Energy-aware Scheduling of Streaming Applications on Edge-devices in IoT based Healthcare

Abstract : The reliance on Network-on-Chip (NoC)-based Multiprocessor Systems-on-Chips (MPSoCs) is proliferating in modern embedded systems to satisfy the higher performance requirement of multimedia streaming applications. Task level coarse grained software pipeling also called re-timing when combined with Dynamic Voltage and Frequency Scaling (DVFS) has shown to be an effective approach in significantly reducing energy consumption of the multiprocessor systems at the expense of additional delay. In this article we develop a novel energy-aware scheduler considering tasks with conditional constraints on Voltage Frequency Island (VFI)-based heterogeneous NoC-MPSoCs deploying re-timing integrated with DVFS for real-time streaming applications. We propose a novel task level re-timing approach called R-CTG and integrate it with non linear programming-based scheduling and voltage scaling approach referred to as ALI-EBAD. The R-CTG approach aims to minimize the latency caused by re-timing without compromising on energy-efficiency. Compared to R-DAG, the state-of-the-art approach designed for traditional Directed Acyclic Graph (DAG)-based task graphs, R-CTG significantly reduces the re-timing latency because it only re-times tasks that free up the wasted slack. To validate our claims we performed experiments on using 12 real benchmarks, the results demonstrate that ALI-EBAD out performs CA-TMES-Search and CA-TMES-Quick task schedulers in terms of energy-efficiency.

 EXISTING SYSTEM :

 ? Existing solutions make simplifying assumptions to estimate the cost of interference due to operator co-location. ? Likewise, existing framework-agnostic solutions for operator placement make simplifying assumptions about the interference costs of co-located operators. ? Existing solutions have varied objectives, such as minimizing network use, minimizing inter-node traffic, minimizing the makespan or response time of an operator graph. ? More importantly, however, to the best of our knowledge, existing works on makespan minimization do not consider the impact of operator co-location and hence the interference effects on response time, while our solution expressly considers such an impact.

 DISADVANTAGE :

 ? In this paper we investigated complex scheduling problem for tasks, both with and without conditional precedence constraints by deploying a VFI-NoCMPSoC computing platform. ? Task scheduling is NP-hard problem therefore, different heuristics have been developed to achieve energy-efficient solutions . ? Other researchers investigated scheduling problems integrated with DVFS for tasks with precedence constraints to reduce the power overhead. ? While the investigations performed for task scheduling problems on MPSoC in only focus on dependent tasks represented by Directed Acyclic Graph (DAG).

 PROPOSED SYSTEM :

 • To address this concern, the edge computing paradigm has been proposed to enable computations to execute near the source of data on low-cost edge devices and small-scale data-centers called cloudlets. • Due to the simplicity of the linear structures, the proposed approach is able to significantly reduce the space over which the latency prediction model needs to be learned. • In, the authors have proposed a greedy algorithm for operator placement with the objective of minimizing the end-to-end response time of operator graphs/DAGs. • The evaluation of the proposed scheme is, however, conducted through a simulation study implemented using OMNET++ simulator.

 ADVANTAGE :

 ? Data extensive real-time applications are increasing in IoT, increasing numbers of processors elements are therefore desirable in MPSoCs design to meet the performance needs. ? Proper task scheduling approaches can reduce energy consumption and increase the performance and reliability of an embedded system. ? Multiprocessor systems are becoming de-facto computing platforms due to their excellent high-performance and exceptional QoS. ? This further reduction in energy consumption occurs because ALI-EBAD maps higher energy consuming tasks on high energy-efficient and low-performance processor. ? In other words it considers the energy performance profiles of the processors during task scheduling.