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Packing Jobs onto Machines in Datacenters

Packing Jobs onto Machines in Datacenters. Cliff Stein Columbia University. Modelling. Partly from Rodero et. al. Partly from some google experience M heterogeneous machines (RAM, CPU, disk) N jobs (RAM, CPU, disk, processing time, arrival time) On-line

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Packing Jobs onto Machines in Datacenters

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  1. Packing Jobs onto Machines in Datacenters Cliff Stein Columbia University

  2. Modelling • Partly from Rodero et. al. • Partly from some google experience • M heterogeneous machines (RAM, CPU, disk) • N jobs (RAM, CPU, disk, processing time, arrival time) • On-line • Objectives: response time, energy • Alternative Objective: minimum number of machines

  3. Power saving assumptions • If a machine is idle, it can be shut down (0 power) • If a machine has light processing requirements, and high memory, the processor can be slowed down • If a machine has low memory utilization, the memory can be slowed down • If a machine doesn’t use disk much, the disk can be shut off (use network instead)

  4. Table from Rodero

  5. First problem • Off Line • Pack Jobs onto Machines • Flow Time constrained to be at most α (lower bound) • Energy model. At any time on any machine, power is a function of (memory, cpu) as from previous table. • Consider either three-state (off, low, high), or linear interpolation based on load. • Minimize total energy used.

  6. Second problem • On-line • Allow migration • Deadlines?

  7. A different problem

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