2015/02/20 by Javad Ghaderi, Sanjay Shakkottai, Ghaderi, Javad +3 · 1 citation
Computer Science · Physics and Astronomy · #Caching and Content Delivery #Complex Network Analysis Techniques #Distributed #FOS: Computer and information sciences #FOS: Mathematics #Networking and Internet Architecture (cs.NI) #Optimization and Control (math.OC) #Optimization and Search Problems #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1502.05968
openalex publication_date 2015/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 trade-off 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.