Near-optimal deployment of dataflow applications on many-core platforms with real-time guarantees

Safe and optimal deployment of data-streaming applications on many-core platforms requires the realistic estimation of task Worst-Case Execution Time (WCET). On the other hand, task WCET depends on the deployment solution, due to the varying number of interferences on shared resources, thus introduc...

Full description

Saved in:
Bibliographic Details
Main Authors: Skalistis, Stefanos, Simalatsar, Alena
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Data models
Delays
Optimization
Real-time systems
Resource management
Scheduling
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Safe and optimal deployment of data-streaming applications on many-core platforms requires the realistic estimation of task Worst-Case Execution Time (WCET). On the other hand, task WCET depends on the deployment solution, due to the varying number of interferences on shared resources, thus introducing a cyclic dependency. Moreover, WCET is still an over-approximation of the Actual Execution Time (AET), thus leaving room for run-time optimisation. In this paper we introduce an offline/online optimisation approach. In the offline phase, we first break the cyclic dependency and acquire safe and near-optimal solutions for tasks partitioning/placement, mapping, scheduling and buffer allocation. Then, we tighten the WCETs and update the scheduling function accordingly. In the online phase we introduce a safe distributed readjustment of the offline schedule, based on the AET. Experiments on a Kalray MPPA-256 platform show a tightening of the guaranteed latency up to 46% in the offline phase, and 41% latency reduction in the online phase. In total, we achieve more than 50% of latency reduction.
ISSN:1558-1101
DOI:10.23919/DATE.2017.7927090