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Cost- and Energy-Aware Load Distribution Across Data Centers Presented by Shameem Ahmed Kien Le, Ricardo Bianchini, Margaret Martonosi, and Thu D. Nguyen (HotPower 2009) Rutgers University and Princeton University
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Motivations Large org has multiple Data Centers (DC) Business distribution High availability Disaster tolerance Uniform access times to widely distributed client sites Problems Consumes lots of energy Financial and environmental cost How can we exploit the geographical distribution of DCs for optimizing energy consumption? 1. Different & variable electricity prices (hourly pricing) 2. Exploit DCs in different time zones (peak/off-peak demand price) 3. Exploit DCs located near sites that produce “green” electricity 2
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Assumptions Multi-DC Internet services (e.g. Google, iTunes) DCs are behind a set of front-end devices Each service has single SLA (Service Level Agreement) with customers SLA (L,P) = At least P% req must complete in <L time Req can be served by 2 or 3 mirror DC Further replication increases state-consistency traffic But no meaningful benefit in availability or performance 4 Is it True?
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Contributions Framework for optimization-based request distribution policy What % of client req should be directed to each DC Front-ends periodically solve optimization problem After % computation, front-ends abide by them until they are recomputed A greedy heuristic policy for comparison Same goal and constraints First exploits DC with best power efficiency Then exploits DC with cheapest electricity 5
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Prior Research Energy management on a single data center A. Qureshi. HotNets 2008 Shut down entire data centers when electricity costs are relatively high K. Le et al. Middleware 2007 Did not address energy issues, time zones, or heuristics 6
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Request Distribution Policies 7
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Principles and Guidelines Only minimizing energy cost is not enough Must also guarantee high performance and availability Respect these requirements by having the front-ends: Prevent DC overloads Monitor response time of DCs and adjust req distribution accordingly Each DC reconfigures itself by Leaving as many servers active as necessary + 20% slack for unexpected load increase Other servers are turned off 8
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“base” energy cost (servers are idle) energy cost of processing the client req Optimization Based Distribution (1/4) Problem Formulation Policy EPrice: Leveraging time zones & variable electricity prices Doesn’t distinguish DCs based on energy source SymbolMeaning f i (t)% req to be forwarded to DC i LT(t)Expected total # of req Cost i (t)Avg cost ($) of a req at DC i BCost i (offered i, t)Base energy cost ($) of DC i under offered i load LR(t)Expected Peak service rate (req/s) offered i LR(t) * f i (t) LCiLoad Capacity (req/s) of DC i CDF i Expected % of req that complete within L time, given offered i load
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Optimization Based Distribution (2/4) Problem Formulation Policy GreenDC: Leveraging DCs powered by green energy 10 energy cost of processing the client req“base” energy cost that is spent when active servers are idle Assumptions: 1.DCs will increasingly be located near green energy source 2.Green energy supply may not be enough to power DC entire period; Need backup (regular electricity) Assumptions: 1.DCs will increasingly be located near green energy source 2.Green energy supply may not be enough to power DC entire period; Need backup (regular electricity)
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Instantiating parameters Typical approach: front ends communicate & coordinate Proposed approach: Each front end independently solves optimization problem LT(t), LR(t), and offered i are defined for each front-end Load capacity (LC) of each DC is divided by # of front-ends CDF i instantiation CDF i = Expected % of req that complete within L time Each Front end Collects recent history of response time of DCi Maintains a table of for each DC Similar table for BCost i : SymbolMeaning f i (t)% req to be forwarded to DC I Cost i (t)Avg cost ($) of a req at DC I BCost i (offered i, t)Base energy cost ($) of DC i under offered i load LC i Load Capacity (req/s) of DC i LT(t)Expected total # of req LR(t)Expected Peak service rate (req/s) offered i LR(t) * f i (t) CDF i Expected % of req that complete within L time, given offered i load Optimization Based Distribution (3/4) 11 Does this approach satisfy the constraints globally?
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Solving Optimization Problem Electricity price prediction: Ameren Load intensity prediction: ARMA CDFi prediction: Current CDFi tables Can’t use LP solvers Solving for entire day at once involves non-linear functions (e.g. BCosti, CDFi) Use Simulated Annealing Divide the day into six 4-hour epochs Solution recomputation (e.g. data center becomes unavailable) Optimization Based Distribution (4/4) 12
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Heuristic-Based Request Distribution (1/2) Cost-aware but simple For each epoch (1 hr), each front-end computes R = P x E P = % of req must complete within L time (SLA) E = # of req front-end expects in next epoch (use ARMA) R = # of req that must complete within L time Each front-end orders DCs that have CDF i (L, LC i )>= P according tofrom lowest to highest ratio Remaining DCs are ordered by same ratio Concatenate two lists of DC to create final list (MainOrder) 13
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Heuristic-Based Request Distribution (2/2) Request forward policy Req are forwarded to first DC in MainOrder until its capacity is met New req is forwarded to next DC on the list and so on After front-end has served R req within L time, it can disregard MainOrder and start forwarding req to cheapest DC until capacity is met What will happen if prediction is inaccurate? Adjusts R for next epoch 14
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Optimization-based vs Heuristics-based CharacteristicsOptimization (EPrice and GreenDC) CA-heuristic Accounting Period1 day Epoch length4 hrs1 hr Load PredictionsPer front-end for entire dayPer front-end for next hour Energy price predictions Entire dayNext hr Recomputation decision Epoch boundary Comm w/ DCsYes 15
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Evaluation 16
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Methodology (1/4) Implemented a simulator for large Internet service Simulate only a single front-end (East US) Front-end distributes req to 3 DC 17 Data CenterBrown energy (cents/KWh) Green Energy (cents/KWh) Capacity (reqs/s) DC1 (West US)11.115.0 (solar)125 DC2 (East US)11.7-215 DC3 (Europe)9.78.0 (wind)125
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Methodology (2/4) Request Trace Day-long trace received by Ask.com Trace doesn’t include response time To generate realistic DC response times: Installed a simple service on 3 PlanetLab machines Req are made from a machine at Rutgers (front-end) Assumption: avg processing time of each req = 200 ms How to mimic effect of load intensity and network congestion 5% increase in response time for each 25% increase in load 18 ARMA
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Methodology (3/4) Electricity Prices, Sources, and time zones Three price scheme Constant rate, two rate (on/off peak), hourly prices How to mimic different brown electricity prices for each DC? Shift default prices 3 hrs earlier or 6 hrs later Assumptions Electricity price for Green DC is constant Green energy at each green site is enough to process 25% req 19 Ameren
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Methodology (4/4) Other parameters Assumptions A req consumes 60 J to process by 2 machines (including cooling, conversion, and delivery overheads) SLA requires 90% of req to complete in 700 ms Cost-unaware distribution policy Used for comparison basis Approach: similar to CA-heuristic Orders DCs according to performance [CDF i (L, LC i )] from highest to lowest Req are forwarded to first DC until its capacity is met New req are forwarded to next DC and so on 20
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Effect of cost-awareness and pricing scheme (brown electricity) No cost for base energy Result (1/4) 21 (1)Both cost-aware policies reduce costs even under constant pricing (2)On/Off and Dynamic schemes reduce cost significantly (3)EPrice always achieves lowest cost EPrice: Optimization-based distribution (No green energy) CA-Heuristic: Cost-Aware Heuristic (consider Costi/CDFi) CU-Heuristic: Cost-Unaware Heuristic (consider CDFi) EPrice: Optimization-based distribution (No green energy) CA-Heuristic: Cost-Aware Heuristic (consider Costi/CDFi) CU-Heuristic: Cost-Unaware Heuristic (consider CDFi)
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Result (2/4) 22 Why does EPrice behave better than CA-Heuristic? Data CenterBrown energy (cents/KWh)Capacity (req/s) DC1 (West US)11.1125 DC2 (East US)11.7215 DC3 (Europe)9.7125 Can Compensate DC3’s poor performance during future periods of low load.
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Effect of green DC Considers only dynamic pricing Results are normalized against EPrice results w/ dynamic pricing No cost for base energy Result (3/4) 23 Brown energy consumption is reduced by 35% by using green DC (3% cost increase) Why do heuristic policies have higher cost than GreenDC?
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Result (4/4) Effect of Base Energy Assumption: Server consumes power even when idle No DC consumes green energy 24 Base energy Cost savings
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Conclusion Optimization framework for request distribution in multi-DC To reduce energy consumption and cost To respect SLAs Policies take advantage of time zones, variable electricity prices, and green energy Propose a heuristic for achieving the same goal Evaluation using a day-long trace from a commercial service 25
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Discussions Only used 1 Front-end in experiment More front-ends will satisfy global constraints? How to ensure end-to-end QoS guarantee Can we combine SLA guarantee with QoS requirement provided by clients? How to handle services with session state Soft state: Only lasts a user’s session with the service All req of a session must be sent to same DC Can we apply the similar concept for multi-cloud structure? Optimize power Optimize monetary cost for online service provider In multi-cloud computing, is it good to assume that data will be available in clouds beforehand? Pros and Cons 26
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