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Network-aware HEFT scheduling for grid.

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Summary

We introduce a network-aware Heterogeneous Earliest Finish Time (HEFT) algorithm. This enhanced HEFT accounts for parallel data transfers, providing realistic schedules and makespans for distributed computing environments like Grids.

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Area of Science:

  • Computer Science
  • Distributed Computing
  • Network Engineering

Background:

  • The original Heterogeneous Earliest Finish Time (HEFT) algorithm is widely used for task scheduling in distributed systems.
  • HEFT does not explicitly consider the impact of parallel network flows on data transfer times, especially in geographically distributed environments.
  • Ignoring network contention can lead to unrealistic schedule makespans and suboptimal resource allocation.

Purpose of the Study:

  • To propose a network-aware adaptation of the HEFT scheduling algorithm.
  • To accurately model the completion time of data transfers considering network bottlenecks.
  • To demonstrate the benefits of incorporating network awareness into scheduling for Grid applications.

Main Methods:

  • Modification of the original HEFT algorithm to incorporate network-aware data transfer time estimations.
  • Simulation of parallel data transfers sharing network links to assess their impact on completion times.
  • Comparison of scheduling makespans between the original HEFT and the proposed network-aware HEFT.

Main Results:

  • The network-aware HEFT accurately stretches data transfer times to reflect realistic completion durations.
  • Schedules generated by the network-aware HEFT provide a more realistic makespan compared to the original HEFT.
  • Ignoring parallel data transfer impacts leads to misleadingly optimistic schedule makespans.

Conclusions:

  • Network-aware HEFT provides a more accurate scheduling solution for distributed computing environments with distant nodes.
  • The proposed approach mitigates the inaccuracies caused by neglecting network contention in parallel data transfers.
  • This enhanced HEFT algorithm offers significant benefits for optimizing Grid applications by improving schedule realism.