Geographical load balancing

Transcription

1 Geographical load balancing Jonathan Lukkien 31 mei / 27 Jonathan Lukkien Geographical load balancing

2 Overview Introduction The Model Algorithms & Experiments Extensions Conclusion 2 / 27 Jonathan Lukkien Geographical load balancing

3 1. Introduction 3 / 27 Jonathan Lukkien Geographical load balancing

4 Problem Introduction 1. Data centers are big players in energy consumption 2. How can we get data centers to use less (fossil) fuel? 4 / 27 Jonathan Lukkien Geographical load balancing

5 Solutions Introduction 1. Make data centers more energy effective 2. Make data center routing follow the renewables 5 / 27 Jonathan Lukkien Geographical load balancing

6 2. 6 / 27 Jonathan Lukkien Geographical load balancing

7 Current practice is used to reduce costs. Paradox: cost reduction by increasing energy consumption! 7 / 27 Jonathan Lukkien Geographical load balancing

8 2.1 The Model 8 / 27 Jonathan Lukkien Geographical load balancing

9 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. 9 / 27 Jonathan Lukkien Geographical load balancing

10 Introducing variables, energy cost ξ i (t) = g i (t, m i (t), λ i (t)) with ξ i (t) denoting the energy cost for data center i. m i (t) denoting the amount of servers active at data center i. λ i (t) denoting the load at data center i. 10 / 27 Jonathan Lukkien Geographical load balancing

11 Introducing variables, delay cost δ i (t) = j J λ ij(t)r(f i (m i (t), λ i (t)) + d ij (t)) with δ i (t) denoting the delay cost for data center i. λ ij (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. f i (m i, λ i ) denoting the amount of delay caused by queueing. d ij (t) denoting the amount of delay caused by the network propagation. 11 / 27 Jonathan Lukkien Geographical load balancing

12 The GLB min T t=1 i N (ξ i(t) + δ i (t)) s.t. i N λ ij(t) = L j (t) j J λ ij (t) 0 i N, j J 0 m i (t) M i i N m i (t) N i N This is annoying to solve. 12 / 27 Jonathan Lukkien Geographical load balancing

13 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? 13 / 27 Jonathan Lukkien Geographical load balancing

14 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! 14 / 27 Jonathan Lukkien Geographical load balancing

15 2.2 Algorithms & Experiments 15 / 27 Jonathan Lukkien Geographical load balancing

16 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 16 / 27 Jonathan Lukkien Geographical load balancing

17 Case Studies Two case studies were done: 1. Case study 1 considers data center perspective 2. Case study 2 considers environmental perspective 17 / 27 Jonathan Lukkien Geographical load balancing

18 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. 18 / 27 Jonathan Lukkien Geographical load balancing

19 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. 19 / 27 Jonathan Lukkien Geographical load balancing

20 Case study 1: results The optimal algorithm suggested by the authors is best. 20 / 27 Jonathan Lukkien Geographical load balancing

21 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. 3. A model for the social objective. 21 / 27 Jonathan Lukkien Geographical load balancing

22 Case study 2: results Geographical load balancing has a trend to follow renewables. However, some renewables are more beneficial than others to follow. 22 / 27 Jonathan Lukkien Geographical load balancing

23 3. Extensions 23 / 27 Jonathan Lukkien Geographical load balancing

24 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. 24 / 27 Jonathan Lukkien Geographical load balancing

25 4. Conclusion 25 / 27 Jonathan Lukkien Geographical load balancing

26 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. 26 / 27 Jonathan Lukkien Geographical load balancing

27 Discussion Conclusion In practice not widely used for following renewables... yet! How long will it take? 27 / 27 Jonathan Lukkien Geographical load balancing