Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- I Introduction to Queueing
- II Necessary Probability Background
- III The Predictive Power of Simple Operational Laws: “What-If” Questions and Answers
- IV From Markov Chains to Simple Queues
- V Server Farms and Networks: Multi-server, Multi-queue Systems
- VI Real-World Workloads: High Variability and Heavy Tails
- 20 Tales of Tails: A Case Study of Real-World Workloads
- 21 Phase-Type Distributions and Matrix-Analytic Methods
- 22 Networks with Time-Sharing (PS) Servers (BCMP)
- 23 The M/G/1 Queue and the Inspection Paradox
- 24 Task Assignment Policies for Server Farms
- 25 Transform Analysis
- 26 M/G/1 Transform Analysis
- 27 Power Optimization Application
- VII Smart Scheduling in the M/G/1
- Bibliography
- Index
24 - Task Assignment Policies for Server Farms
from VI - Real-World Workloads: High Variability and Heavy Tails
Published online by Cambridge University Press: 05 February 2013
- Frontmatter
- Contents
- Preface
- Acknowledgments
- I Introduction to Queueing
- II Necessary Probability Background
- III The Predictive Power of Simple Operational Laws: “What-If” Questions and Answers
- IV From Markov Chains to Simple Queues
- V Server Farms and Networks: Multi-server, Multi-queue Systems
- VI Real-World Workloads: High Variability and Heavy Tails
- 20 Tales of Tails: A Case Study of Real-World Workloads
- 21 Phase-Type Distributions and Matrix-Analytic Methods
- 22 Networks with Time-Sharing (PS) Servers (BCMP)
- 23 The M/G/1 Queue and the Inspection Paradox
- 24 Task Assignment Policies for Server Farms
- 25 Transform Analysis
- 26 M/G/1 Transform Analysis
- 27 Power Optimization Application
- VII Smart Scheduling in the M/G/1
- Bibliography
- Index
Summary
In this chapter we revisit server farms, however this time in the context of high-variability job sizes (indicative of the workloads described in Chapter 20), rather than Exponential job sizes.
The server farm architecture is ubiquitous in computer systems. Rather than using a single, powerful server to handle all incoming requests (assuming such a beast can even be purchased), it is more cost efficient to buy many slow, inexpensive servers and pool them together to harness their combined computational power. The server farm architecture is also popular for its flexibility: It is easy to add servers when load increases and easy to take away servers when the load drops. The term server farm is used to connote the fact that the servers tend to be co-located, in the same room or even on one rack.
Thus far, we have primarily studied server farms with a central queue, as in the M/M/k system, which we initially examined in Chapter 14 and then revisited from a capacity provisioning perspective in Chapter 15. In the M/M/k, jobs are held in a central queue, and only when a server is free does it take on the job at the head of the queue.
By contrast, in computer systems, most server farms do immediate dispatching (also known as task assignment), whereby incoming jobs are immediately assigned to servers (also known as hosts). There is typically no central queue; instead the queues are at the individual hosts.
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- Performance Modeling and Design of Computer SystemsQueueing Theory in Action, pp. 408 - 432Publisher: Cambridge University PressPrint publication year: 2013