A General AuctionBased Architecture for Resource Allocation

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A General Auction-Based Architecture for Resource
Allocation

• Weidong Cui, Matthew C. Caesar, and Randy H. Katz
• EECS, UC Berkeley
• wdc, mccaesar, randy_at_eecs.berkeley.edu

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Motivation

• Desired characteristics
• General can be applied to different kinds of
  resources.
• Flexible components are application-aware and
  can adapt to a variety of workloads.
• Efficient high resource utilization with low
  overhead.
• Responsive adapt quickly to dynamic client
  demand.
• Fair fairness under contention
• Common techniques
• Brings applications into the control loop
• Uses prediction to leverage traffic stationarity
• Abstracts resource requirements as application
  queues and tokens
• Support dynamic priority
• No single scheme implements all of them.

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Auction-based Approach

• Our scheme
• Uses auction-based techniques to achieve good
  performance
• Why use auctions?
• Brings applications into the control loop
• Bidders can place bids based on application
  requirements and contention level.
• Uses prediction to leverage traffic stationarity
• Bidders can place bids for near future resource
  requirements based on recent history.
• Abstracts resource requirements as application
  queues and tokens
• Bidder can express both utility and priority to
  auctioneer.
• Auctioneer can alter node priority by changing
  the token allocation rate.
• Support dynamic priority
• Auctioneer can allocate resources to clients
  based on their dynamic needs.

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Related Work

• Economic based schemes
• SPAWN
• U-Mich. TAC
• Bandwidth allocation
• Weighted Fair Queuing
• GAMA
• CSMA
• CPU scheduling
• Lottery scheduling
• Fair share

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Resource Allocation Process

• Frame-based
• Single-round bids
• Synchronized

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Architecture
App
App
App
App
App
App
Queue
Queue
Queue
Queue
Queue
Queue
Bidder
Dispatcher
Bidder
Dispatcher
Auctioneer
Resource Pool
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System Design

• Resource Abstraction
• Multiple-unit time slots
• Examples wireless bandwidth, CPU, memory
• Tokens
• Fake money for bidding resources
• Depleted and periodically disbursed
• Functional Entities
• Auctioneer
• Bidder
• Application Queues
• An abstraction for clients dynamic demand
• Techniques
• Adaptation
• Robustness

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Auctioneer Design

• Multiple Unit First Price Auction
• A bidder gets the amount left after all other
  bidders with higher bids,
• and pays for it the price she bids.
• Progressive Second Price Auction
• A bidder gets the amount left after all other
  bidders with higher bids,
• and pays for her allocation so as to exactly
  cover the social opportunity cost.
• Break Ties
• Assign random numbers to each bidder with ties.
• The random numbers will determine the order of
  bids.

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Bidder Design

• Bids are dependent on a few factors
• Current application queue size
• Estimated resource request arrival rate
• Tokens left
• Auction history
• Amount of resources under auction
• Bidding Strategies
• Aggressive vs. Conservative
• Risky vs. Safe
• A major area of research

Asks
Prediction Engine
Token Pool
Bidding Engine
Bids
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Adaptation techniques

• Token disbursement rate determines the ratio of
  each clients share of resources in the long run.
• Research issue adaptively change the token
  disbursement rate with node priority.
• Frequency of auction rounds affects the tradeoff
  between resource utilization and latency.
• Research issue adaptively change the frequency
  of auction rounds based on bidding history.

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Forward Allocation

• Put future resources into auctions
• Leverage usage prediction
• Prediction algorithms exponential average, HMM,
  etc.
• Advantages
• Average the risk of starvation.
• Decrease latency.
• Disadvantages
• Over estimation may decrease resource
  utilization.

Now
Now1
Now2
Now3
Now4
Now5
Time
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Robustness

• Possible failures
• Auctioneer failure
• Bidder failure
• Asks/bids/allocations may be dropped
• Research issues
• Design a robust auctioneer-bidder communication
  protocol
• Auctioneer election and failover protocol

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Scenario Wireless Spectrum Allocation

• Instances
• Cellular
• Basestation-based centralized allocation
• Ad-hoc / Peer to Peer networking
• Distributed allocation
• Etiquette rules in unlicensed bands
• Potential benefits
• Prediction with dynamic allocation can improve
  utilization and response time
• Policing protocols monitor usage
• Nodes can vote to penalize offender
• Tokens allow nodes to express criticality and
  priority

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Overhead Analysis (responsiveness vs. efficiency)

• Example 3 Asks, n m, ? 1.0

• Slot size 1Kbyte
• Send Rate 1Mbps
• n number of slots in a frame
• m number of nodes
• ? usage ratio

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Experimental Results

• Weighted proportional fairness

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Experimental Results

• Response time

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Conclusion/Summary

• Simple strategies can provide fair resource
  allocations with low overhead.
• System can be tuned to give fast response time.
• Dynamic auction-based strategies offer
  significant advantages over static schemes.
• Limitations
• Doesn't support combinatorial auctions
• Cant support very large numbers of nodes
• Future work
• Improve prediction, bidding, and auctioning
  strategies
• Make auction protocol resilient to losses and
  node failures.
• Design techniques to dynamically adapt round
  frequency and token disbursion rate