Energy-efficient, Thermal-aware Data Placement, Replication, and Scheduling in Data Centers

1. Energy-efficient, Thermal-aware Data Placement, Replication, and Scheduling in Data Centers Amol Deshpande SamirKhuller Department of Computer Science and UMIACS University of Maryland at College Park

2. Motivation: Data Centers • Large data centers are a key to handle rapidly growing data management needs • Consume increasingly large amounts of energy both for computing itself, and for cooling • Trend toward higher density of components raises many new challenges w.r.t. thermal issues and energy costs • Hotspots: cooling systems cannot deal effectively with hotspots • Temperature constraints: component temperatures cannot exceed hard thresholds – higher failure rates • Spatial effects: temperature increase at a machine affects temperatures at components nearby • Temperature-dependent power draw: leakage power increases exponentially with temperature

3. Rethinking Optimization • Data replication, placement, and migration • Given an expected workload, find a data placement that results in better energy efficiency and avoids thermal hotspots • Energy efficiencyclustering related data items together • But may result in thermal hotspots • How to use the inherent replication in data centers to optimize for these new, often conflicting, optimization goals • Task assignment and scheduling • Energy efficiency switchon as few machines as possible • But thermal balancing spread out tasks over time and space • Controlling disk and processor speeds • New hardware often comes with knobs to control performance • How to use those to achieve energy efficiency w/o affecting performance?

4. Preliminary Results • Energy-efficient scheduling • Goal: Given performance constraints, minimize the total activation cost, i.e., turn on as few machines as possible, to execute the workload • Designed approximation algorithms with provable bounds [KLS’10, LK’11] • Workload-aware data placement and replication • Can be modeled as a hypergraph partitioning problem • Designed several algorithms that try to minimize the number of machines involved in answering a query [KDK’11] HMetis: State-of-the-art Hypergraph Partitioning Algorithm LMBR: A greedy algorithm that does sophisticated local moves Significant energy savings possible by doing workload-driven optimization

5. Challenges • How to model power consumption as a function of load and temperature? • Too much variance across different hardware platforms • Hardware components often have their own mechanisms to handle undesirable situations (e.g., throttling down if temperature too high) • How to model the temperature in a data center? • Spatial effects are best modeled using computational fluid dynamics • Infeasible for large-scale data centers • Temporal (cooling) effects can be modeled using Fourier’s Law • Unclear if optimization problems can be solved under that model • Need simpler models that approximate the behavior sufficiently well • Developing robust abstractions that are useful across a variety of hardware platforms and component mixes • Infrastructure and/or simulation frameworks for evaluation