1 Carnegie Mellon Robust Distributed Services in Embedded Networks Michael Reiter.

Slides:



Advertisements
Similar presentations
Scalable and Dynamic Quorum Systems Moni Naor & Udi Wieder The Weizmann Institute of Science.
Advertisements

Winter 2004 UCSC CMPE252B1 CMPE 257: Wireless and Mobile Networking SET 3f: Medium Access Control Protocols.
Carnegie Mellon Approved for Public Release, Distribution Unlimited Increasing Intrusion Tolerance Via Scalable Redundancy Michael Reiter
TOPOLOGIES FOR POWER EFFICIENT WIRELESS SENSOR NETWORKS ---KRISHNA JETTI.
Sogang University ICC Lab Using Game Theory to Analyze Wireless Ad Hoc networks.
Hierarchical Decompositions for Congestion Minimization in Networks Harald Räcke 1.
The Structure of Networks with emphasis on information and social networks T-214-SINE Summer 2011 Chapter 8 Ýmir Vigfússon.
The Cache Location Problem. Overview TERCs Vs. Proxies Stability Cache location.
Wireless Mesh Networks 1. Architecture 2 Wireless Mesh Network A wireless mesh network (WMN) is a multi-hop wireless network that consists of mesh clients.
Beneficial Caching in Mobile Ad Hoc Networks Bin Tang, Samir Das, Himanshu Gupta Computer Science Department Stony Brook University.
Math443/543 Mathematical Modeling and Optimization
Network Design Adam Meyerson Carnegie-Mellon University.
Implicit Hitting Set Problems Richard M. Karp Harvard University August 29, 2011.
Dynamic Hypercube Topology Stefan Schmid URAW 2005 Upper Rhine Algorithms Workshop University of Tübingen, Germany.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Mobile Ad Hoc Networks Theory of Data Flow and Random Placement.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Parallel Programming in C with MPI and OpenMP Michael J. Quinn.
SkipNet: A Scalable Overlay Network with Practical Locality Properties Nick Harvey, Mike Jones, Stefan Saroiu, Marvin Theimer, Alec Wolman Microsoft Research.
ICNP'061 Benefit-based Data Caching in Ad Hoc Networks Bin Tang, Himanshu Gupta and Samir Das Department of Computer Science Stony Brook University.
Online Oblivious Routing Nikhil Bansal, Avrim Blum, Shuchi Chawla & Adam Meyerson Carnegie Mellon University 6/7/2003.
Ad Hoc Mobility Management with Uniform Quorum Systems.
CPSC 689: Discrete Algorithms for Mobile and Wireless Systems Spring 2009 Prof. Jennifer Welch.
Building a Strong Foundation for a Future Internet Jennifer Rexford ’91 Computer Science Department (and Electrical Engineering and the Center for IT Policy)
Connected Dominating Sets in Wireless Networks My T. Thai Dept of Comp & Info Sci & Engineering University of Florida June 20, 2006.
Tradeoffs in CDN Designs for Throughput Oriented Traffic Minlan Yu University of Southern California 1 Joint work with Wenjie Jiang, Haoyuan Li, and Ion.
Architectural Design Establishing the overall structure of a software system Objectives To introduce architectural design and to discuss its importance.
1 Latency Equalization: A Programmable Routing Service Primitive Minlan Yu Joint work with Marina Thottan, Li Li at Bell Labs.
The Structure of Networks with emphasis on information and social networks T-214-SINE Summer 2011 Chapter 8 Ýmir Vigfússon.
A User Experience-based Cloud Service Redeployment Mechanism KANG Yu.
1 The Google File System Reporter: You-Wei Zhang.
By: Gang Zhou Computer Science Department University of Virginia 1 A Game-Theoretic Framework for Congestion Control in General Topology Networks SYS793.
Online Oblivious Routing Nikhil Bansal, Avrim Blum, Shuchi Chawla & Adam Meyerson Carnegie Mellon University 6/7/2003.
Energy Efficient Routing and Self-Configuring Networks Stephen B. Wicker Bart Selman Terrence L. Fine Carla Gomes Bhaskar KrishnamachariDepartment of CS.
“Intra-Network Routing Scheme using Mobile Agents” by Ajay L. Thakur.
+ Mayukha Bairy Disk Intersection graphs and CDS as a backbone in wireless ad hoc networks.
CS774. Markov Random Field : Theory and Application Lecture 13 Kyomin Jung KAIST Oct
1 Heterogeneity in Multi-Hop Wireless Networks Nitin H. Vaidya University of Illinois at Urbana-Champaign © 2003 Vaidya.
A novel approach of gateway selection and placement in cellular Wi-Fi system Presented By Rajesh Prasad.
Fast Handoff for Seamless wireless mesh Networks Yair Amir, Clauiu Danilov, Michael Hilsdale Mobisys’ Jeon, Seung-woo.
Coverage Criteria for Testing of Object Interactions in Sequence Diagrams Atanas (Nasko) Rountev Scott Kagan Jason Sawin Ohio State University.
Optimal Content Delivery with Network Coding Derek Leong, Tracey Ho California Institute of Technology Rebecca Cathey BAE Systems CISS 2009 March 19, 2009.
Investigating Survivability Strategies for Ultra-Large Scale (ULS) Systems Vanderbilt University Nashville, Tennessee Institute for Software Integrated.
InterConnection Network Topologies to Minimize graph diameter: Low Diameter Regular graphs and Physical Wire Length Constrained networks Nilesh Choudhury.
NTU IM Page 1 of 35 Modelling Data-Centric Routing in Wireless Sensor Networks IEEE INFOCOM Author: Bhaskar Krishnamachari Deborah Estrin Stephen.
Lecture 4 TTH 03:30AM-04:45PM Dr. Jianjun Hu CSCE569 Parallel Computing University of South Carolina Department of.
Intradomain Traffic Engineering By Behzad Akbari These slides are based in part upon slides of J. Rexford (Princeton university)
11 CLUSTERING AND AVAILABILITY Chapter 11. Chapter 11: CLUSTERING AND AVAILABILITY2 OVERVIEW  Describe the clustering capabilities of Microsoft Windows.
1 Secure Peer-to-Peer File Sharing Frans Kaashoek, David Karger, Robert Morris, Ion Stoica, Hari Balakrishnan MIT Laboratory.
Re-Configurable Byzantine Quorum System Lei Kong S. Arun Mustaque Ahamad Doug Blough.
1 Revisiting Hierarchical Quorum Systems Nuno Preguiça, J. Legatheaux Martins Henry Canivel.
A User Experience-based Cloud Service Redeployment Mechanism KANG Yu Yu Kang, Yangfan Zhou, Zibin Zheng, and Michael R. Lyu {ykang,yfzhou,
KAIS T On the problem of placing Mobility Anchor Points in Wireless Mesh Networks Lei Wu & Bjorn Lanfeldt, Wireless Mesh Community Networks Workshop, 2006.
High-Speed Policy-Based Packet Forwarding Using Efficient Multi-dimensional Range Matching Lakshman and Stiliadis ACM SIGCOMM 98.
1 The Encoding Complexity of Network Coding Michael Langberg California Institute of Technology Joint work with Jehoshua Bruck and Alex Sprintson.
Optical Networking University of Southern Queensland.
Resource Allocation in Network Virtualization Jie Wu Computer and Information Sciences Temple University.
1 An Arc-Path Model for OSPF Weight Setting Problem Dr.Jeffery Kennington Anusha Madhavan.
CS 6401 Overlay Networks Outline Overlay networks overview Routing overlays Resilient Overlay Networks Content Distribution Networks.
Self-stabilizing energy-efficient multicast for MANETs.
Distributed Storage Systems: Data Replication using Quorums.
Load Balanced Link Reversal Routing in Mobile Wireless Ad Hoc Networks Nabhendra Bisnik, Alhussein Abouzeid ECSE Department RPI Costas Busch CSCI Department.
TRUST Self-Organizing Systems Emin G ü n Sirer, Cornell University.
Errol Lloyd Design and Analysis of Algorithms Approximation Algorithms for NP-complete Problems Bin Packing Networks.
Data Center Network Architectures
PROTEAN: A Scalable Architecture for Active Networks
Managing the performance of multiple radio Multihop ESS Mesh Networks.
Intra-Domain Routing Jacob Strauss September 14, 2006.
Chapter 16: Distributed System Structures
Towards Next Generation Panel at SAINT 2002
Haim Kaplan and Uri Zwick
Parallel Programming in C with MPI and OpenMP
Presentation transcript:

1 Carnegie Mellon Robust Distributed Services in Embedded Networks Michael Reiter

2 Carnegie Mellon Take-Away Message An analogy Users on the Internet are not satisfied with only connectivity  Higher-level services attract users and applications Same theme is arising in mobile handheld applications Similarly, we believe that ensuring connectivity is only part of the picture for embedded / ad-hoc / … networks Users and applications will require services, databases, and other “pull-style” information backplanes

3 Carnegie Mellon What Makes This Difficult? If your embedded / ad-hoc network is autonomous, it may have no servers!  At least not in the typical sense of that word A server is typically  Well provisioned and maintained  Reliably connected  Relatively trustworthy Embedded / ad hoc networks may lack any such nodes

4 Carnegie Mellon Survivable Distributed Services Service, or object, abstraction Implementation pushpopsort invocation response

5 Carnegie Mellon inv Traditional Approach: State Machine Replication Offers no load dispersion, and degrades as system scales Servers inv

6 Carnegie Mellon Quorum Systems Quorum systems:  Basic tool for synchronization in distributed systems  A set of subsets ( quorums ) of a universe U of logical elements, having intersection property (any pair of quorums intersect) Majority Grid

7 Carnegie Mellon Byzantine Quorum Systems A quorum system is a data redundancy technique that supports load dispersion among servers Only a subset of servers are accessed in each operation  Good servers in intersection must be enough to “out vote” bad servers Ex: Grid with n =49, b =3

8 Carnegie Mellon Protocols for Survivable Services [w/ Abd-El-Malek, Ganger, Goodson, and Wylie] New protocols for  Read/write objects  Arbitrary services (Q/U) combining  Quorum systems  Optimistic execution  Fast cryptographic primitives Graphs on right show that quorum protocols can scale better than SMR in real systems  But these were well-connected settings

9 Carnegie Mellon Dealing with Network Effects Network effects are likely to be just as important in embedded / ad hoc networks as load dispersion Even worse, minimizing network delays for accessing quorums can be in conflict with load dispersion  May have to bypass a close but heavily-loaded quorum in favor of a less-loaded but more distant quorum Can we balance this tradeoff?

10 Carnegie Mellon Quorum Placement Problems Place “good” quorum systems on network  to minimize network-specific measures  preserve “goodness” Goodness = load  Assume each quorum Q is accessed with probability p(Q)  load p (u) = ∑ Q: u  Q p(Q) Network measures:  Average delay observed by clients when accessing quorum system  Network congestion induced by clients accessing quorum system

11 Carnegie Mellon Network Measures  quorum system Q over U  access strategy p: Q → [0, 1]  placement f : U →V Given  network G = ( V, E )  delay d : E → R +  edge_cap: E → R + Average max-delay:  d(v, f(Q)) = max u  Q d(v, f(u))  d(v, f( Q )) = E p [d(v, f(Q))] = Δ f (v)  avg_delay f = Avg v  V [Δ f (v)] Network congestion:  flow g v,f(u) : E → R +  traff e (v, f(Q)) = ∑ u  Q g v,f(u) (e)  traff e = Avg v  V E p [traffic e (v, f(Q))]  cong f = max e  E traff e /edge_cap(e)

12 Carnegie Mellon Quorum Placement Problem for Delay (QPPD) Given  graph G = (V, E),  with distances d: E → R +  and capacity node_cap(v) for all v  V  a quorum system Q  with a distribution p s.t. each Q i is accessed with prob. p(Q i ) find placement f  minimizing average max-delay, Avg v  V [Δ f (v)]  subject to load constraints: load f (v) ≤ node_cap(v), for all v  V /3 f = ?

13 Carnegie Mellon Results for QPPD [w/ Gupta, Maggs, Oprea] QPPD is NP-hard For any  > 1, there is a (5  /(  1),  +1) approximation:  If we allow capacities to be exceeded by a factor of  +1, then we can achieve average max-delay within a factor of 5  /(  1) of optimal for all capacity-respecting solutions For Majority and Grid, if node capacities equal the optimal load of the quorum system, there is a (5, 1) -approximation.

14 Carnegie Mellon Quorum Placement for Congestion (QPPC) Two routing models:  Fixed paths (given as input)  Arbitrary paths (chosen probabilistically) Given:  graph G = (V, E),  node capacities node_cap(v) for all v  V,  and edge capacities edge_cap(e) for all e  E  a quorum system Q  with a distribution p s.t. each Q i is accessed with prob. p(Q i ) find placement f  minimizing max relative-congestion, Max e  E [cong f (e)]  subject to load constraints: load f (v) ≤ node_cap(v), for all v  V

15 Carnegie Mellon Results for QPPC [w/ Golovin, Gupta, Maggs, Oprea] QPPC is NP-hard in either model  Even finding any node-capacity-respecting solution is NP-hard Arbitrary paths: There is an (O(log 2 n log log n), 2) - approximation.  If we allow node capacities to be exceeded by a factor of 2, then we can achieve max relative-congestion to within a factor of O(log 2 n log log n) of optimal for all node-capacity-respecting solutions If G is a tree, there is a (5, 2) -approximation. Fixed paths: There is an (O(η log n / log log n), 2) –approximation, where η is the size of the set {  log 2 (load(u))  | u  U }

16 Carnegie Mellon Theory vs. Practice We have some initial theory results  But many theoretical questions remain unanswered But how does the theory correspond to practice?  Example: Network delay is only one component of client response time, the other being server load  So, network delay and server load are not easily separable for this measure These problems still need to be explored even in fixed- infrastructure networks

17 Carnegie Mellon Embedded / Ad Hoc Networks Importance of addressing faults  Not only due to disabling quorum elements, but also due to impinging on quorum reachability If population is dynamic  Need to consider migrating quorum elements If mobility is involved  Continually need to re-evaluate quorum placements