Karim Kanoun

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—Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric mobile-cardiology systems. A WBSN-enabled electrocardiogram (ECG) monitor consists of wearable, miniaturized and wireless sensors able to measure and wirelessly report cardiac signals to a WBSN coordinator, which is responsible for reporting them to the(More)
—We consider the problem of energy-efficient on-line scheduling for slice-parallel video decoders on multicore systems with Dynamic Voltage Frequency Scaling (DVFS) enabled processors. In the past, scheduling and DVFS policies in multi-core systems have been formulated heuristically due to the inherent complexity of the on-line multicore scheduling problem.(More)
—In the last years the process of examining large amounts of different types of data, or Big-Data, in an effort to uncover hidden patterns or unknown correlations has become a major need in our society. In this context, stream mining applications are now widely used in several domains such as financial analysis, video annotation, surveillance, medical(More)
—Several techniques have been proposed to adapt Big-Data streaming applications to resource constraints. These techniques are mostly implemented at the application layer and make simplistic assumptions about the system resources and they are often agnostic to the system capabilities. Moreover, they often assume that the data streams characteristics and(More)
— Numerous Directed-Acyclic Graph (DAG) sched-ulers have been developed to improve the energy efficiency of various multi-core systems. However, the DAG monitoring modules proposed by these schedulers make a priori assumptions about the workload and relationship between the task dependencies. Thus, schedulers are limited to work on a limited subset of DAG(More)
—Numerous directed acyclic graph (DAG) schedulers have been developed to improve the energy efficiency of various multicore platforms. However, these schedulers make a priori assumptions about the relationship between the task dependencies , and they are unable to adapt online to the characteristics of each application without offline profiling data.(More)
—Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and resources allocated to the various streaming tasks depending on dynamically changing data stream(More)
We consider the problem of energy-efficient scheduling for slice-parallel video decoders on multicore systems with Dynamic Voltage Frequency Scaling (DVFS) enabled processors. We rigorously formulate the problem as a Markov decision process (MDP), which simultaneously considers the on-line scheduling and per-core DVFS capabilities; the power consumption of(More)
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