Key result
An adaptive scheduling algorithm based on estimating higher order moments was proposed and compared to FIFO, SJF, and RR policies using synthetic ECG data.
An adaptive scheduling algorithm based on higher order moments of process size distribution was proposed and benchmarked against standard policies using synthetic ECG data.
Hypothesis-generating for efficient biosignal scheduling; real-world validation on clinical data required before adoption.
Efficient biosignal processing requires a solid algorithmic background as well as lightweight infrastructure. Job scheduling, namely the policy determining the maximum process number and the order of the actual process execution from the CPU, is closely related not only to performance but also to quality of service (QoS). Thus, scheduling policies are important infrastructure components along with hardware, network protocols, and memory management algorithms to name a few. In a typical workstation setting scheduling is local and constitutes part of the operating system, whereas in distributed systems such as Hadoop there is an additional global scheduling layer at the cluster level. An adaptive scheduling algorithm at the operating system level suitable for CPU bound processes and based on estimating higher order moments of the process size distribution is proposed. Then it is compared to FIFO, SJF, and RR policies with synthetic data derived from the standard MIT-BIH electrocardiogram (ECG) dataset serving as performance and quality benchmark.
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Δρακόπουλος et al. (2016) studied Biosignal processing (ECG). Adaptive scheduling algorithm based on estimating higher order moments vs. FIFO, SJF, and RR policies was evaluated on Performance and quality benchmark. An adaptive scheduling algorithm based on estimating higher order moments was proposed and compared to FIFO, SJF, and RR policies using synthetic ECG data.
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