Cost Aware Resource Management for Decentralized Network Services

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Cost Aware Resource Management for Decentralized
Network Services

• Venugopalan Ramasubramanian (Rama)
• Microsoft Research Silicon Valley / Cornell
  University

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Introduction

• decentralized services have become increasingly
  important
• e.g. name systems, CDNs, publish-subscribe
• low latency, constant availability, and high
  scalability
• current services often fall short of required
  performance
• ad hoc techniques

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Problems with Ad hoc Techniques

• no performance guarantees
• unable to quantify/bound performance
• unable to tune resource utilization to meet
  performance targets
• tailored to specific workloads
• e.g. opportunistic caching on 90/10 rule
• heavy-tailed popularity distributions
• mutable objects

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Principled Approach

• fundamental cost-performance tradeoff
• e.g. lookup latency vs. memory / bandwidth
  consumption
• resource allocation problem
• which node hosts which object?
• depends on popularity, size, update rate, etc.

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Prior Work

• Scalability
• high complexity even to express the problem
• number of objects x number of nodes (M x N)
• Decentralization
• objects are distributed among multiple nodes
• expensive to perform resource allocation centrally

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Cost-Aware Resource Management Framework

• high performance, robust, and scalable services
• Mathematical Optimization
• system-wide performance goals become constraints
  to optimization problems
• Min. cost s.t. performance meets target
• Max. performance s.t. cost limit
• Structured Overlays
• decentralization and self-organization
• well-defined topology with bounded diameter and
  node degree

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Decentralized Internet Services

• name service for the Internet
• Cooperative Domain Name System (CoDoNS)
• content distribution network
• Cooperative Beehive Web (CoBWeb)
• on-line data monitoring
• Cornell On-line News Aggregator (CorONA)

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Scalable Resource Allocation

• structured overlay
• each object has a home node
• DAG rooted at home node reaching all nodes
• uniform branching-factor
• allocate resources at well-defined levels
• level l means all nodes l hops away from home
  node
• low complexity resource allocation
• Number of objects x Diameter (e.g. M x log N)
• practical and scalable

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Structured Overlays Pastry
prefix-matching logbN hops
2012
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Opportunistic Caching in Pastry
2012
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Structured Resource Allocation

• analytically model performance-overhead tradeoff
• object replicated at all nodes with l matching
  prefix-digits
• lookup latency l hops
• replicas N/bl
• inexpensive to locate and update replicas

0021
0112
0122
2012
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Outline

• Introduction
• Honeycomb Framework
• Optimization Analysis
• Implementation
• Applications
• Evaluation
• Conclusions

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Analytical Modeling

• level of allocation (l)
• object hosted at all nodes l hops from the home
  node
• optimization problem find optimal values of li
• min. ? Ci(li), s.t. ? Pi(li) ? T
• max. ? Pi(li), s.t. ? Ci(li) ? T
• performance variables
• lookup latency, update latency
• cost variables
• memory consumption, network overhead, number of
  nodes

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Optimization Problem Lookup Latency

• min. ? ci . bli s.t., ? qi (D - li) ? TL

total overhead
avg. lookup latency
TL target lookup latency in hops qi relative
query frequency ci replication cost of object i
objects M, nodes N, branching factor b, diameter D
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Resource Allocation for Lookup Performance

• target avg. lookup latency hops
• sub-one hop, fractional values (e.g., 0.5 hops)
• indirectly specifies cache hit ratio
• worst case lookup latency
• lower bound on l
• optimizes multiple overhead metrics
• number of nodes c 1
• memory c size of object
• bandwidth c size x update rate

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Analytical Optimization (Beehive)

• Zipf popularity distribution (e.g. DNS, Web, RSS)
• analytically tractable (one parameter ?)
• closed-form solution
• inexpensive to compute and apply

Ramasubramanian and Sirer NSDI 04
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Numerical Optimization

• general-purpose approach
• any popularity distribution (including Zipf)
• many cost metrics (fine-grained bandwidth
  consumption)
• many performance metrics (update latency)
• optimization problem is NP-Hard
• Multiple choice Knapsack problem
• discrete, convex, and separable
• fast and accurate approximation algorithm
• O(M D log(M D)) running time
• at most one object per node (more or less than
  optimum)

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Numerical Optimization 2

• Lagrange multiplier
• min. ? C(lm) ? ? P(lm) T
• bisection-based bracketing algorithm
• upper and lower bound solutions that differ in
  one channel yields near-optimal solution
• pre-computation and sorting of ?s before
  iterating yields O(MD log (MD)) algorithm

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Honeycomb

• cost-aware resource allocation framework for
  structured overlays
• properties
• system-wide performance goals
• scalability and failure resilience
• quick adaptation to workload
• fast update propagation

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Scalable Resource Management

• independent decisions
• local aggregation
• estimate popularity
• communication only with overlay neighbors
• replicas managed by one-hop neighbors

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Scalable Resource Management

• independent decisions
• local aggregation
• estimate popularity
• communication only with overlay neighbors
• replicas managed by one-hop neighbors

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Decentralized Optimization

• global optimum requires global information
• Using local knowledge alone leads to sub-optimal
  solutions
• solution
• approximate tradeoffs for non-local channels
• aggregate coarse-grained information between
  neighbors

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Decentralized Optimization 2

• approximate parameters
• cluster channels with similar values of P(l) /
  C(l)
• constant number of clusters per level

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Decentralized Optimization 3

• Aggregating Clusters
• Exchange clusters with one-hop neighbors
• Hierarchical aggregation through structured
  overlay

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Adaptation to Workload Changes

• popularity of objects may change drastically
• flash-crowds, denial of service attacks
• nodes measure popularity for local objects and
  aggregate popularity estimates with neighbors

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Adaptation to Workload Changes 2

• orders of magnitude difference in query rates of
  popular and unpopular objects
• solution combine inter-arrival times and query
  counts
• estimation times proportional to the query rate
  of the object
• monitoring overhead proportional to the query
  rate of the object
• quick detection of large increases in query rate

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Honeycomb Fast Update Propagation

• single integer (replication level) indicates
  locations of all objects
• no TTL required
• proactively propagate updates
• use neighbors in the underlying overlay
• increasing version numbers differentiate versions
• lazy updates in background

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Outline

• Introduction
• Honeycomb Framework
• Applications
• Name service (CoDoNS)
• Content distribution network (CoBWeb)
• On-line data monitoring system (CorONA)
• Evaluation
• Conclusions

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CoDoNS Cooperative Domain Name System

• legacy DNS has fundamental problems
• poor failure resilience due to limited
  replication
• high response times due to multi-hop lookups
• no support for spontaneous updates
• cooperative cache for DNS bindings

LegacyDNS
Ramasubramanian and Sirer SIGCOMM 04
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CoDoNS Cooperative Domain Name System

• structured, proactive caching of name-data
  mappings
• targets avg. lookup latency of (0.5 hops)
• minimizes memory consumption
• updates pushed proactively to all caching nodes
• self-certifying data to preserve integrity
  (DNS-SEC)
• incremental deployment path
• safety-net for legacy DNS
• deployed on Planet-Lab

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CobWeb Cooperative Beehive Web

• Web caches
• passive, client driven
• Content Distribution Networks
• active, replication driven
• e.g. Akamai, Digital Island (commercial), CoDeeN,
  CoralCDN (academia)
• web caching solutions based on heuristics
• ideal cache hit rate (60-70) Wolman et al. 01
• achieved cache hit rate (20-40) Breslao et al.
  99, Wolman et al. 01

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CobWeb Cooperative Beehive Web

• CobWeb is a cooperative web cache
• high cache hit rate through structured, proactive
  caching
• low network overhead using object size and update
  rate
• adaptation to flash crowds
• CobWeb performance goals
• min. network bandwidth s.t. cache hit rate meets
  a target
• max. cache hit rate s.t. network bandwidth is all
  consumed

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CobWeb Cooperative Beehive Web

• user interfaces
• append cob-web.org to urls
• e.g., http//slashdot.org.cob-web.org8888
• DNS redirection, URL rewriting
• Meridian finds closest node to the client
• deployed on Planet-Lab
• greater than10 million requests per day

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Corona Monitoring Online Data

• continuously monitoring and detecting changes is
  crucial
• e.g., web pages, sensors, databases
• content servers only provide query-based
  interface
• naïve approach through repeated, independent
  polling
• bad update performance
• high server load

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Corona Monitoring Online Data

• publish-subscribe interface for monitoring web
  urls
• cooperative polling
• resource allocation decides how many nodes poll
  each channel

Ramasubramanian, Peterson, and Sirer NSDI 06
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Corona Performance Goals

• Corona Lite
• Min. update detection time s.t. network load is
  bounded
• Corona Fast
• Min. network load s.t. update detection time
  meets a target
• Corona Fair
• Min. relative update detection time s.t. network
  load is bounded
• ratio of update detection time to update interval

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Outline

• Introduction
• Honeycomb Framework
• Applications
• Evaluation
• Conclusions

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CoDoNS Lookup Latency
MIT-DNS trace 265111 queries, 30000 names, 65
nodes
CoDoNS gives 1.5 to 2 times better latency
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CoBWeb Lookup Performance
NLANR Workload 1024 nodes, 10,000 objects, 100,
000 queries
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CoBWeb vs. Opportunistic Caching
Lookup Latency
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CoBWeb vs. Opportunistic Caching
Storage Overhead
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CoBWeb Flash Crowd
Lookup Latency
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CoBWeb Flash Crowd
Network Bandwidth
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Corona Update Performance
Corona improves update detection time from 15 min
to 45 sec
Corona keeps load lower than Legacy RSS
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Corona Update Performance
Heuristics vs. Corona
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Conclusions

• enables high performance, robust, and scalable
  network services
• principled approach for achieving performance
  goals in distributed systems
• mathematical optimization and structured overlays
• CoDoNS, CobWeb, and Corona

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Other Research in Wireless Networks

• Sharp hybrid adaptive routing prorocol for mobile
  ad hoc networks Mobihoc 03
• combines proactive and reactive approaches to
  routing to achieve high performance efficiently
• SRL bidirectional abstraction to support routing
  protocols on asymmetric mobile ad hoc networks
  INFOCOM 02
• Anonymous Gossip improving multicast reliability
  on mobile ad hoc networks ICDCS 01

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