A General Auction-Based Architecture for Resource Allocation

1. A General Auction-Based Architecture for Resource Allocation Weidong Cui, Matthew C. Caesar, and Randy H. Katz EECS, UC Berkeley {wdc, mccaesar, randy}@eecs.berkeley.edu

2. 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.

3. 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.

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

5. Auctioneer Bidders Asks Bids Allocs Consume Resource Asks Bids Allocs Resource Allocation Process • Frame-based • Single-round bids • Synchronized

6. Architecture App App App App App App Queue Queue Queue Queue Queue Queue Dispatcher Bidder Bidder Dispatcher Auctioneer Resource Pool

7. 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 client’s dynamic demand • Techniques • Adaptation • Robustness

8. 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.

9. 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 Token Pool Prediction Engine Bidding Engine Bids

10. Adaptation techniques • Token disbursement rate determines the ratio of each client’s 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.

11. 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 Now+1 Now+2 Now+3 Now+4 Now+5 Time

12. 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

13. 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

14. 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

15. Experimental Results • Weighted proportional fairness

16. Experimental Results • Response time

17. 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 • Can’t 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