Handling heterogeneous storage devices in clusters

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Handling heterogeneous storage devices in clusters"

Transcription

1 Handling heterogeneous storage devices in clusters André Brinkmann University of Paderborn Toni Cortes Barcelona Supercompu8ng Center

2 Randomized Data Placement Schemes n Randomized Data Placement Schemes Introduc8on Randomiza8on Balls into bins Randomized Data Placement Schemes Distributed Hash Tables Consistent Hashing and Share Redundancy and Randomized Data Placement Schemes Distributed Metadata Management

3 Introduc?on Randomiza?on n Determinis?c data placement schemes suffered many drawbacks for a long?me Heterogeneity has been an issue It has been costly to adapt to new storage systems It is difficult to support storage- on- demand concepts n Is there an alterna?ve to determinis?c schemes? n Yes, Randomiza?on can help to overcome these drawbacks, but new challenges are introduced!

4 Balls into bins Games I n Basic tasks of balls into bins games Assign a set of m balls to n bins n Mo?va?on Bins = Hard disks Balls = Data items L = max number of data items on each disk Where should I place the next item??

5 Balls into bins Games II n Basic Results: Assign n balls to n bins For every ball, choose one bin independently, uniformly at random Maximum load is sharply concentrated: where w.h.p. abbreviates with probability at least, for any fixed

6 Balls into bins Games III n This sounds terrible: The maximum loaded hard disk stores - 8mes more data than the average This seems not to be not scalable, or n The model assumes that only very few data items are stored inside the environment, but each disk is able to store many objects Let s assume that many objects means Then it holds w.h.p. that see, e.g, M. Raab, A. Steger: Balls into Bins - A Simple and Tight Analysis

7 Distributed Hash Tables n Randomiza?on introduces some (well known) challenges n Key ques?ons are: How can we retrieve a stored data item? How can we adapt to a changing number of disks? How can we handle heterogeneity? How can we support redundancy? n Key Tasks of Distributed Hash Tables (DHTs)

8 Consistent Hashing I n Introduced in the context of Web Caching n Bins are mapped by a pseudo- random hash func?on h: on a ring of length 1 n Bins become responsible for their interval n Balls are mapped by an addi?onal hash func?on g: onto the ring n Each bin stores balls in its interval See D. Karger, E. Lehman et al.: Consistent Hashing and Random Trees: Tools for Relieving Hot Spots on the World Wide Web

9 Consistent Hashing II n Average load of each bin is, but devia?on from average can be high: The maximum arc length on the ring becomes w.h.p. n Solu?on: Each bin is mapped by a set of independent hash func?ons to mul?ple points on the ring The maximum arc length assigned to a bin can be reduced to for an arbitrary small constant, if virtual bins are used for each physical bin See I. Stoica, R. Morris, et al.: Chord: A Scalable Peer- To- Peer Lookup Service for Internet Applica8ons.

10 Join and Leave- Opera?ons I n In a dynamic network, nodes can join and leave any?me n The main goal of a DHT is to have the ability to locate every key in the network at (nearly) any?me n (Planned) removal of bins changes the length of their neighbor intervals Data has to be moved to neighbor n Inser?on of bins changes interval length of their new neighbors

11 Join and Leave- Opera?ons II n Defini?on of a View V: A view V is a set of bins of which a particular client is aware of. n Monotonicity: A ranged hash function f is monotone if for all views implies n Monotonicity implies that in case of a join opera?on of a bin i, all moved data items have des?na?on i n Consistent Hashing has property of monotonicity

12 Heterogeneous Bins n Consistent Hashing is (nearly) op?mally suited for homogeneous environment, where all bins (disks) have same capacity and performance n Heterogeneous bins can be mapped to Consistent Hashing by using a different number of virtual bins for each physical bin n The rela?on between the number of different bins constantly changes n Monotonicity (and some other proper?es) can not be kept up

13 Share Strategy I g(d) l(c d ) 0 1 d p o n Share Strategy tries to map heterogeneous problem to homogeneous solu?on n Each bin d is assigned by a hash func?on g: to a start point g(d) inside [0,1)- interval n The length l of the interval is propor?onal to the capacity c i (performance, or other metric) of bin i See A. Brinkmann, K. Salzwedel, C. Scheideler: Compact, adap8ve placement schemes for non- uniform distribu8on requirements.

14 Share Strategy II 0 x h(x) n How to retrieve loca?on of a data item x inside this heterogeneous sebng? n Use hash func?on h: to map x to [0,1)- Interval n Use DHT for homogeneous bins to retrieve loca?on of x from all intervals cubng h(x)

15 Share Strategy III 0 x h(x) n Proper?es: (Arbitrary) op8mal distribu8on of balls and bins Computa8onal Complexity in O(1) Compe88ve Ra8o concerning Join and Leave is (1+ε) for every ε>0 n But: Share has been op8mized for usage in data center environments Share is not monotone and only par8ally suited for P2P networks

16 V:Drive SAN MDA n V:Drive out- of- band virtualiza8on environment each (Linux) server includes addi8onal block- level driver module metadata appliance ensures consistent view on storage and servers Share strategy used as data distribu8on strategy See A. Brinkmann, S. Effert, et al.: Influence of Adap8ve Data Layouts on Performance in dynamically changing Storage Environments

17 Performance V:Drive - Sta?c Throughput (MB/s) Synthe8c random I/O benchmark, sta8c configura8on Physical Volumes VDrive LVM Avg. latency (ms) Physical volumes VDrive LVM

18 Performance V:Drive Dynamic Throughput (MB/s) Synthe8c random I/O benchmark, dynamic configura8on Avg. latency (ms) Physical volumes VDrive LVM Physical volumes VDrive LVM

19 V:Drive - Reconfigura?on Overhead

20 Randomiza?on and Redundancy n Randomized data distribu?on schemes do not include mechanisms to safe data against dist failures n Ques?on: How to use Randomiza8on and RAID schemes together n Assump?on: n copies of a data block have to be distributed over n disks No two copies of a data block are allowed to be stored on the same disk

21 Trivial Solu?ons n Trivial Solu?on I: Divide storage systems into n storage pools Distribute first copies over first pool,, n- th copies over n- th pool Ø Missing flexibility n Trivial Solu?on II: First copy will be distributed over all disks Second copy will be distributed about all but the previously chosen disk, Ø Not able to use capacity efficiently p = ( 1 2 ) 3 p = ( 1 1 ) 2 p = Second Copy ( 1 1 ) 4 First Copy

22 Observa?on n Trivial Solu?on II is not able to use capacity efficiently, because big storage systems will be penalized compared to smaller devices n Theorem: Assume a trivial replication strategy that has to distribute k copies of m balls over n > k bins. Furthermore, the biggest bin has a capacity c max that is at least (1 + ε) c j of the next biggest bin j. In this case, the expected load of the biggest bin will be smaller than the expected load required for an optimal capacity efficiency. See A. Brinkmann, S. Effert, et al.: Dynamic and Redundant Data Placement

23 Idea n Algorithm has to ensure that bigger bins get data items according to their capaci?es n This can be ensured by an algorithm that iterates over a sorted list of bins 1. At each itera8on, the algorithm randomly decides, whether or whether not to place the ball 2. If one of k copies of a ball has been placed, use op8mal strategy for (k- 1) with remaining bins as input n Challenge: How to make random decision in step 1 of each itera8on

24 Example for Mirroring (k=2) 100 GB 100 GB 80 GB 80 GB 60 GB n n n denotes the rela?ve capacity of disk i to all disks denotes the rela?ve capacity of disk i to all disks star?ng with index i is the weight for the random decision!

25 Example for Mirroring (k=2) 100 GB 100 GB 80 GB 80 GB 60 GB n If, e.g., disk 2 is chosen as first copy of a mirror, just distribute the second copy according to Share over disks 3, 4, and 5 n Some adapta?on is necessary, if disk 3 is chose, because weight of disk 4 is greater 1

26 Observa?ons 100 GB 100 GB 80 GB 80 GB 60 GB n Strategy can easily be extended to arbitrary k n Data distribu?on is op?mal n Redistribu?on of data in dynamic environment is k 2 - compe??ve n Computa?onal complexity can be reduced to O(k)

27 Fairness of k- fold Replica?on

28 Adap?vity of k- fold Replica?on

29 Metadata Management n Assignment of data items to disks can be solved efficiently for random data distribu?on schemes Very good distribu8on of data and requests Computa8onal complexity low Adap8vity to new infrastructures op8mal without redundancy, ok with redundancy Over- provisioning can be efficiently integrated n but how to find posi?on of data item on the disks? Equal to the dic8onary problem Requires O(n) entries to find loca8on of n objects! Defines bulk set of metadata

30 Dic?onary Problem Extent Size vs. Volume Size 4 KB 16 KB 256 KB 4MB 16MB 256 MB 1 GB 1 GB 8 MB 2 MB 128 KB 8 KB 2 KB 128 Byte 32 Byte 64 GB 512 MB 128 MB 8 MB 512 KB 128 KB 8 KB 2 KB 1 TB 8 GB 2 GB 128 MB 8 MB 2 MB 128 KB 32 KB 64 TB 512 GB 128 GB 8 GB 512 MB 128 MB 8 MB 2 MB 1 PB 8 TB 2 TB 128 GB 8 GB 2 GB 128 MB 32 MB n Extent: Smallest con?nuous unit that can be addressed by virtualiza?on solu?on n Dic?onary easily becomes too big to be stored inside each server system for small extent sizes n Solu?ons Caching Huge extent sizes Object Based Storage Systems

31 Summary and Conclusions n Introduc?on into Disk Arrays n Why Heterogeneity? n Determinis?c Data Placement Schemes n Randomized Data Placement Schemes n Summary and Conclusions

32 Summary n Problem to be solved: scalable storage systems suppor?ng heterogeneous devices n Two solu?ons developed concurrently Determinis8c Modify RAID technology keeping its flavor Non- determinis8c Distribute data blocks by using randomiza8on RAID encoding on top of randomiza8on process

33 Conclusions n Advantages of each version Determinis8c Easy metadata management Easy recovery Non- determinis8c Good support for storage- on- demand concepts Less probability to get to a degraded state? n Both approaches are complementary concerning the advantages, but have many similari?es A zone is very similar to a group of extents Not fully described in the tutorial n Next step: Work on a mixed version

34 Bibliography I n A. Brinkmann, S. Effert, F. Meyer auf der Heide, C. Scheideler: Dynamic and Redundant Data Placement. In Proceedings of the 27th IEEE Interna8onal Conference on Distributed Compu8ng Systems (ICDCS ), 2007 n A. Brinkmann, S. Effert, M. Heidebuer, M. Vodisek: Influence of Adap?ve Data Layouts on Performance in dynamically changing Storage Environments. In Proceedings of the 14th Euromicro Conference on Parallel, Distributed and Network based Processing, 2006 n A. Brinkmann, K. Salzwedel, C. Scheideler: Compact, adap?ve placement schemes for non- uniform distribu?on requirements. In Proceedings of the 14th ACM Symposium on Parallel Algorithms and Architectures (SPAA), 2002 n T. Cortes and J. Labarta: Taking Advantage of Heterogeneity in Disk Arrays: Journal on Parallel and Distributed Compu8ng (JPDC), Volume 63, number 4, pp , April 2003 n J.L. Gonzalez and Toni Cortes: An Adap?ve Data Block Placement based on Determinis?c Zones (Adap?veZ): Interna8onal Conference on Grid compu8ng, high- performance and Distributed Applica8ons (GADA'07) Vilamoura, Algarve, Portugal, Nov 29-30, 2007

35 Bibliography II n J. L. Gonzalez, T. Cortes: Evalua?ng the Effects of Upgrading Heterogeneous Disk Arrays: Interna8onal Symposium on Performance Evalua8on of Computer and Telecommunica8on Systems (SPECTS 2006), Calgary, Canada, July 31 - August 2, 2006 n M. Holland G.A. Gibson: Parity declustering for con?nuous opera?on in redundant disk arrays: In Proceedings of the fish interna8onal conference on Architectural support for programming languages and opera8ng systems, Boston, Massachusets, 1992 n D. Karger, E. Lehman, T. Leighton, M. Levine, D. Lewin, and R. Panigrahy: Consistent Hashing and Random Trees: Tools for Relieving Hot Spots on the World Wide Web. In Proceedings of Symposium on Theory of Compu8ng (STOC), n Peter Lyman and Hal R. Varian. How much informa?on 2003?. School of Informa8on Management and Systems. University of California at Berkeley n D. A. Paterson, G. A. Gibson, R. H. Katz: A Case for Redundant Arrays of Inexpensive Disks (RAID). In Proceedings of the Interna8onal Conference on Management of Data (SIGMOD), 1988

36 Bibliography III n M. Raab, A. Steger: Balls into Bins - A Simple and Tight Analysis. In Proceedings of the 2nd Workshop on Randomiza8on and Approxima8on Techniques in Computer Science (RANDOM'98), 1998 n I. Stoica, R. Morris, D. Karger, F. Kaashoek, and H. Balakrishnan: Chord: A Scalable Peer- To- Peer Lookup Service for Internet Applica?ons. In Proceedings of the 2001 ACM SIGCOMM Conference, 2001 n Ron Yellin. The data storage evolu?on. Has disk capacity outgrown its usefulness? Terada magazine 2006

hashfs Applying Hashing to Op2mize File Systems for Small File Reads

hashfs Applying Hashing to Op2mize File Systems for Small File Reads hashfs Applying Hashing to Op2mize File Systems for Small File Reads Paul Lensing, Dirk Meister, André Brinkmann Paderborn Center for Parallel Compu2ng University of Paderborn Mo2va2on and Problem Design

More information

6 Distributed data management I Hashing

6 Distributed data management I Hashing 6 Distributed data management I Hashing There are two major approaches for the management of data in distributed systems: hashing and caching. The hashing approach tries to minimize the use of communication

More information

Distributed Two-way Trees for File Replication on Demand

Distributed Two-way Trees for File Replication on Demand Distributed Two-way Trees for File Replication on Demand Ramprasad Tamilselvan Department of Computer Science Golisano College of Computing and Information Sciences Rochester, NY 14586 rt7516@rit.edu Abstract

More information

Building a low-latency, proximity-aware DHT-based P2P network

Building a low-latency, proximity-aware DHT-based P2P network Building a low-latency, proximity-aware DHT-based P2P network Ngoc Ben DANG, Son Tung VU, Hoai Son NGUYEN Department of Computer network College of Technology, Vietnam National University, Hanoi 144 Xuan

More information

Dynamic Load Sharing in Peer-to-Peer Systems: When some Peers are more Equal than Others

Dynamic Load Sharing in Peer-to-Peer Systems: When some Peers are more Equal than Others Dynamic Load Sharing in Peer-to-Peer Systems: When some Peers are more Equal than Others Sabina Serbu, Silvia Bianchi, Peter Kropf and Pascal Felber Computer Science Department, University of Neuchâtel

More information

Search Engines. Informa1on Retrieval in Prac1ce. Annotations by Michael L. Nelson

Search Engines. Informa1on Retrieval in Prac1ce. Annotations by Michael L. Nelson Search Engines Informa1on Retrieval in Prac1ce Annotations by Michael L. Nelson All slides Addison Wesley, 2008 Indexes Indexes are data structures designed to make search faster Text search has unique

More information

Chord: A Scalable Peer-to-peer Lookup Service For Internet Applications

Chord: A Scalable Peer-to-peer Lookup Service For Internet Applications Chord: A Scalable Peer-to-peer Lookup Service For Internet Applications Ion Stoica, Robert Morris, David Karger, M. Frans Kaashoek, Hari Balakrishnan Presented by Jibin Yuan ION STOICA Professor of CS

More information

Scalability In Peer-to-Peer Systems. Presented by Stavros Nikolaou

Scalability In Peer-to-Peer Systems. Presented by Stavros Nikolaou Scalability In Peer-to-Peer Systems Presented by Stavros Nikolaou Background on Peer-to-Peer Systems Definition: Distributed systems/applications featuring: No centralized control, no hierarchical organization

More information

Lecture 15 October 31

Lecture 15 October 31 CS559: ALGORITHMIC ASPECTS OF COMPUTER NETWORKSFall 2007 Lecture 15 October 31 Lecturer: John Byers BOSTON UNIVERSITY Scribe: Georgios Smaragdakis In today s lecture, we elaborate more on structured eer-to-eer

More information

Time-related replication for p2p storage system

Time-related replication for p2p storage system Seventh International Conference on Networking Time-related replication for p2p storage system Kyungbaek Kim E-mail: University of California, Irvine Computer Science-Systems 3204 Donald Bren Hall, Irvine,

More information

A Framework for Peer-To-Peer Lookup Services based on k-ary search

A Framework for Peer-To-Peer Lookup Services based on k-ary search A Framework for Peer-To-Peer Lookup Services based on k-ary search Sameh El-Ansary Swedish Institute of Computer Science Kista, Sweden Luc Onana Alima Department of Microelectronics and Information Technology

More information

Athens University of Economics and Business. Dept. of Informatics

Athens University of Economics and Business. Dept. of Informatics Athens University of Economics and Business Athens University of Economics and Business Dept. of Informatics B.Sc. Thesis Project report: Implementation of the PASTRY Distributed Hash Table lookup service

More information

Consistent Hashing. Overview. Ranged Hash Functions. .. CSC 560 Advanced DBMS Architectures Alexander Dekhtyar..

Consistent Hashing. Overview. Ranged Hash Functions. .. CSC 560 Advanced DBMS Architectures Alexander Dekhtyar.. .. CSC 56 Advanced DBMS Architectures Alexander Dekhtyar.. Overview Consistent Hashing Consistent hashing, introduced in [] is a hashing technique that assigns items (keys) to buckets in a way that makes

More information

Storwize in IT Environments Market Overview

Storwize in IT Environments Market Overview Storwize in IT Environments Market Overview Topic Challenges in Tradi,onal IT Environment Types of informa,on Storage systems required Storage for private clouds where tradi,onal IT is involved Storwize

More information

Today s Objec4ves. Data Center. Virtualiza4on Cloud Compu4ng Amazon Web Services. What did you think? 10/23/17. Oct 23, 2017 Sprenkle - CSCI325

Today s Objec4ves. Data Center. Virtualiza4on Cloud Compu4ng Amazon Web Services. What did you think? 10/23/17. Oct 23, 2017 Sprenkle - CSCI325 Today s Objec4ves Virtualiza4on Cloud Compu4ng Amazon Web Services Oct 23, 2017 Sprenkle - CSCI325 1 Data Center What did you think? Oct 23, 2017 Sprenkle - CSCI325 2 1 10/23/17 Oct 23, 2017 Sprenkle -

More information

Amol Deshpande, University of Maryland Lisa Hellerstein, Polytechnic University, Brooklyn

Amol Deshpande, University of Maryland Lisa Hellerstein, Polytechnic University, Brooklyn Amol Deshpande, University of Maryland Lisa Hellerstein, Polytechnic University, Brooklyn Mo>va>on: Parallel Query Processing Increasing parallelism in compu>ng Shared nothing clusters, mul> core technology,

More information

LessLog: A Logless File Replication Algorithm for Peer-to-Peer Distributed Systems

LessLog: A Logless File Replication Algorithm for Peer-to-Peer Distributed Systems LessLog: A Logless File Replication Algorithm for Peer-to-Peer Distributed Systems Kuang-Li Huang, Tai-Yi Huang and Jerry C. Y. Chou Department of Computer Science National Tsing Hua University Hsinchu,

More information

OPTIMAL ROUTING VS. ROUTE REFLECTOR VNF - RECONCILE THE FIRE WITH WATER

OPTIMAL ROUTING VS. ROUTE REFLECTOR VNF - RECONCILE THE FIRE WITH WATER OPTIMAL ROUTING VS. ROUTE REFLECTOR VNF - RECONCILE THE FIRE WITH WATER Rafal Jan Szarecki #JNCIE136 Solu9on Architect, Juniper Networks. AGENDA Route Reflector VNF - goals Route Reflector challenges and

More information

Effect of Links on DHT Routing Algorithms 1

Effect of Links on DHT Routing Algorithms 1 Effect of Links on DHT Routing Algorithms 1 Futai Zou, Liang Zhang, Yin Li, Fanyuan Ma Department of Computer Science and Engineering Shanghai Jiao Tong University, 200030 Shanghai, China zoufutai@cs.sjtu.edu.cn

More information

Monitoring IPv6 Content Accessibility and Reachability. Contact: R. Guerin University of Pennsylvania

Monitoring IPv6 Content Accessibility and Reachability. Contact: R. Guerin University of Pennsylvania Monitoring IPv6 Content Accessibility and Reachability Contact: R. Guerin (guerin@ee.upenn.edu) University of Pennsylvania Outline Goals and scope So=ware overview Func@onality, performance, and requirements

More information

A Scalable Content- Addressable Network

A Scalable Content- Addressable Network A Scalable Content- Addressable Network In Proceedings of ACM SIGCOMM 2001 S. Ratnasamy, P. Francis, M. Handley, R. Karp, S. Shenker Presented by L.G. Alex Sung 9th March 2005 for CS856 1 Outline CAN basics

More information

MODERN FILESYSTEM PERFORMANCE IN LOCAL MULTI-DISK STORAGE SPACE CONFIGURATION

MODERN FILESYSTEM PERFORMANCE IN LOCAL MULTI-DISK STORAGE SPACE CONFIGURATION INFORMATION SYSTEMS IN MANAGEMENT Information Systems in Management (2014) Vol. 3 (4) 273 283 MODERN FILESYSTEM PERFORMANCE IN LOCAL MULTI-DISK STORAGE SPACE CONFIGURATION MATEUSZ SMOLIŃSKI Institute of

More information

Adaptive Load Balancing for DHT Lookups

Adaptive Load Balancing for DHT Lookups Adaptive Load Balancing for DHT Lookups Silvia Bianchi, Sabina Serbu, Pascal Felber and Peter Kropf University of Neuchâtel, CH-, Neuchâtel, Switzerland {silvia.bianchi, sabina.serbu, pascal.felber, peter.kropf}@unine.ch

More information

Implementation and Performance Evaluation of RAPID-Cache under Linux

Implementation and Performance Evaluation of RAPID-Cache under Linux Implementation and Performance Evaluation of RAPID-Cache under Linux Ming Zhang, Xubin He, and Qing Yang Department of Electrical and Computer Engineering, University of Rhode Island, Kingston, RI 2881

More information

An Empirical Study of Data Redundancy for High Availability in Large Overlay Networks

An Empirical Study of Data Redundancy for High Availability in Large Overlay Networks An Empirical Study of Data Redundancy for High Availability in Large Overlay Networks Giovanni Chiola Dipartimento di Informatica e Scienze dell Informazione (DISI) Università di Genova, 35 via Dodecaneso,

More information

Ch06. NoSQL Part III.

Ch06. NoSQL Part III. Ch06. NoSQL Part III. Joonho Kwon Data Science Laboratory, PNU 2017. Spring Adapted from Dr.-Ing. Sebastian Michel s slides Recap: Configurations R/W Configuration Kind ofconsistency W=N and R=1 Read optimized

More information

DYNAMIC TREE-LIKE STRUCTURES IN P2P-NETWORKS

DYNAMIC TREE-LIKE STRUCTURES IN P2P-NETWORKS DYNAMIC TREE-LIKE STRUCTURES IN P2P-NETWORKS Herwig Unger Markus Wulff Department of Computer Science University of Rostock D-1851 Rostock, Germany {hunger,mwulff}@informatik.uni-rostock.de KEYWORDS P2P,

More information

: Scalable Lookup

: Scalable Lookup 6.824 2006: Scalable Lookup Prior focus has been on traditional distributed systems e.g. NFS, DSM/Hypervisor, Harp Machine room: well maintained, centrally located. Relatively stable population: can be

More information

Mul$media Networking. #9 CDN Solu$ons Semester Ganjil 2012 PTIIK Universitas Brawijaya

Mul$media Networking. #9 CDN Solu$ons Semester Ganjil 2012 PTIIK Universitas Brawijaya Mul$media Networking #9 CDN Solu$ons Semester Ganjil 2012 PTIIK Universitas Brawijaya Schedule of Class Mee$ng 1. Introduc$on 2. Applica$ons of MN 3. Requirements of MN 4. Coding and Compression 5. RTP

More information

CSE Opera+ng System Principles

CSE Opera+ng System Principles CSE 30341 Opera+ng System Principles Lecture 2 Introduc5on Con5nued Recap Last Lecture What is an opera+ng system & kernel? What is an interrupt? CSE 30341 Opera+ng System Principles 2 1 OS - Kernel CSE

More information

A Directed-multicast Routing Approach with Path Replication in Content Addressable Network

A Directed-multicast Routing Approach with Path Replication in Content Addressable Network 2010 Second International Conference on Communication Software and Networks A Directed-multicast Routing Approach with Path Replication in Content Addressable Network Wenbo Shen, Weizhe Zhang, Hongli Zhang,

More information

Distributed Systems INF Michael Welzl

Distributed Systems INF Michael Welzl Distributed Systems INF 3190 Michael Welzl What is a distributed system (DS)? Many defini8ons [Coulouris & Emmerich] A distributed system consists of hardware and sodware components located in a network

More information

Relaxing Routing Table to Alleviate Dynamism in P2P Systems

Relaxing Routing Table to Alleviate Dynamism in P2P Systems Relaxing Routing Table to Alleviate Dynamism in P2P Systems Hui FANG 1, Wen Jing HSU 2, and Larry RUDOLPH 3 1 Singapore-MIT Alliance, National University of Singapore 2 Nanyang Technological University,

More information

Staggeringly Large File Systems. Presented by Haoyan Geng

Staggeringly Large File Systems. Presented by Haoyan Geng Staggeringly Large File Systems Presented by Haoyan Geng Large-scale File Systems How Large? Google s file system in 2009 (Jeff Dean, LADIS 09) - 200+ clusters - Thousands of machines per cluster - Pools

More information

Sta$c Single Assignment (SSA) Form

Sta$c Single Assignment (SSA) Form Sta$c Single Assignment (SSA) Form SSA form Sta$c single assignment form Intermediate representa$on of program in which every use of a variable is reached by exactly one defini$on Most programs do not

More information

From Connected Cars to Smart Ci9es: Novel Applica9ons for Wireless Communica9on

From Connected Cars to Smart Ci9es: Novel Applica9ons for Wireless Communica9on Distributed Embedded Systems University of Paderborn From Connected Cars to Smart Ci9es: Novel Applica9ons for Wireless Communica9on Falko Dressler dressler@ccs-labs.org Science Brunch, Zurich From Connected

More information

Load Balancing in Structured P2P Systems

Load Balancing in Structured P2P Systems 1 Load Balancing in Structured P2P Systems Ananth Rao Karthik Lakshminarayanan Sonesh Surana Richard Karp Ion Stoica fananthar, karthik, sonesh, karp, istoicag@cs.berkeley.edu Abstract Most P2P systems

More information

Performance Evaluation of a MongoDB and Hadoop Platform for Scientific Data Analysis

Performance Evaluation of a MongoDB and Hadoop Platform for Scientific Data Analysis Performance Evaluation of a MongoDB and Hadoop Platform for Scientific Data Analysis Elif Dede, Madhusudhan Govindaraju Lavanya Ramakrishnan, Dan Gunter, Shane Canon Department of Computer Science, Binghamton

More information

Virtual Allocation: A Scheme for Flexible Storage Allocation

Virtual Allocation: A Scheme for Flexible Storage Allocation Virtual Allocation: A Scheme for Flexible Storage Allocation Sukwoo Kang, and A. L. Narasimha Reddy Dept. of Electrical Engineering Texas A & M University College Station, Texas, 77843 fswkang, reddyg@ee.tamu.edu

More information

Link Layer. w/ credit to Rick Graziani (Cabrillo) for some of the anima<ons

Link Layer. w/ credit to Rick Graziani (Cabrillo) for some of the anima<ons Link Layer w/ credit to Rick Graziani (Cabrillo) for some of the anima

More information

Today s Papers. Array Reliability. RAID Basics (Two optional papers) EECS 262a Advanced Topics in Computer Systems Lecture 3

Today s Papers. Array Reliability. RAID Basics (Two optional papers) EECS 262a Advanced Topics in Computer Systems Lecture 3 EECS 262a Advanced Topics in Computer Systems Lecture 3 Filesystems (Con t) September 10 th, 2012 John Kubiatowicz and Anthony D. Joseph Electrical Engineering and Computer Sciences University of California,

More information

CS535 Big Data Fall 2017 Colorado State University 11/7/2017 Sangmi Lee Pallickara Week 12- A.

CS535 Big Data Fall 2017 Colorado State University  11/7/2017 Sangmi Lee Pallickara Week 12- A. CS Big Data Fall Colorado State University http://www.cs.colostate.edu/~cs // Week - A // CS Big Data - Fall Week -A- CS BIG DATA FAQs PA deadline has been extended (/) PART. SCALABLE FRAMEWORKS FOR REAL-TIME

More information

NodeId Verification Method against Routing Table Poisoning Attack in Chord DHT

NodeId Verification Method against Routing Table Poisoning Attack in Chord DHT NodeId Verification Method against Routing Table Poisoning Attack in Chord DHT 1 Avinash Chaudhari, 2 Pradeep Gamit 1 L.D. College of Engineering, Information Technology, Ahmedabad India 1 Chaudhari.avi4u@gmail.com,

More information

EFFICIENT ROUTING OF LOAD BALANCING IN GRID COMPUTING

EFFICIENT ROUTING OF LOAD BALANCING IN GRID COMPUTING EFFICIENT ROUTING OF LOAD BALANCING IN GRID COMPUTING MOHAMMAD H. NADIMI-SHAHRAKI*, FARAMARZ SAFI, ELNAZ SHAFIGH FARD Department of Computer Engineering, Najafabad branch, Islamic Azad University, Najafabad,

More information

Linux Software RAID Level 0 Technique for High Performance Computing by using PCI-Express based SSD

Linux Software RAID Level 0 Technique for High Performance Computing by using PCI-Express based SSD Linux Software RAID Level Technique for High Performance Computing by using PCI-Express based SSD Jae Gi Son, Taegyeong Kim, Kuk Jin Jang, *Hyedong Jung Department of Industrial Convergence, Korea Electronics

More information

Storage System. Distributor. Network. Drive. Drive. Storage System. Controller. Controller. Disk. Disk

Storage System. Distributor. Network. Drive. Drive. Storage System. Controller. Controller. Disk. Disk HRaid: a Flexible Storage-system Simulator Toni Cortes Jesus Labarta Universitat Politecnica de Catalunya - Barcelona ftoni, jesusg@ac.upc.es - http://www.ac.upc.es/hpc Abstract Clusters of workstations

More information

Op#mizing MapReduce for Highly- Distributed Environments

Op#mizing MapReduce for Highly- Distributed Environments Op#mizing MapReduce for Highly- Distributed Environments Abhishek Chandra Associate Professor Department of Computer Science and Engineering University of Minnesota hep://www.cs.umn.edu/~chandra 1 Big

More information

Cluster-Level Google How we use Colossus to improve storage efficiency

Cluster-Level Google How we use Colossus to improve storage efficiency Cluster-Level Storage @ Google How we use Colossus to improve storage efficiency Denis Serenyi Senior Staff Software Engineer dserenyi@google.com November 13, 2017 Keynote at the 2nd Joint International

More information

Soft GPGPUs for Embedded FPGAS: An Architectural Evaluation

Soft GPGPUs for Embedded FPGAS: An Architectural Evaluation Soft GPGPUs for Embedded FPGAS: An Architectural Evaluation 2nd International Workshop on Overlay Architectures for FPGAs (OLAF) 2016 Kevin Andryc, Tedy Thomas and Russell Tessier University of Massachusetts

More information

CS 465 Final Review. Fall 2017 Prof. Daniel Menasce

CS 465 Final Review. Fall 2017 Prof. Daniel Menasce CS 465 Final Review Fall 2017 Prof. Daniel Menasce Ques@ons What are the types of hazards in a datapath and how each of them can be mi@gated? State and explain some of the methods used to deal with branch

More information

IN recent years, the amount of traffic has rapidly increased

IN recent years, the amount of traffic has rapidly increased , March 15-17, 2017, Hong Kong Content Download Method with Distributed Cache Management Masamitsu Iio, Kouji Hirata, and Miki Yamamoto Abstract This paper proposes a content download method with distributed

More information

Disk Scheduling COMPSCI 386

Disk Scheduling COMPSCI 386 Disk Scheduling COMPSCI 386 Topics Disk Structure (9.1 9.2) Disk Scheduling (9.4) Allocation Methods (11.4) Free Space Management (11.5) Hard Disk Platter diameter ranges from 1.8 to 3.5 inches. Both sides

More information

Distributed File Systems II

Distributed File Systems II Distributed File Systems II To do q Very-large scale: Google FS, Hadoop FS, BigTable q Next time: Naming things GFS A radically new environment NFS, etc. Independence Small Scale Variety of workloads Cooperation

More information

Link Layer. w/ much credit to Cisco CCNA and Rick Graziani (Cabrillo)

Link Layer. w/ much credit to Cisco CCNA and Rick Graziani (Cabrillo) Link Layer w/ much credit to Cisco CCNA and Rick Graziani (Cabrillo) Administra>via How are the labs going? Telnet- ing into Linux as root In /etc/pam.d/remote comment out line auth required pam_securely.so

More information

Comparing Chord, CAN, and Pastry Overlay Networks for Resistance to DoS Attacks

Comparing Chord, CAN, and Pastry Overlay Networks for Resistance to DoS Attacks Comparing Chord, CAN, and Pastry Overlay Networks for Resistance to DoS Attacks Hakem Beitollahi Hakem.Beitollahi@esat.kuleuven.be Geert Deconinck Geert.Deconinck@esat.kuleuven.be Katholieke Universiteit

More information

The Google File System

The Google File System The Google File System Sanjay Ghemawat, Howard Gobioff and Shun Tak Leung Google* Shivesh Kumar Sharma fl4164@wayne.edu Fall 2015 004395771 Overview Google file system is a scalable distributed file system

More information

Spanning Tree and Datacenters

Spanning Tree and Datacenters Spanning Tree and Datacenters EE 122, Fall 2013 Sylvia Ratnasamy http://inst.eecs.berkeley.edu/~ee122/ Material thanks to Mike Freedman, Scott Shenker, Ion Stoica, Jennifer Rexford, and many other colleagues

More information

18-hdfs-gfs.txt Thu Nov 01 09:53: Notes on Parallel File Systems: HDFS & GFS , Fall 2012 Carnegie Mellon University Randal E.

18-hdfs-gfs.txt Thu Nov 01 09:53: Notes on Parallel File Systems: HDFS & GFS , Fall 2012 Carnegie Mellon University Randal E. 18-hdfs-gfs.txt Thu Nov 01 09:53:32 2012 1 Notes on Parallel File Systems: HDFS & GFS 15-440, Fall 2012 Carnegie Mellon University Randal E. Bryant References: Ghemawat, Gobioff, Leung, "The Google File

More information

Extending Heuris.c Search

Extending Heuris.c Search Extending Heuris.c Search Talk at Hebrew University, Cri.cal MAS group Roni Stern Department of Informa.on System Engineering, Ben Gurion University, Israel 1 Heuris.c search 2 Outline Combining lookahead

More information

P2P: Distributed Hash Tables

P2P: Distributed Hash Tables P2P: Distributed Hash Tables Chord + Routing Geometries Nirvan Tyagi CS 6410 Fall16 Peer-to-peer (P2P) Peer-to-peer (P2P) Decentralized! Hard to coordinate with peers joining and leaving Peer-to-peer (P2P)

More information

CS 188: Ar)ficial Intelligence

CS 188: Ar)ficial Intelligence CS 188: Ar)ficial Intelligence Search Instructors: Pieter Abbeel & Anca Dragan University of California, Berkeley [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley

More information

Database design and implementation CMPSCI 645. Lecture 08: Storage and Indexing

Database design and implementation CMPSCI 645. Lecture 08: Storage and Indexing Database design and implementation CMPSCI 645 Lecture 08: Storage and Indexing 1 Where is the data and how to get to it? DB 2 DBMS architecture Query Parser Query Rewriter Query Op=mizer Query Executor

More information

Fault Resilience of Structured P2P Systems

Fault Resilience of Structured P2P Systems Fault Resilience of Structured P2P Systems Zhiyu Liu 1, Guihai Chen 1, Chunfeng Yuan 1, Sanglu Lu 1, and Chengzhong Xu 2 1 National Laboratory of Novel Software Technology, Nanjing University, China 2

More information

The Google File System

The Google File System October 13, 2010 Based on: S. Ghemawat, H. Gobioff, and S.-T. Leung: The Google file system, in Proceedings ACM SOSP 2003, Lake George, NY, USA, October 2003. 1 Assumptions Interface Architecture Single

More information

SELF- OPTIMIZING DATA GRIDS. Collabora'on Mee'ng with Op'mis, Sept. 2011, Rome

SELF- OPTIMIZING DATA GRIDS. Collabora'on Mee'ng with Op'mis, Sept. 2011, Rome SELF- OPTIMIZING DATA GRIDS Collabora'on Mee'ng with Op'mis, 20-21 Sept. 2011, Rome Project Goals Develop an open- source middleware for the Cloud: 1. Providing a simple and intui've programming model:

More information

An Agenda for Robust Peer-to-Peer Storage

An Agenda for Robust Peer-to-Peer Storage An Agenda for Robust Peer-to-Peer Storage Rodrigo Rodrigues Massachusetts Institute of Technology rodrigo@lcs.mit.edu Abstract Robust, large-scale storage is one of the main applications of DHTs and a

More information

ECS 165B: Database System Implementa6on Lecture 3

ECS 165B: Database System Implementa6on Lecture 3 ECS 165B: Database System Implementa6on Lecture 3 UC Davis April 4, 2011 Acknowledgements: some slides based on earlier ones by Raghu Ramakrishnan, Johannes Gehrke, Jennifer Widom, Bertram Ludaescher,

More information

Should we build Gnutella on a structured overlay? We believe

Should we build Gnutella on a structured overlay? We believe Should we build on a structured overlay? Miguel Castro, Manuel Costa and Antony Rowstron Microsoft Research, Cambridge, CB3 FB, UK Abstract There has been much interest in both unstructured and structured

More information

Teach A level Compu/ng: Algorithms and Data Structures

Teach A level Compu/ng: Algorithms and Data Structures Teach A level Compu/ng: Algorithms and Data Structures Eliot Williams @MrEliotWilliams Course Outline Representa+ons of data structures: Arrays, tuples, Stacks, Queues,Lists 2 Recursive Algorithms 3 Searching

More information

Back-Up Chord: Chord Ring Recovery Protocol for P2P File Sharing over MANETs

Back-Up Chord: Chord Ring Recovery Protocol for P2P File Sharing over MANETs Back-Up Chord: Chord Ring Recovery Protocol for P2P File Sharing over MANETs Hong-Jong Jeong, Dongkyun Kim, Jeomki Song, Byung-yeub Kim, and Jeong-Su Park Department of Computer Engineering, Kyungpook

More information

The Google File System

The Google File System The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung December 2003 ACM symposium on Operating systems principles Publisher: ACM Nov. 26, 2008 OUTLINE INTRODUCTION DESIGN OVERVIEW

More information

A DHT-Based Grid Resource Indexing and Discovery Scheme

A DHT-Based Grid Resource Indexing and Discovery Scheme SINGAPORE-MIT ALLIANCE SYMPOSIUM 2005 1 A DHT-Based Grid Resource Indexing and Discovery Scheme Yong Meng TEO 1,2, Verdi March 2 and Xianbing Wang 1 1 Singapore-MIT Alliance, 2 Department of Computer Science,

More information

P2P Network Structured Networks: Distributed Hash Tables. Pedro García López Universitat Rovira I Virgili

P2P Network Structured Networks: Distributed Hash Tables. Pedro García López Universitat Rovira I Virgili P2P Network Structured Networks: Distributed Hash Tables Pedro García López Universitat Rovira I Virgili Pedro.garcia@urv.net Index Description of CHORD s Location and routing mechanisms Symphony: Distributed

More information

Compiler Optimization Intermediate Representation

Compiler Optimization Intermediate Representation Compiler Optimization Intermediate Representation Virendra Singh Associate Professor Computer Architecture and Dependable Systems Lab Department of Electrical Engineering Indian Institute of Technology

More information

Mondrian Mul+dimensional K Anonymity

Mondrian Mul+dimensional K Anonymity Mondrian Mul+dimensional K Anonymity Kristen Lefevre, David J. DeWi

More information

Objec0ves. Gain understanding of what IDA Pro is and what it can do. Expose students to the tool GUI

Objec0ves. Gain understanding of what IDA Pro is and what it can do. Expose students to the tool GUI Intro to IDA Pro 31/15 Objec0ves Gain understanding of what IDA Pro is and what it can do Expose students to the tool GUI Discuss some of the important func

More information

Architectures for Distributed Systems

Architectures for Distributed Systems Distributed Systems and Middleware 2013 2: Architectures Architectures for Distributed Systems Components A distributed system consists of components Each component has well-defined interface, can be replaced

More information

Simple Efficient Load Balancing Algorithms for Peer-to-Peer Systems

Simple Efficient Load Balancing Algorithms for Peer-to-Peer Systems Simple Efficient Load Balancing Algorithms for Peer-to-Peer Systems David R. Karger MIT karger@lcs.mit.edu Matthias Ruhl IBM Almaden ruhl@almaden.ibm.com Abstract Load balancing is a critical issue for

More information

CONTAINERIZING JOBS ON THE ACCRE CLUSTER WITH SINGULARITY

CONTAINERIZING JOBS ON THE ACCRE CLUSTER WITH SINGULARITY CONTAINERIZING JOBS ON THE ACCRE CLUSTER WITH SINGULARITY VIRTUAL MACHINE (VM) Uses so&ware to emulate an en/re computer, including both hardware and so&ware. Host Computer Virtual Machine Host Resources:

More information

DATA. The main challenge in P2P computing is to design and implement LOOKING UP. in P2P Systems

DATA. The main challenge in P2P computing is to design and implement LOOKING UP. in P2P Systems LOOKING UP DATA in P2P Systems By Hari Balakrishnan, M. Frans Kaashoek, David Karger, Robert Morris, and Ion Stoica The main challenge in P2P computing is to design and implement a robust and scalable

More information

Replication, Load Balancing and Efficient Range Query Processing in DHTs

Replication, Load Balancing and Efficient Range Query Processing in DHTs Replication, Load Balancing and Efficient Range Query Processing in DHTs Theoni Pitoura, Nikos Ntarmos, and Peter Triantafillou R.A. Computer Technology Institute and Computer Engineering & Informatics

More information

The Google File System

The Google File System The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung Google SOSP 03, October 19 22, 2003, New York, USA Hyeon-Gyu Lee, and Yeong-Jae Woo Memory & Storage Architecture Lab. School

More information

Mul$media Streaming. Digital Audio and Video Data. Digital Audio Sampling the analog signal. Challenges for Media Streaming.

Mul$media Streaming. Digital Audio and Video Data. Digital Audio Sampling the analog signal. Challenges for Media Streaming. Mul$media Streaming Digital Audio and Video Data Jennifer Rexford COS 461: Computer Networks Lectures: MW 10-10:50am in Architecture N101 hhp://www.cs.princeton.edu/courses/archive/spr12/cos461/ 2 Challenges

More information

The Google File System

The Google File System The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung Google* 정학수, 최주영 1 Outline Introduction Design Overview System Interactions Master Operation Fault Tolerance and Diagnosis Conclusions

More information

Towards Scalable and Robust Overlay Networks

Towards Scalable and Robust Overlay Networks Towards Scalable and Robust Overlay Networks Baruch Awerbuch Department of Computer Science Johns Hopkins University Baltimore, MD 21218, USA baruch@cs.jhu.edu Christian Scheideler Institute for Computer

More information

TSIN02 Internetworking. Lecture 5 Op8cal Networking The Internet backbone

TSIN02 Internetworking. Lecture 5 Op8cal Networking The Internet backbone TSIN02 Internetworking Lecture 5 Op8cal Networking The Internet backbone 1 Outline Networking Hierarchy Evolu8on of Op8cal Networks Op8cal Network Node and Switching Elements Rou8ng and Wavelength Assignment

More information

Decentralized Distributed Storage System for Big Data

Decentralized Distributed Storage System for Big Data Decentralized Distributed Storage System for Big Presenter: Wei Xie -Intensive Scalable Computing Laboratory(DISCL) Computer Science Department Texas Tech University Outline Trends in Big and Cloud Storage

More information

Design of a New Hierarchical Structured Peer-to-Peer Network Based On Chinese Remainder Theorem

Design of a New Hierarchical Structured Peer-to-Peer Network Based On Chinese Remainder Theorem Design of a New Hierarchical Structured Peer-to-Peer Network Based On Chinese Remainder Theorem Bidyut Gupta, Nick Rahimi, Henry Hexmoor, and Koushik Maddali Department of Computer Science Southern Illinois

More information

AOTO: Adaptive Overlay Topology Optimization in Unstructured P2P Systems

AOTO: Adaptive Overlay Topology Optimization in Unstructured P2P Systems AOTO: Adaptive Overlay Topology Optimization in Unstructured P2P Systems Yunhao Liu, Zhenyun Zhuang, Li Xiao Department of Computer Science and Engineering Michigan State University East Lansing, MI 48824

More information

Monitoring & Analy.cs Working Group Ini.a.ve PoC Setup & Guidelines

Monitoring & Analy.cs Working Group Ini.a.ve PoC Setup & Guidelines Monitoring & Analy.cs Working Group Ini.a.ve PoC Setup & Guidelines Copyright 2017 Open Networking User Group. All Rights Reserved Confiden@al Not For Distribu@on Outline ONUG PoC Right Stuff Innova@on

More information

CA485 Ray Walshe Google File System

CA485 Ray Walshe Google File System Google File System Overview Google File System is scalable, distributed file system on inexpensive commodity hardware that provides: Fault Tolerance File system runs on hundreds or thousands of storage

More information

Distributed Hash Tables Chord and Dynamo

Distributed Hash Tables Chord and Dynamo Distributed Hash Tables Chord and Dynamo (Lecture 19, cs262a) Ion Stoica, UC Berkeley October 31, 2016 Today s Papers Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications, Ion Stoica,

More information

A Method for Designing Proximity-aware Routing Algorithms for Structured Overlays

A Method for Designing Proximity-aware Routing Algorithms for Structured Overlays A Method for Designing Proximity-aware Routing Algorithms for Structured Overlays Takehiro Miyao, Hiroya Nagao, Kazuyuki Shudo Tokyo Institute of Technology 2-12-1 Ookayama, Meguro-ku, Tokyo, JAPAN Email:

More information

Operating Systems. Lecture File system implementation. Master of Computer Science PUF - Hồ Chí Minh 2016/2017

Operating Systems. Lecture File system implementation. Master of Computer Science PUF - Hồ Chí Minh 2016/2017 Operating Systems Lecture 7.2 - File system implementation Adrien Krähenbühl Master of Computer Science PUF - Hồ Chí Minh 2016/2017 Design FAT or indexed allocation? UFS, FFS & Ext2 Journaling with Ext3

More information

Providing File Services using a Distributed Hash Table

Providing File Services using a Distributed Hash Table Providing File Services using a Distributed Hash Table Lars Seipel, Alois Schuette University of Applied Sciences Darmstadt, Department of Computer Science, Schoefferstr. 8a, 64295 Darmstadt, Germany lars.seipel@stud.h-da.de

More information

Understanding Chord Performance

Understanding Chord Performance CS68 Course Project Understanding Chord Performance and Topology-aware Overlay Construction for Chord Li Zhuang(zl@cs), Feng Zhou(zf@cs) Abstract We studied performance of the Chord scalable lookup system

More information

Distributed Meta-data Servers: Architecture and Design. Sarah Sharafkandi David H.C. Du DISC

Distributed Meta-data Servers: Architecture and Design. Sarah Sharafkandi David H.C. Du DISC Distributed Meta-data Servers: Architecture and Design Sarah Sharafkandi David H.C. Du DISC 5/22/07 1 Outline Meta-Data Server (MDS) functions Why a distributed and global Architecture? Problem description

More information

No compromises: distributed transac2ons with consistency, availability, and performance

No compromises: distributed transac2ons with consistency, availability, and performance No compromises: distributed transac2ons with consistency, availability, and performance Aleksandar Dragojevic, Dushyanth Narayanan, Edmund B. Nigh2ngale, MaDhew Renzelmann, Alex Shamis, Anirudh Badam,

More information

Efficient Multi-source Data Dissemination in Peer-to-Peer Networks

Efficient Multi-source Data Dissemination in Peer-to-Peer Networks Efficient Multi-source Data Dissemination in Peer-to-Peer Networks Zhenyu Li 1,2, Zengyang Zhu 1,2, Gaogang Xie 1, Zhongcheng Li 1 1 Institute of Computing Technology, Chinese Academy of Sciences 2 Graduate

More information

GreenCHT: A Power-Proportional Replication Scheme for Consistent Hashing based Key Value Storage Systems

GreenCHT: A Power-Proportional Replication Scheme for Consistent Hashing based Key Value Storage Systems GreenCHT: A Power-Proportional Replication Scheme for Consistent Hashing based Key Value Storage Systems Nannan Zhao 12, Jiguang Wan 12, Jun Wang 3, and Changsheng Xie 12 1 Wuhan National Laboratory for

More information