Scheduling parallel program tasks onto arbitrary target machines
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
Task Assignment for Distributed Computing
APDC '97 Proceedings of the 1997 Advances in Parallel and Distributed Computing Conference (APDC '97)
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Triplet: A Clustering Scheduling Algorithm for Heterogeneous Systems
ICPPW '01 Proceedings of the 2001 International Conference on Parallel Processing Workshops
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A semi-static approach to mapping dynamic iterative tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing
A low-cost rescheduling policy for efficient mapping of workflows on grid systems
Scientific Programming - AxGrids 2004
A scalable application placement controller for enterprise data centers
Proceedings of the 16th international conference on World Wide Web
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
SLIPstream: scalable low-latency interactive perception on streaming data
Proceedings of the 18th international workshop on Network and operating systems support for digital audio and video
Object recognition and full pose registration from a single image for robotic manipulation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Exploiting multi-level parallelism for low-latency activity recognition in streaming video
MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
Controlling your TV with gestures
Proceedings of the international conference on Multimedia information retrieval
The Iso-level scheduling heuristic for heterogeneous processors
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
Odessa: enabling interactive perception applications on mobile devices
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
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Interactive perception applications, such as gesture recognition and vision-based user interfaces, process high-data rate streams with compute intensive computer vision and machine learning algorithms. These applications can be represented as data flow graphs comprising several processing stages. Such applications require low latency to be interactive so that the results are immediately available to the user. To achieve low latency, we exploit the inherent coarse grained task and data parallelism of these applications by running them on clusters of machines. This paper addresses an important problem that arises: how to place the stages of these applications on machines to minimize the latency, and in particular, how to adjust an existing schedule in response to changes in the operating conditions (perturbations) while minimizing the disruption in the existing placement (churn). To this end, we propose four incremental placement heuristics which use the HEFT scheduling algorithm as their primary building block. Through simulations and experiments on a real implementation, using diverse workloads and a range of perturbation scenarios, we demonstrate that dynamic adjustment of the schedule can improve latency by as much as 36%, while producing little churn.