Geographical load balancing

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Geographical load balancing

Jonathan Lukkien 31 mei 2013

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Overview

Introduction

Geographical Load Balancing

The Model

Algorithms & Experiments

Extensions

Conclusion

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Problem

Introduction

1. Data centers are big players in energy consumption

2. How can we get data centers to use less (fossil) fuel?

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Solutions

Introduction

1. Make data centers more energy effective

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2. Geographical Load Balancing

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Current practice

Geographical Load Balancing is used to reduce costs. Paradox: cost reduction by increasing energy consumption!

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2.1 The Model

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General Idea

Considering |N | data centers and |J | sources from queries to these data centers. Model the energy cost per data center for handling some query. Model the lost revenue “cost” incurred by delay on response per data center for some query. Combine these to model complete cost of query handling.

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Introducing variables, energy

cost

ξi(t) = gi(t, mi(t), λi(t))

with

ξi(t) denoting the energy cost for data center i.

mi(t) denoting the amount of servers active at data center i.

λi(t) denoting the load at data center i.

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Introducing variables, delay

cost

δi(t) =P_j∈Jλij(t)r(fi(mi(t), λi(t)) + dij(t))

with

δi(t) denoting the delay cost for data center i.

λij(t) denoting the amount of work sent to data center i from

source j.

r(d) denoting the revenue lost for a job given delay d. fi(mi, λi) denoting the amount of delay caused by queueing.

dij(t) denoting the amount of delay caused by the network

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The GLB

Geographical Load Balancing minPT t=1 P i∈N(ξi(t) + δi(t)) s.t. P i∈Nλij(t) = Lj(t) ∀j ∈ J λij(t) ≥ 0 ∀i ∈ N, ∀j ∈ J 0 ≤ mi(t) ≤ Mi ∀i ∈ N mi(t) ∈ N ∀i ∈ N

This is annoying to solve.

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Easier GLB

We drop the integrality constraint and instead we round up when we find a solution.

Now we have an easier problem to solve. But still problems remain, some suggestions?

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Some sidenotes

1. An optimal solution which has non-sparse routing can be transformed to one that has sparse routing.

2. Rerouting of queries is necessary for this to work, research has been done on this subject.

3. Adapting capacity of data centers should be possible on the fly, also research being done.

4. Estimation of propagation delay should be reasonably accurate.

5. We do not want to solve it centrally!

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A distributed approach

Two algorithms proposed

1. Algorithm 1 has every source solving the LP with some partial information

2. Algorithm 2 has every source solving steepest gradient with additional smart things

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Case Studies

Two case studies were done:

1. Case study 1 considers data center perspective

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Case study 1: setup

Study used historical data to model input data. 14 data centers were used.

Service time per query is set to 1. Static pricing of electricity is used.

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Case study 1:

benchmarks

Three alternative strategies are also tested to provide some context

1. Baseline 1: does not consider energy pricing when minimizing cost.

2. Baseline 2: does not consider network delay when minimizing cost.

3. Baseline 3: considers neither network delay nor energy pricing.

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Case study 1: results

The optimal algorithm suggested by the authors is best.

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Case study 2: setup

Building on the setup of case study 1 three things are added:

1. A model for availability of renewable energy sources.

2. The pricing for renewable energy.

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Case study 2: results

Geographical load balancing has a trend to follow renewables. However, some renewables are more beneficial than others to follow.

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1. Taking in to account switching on and off of servers.

2. Providing better estimates for delays in the system.

3. Incorporate more renewables in the model.

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We have seen the idea of geographical load balancing. Distributed algorithms are in place to reduce energy costs. Dynamic pricing is needed to make sure renewables can be in corporated.

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Discussion

Conclusion

In practice not widely used for following renewables... yet! How long will it take?