Quantifying the Benefits of Resource Multiplexing in On-Demand Data Centers

1
Quantifying the Benefits of Resource Multiplexing
in On-Demand Data Centers

• Abhishek Chandra
• Prashant Shenoy
• UMASS Amherst

Pawan Goyal IBM Almaden, San Jose
2
Motivation

• On-demand Data Centers
• Server farms
• Rent computing and storage resources to
  applications
• Revenue for meeting application workload levels

• Goals
• Satisfy dynamically changing application
  requirements
• Maximize resource utilization of the platform
• Robustness against Slashdot effects

3
Dynamic Resource Allocation

• Existing techniques
• Oceano Appleby01, HP Utility Data Center
  Rolia00, MUSE Chase01, COD Doyle02, SHARC
  Uragaon02
• Differ in allocation policies and mechanisms
• Common features
• Periodically re-allocate resources among
  applications
• Estimate workloads for near future
• Statistical multiplexing of resources
• Question Which techniques work best and when?

4
On-demand Allocation Practical Issues

• How often and how fine should the re-allocation
  be done?
• How well can the application requirements be
  estimated?
• How much head room should be allowed to absorb
  transient loads?
• Do large number of customers lead to better
  statistical multiplexing?

5
Talk Outline

• Motivation
• System Model and Metrics
• Performance Study
• Conclusions and Future Work

6
System Model

• Cluster of servers
• Homogeneous pool of resources
• No constraints on application placement

• Time granularity (?t) Period of re-allocation
• E.g. re-allocate once every minute, hour, day
• Space granularity (?s) Resource allocation unit
• E.g re-allocate partial/whole server, server
  group

7
Optimal Resource Allocation

• Infinitesimally small allocation granularity
• Allocates precise amount of resource
• No resource wastage

Ropt
Resource Allocation
Time
8
Practical Resource Allocation

• Allocation done periodically and in fixed quanta
• Fixed resource allocation for next period
• Clairvoyant scheme Predict peak application
  requirements for the next allocation period

Resource Allocation
Time
9
Capacity Overhead
Rpract
?
Ropt
Resource Allocation
Time
10
Performance Study

• Workload
• 3 e-commerce traces
• 24-hour long

Workload Number of Requests Avg. Request Size Peak bit-rate
Ecommerce1 1,194,137 3.95 KB 458.1 KB/s
Ecommerce2 1,674,672 3.85 KB 1631.0 KB/s
Ecommerce3 251,352 7.24 KB 1346.9 KB/s
11
Effect of Allocation Granularity
Space granularity
Time granularity

• Fine time scale with reasonably fine resource
  unit desirable

12
Effect of Prediction Inaccuracy

• Fine allocation is better even with inaccurate
  prediction

13
Effect of Overprovisioning

• Finer allocation achieves same head room with
  less overhead

14
Effect of Number of Customers

• Large number of customers provide more
  opportunity for statistical multiplexing

15
Data Center Architectures

• Dedicated
• Allocation of whole servers
• Typical reallocation in order of 30 minutes
• Shared
• Fractional server resources
• Reallocation in seconds or minutes
• Fast Reallocation
• Reserved server pools, remote booting
• Reallocation in a few minutes

16
Comparison of Architectures
Data Center Configuration Number of customers Optimal Reqmt (Num of servers) Dedicated Architecture (Num of servers) Fast Reallocation (Num of servers) Shared Architecture (Num of servers)
Small 3 20 34 31 25
Medium 15 100 388 304 148
Large 30 1000 5017 3759 1739
17
Implications and Opportunities

• Cost of re-allocation
• Partial server 1 syscall/min
• Full server Rebooting, disk scrubbing, etc.
• Virtual machines Low cost of reallocation with
  encapsulation
• Prediction
• Work-conserving scheduler at fine time-scales
• Accurate prediction possible at minutes, hours

18
Conclusions and Future Work

• Dynamic Resource Allocation for data centers
• Fine allocation granularity desirable
• Even with inaccurate prediction
• To achieve more head room
• Large number of customers lead to higher
  multiplexing benefits
• Future Work
• Effect of affinity, placement constraints
• Re-allocation overhead
• Stability of resource allocation