Transcript Consumer Decision Support for Resource Procurement in IaaS Markets Kurt Vanmechelen, Ruben van den Bossche Research Group Computational Programming Cloud Futures 2010 Workshop, 8th.

Consumer Decision Support for
Resource Procurement in IaaS Markets
Kurt Vanmechelen, Ruben van den Bossche
Research Group Computational Programming
Cloud Futures 2010 Workshop, 8th April
Search trends…
Approximated number of
EC2 Instances launched per day
(Observations by RightScale)
EC2 activity growth
Defining a cloud…
© annegret richter
Service models
SaaS
PaaS
IaaS
IT infrastructure as a utility
On-demand access to resources
Elasticity
Pay-per-use
D. Parkhill, The challenge of the Computer Utility. Addison Wesley Educational Publishers, 1966
N. Carr, The Big Switch, W.W. Norton & Co., 2008
Etymology
Etymology
The Cloud?
Provider differentiation
Drivers for commoditization
•
•
•
Consumers fear vendor-lock in

CAPEX -> OPEX

What OPEX?

Provider capabilities
Standardization

VM Image format (OVF)

OGF’s Open Cloud Computing Interface (OCCI)

…
Tooling for interoperability / integration

Condor

Service Domain Manager (SGE)

Wolfram Matlab

…
Product diversification
High CPU and
Medium CPU
Instances
05/2008
EC2 Launch
08/2006
Large and
Extra Large
Instances
10/2007
Double XL
and
Quadruple XL
instances
10/2009
EC2 reserved
instances
03/2009
New S3 tiered
pricing model
10/2008
Physical Data
Carrier
Import/Export
05/2009
EC2 Spot
Instances
12/2009
Product diversification
High CPU and
Medium CPU
Instances
05/2008
EC2 Launch
08/2006
Large and
Extra Large
Instances
10/2007
Double XL
and
Quadruple XL
instances
10/2009
EC2 reserved
instances
03/2009
New S3 tiered
pricing model
10/2008
Physical Data
Carrier
Import/Export
05/2009
EC2 Spot
Instances
12/2009
EC2 on-demand instances
Feature
Small
Large
Extra
Large
High CPU
Medium
High CPU
XL
High
Memory
XL
High
Memory
Double
XL
High
Memory
Quadruple
XL
# ECU1
1
4
8
5
20
6.5
13
26
# Vcores
1
2
4
2
8
2
4
8
RAM (GB)
1.7
7.5
15
1.7
7
17.1
34.2
68.4
Storage
160GB
850GB
1,7TB
350GB
1,7TB
420GB
850GB
1,7TB
Platform
32-bit
64-bit
64-bit
32-bit
64-bit
64-bit
64-bit
64-bit
I/O Perf.
Moderate
High
High
Moderate
High
Moderate
High
High
Price
$0.085
$0.34
$0.68
$0.17
$0.68
$0.50
$1.20
$2.40
1
One ECU delivers performance of a 2007 Opteron @ 1.0-1.2 GHz
Performance
Dejun et al. (2009) EC2 Performance Analysis for Resource Provisioning of Service-Oriented Applications,
3rd Workshop on Non-Functional Properties and SLA Management in Service-Oriented Computing.
Performance
S. Ostermann et al. (2009) A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing,
Proceedings of CloudComp 2009, LNICST 34, pp. 115-131.
Drivers for non-uniform pricing
•
Locality-dependence of DC costs

Real estate

Power

Bandwidth

Taxation
A. Qureshi et al. (2009) Cutting the Electric Bill for Internet-Scale Systems, ACM SIGCOMM, pp. 123-134 .
Drivers for non-uniform pricing
•
•
•
Temporal and locality dependence

Demand

Supply
Provider cost differentiation

Network

Storage

Compute
Compute resources are non-storable commodities

Cost of underutilization

Transfer of risk through reservations

Reduction of risk through volume discounts and oversubscription
EC2 reserved instances
EC2 Spot Market
http://cloudexchange.org/
Decision support
Allocation problem
Valuation problem
Scope
•
Not whether to move to the cloud

Data confidentiality, security, reliability, legal issues, …

Lease or buy decision (economics of private DC) [Walker2009]

Organizational impact

…
•
…but how
•
Current focus on computational workloads
E. Walker (2009) The real cost of a CPU hour, Computer Vol. 42(4), pp. 35-41.
Allocation problem
•
•
Given

Application set A = {A1, …, An}

Workload Wi = {CPUreq, Storagereq, I/Oreq, DAG}

QoS requirements Qi = {Q1, …, Qm}

Provider product offering set P = {P1, …, Pk}
Devise an allocation over P that adheres to Q at minimum cost

•
Performance modeling
•
Impact of VM properties
•
Degree and level of parallelism
•
Benchmarking studies (Ostermann, Dejun, Iosup, …)

Workload modeling (Smith, Iverson, Hellinckx, …)

Automated bidding / portfolio management
Complexity

Consumers have access to internal resources (hybrid cloud)

Minor technical issues (e.g. hourly vs real-time usage metering)
Automated bidding
•
Modeling of spot price dynamics

•
Bid timing and bid values

•
•
ZI, ZIP, Gjerstadt-Dickhaut, AA, …
Spot price uncertainty driver for derivatives

•
Fundamental / Stochastic (technical)
Comprehensive body of work for financial and electricity markets [Burger2004]
Parallels to electricity market

Non-storable goods

(Unanticipated) events can cause market shocks

Delivery delay and network congestion

Local spot markets hosted by RTOs (balancing markets) through UPA

Impact of Smart Grids on demand elasticity
Unclear what spot dynamics will emerge

EC2 spot market is fully Amazon controlled

Dynamics might be flattened out by EC2 pricing policies
J. K. Mason et al. (2006) Automated Markets and Trading Agents, Handbook of Computational Economics, Chapter 28, pp. 1381-1431
M.Burger et al. (2004) A spot market model for pricing derivatives in electricity markets, Quantitative Finance Vol.4(1), pp. 109-122
Market-based control
•
Body of work for distributed systems

Efficiency through value-centric resource allocation

Incentivizes well-considered resource usage

Decentralized decision making

…in pursuit of sustainability and openess of grid systems

•
CDA, Proportional share, English/Dutch/First-Price/Vickrey auctions,
Single-unit / multi-unit / combinatorial, …
…never made it to production on a large scale

Virtual currency

Mechanism complexity

Computational tractability

Value elicitation
I. E. Sutherland (1968) A futures market in computer time, Communications of the ACM, Vol.11(6), pp.449–451.
S. Clearwater (1995) Market-based control: A paradigm for distributed resource allocation, World Scientific.
N. Dube (2008) SuperComputing Futures: The Next Sharing Paradigm for HPC Resources, PhD, Laval.
K. Vanmechelen (2009) Economic Grid Resource Management using Spot and Futures Markets, PhD, University of Antwerp
A. Byde: A comparison between mechanisms for sequential compute resource auctions, Proceedings of AMAAS 2006, pp. 1199-1201
Valuation problem
•
Clouds turn users into choosers [Yanosky2008]
•
Freedom of choice induces complexity, overhead, inefficiencies


•
Local infrastructure no longer constrains user’s options
•
System architecture
•
Delivered quality of service
Dealing with the notion of cost / utility
Expression of value

Oftentimes taken as a given in the economic literature

Hard in practice but positive results exist [Lee2006]
R. Yanosky (2008) From Users to Choosers: The Cloud and the Changing Shape of the Enterprise, In The Tower and The Cloud.
C. B. Lee and A. Snavely (2006) On the User-Scheduler Dialogue: Studies of User-Provided Runtime Estimates and Utility Functions,
International Journal of High Performance Computing Applications, Vol. 20(4).
Valuation problem
•
What is my value for running Ai on Pl ?
•
How does the cost of Ai evolve when changing Wi or Qi?

Unforeseen immediacy costs!
Use 1 server for 24 hours

•
Use 24 servers spread over 3 racks for 1 hour
Non-linear effects
•
Portfolio of reserved instances
•
Spot market prices
•
Utilization of private cloud
User-oriented metrics

Absolute costs (historical data, statistics)

$/minute reduction of makespan

$/unit of accuracy increase within deadline
Component overview
Simple problem instance
•
•
Applications with batch workloads

Trivially parallel DAG

No I/O performance model, storage requirements

Tasks
•
Preemptible but cannot switch to different instance type / provider
•
Runtime associated with each valid instance type

Single QoS property (hard deadline)

Inbound / outbound network traffic per task
Resource supply

On-demand, posted price model

Instance types

•
# CPUs (normalized)
•
Available memory
Allocation granularity of one hour
Binary Integer Program
Evaluation
•
•
Software

AMPL for model definition

CPLEX solver 12.1

Ubuntu 9.10
Hardware

•
Intel quad core @ 2.83GHz / 6MB L2 / 8 GB RAM
Public cloud setup

50 apps / 3 providers / S, L, XL

20 samples / point
Outcome (public)
CPLEX Performance (hybrid)
•
Addition of zero-cost private cloud with 512 CPUs
•
MIPGAP of 1%
•
CPU time
Greedy heuristic
•
Order instance types on cost
•
Schedule tasks with a CDT policy
•
Solves within a second
•
Efficiency (40 apps, 20 samples)
CPLEX Cost
Heuristic Cost
Ratio
0,00
1,46
/
44,68
146,88
3,29
130,53
274,15
2,10
0,00
14,40
/
158,23
331,16
2,09
0,00
0,00
0,00
0,00
0,74
/
58,51
242,16
4,14
0,00
0,00
0,00
0,00
1,47
/
0,00
0,00
0,00
1100,09
1495,17
1,36
68,92
300,97
4,37
0,00
58,64
/
376,05
575,70
1,53
27,49
91,96
3,34
0,00
0,69
/
0,00
52,89
/
0,00
0,00
0,00
0,00
8,90
/
Market structure
Clients
Providers
Market structure
Providers
Clients
Broker
Market structure
Clients
Brokers
Brokers
Market
Providers
MS also takes interest…
Education
Cluster Computing
(Bachelor)
• Infrastructure
know-how
• Administration and
configuration
• Programming (MPI,
batch processing, …)
Distributed
Computing
Paradigms (Master)
• Web services
technology stack
• Service Oriented
Architecture
Topics in Distributed
Computing (Master)
• Virtualization
technology
• Cloud Computing
• Hadoop
• Hands-on work
with EC2 planned
• Azure evaluation
Theses topics /
Internships
• Market
mechanisms
• Bidding strategies
• Technical
realization of
hybrid clouds
• Collaboration with
Applied Economics
Questions?
[email protected]