Online robust optimization framework for QoS guarantees in distributed soft real-time systems
EMSOFT '10 Proceedings of the tenth ACM international conference on Embedded software
Jockey: guaranteed job latency in data parallel clusters
Proceedings of the 7th ACM european conference on Computer Systems
Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Journal of Systems and Software
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As distributed real-time applications gain in popularity, a key challenge is to allocate resources so that diverse real-time requirements (including non-real-time applications), distributed application components and varying workloads can all be accommodated without violating timeliness constraints. We examine the problem of resource allocation in distributed soft real-time systems, where both network and CPU resources are consumed. The timeliness constraints of applications are expressed through utility functions, which compute "benefit" as a function of end-to-end latency. We present LLA (Lagrangian Latency Assignment), a scalable and efficient distributed algorithm which maximizes aggregate utility by computing an optimal trade-off between end-to-end latency and allocated resources. The algorithm runs continuously and adapts to both workload and resource variations. LLA is guaranteed to converge if the workload and resource requirements stabilize. We evaluate the quality of results and convergence characteristics under various workloads, using both simulation and real-world experimentation.