## Abstract

Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. Each graph is a job, where nodes represent compute tasks and edges indicate data flows between these compute tasks. Jobs (graphs) arrive randomly over time and, upon completion, leave the system. When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations. Specifically, neighboring compute tasks in the graph that are mapped to different servers incur load on the network; thus a mapping of the jobs among the servers incurs a cost that is proportional to the number of “broken edges.” We propose a low-complexity randomized scheduling algorithm that, without service preemptions, stabilizes the system with graph arrivals/departures; more importantly, it allows a smooth tradeoff between minimizing average partitioning cost and average queue lengths. Interestingly, to avoid service preemptions, our approach does not rely on a Gibbs sampler; instead, we show that the corresponding limiting invariant measure has an interpretation stemming from a loss system.

Original language | English (US) |
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Article number | a14 |

Journal | ACM Transactions on Modeling and Performance Evaluation of Computing Systems |

Volume | 1 |

Issue number | 4 |

DOIs | |

State | Published - Sep 2016 |

## Keywords

- Dynamic resource allocation
- Graph partitioning
- Markov chains
- Stability

## ASJC Scopus subject areas

- Computer Science (miscellaneous)
- Software
- Information Systems
- Media Technology
- Safety, Risk, Reliability and Quality
- Hardware and Architecture
- Computer Networks and Communications