pydcop.distribution.heur_comhost

Distribution heuristic based on hosting costs and communication cost, while respecting agents’ capacity.

Greedy algorithm: We place first the computation with the highest footprint.

When placing a computation we select an agent that has enough capacity left to host this computation and has the smallest aggregated hosting and communication cost incurred by hosting the computation on that agent, according to the computations that have been already distributed.

This means that the first computations (with biggest footprint) are mostly placed based on their hosting cost, as most other computations are not distributed yet and we cannot evaluate their communication cost.

candidate_hosts(computation: ComputationNode, footprint: float, computations: List[Tuple], agents: Iterable[AgentDef], communication_load: Callable[[ComputationNode, str], float], mapping: Dict[str, str])

Build a list of candidate agents for a computation.

The list includes agents that have enough capacity to host this computation and is sorted by cost (cheapest cost) where cost is the aggregated hosting and communication cost incurred by hosting the computation on that agent, according to the computation that have been already distributed. This means that the first computations are mostly placed depending on their hosting cost, as most other computations are not distributed yet and we cannot evaluate their communication cost.

Parameters:

• computation –

• footprint –

• computations –

• agents –

• communication_load –

• mapping –

distribute(computation_graph: ComputationGraph, agentsdef: Iterable[AgentDef], hints: DistributionHints | None = None, computation_memory: Callable[[ComputationNode], float] | None = None, communication_load: Callable[[ComputationNode, str], float] | None = None, timeout=None) → Distribution

Parameters:

• computation_graph –

• agentsdef –

• hints –

• computation_memory –

• communication_load –