORG

(ASE 18 US 8,370.224 B2 Page 2

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
(ASE 18 US 8,370.224 B2 Page 2 USOO8370224B2 (12) United States Patent (10) Patent No.: US 8,370.224 B2 Grewal (45) Date of Patent: Feb. 5, 2013 (54) GRAPHICAL INTERFACE FOR DISPLAY OF 6,574,779 B2 6/2003 Allen et al. ASSETS IN AN ASSET MANAGEMENT 6,581,045 B1 6/2003 Watson 6,631,552 B2 10/2003 Yamaguchi SYSTEM 6,650,346 B1 1 1/2003 Jaeger et al. 6,691,115 B2 2/2004 Mosher, Jr. et al. (75) Inventor: Ardaman Singh Grewal, Milwaukee, 6,735,752 B2 5, 2004 Manoo WI (US) 6,738,958 B2 5, 2004 Manoo 6,763,377 B1 7/2004 Belknap et al. 6,775,576 B2 8/2004 Spriggs et al. (73) Assignee: Rockwell Automation Technologies, 6,782,399 B2 8/2004 Mosher, Jr. Inc., Mayfield Heights, OH (US) 6,789,214 B1 9/2004 De Bonis-Hamelin et al. 6,806,813 B1 10/2004 Cheng et al. (*) Notice: Subject to any disclaimer, the term of this 6,847,982 B2 1/2005 Parker et al. patent is extended or adjusted under 35 6,889,096 B2 * 5/2005 Spriggs et al. .................. 7OO/17 U.S.C. 154(b) by 247 days. (Continued) (21) Appl. No.: 11/535,549 FOREIGN PATENT DOCUMENTS GB 2414568 A 11, 2005 (22) Filed: Sep. 27, 2006 WO 2006/081024 A2 8, 2006 (65) Prior Publication Data OTHER PUBLICATIONS US 2008/OO77512 A1 Mar. 27, 2008 “Gibson Petroleum Selects Couay to Add Location Intelligence to Fleet Management System.” Canada NewsWire Nov. 29, 2001 (51) Int. Cl. ProQuest Newsstand, ProQuest. Web. Aug. 9, 2012.* AIK 5/02 (2006.01) G06F 17/50 (2006.01) (Continued) (52) U.S. Cl. ......................................................... 705/29 Primary Examiner — Seye Iwarere (58) Field of Classification Search .................... 705/29; (74) Attorney, Agent, or Firm — Turocy & Watson, LLP: 71.5/853 See application file for complete search history. Alexander R. Kuszewski; John M. Miller (57) ABSTRACT (56) References Cited The claimed Subject matter provides a system and/or method U.S. PATENT DOCUMENTS that facilitates employing a graphical user interface to moni 4,672,039 A 6, 1987 Lundblom tor and/or manage an asset within an industrial environment. 5,414,812 A 5/1995 Filip et al. A graphical user interface can facilitate asset management 5,455,775 A 10, 1995 Huber et al. including a first field that provides a user with a hierarchical 5,638,071 A 6/1997 Capofreddi et al. representation of assets within an industrial environment. The 5,917,433 A 6, 1999 Keillor et al. 6,005,571 A 12/1999 Pachauri graphical user interface can further include a second field that 6.256,768 B1 7/2001 Igusa displays available management functionality associated with 6,366,916 B1 4/2002 Baer et al. an asset selected within the first field. 6.421,571 B1 7/2002 Spriggs et al. 6,430,536 B2 8/2002 Irving et al. 27 Claims, 11 Drawing Sheets - O iSERNP 4. iNISTRAL ENWRCNENE GCCPCNEN s - as (ASE 18 US 8,370.224 B2 Page 2 U.S. PATENT DOCUMENTS 2003. O195764 A1 10, 2003 Baker et al. 2003,0225650 A1 12/2003 Wilson et al. 6.961,687 B1 * 1 1/2005 Myers et al. ...................... TO3/6 2003/0233438 A1 12/2003 Hutchinson et al. 6,989,751 B2 1/2006 Richards 2004, OO15367 A1 1/2004 Nicastro et al. D522,523 S 6, 2006 Parta 2004.0024660 A1 2/2004 Ganesh et al. 7,058,154 B1 6, 2006 Stark et al. T.O62.455 B1 6, 2006 Tob 2004.01.11335 A1 6/2004 Black et al. W was obey 2004/O153437 A1* 8, 2004 Buchan ............................. 707/1 7,069,558 B1 6, 2006 Stone et al. 2004/O186927 A1 9/2004 Eryurek et al. 7,102,4937,110,110 B1B2 9,9/2006 2006 CoppingerFink et al. 2004/0215544 A1 10, 2004 Formal et al. J. J. W. 2005/0043977 A1 2/2005 Ahern et al. ...................... 705/7 7.117.4437,116.228 B1 10/200610, 2006 ZikaSingleton et all 2005, 0120288 A1 6/2005 Boehme et al. J. J. Ilka et al. 2005/0242181 A1 1 1/2005 Cunningham et al. 7,313,592 B1* 12/2007 Huboi et al. .................. TO9,203 2005/025895.6 A1 11/2005 Neuwirth 7.584, 165 B2* 9/2009 Buchan .......... ... 70660 2005/0283718 A1 12/2005 Wilson et al. 2001/0027378 A1* 10, 2001 Tennison et al. .............. TO1,213 2006, OO85242 A1 4/2006 Mark 2001/0052013 A1* 12/2001 Munguia et al. .............. 709,225 2006, O161593 A1 7/2006 Mori et al. 2002/0065698 A1* 5, 2002 Schicket al. ..................... 70.5/8 ck 2002fO116308 A1 8/2002 Cunningham 2006, O18454.0 A1 ck 8/2006 Kung et al. ..................... 707/10 2007/0214179 A1 9/2007 Hoang ... 707/104.1 2003/0028269 A1* 2/2003 Spriggs et al. .................. TOO/83 2008.0015955 A1 1/2008 Ehrman et al. .................. 705/28 2003/0028544 A1 2/2003 Viraget al. 2003/0055666 A1* 3/2003 Roddy et al. ...................... 70.5/1 OTHER PUBLICATIONS 2003/006 1159 A1 3f2003 Adams et al. .. ... TOS/40 2003/0078960 A1* 4, 2003 Murren et al. 709f2O3 European Search Report for EP Application Serial No. 071 17179.7. 2003/0084067 A1* 5/2003 Obiaya ...................... TO7 104.1 dated Sep. 27, 2010, 7 pages. 2003. O14493.0 A1 7/2003 Kulkarni et al. 2003. O154199 A1 8, 2003 Thomas et al. * cited by examiner U.S. Patent Feb. 5, 2013 Sheet 1 of 11 US 8,370.224 B2 V, U.S. Patent US 8,370.224 B2 U.S. Patent US 8,370.224 B2 U.S. Patent US 8,370.224 B2 U.S. Patent Feb. 5, 2013 Sheet 6 of 11 US 8,370.224 B2 -60) ENTERRISE 5)4. Ét AREA ENE WRRCE FIG. 6 U.S. Patent Feb. 5, 2013 Sheet 7 of 11 US 8,370.224 B2 þTIg?s,??I U.S. Patent Feb. 5, 2013 Sheet 8 of 11 US 8,370.224 B2 800 RERESENT ASSETS WTFN AN 82. NESTRALEWERONMENT INA ERACHCA. MANNER BASE) FON CRACERST EMCY A GRA: A SER 3): NERFACE TO ROWE ASSET MANAGEMENTAN)-R MONITORNG RCEWE AN ENPS TO SELECAT 8) iASSE ASSETOEPOY WONORNG ANDRMANAGIN CFFHE SELECTED ASSET F.G. S. U.S. Patent Feb. 5, 2013 Sheet 9 of 11 US 8,370.224 B2 900 illERARCHICALLY STRUCTURE -92 TW) OR WORE ASSEES WTHIN AN 1 NOJSRA ENVIRONMEN N A DAA REPOSTORY EMPLOYA (RAPHICAL jSER ENERFACEO OBSERVE AND/OR 4. MANAGE HE HERARECALLY SRUCERE ASSE WHEN 'I, in CSEREAL ENWRONMENT 9) PROWEEN ASSE GRAF-C RESFECTWE TO ASE.ECTED ASSET -968 ;:ST AWALABLE FUNCTIONALTY - RESPECTWE TO ASEECE ASSET All OW DETALS ANDOR UPDATE RELATED TOCATA ASSOCATE) WH THE GRAF-CAL SER ENERFACE O 3E SPLAYE) FG, 9 U.S. Patent Feb. 5, 2013 Sheet 10 of 11 US 8,370.224 B2 -joo EO CENT SERVER OAA, AA STORES) STORE{S} COMMENCATION ES FRAWEEWORK 1936 O4) F.G. ) U.S. Patent Feb. 5, 2013 Sheet 11 of 11 US 8,370.224 B2 ... 28 APPLICATIONS: ---------------- '3? E T-IMQUES in I FI Liu II L II. 1134 EAA ; : O'' } EEWICE{S} E NTERFACE PORTS) NP iEVCE(S) 35 NEWORK COMMUNICATION H J NTERFACE CONNECTION(S) 43 EMORY SORASE 46 F.G. 1 US 8,370.224 B2 1. 2 GRAPHICAL INTERFACE FOR DISPLAY OF ing of some aspects described herein. This Summary is not an ASSETS IN AN ASSET MANAGEMENT extensive overview, and is not intended to identify key/critical SYSTEM elements or to delineate the scope of the claimed subject matter. Its sole purpose is to present some concepts in a TECHNICAL FIELD simplified form as a prelude to the more detailed description that is presented later. The claimed Subject matter relates generally to asset man The Subject innovation relates to systems and/or methods agement in a facility and, more particularly, to representing that facilitate employing a graphical user interface to monitor assets within an industrial facility. and/or manage an asset within an industrial environment. A 10 graphical user interface can be employed to traverse through BACKGROUND a hierarchy of assets to select a desired asset, wherein Such Due to advances in computing technology, businesses selection can allow the management and/or observation of today are able to operate more efficiently when compared to assets, asset details, available functionality, hierarchy loca Substantially similar businesses only a few years ago. For tion, etc. For instance, management can relate to validating an example, internal networking enables employees of a com 15 asset, backing up an asset, archiving data associated with an pany to communicate instantaneously by email, quickly asset, updating an asset with new or additional Software or transfer data files to disparate employees, manipulate data firmware, and the like. Further, an asset can be a physical files, share data relevant to a project to reduce duplications in device. Such as a programmable logic controller, a pump, a work product, etc. Furthermore, advancements in technology press, a valve, a drain, a heater, a cooler, a Switch, a sensor, a have enabled factory applications to become partially or com conveyor, and/or a portion thereof, as well as Software, firm pletely automated. For instance, operations that once required ware, etc. The graphical user interface can include a first field workers to put themselves proximate to heavy machinery and that includes an expandable and/or collapsible hierarchy of other various hazardous conditions can now be completed at assets, wherein buttons or the like can be selected by a point a safe distance therefrom.
Load more

Recommended publications
  • Reflex: Remote Flash at the Performance of Local Flash
    ReFlex: Remote Flash at the Performance of Local Flash Ana Klimovic Heiner Litz Christos Kozyrakis Flash Memory Summit 2017 Santa Clara, CA Stanford MAST1 ! Flash in Datacenters § NVMe Flash • 1,000x higher throughput than disk (1MIOPS) • 100x lower latency than disk (50-70usec) § But Flash is often underutilized due to imbalanced resource requirements Example Datacenter Flash Use-Case Applica(on Tier Datastore Tier Datastore Service get(k) Key-Value Store So9ware put(k,val) App App Tier Tier App Clients Servers TCP/IP NIC CPU RAM Hardware Flash 3 Imbalanced Resource Utilization Sample utilization of Facebook servers hosting a Flash-based key-value store over 6 months 4 [EuroSys’16] Flash storage disaggregation. Ana Klimovic, Christos Kozyrakis, Eno Thereska, Binu John, Sanjeev Kumar. Imbalanced Resource Utilization Sample utilization of Facebook servers hosting a Flash-based key-value store over 6 months 5 [EuroSys’16] Flash storage disaggregation. Ana Klimovic, Christos Kozyrakis, Eno Thereska, Binu John, Sanjeev Kumar. Imbalanced Resource Utilization u"lizaon Sample utilization of Facebook servers hosting a Flash-based key-value store over 6 months 6 [EuroSys’16] Flash storage disaggregation. Ana Klimovic, Christos Kozyrakis, Eno Thereska, Binu John, Sanjeev Kumar. Imbalanced Resource Utilization u"lizaon Flash capacity and bandwidth are underutilized for long periods of time 7 [EuroSys’16] Flash storage disaggregation. Ana Klimovic, Christos Kozyrakis, Eno Thereska, Binu John, Sanjeev Kumar Solution: Remote Access to Storage § Improve resource utilization by sharing NVMe Flash between remote tenants § There are 3 main concerns: 1. Performance overhead for remote access 2. Interference on shared Flash 3. Flexibility of Flash usage 8 Issue 1: Performance Overhead 4kB random read 1000 Local Flash iSCSI (1 core) 800 libaio+libevent (1core) 600 4x throughput drop 400 200 p95 read latency(us) 2× latency increase 0 0 50 100 150 200 250 300 IOPS (Thousands) • Traditional network/storage protocols and Linux I/O libraries (e.g.
    [Show full text]
  • Multi-Resolution Storage and Search in Sensor Networks Deepak Ganesan University of Massachusetts Amherst
    University of Massachusetts Amherst ScholarWorks@UMass Amherst Computer Science Department Faculty Publication Computer Science Series 2003 Multi-resolution Storage and Search in Sensor Networks Deepak Ganesan University of Massachusetts Amherst Follow this and additional works at: https://scholarworks.umass.edu/cs_faculty_pubs Part of the Computer Sciences Commons Recommended Citation Ganesan, Deepak, "Multi-resolution Storage and Search in Sensor Networks" (2003). Computer Science Department Faculty Publication Series. 79. Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/79 This Article is brought to you for free and open access by the Computer Science at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Computer Science Department Faculty Publication Series by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Multi-resolution Storage and Search in Sensor Networks Deepak Ganesan Department of Computer Science, University of Massachusetts, Amherst, MA 01003 Ben Greenstein, Deborah Estrin Department of Computer Science, University of California, Los Angeles, CA 90095, John Heidemann University of Southern California/Information Sciences Institute, Marina Del Rey, CA 90292 and Ramesh Govindan Department of Computer Science, University of Southern California, Los Angeles, CA 90089 Wireless sensor networks enable dense sensing of the environment, offering unprecedented op- portunities for observing the physical world. This paper addresses two key challenges in wireless sensor networks: in-network storage and distributed search. The need for these techniques arises from the inability to provide persistent, centralized storage and querying in many sensor networks. Centralized storage requires multi-hop transmission of sensor data to internet gateways which can quickly drain battery-operated nodes.
    [Show full text]
  • Network-Compute Co-Design for Distributed In-Memory Computing” Advisors: Prof
    Network–Compute Co-Design for Distributed In-Memory Computing Alexandros Daglis Ph.D. Thesis July 6, 2018 Thesis Committee Prof. Babak Falsafi, Prof. Edouard Bugnion, thesis directors Prof. Paolo Ienne, jury president Dr. Paolo Faraboschi, external examiner Prof. James Larus, internal examiner Prof. Gurindar Sohi, external examiner Communication and Information Sciences École Polytechnique Fédérale de Lausanne Lausanne, Switzerland T¨c paideÐac êfh tὰς μὲn ûÐzac εἶnai πικράς, tὸn δὲ καρπὸn γλυκύν. — Aristotèlhc The roots of education are bitter, but the fruit is sweet. — Aristotle To my family Acknowledgements My PhD journey has undoubtedly been the most challenging and, at the same time, most rewarding so far in my life. A journey that transformed me into a better person and taught me the true value of perseverance, team work, empathy, and patience. I would not have been able to reach the finish line if it weren’t for the wonderful people around me; people who were always there to magnify the joy of the best moments; people who would always support and help me push through the hardest times. To all of these people, I owe my deepest gratitude. It is therefore apposite to start this thesis by thanking them. First and foremost, I am grateful to my advisors, Babak and Ed. Babak has been a constant source of stimulation to keep pushing myself out of my comfort zone, a practice instrumental to my success. He taught me the importance of asking the right questions and taking a step back to look at the big picture. Babak’s attention to detail and quest for perfection confirms that the "the path of virtues goes through toils".
    [Show full text]
  • Succinct: Enabling Queries on Compressed Data
    Succinct: Enabling Queries on Compressed Data Rachit Agarwal, Anurag Khandelwal, and Ion Stoica, University of California, Berkeley https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/agarwal This paper is included in the Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’15). May 4–6, 2015 • Oakland, CA, USA ISBN 978-1-931971-218 Open Access to the Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’15) is sponsored by USENIX Succinct: Enabling Queries on Compressed Data Rachit Agarwal Anurag Khandelwal Ion Stoica UC Berkeley UC Berkeley UC Berkeley Abstract that indexes can be as much as 8× larger than the in- Succinct is a data store that enables efficient queries di- put data size. Traditional compression techniques can rectly on a compressed representation of the input data. reduce the memory footprint but suffer from degraded Succinct uses a compression technique that allows ran- throughput since data needs to be decompressed even dom access into the input, thus enabling efficient stor- for simple queries. Thus, existing data stores either re- age and retrieval of data. In addition, Succinct natively sort to using complex memory management techniques supports a wide range of queries including count and for identifying and caching “hot” data [5, 6, 26, 35] or search of arbitrary strings, range and wildcard queries. simply executing queries off-disk or off-SSD [25].In What differentiates Succinct from previous techniques either case, latency and throughput advantages of in- is that Succinct supports these queries without storing dexes drop compared to in-memory query execution.
    [Show full text]
  • Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory
    Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory Shivaram Venkataraman†∗, Niraj Tolia‡, Parthasarathy Ranganathan†, and Roy H. Campbell∗ †HP Labs, Palo Alto, ‡Maginatics, and ∗University of Illinois, Urbana-Champaign Abstract ther disk or flash. Not only are they byte-addressable and low-latency like DRAM but, they are also non-volatile. The predicted shift to non-volatile, byte-addressable Projected cost [19] and power-efficiency characteris- memory (e.g., Phase Change Memory and Memristor), tics of Non-Volatile Byte-addressable Memory (NVBM) the growth of “big data”, and the subsequent emergence lead us to believe that it can replace both disk and mem- of frameworks such as memcached and NoSQL systems ory in data stores (e.g., memcached, database systems, require us to rethink the design of data stores. To de- NoSQL systems, etc.) but not through legacy inter- rive the maximum performance from these new mem- faces (e.g., block interfaces or file systems). First, the ory technologies, this paper proposes the use of single- overhead of PCI accesses or system calls will dominate level data stores. For these systems, where no distinc- NVBM’s sub-microsecond access latencies. More im- tion is made between a volatile and a persistent copy of portantly, these interfaces impose a two-level logical sep- data, we present Consistent and Durable Data Structures aration of data, differentiating between in-memory and (CDDSs) that, on current hardware, allows programmers on-disk copies. Traditional data stores have to both up- to safely exploit the low-latency and non-volatile as- date the in-memory data and, for durability, sync the data pects of new memory technologies.
    [Show full text]
  • Bluecache: a Scalable Distributed Flash-Based Key-Value Store
    BlueCache: A Scalable Distributed Flash-based Key-value Store Shuotao Xuz Sungjin Leey Sang-Woo Junz Ming Liuz Jamey Hicks[ Arvindz zMassachusetts Institute of Technology yInha University [Accelerated Tech, Inc fshuotao, wjun, ml, [email protected] [email protected] [email protected] ABSTRACT KVS caches are used extensively in web infrastructures because of A key-value store (KVS), such as memcached and Redis, is widely their simplicity and effectiveness. used as a caching layer to augment the slower persistent backend Applications that use KVS caches in data centers are the ones storage in data centers. DRAM-based KVS provides fast key-value that have guaranteed high hit-rates. Nevertheless, different appli- access, but its scalability is limited by the cost, power and space cations have very different characteristics in terms of the size of needed by the machine cluster to support a large amount of DRAM. queries, the size of replies and the request rate. KVS servers are This paper offers a 10X to 100X cheaper solution based on flash further subdivided into application pools, each representing a sepa- storage and hardware accelerators. In BlueCache key-value pairs rate application domain, to deal with these differences. Each appli- are stored in flash storage and all KVS operations, including the cation pool has its own prefix of the keys and typically it does not flash controller are directly implemented in hardware. Further- share its key-value pairs with other applications. Application mixes more, BlueCache includes a fast interconnect between flash con- also change constantly, therefore it is important for applications to trollers to provide a scalable solution.
    [Show full text]
  • Building Distributed Storage with Specialized Hardware
    Research Collection Doctoral Thesis Building Distributed Storage with Specialized Hardware Author(s): Istvan, Zsolt Publication Date: 2018-06 Permanent Link: https://doi.org/10.3929/ethz-b-000266096 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library DISS. ETH NO. 24858 Building Distributed Storage with Specialized Hardware A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH¨ (Dr. sc. ETH Z¨urich) presented by ZSOLT ISTVAN´ Master of Science ETH in Computer Science, ETH Z¨urich born on 08.09.1988 accepted on the recommendation of Prof. Dr. Gustavo Alonso (ETH Z¨urich), examiner Prof. Dr. Torsten Hoefler (ETH Z¨urich), co-examiner Prof. Dr. Timothy Roscoe (ETH Z¨urich), co-examiner Dr. Ken Eguro (MSR Redmond), co-examiner 2017 Abstract In an effort to keep up with increasing data sizes, applications in the datacenter are scaled out to large number of machines. Even though this allows them to tackle complex problems, data movement bottlenecks of various types appear, limiting overall performance and scalability. Pushing computation closer to the data reduces bottlenecks, and in this work we explore this idea in the context of distributed key-value stores. Efficient compute is important in the datacenter, especially at scale. Driven by the stagna- tion of single-threaded CPU performance, specialized hardware and hybrid architectures are emerging and could hold the answer for more efficient compute and data manage- ment. In this work we use Field Programmable Gate Arrays (FPGA) to break traditional trade-offs and limitations, and to explore design scenarios that were previously infeasible.
    [Show full text]
  • Iot Governance, Privacy and Security Issues EUROPEAN RESEARCH CLUSTER on the INTERNET of THINGS
    Internet of Things IoT Governance, Privacy and Security Issues EUROPEAN RESEARCH CLUSTER ON THE INTERNET OF THINGS January, 2015 “Just as energy is the basis of life itself, and ideas the source of innovation, so is innovation the vital spark of all human change, improvement and progress.” Ted Levitt IERC Coordinators: HINGS Ovidiu Vermesan, Coordinator IERC Cluster, [email protected] T Peter Friess, Coordinator IERC Cluster, European Commission, [email protected] Authors Gianmarco Baldini, (DG JRC-EC) Trevor Peirce (Avanta Global), Maarten Botterman (GNKS Consult) Maria Chiara Talacchini, (DG JRC-EC) NTERNET OF I Angela Pereira (DG JRC – EC) Marcus Handte (University of Duisburg-Essen), Domenico Rotondi (TXT Group), Henrich C. Pöhls (Passau University), Ovidiu Vermesan (SINTEF), Atta Baddii (University of Reading), Bertrand Copigneaux (Inno Ag), Schreckling, Daniel (Passau University), THE ON LUSTER Luca Vigano (University of Verona), C Gary Steri (DG JRC – EC) Salvatore Piccione (TXT Group), Panagiotis Vlacheas (UPRC) Vera Stavroulaki (UPRC) ESEARCH Dimitris Kelaidonis (UPRC) R Ricardo Neisse (DG JRC-EC) Elias Tragos (ICS - FORTH), Philippe Smadja (GEMALTO) Christine Hennebert (CEA LETI) Martin Serrano (National University of Ireland Galway) UROPEAN E Stefano Severi (Jacobs University) - Giuseppe Abreu (Jacobs University) Peter T. Kirstein (University College London) Socrates Varakliotis (University College London) IERC Antonio Skarmeta (University of Murcia) Contributing SDOs, Projects and Initiatives ••• 2 / 128 iCore, GAMBAS, BUTLER, CEN/CENELEC, ETSI, ISO, PROBE-IT, SPaCIoS, IoT@Work, COMPOSE, RERUM, OpenIoT, IoT6, Value-Ageing IERC Acknowledgements The IERC would like to thank the European Commission services for their support in the planning and preparation of this document.
    [Show full text]
  • Zipg: a Memory-Efficient Graph Store for Interactive Queries
    ZipG: A Memory-efficient Graph Store for Interactive Queries Anurag Khandelwal Zongheng Yang Evan Ye UC Berkeley UC Berkeley UC Berkeley Rachit Agarwal Ion Stoica Cornell University UC Berkeley ABSTRACT Achieving high performance for interactive user-facing queries We present ZipG, a distributed memory-efficient graph store for on large graphs, however, remains a challenging problem. serving interactive graph queries. ZipG achieves memory effi- For such interactive queries, the goal is not only to achieve ciency by storing the input graph data using a compressed rep- millisecond-level latency, but also high throughput [24, 29, 35]. resentation. What differentiates ZipG from other graph stores is When the graph data is distributed between memory and sec- its ability to execute a wide range of graph queries directly on ondary storage, potentially across multiple servers, achieving this compressed representation. ZipG can thus execute a larger these goals is particularly hard. For instance, consider the query: fraction of queries in main memory, achieving query interactivity. “Find friends of Alice who live in Ithaca”. One possible way to ZipG exposes a minimal API that is functionally rich enough execute this query is to execute two sub-queries — find friends to implement published functionalities from several industrial of Alice, and, find people who live in Ithaca — and compute the graph stores. We demonstrate this by implementing and evaluat- final result using a join or an intersection of results from two sub-queries. Such joins may be complex1, and may incur high ing graph queries from Facebook TAO, LinkBench, Graph Search 2 and several other workloads on top of ZipG.
    [Show full text]
  • Towards Elastic High-Performance Geo-Distributed Storage in the Cloud
    Towards Elastic High-Performance Geo-Distributed Storage in the Cloud YING LIU School of Information and Communication Technology KTH Royal Institute of Technology Stockholm, Sweden 2016 and Institute of Information and Communication Technologies, Electronics and Applied Mathematics Université catholique de Louvain Louvain-la-Neuve, Belgium 2016 KTH School of Information and Communication Technology TRITA-ICT 2016:24 SE-164 40 Kista ISBN 978-91-7729-092-6 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framlägges till offentlig granskning för avläggande av doktorsexamen i Informations- och kom- munikationsteknik måndagen den 3 oktober 2016 klockan 10:00 i Sal C, Electrum, Kungl Tekniska högskolan, Kistagången 16, Kista. © Ying Liu, October 2016 Tryck: Universitetsservice US AB To my beloved Yi and my parents Xianchun and Aiping. Abstract In this thesis, we have investigated two aspects towards improving the performance of distributed stor- age systems. In one direction, we present techniques and algorithms to reduce request latency of distributed storage services that are deployed geographically. In another direction, we propose and design elasticity controllers to maintain predictable/stable performance of distributed storage systems under dynamic work- loads and platform uncertainties. On the research path towards the first direction, we have proposed a lease-based data consistency algo- rithm that allows a distributed storage system to serve read-dominant workload efficiently in a global scale. Essentially, leases are used to assert the correctness/freshness of data within a time interval. The leasing algorithm allows replicas with valid leases to serve read requests locally. As a result, most of the read requests are served with little latency.
    [Show full text]
  • A Scalable Distributed Flash-Based Key-Value Store
    BlueCache: A Scalable Distributed Flash-based Key-Value Store by Shuotao Xu B.S., University of Illinois at Urbana-Champaign (2012) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2016 © Massachusetts Institute of Technology 2016. All rights reserved. Author............................................................. Department of Electrical Engineering and Computer Science May 20, 2016 Certified by . Arvind Johnson Professor of Computer Science and Engineering Thesis Supervisor Accepted by. Leslie Kolodziejski Chair, Department Committee on Graduate Theses 2 BlueCache: A Scalable Distributed Flash-based Key-Value Store by Shuotao Xu Submitted to the Department of Electrical Engineering and Computer Science on May 20, 2016, in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering Abstract Low-latency and high-bandwidth access to a large amount of data is a key requirement for many web applications in data centers. To satisfy such a requirement, a distributed in- memory key-value store (KVS), such as memcached and Redis, is widely used as a caching layer to augment the slower persistent backend storage (e.g. disks) in data centers. DRAM- based KVS is fast key-value access, but it is difficult to further scale the memory pool size because of cost, power/thermal concerns and floor plan limits. Flash memory offers an alternative as KVS storage media with higher capacity per dollar and less power per byte. However, a flash-based KVS software running on an x86 server with commodity SSD can- not harness the full potential device performance of flash memory, because of overheads of the legacy storage I/O stack and relatively slow network in comparision with faster flash storage.
    [Show full text]
  • Building a Transactional Distributed Data Store with Erlang
    Building a transactional distributed data store with Erlang Alexander Reinefeld, Florian Schintke, Thorsten Schütt Zuse Institute Berlin, onScale solutions GmbH Transactional data store - What for? z Web 2.0 services: shopping, banking, gaming, … − don’ t need full SQL semantics, key/value DB often suffice e.g. Mike Stonebreaker: “One size does not fit all” z Scalability matters − >104 accesses per sec. − many concurrent writes Traditional Web 2.0 hosting Clients Traditional Web 2.0 hosting Clients Traditional Web 2.0 hosting Clients .... Traditional Web 2.0 hosting Clients .... Now think big. Really BIG. NthNot how fas t our cod e is tdtoday, btbut: − Can it “scale out”? − Can it run in parallel? … distributed? − Anyygg common resources causing locking? AAsymptoticsymptotic performance matters! Our Approach: P2P makes it scalable “arbitrary“ number of clients Web 2.0 services with P2P nodes idtin datacen ters x x Our Approach Application Layer crash recovery model Key/Value Store (= simple database) strong data consistency Transaction Layer implements ACID improves availability Replication Layer at the cost of consistency crash stop implements modldel P2P Layer - scalability - eventual consistency unreliable, distributed nodes providing a scalable distributed data store: P2P LAYER Key/Value Store z for storing “items” (= “key/value pairs”) − synonyms: “key/value store”“, dictionary”“, map”, … z just 3 ops − insert(key, value) Turing Award Winners Key Value − delete(key) Backus 1977 − lk(k)lookup(key) Hoare 1980 Karp 1985 Knuth 1974 Wirth 1984 . Chord# - Distributed Key/Value Store z key space: total order on items (strings, numbers, …) z nodes have a random key as their position in ring z items are stored on the successor node (clockwise) (Backus, …, Karp] keys (Karp, …, Knuth] Key Value # Backus 1977 Chord item distributed Hoare 1980 key/value Karp 1985 store Knuth 1974 Wirth 1984 nodes .
    [Show full text]