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

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energy efficient thermal aware data placement replication and scheduling in data centers

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

motivation data centers
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
rethinking optimization
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?
preliminary results
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

challenges
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