1. Online Procurement Auctions for Resource Pooling in Client-Assisted Cloud Storage Systems
Jian Zhao, Xiaowen Chu, Hai Liu, Yiu-Wing Leung
Department of Computer Science
Hong Kong Baptist University
Zongpeng Li
University of Calgary
IEEE INFOCOM 2015, HONG KONG

2. Outline Motivation: why client-assisted? Background: auctions
Model formulation
Some technical details (without equations)
Conclusions
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3. Cloud Storage Services
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4. Most of the time, CSPs are
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Durability: Our data will be there without error forever.
Availability: Data can be accessed anywhere, anytime, from any device.

5. Cloud Outages! But occasionally, they become
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6. Cloud Outages Cloud services could become unavailable because of
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7. Evidence of Cloud Outages
“The worst cloud outages of 201X “ by J. R. Raphael
Inforworld.com has been tracing cloud outages since 2011
Amazon
Google
Microsoft
Apple
Dropbox
Facebook
Adobe …
Big names on the list
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8. Plan A: Cloud Federation
What can we do?
Plan A: Cloud Federation
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9. Plan B: Client-Assisted Cloud Storage
Alternatively
Plan B: Client-Assisted Cloud Storage
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10. Client-Assisted Examples in Academia
Leverage peer bandwidth to mitigate server bandwidth cost
Improve availability and downloading performance
FS2You
Data is kept at a central cloud and replicated among distributed peers
AmazingStore
Peer-assisted architecture with a focus on data consistency
Triton
IEEE INFOCOM 2015, HONG KONG

11. A Counter Example: IEEE INFOCOM 2015, HONG KONG
Wuala was designed to be “client-assisted”.
Servers in datacenters
Client users
casual peers without contribution
storage peers trading local storage for increased storage space
Wuala abandoned the “client-assisted” design in 2012
Hybrid architecture is very complicated
Bandwidth cost is dropping
Contribution from peers are marginal
More focus on business customers
IEEE INFOCOM 2015, HONG KONG

12. Another Story: Symform
Acquired by Quantum in 2014
Observation
“cloud providers use extremely inefficient centralized infrastructure to store stuff.”
“most users had tons of excess local storage just going to waste”
Goal
“Creating the World’s Largest Datacenter”
“Without Building a Datacenter”
Method
“Users that contribute get 1 GB free for every 2 GB contributed”
IEEE INFOCOM 2015, HONG KONG

13. Incentive makes a difference
Total upload = Total download
Public BitTorrent
Tit-for-Tat only
Free-rider problem
Stop seeding after downloading
Limit the uploading bandwidth
Private BitTorrent
(or Darknet)
Sharing Ratio Enforcement
Users fight to contribute as much as they can to survive
Everyone in the community can get high downloading performance
People are willing to pay to get into the community
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14. Auctions
Auctions are economical approaches for allocating resources or trading commodities
Participants: auctioneer + bidders
Auction mechanism: a set of institutions for the buying/selling of goods or services, such as allocation rules and pricing rules
IEEE INFOCOM 2015, HONG KONG

15. Auctions as Incentive
No need to predict users’ demand: users reveal their true information through bids
Balance among supply and demand
Allocate available resources efficiently
Obtain higher revenue
IEEE INFOCOM 2015, HONG KONG

16. How to design the auction mechanism for resource pooling from clients?
Research Problem
How to design the auction mechanism for resource pooling from clients?
For clients: get reasonable monetary return by selling resources (storage space & bandwidth)
For CSPs: save cost (and overcome cloud outages)
Assumption: the cost of providing service by datacenter herself is expensive than procuring resources from clients
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17. Our Approach: Online Procurement Auctions
Online auctions
Different bidders arrive at different times
Auctioneer makes decision about each bid as it is received
Different from the traditional case that the auctioneer receives all the bids before determining the allocation
In line with asynchronous arrivals of user bids and requests
Procurement auctions
Ordinary auctions (or forward auctions): one seller, multiple buyers
Buyers compete
Procurement auctions: one buyer, multiple sellers
Sellers compete
reverse auctions
IEEE INFOCOM 2015, HONG KONG

18. Our Auction Model (I)
Storage & bandwidth are unified as conceptual “resource”
Needs further study
Clients (i.e., bidders) want to sell resources through bids
Bid: (starting time, ending time, amount, unit price)
Valuation: the “true” value of the resource, private to the bidder
Utility = the received payment - the cost of offering the sold resource
Target: maximize utility
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19. Our Auction Model (II)
CSP (i.e., auctioneer ) wants to buy resources from some clients
Set a target S for time period [0, T]
For each incoming bid, determine
how much to procure (allocation rule)
how much to pay (payment rule)
IEEE INFOCOM 2015, HONG KONG

20. Design Objectives Truthful Competitive
If ( truthful bidding always maximizes utility )
Then ( rational bidders will report their true valuations )
Truthful
The CSP’s total cost is bounded by γ times of the total cost of offline optimal auction
Competitive
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21. Our Results Truthfulness Competitiveness
We derive price-based allocation rule and payment rule that result in truthful auction mechanism.
Competitiveness
We find a solution to guarantee a target competitive ratio against offline optimal auctions.
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22. Our Methodology
Start from Myerson’s Principles of Truthfulness (1981)
Gives two conditions to satisfy the truthfulness property
For one single indivisible good
Extend to the case of online procurement auction for divisible goods
We first study the conditions for truthfulness of an online auction
We then derive allocation and payment equations such that the auction mechanism is truthful.
Design marginal pricing function for CSP to achieve a desired competitive ratio
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23. Conclusions
We consider “client-assisted” a promising approach to addressing cloud outages.
We argue that “auctions” can be a good incentive mechanism.
We propose an online procurement auction mechanism, and prove its truthfulness and competitiveness.
IEEE INFOCOM 2015, HONG KONG

24. A
and
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25. Myerson’s Principle of Truthfulness
Consider the auction of a single indivisible good
The auction mechanism is truthful if and only if
The probability of a bidder winning an auction is monotonically non-decreasing in its bid
The payment charged to a bidder is independent of its bid
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26. Extension to Online Procurement Auctions
We first define “allocation monotonicity” for online procurement auctions.
Better bids get more allocation
If allocation rule is monotone, we can find a payment rule that results in truthful online procurement auction.
We design a monotone allocation rule and the corresponding payment rule.
IEEE INFOCOM 2015, HONG KONG

27. Competitive Analysis
Our auction mechanism assumes a non-increasing marginal pricing function for the CSP to procure resources from clients.
The marginal pricing function is a variable
How to minimize the total cost by adjusting the marginal pricing function?
An online algorithm design problem
We find a solution of setting the marginal pricing function to achieve a target competitive ratio.
IEEE INFOCOM 2015, HONG KONG