Energy-aware task scheduling on heterogeneous NoC-Based MPSoCs

We investigate the problem of scheduling a set of nonpreemptible tasks with precedence constraints and individual deadlines on heterogeneous NoC-based, DVFS-enabled MPSoCs with discrete frequencies such that the total energy consumption of all the tasks is minimized, and propose two novel approaches. Our approaches consist of a convex nonlinear programming (NLP)-based algorithm for computing the optimal frequencies of all tasks and communication links under the continuous frequency model, an integer linear programming (ILP)- based algorithm and a polynomial-time heuristic for assigning optimal discrete frequencies to all tasks and communication links. Our experimental results show that in terms of total energy consumption, our approach using ILP outperforms two state-of-the-art approaches, ETFGBF and CA-TMES-Search by up to 69.40% and 48.35%, respectively. Moreover, the performance of our approach using the heuristic is very close to that of our approach using ILP

Big data security in Internet of Things

The Internet-of-Things (IoT) paradigm is an emerging twenty-first century technological revolution, a concept that facilitates to communicate with objects, devices, and machines at unprecedented scale. Nowadays, IoT is extensively applied to numerous applications such as intelligent transportation, smart security, smart grid, and smart home. Now, considering that in the near future, millions of devices will be interconnected and will be producing enormous data, the privacy and security of data going to be challenged and private information may leak at any time. This chapter presents an overview of the IoT and security concerns on big data while we discuss privacy and security approaches for big data with reference to infrastructure, application, and data

Energy-efficient scheduling of tasks with conditional precedence constraints on MPSoCs

In this article, we investigate the problem of energy-efficient scheduling of tasks with conditional precedence constraints on heterogeneous NoC-based MPSoC. We propose a novel offline approach that performs task mapping, scheduling and voltage scaling in an integrated manner. Our approach consists of a scheduling algorithm that constructs a single unified schedule by prioritizing tasks with tight latest finish time bounds. It uses an NLP-based DVFS algorithm to assign continuous frequencies and voltages to tasks and communications, and transforms the assigned frequencies and voltages to tasks and communications to valid discrete frequency and voltage levels using either an ILP or a heuristic-based algorithm. Compared to the state-of-the-art approach designed for the task model with unconditional precedence constraints, our approach using ILP-based algorithm achieves improvements in the range of 9% to 61% and an average improvement of 31%, and our approach using a heuristic-based algorithm achieves improvements in the range of 2% to 46% and an average improvement of 20% in terms of energy reduction. In terms of running time, our approach is approximately 3 times faster than the state-of-the-art approach

Energy efficient task mapping & scheduling on heterogeneous NoC-MPSoCs in IoT based smart city

Multi-Processor System-on-Chips (MPSoCs) are extensively deployed in modern Internet-of-Things based Smart City (IoT-SC) applications to fulfill the ever growing computation demands. The Sensor Nodes (SNs) in IoT-SC are energy constrained and normally powered by a battery source with limited residual energy. Therefore, reduction in energy consumption is one of the challenging technological aspect for IoTSC. In this paper we investigate the problem of scheduling set of tasks with precedence and deadline constraints on Networkon-Chip (NoC) based heterogeneous MPSoCs. Unlike other energy-aware scheduling approaches that separately perform task ordering and voltage assignment from the task mapping, our proposed approach deals with it in an integrated way while explicitly considering the contentions between communications. Moreover, our approach shares the available slack between tasks and communications. We have proposed Energy-aware Integrated Task Mapping, Scheduling and Voltage Scaling (EIMSVS) algorithm. The EIMSVS algorithm uses Earliest Latest Finish Time First (ELFTF) strategy to order the tasks and communications in time. At each optimization step EIMSVS algorithm selects a task or a communication to remap it to a processor and or a voltage level that minimizes total energy consumption. The experiments are conducted on synthetic as well as real-world TGs adopted from Embedded Systems Synthesis Benchmarks (E3S). The experimental results are compared with state of the art approach. The results illustrates that our proposed approach achieves average energy improvement and maximum energy improvement of ~ 21% and ~ 59% respectively

A novel meta-heuristic for green computing on VFI-NoC-HMPSoCs

The number of processors has increased significantly on multiprocessor system therefore, Voltage Frequency Island (VFI) recently adopted for effective energy management mechanism in the large scale multiprocessor chip designs. Heterogeneous VFI, Network-on-Chip (NoC) based Multiprocessor System-on-Chips (MPSoCs) i.e. VFI-NoC-HMPSoCs are widely adopted in computational extensive applications due to their higher performance and an exceptional Quality-of-Service (QoS). Proper task scheduling using search-based algorithms on multiprocessor architectures can significantly improve the performance and energy-efficiency of a battery-constrained embedded system. In this paper, unlike the existing population-based optimization algorithms, we propose a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time for performance trade-off. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm. Our static scheduler ARHS-FATI collectively performs task mapping and ordering. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI based NoC-MPSoCs. We conducted the experiments on 8 real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of 15% and 20% over CA-TMES-Search and CA-TMES-Quick respectively

Energy efficient heuristic algorithm for task mapping on shared-memory heterogeneous MPSoCs

Existing research mostly reduce mapping time and inter-processors communication energy of the multiprocessor system-on-chips (MPSoCs). Unlike other approaches in this paper we have explored energy efficient task mapping on shared-memory heterogeneous MPSoCs considering the energy performance profile of the processors. We propose mitosis heterogeneous-genetic algorithm (MH-GA) for energy aware task mapping on DVFS-enabled processors in order to maximally exploit the inherent heterogeneity in the MPSoC platform while satisfying the application deadline restriction. The proposed heuristic mapping approach has an integrated list scheduler that assigns priority to the tasks with lower deadlines. The experiments are conducted on 4 synthetic and 4 real-world task graphs (TGs) acquired from embedded systems synthesis benchmarks (E3S). The experimental results are compared with the greedy algorithm and our proposed heuristic algorithm achieves maximum energy efficiency of ~53.2% while reduces the average energy consumption ~21.5%

Energy-efficient scheduling of streaming applications in VFI-NoC-HMPSoC based edge devices

Energy-aware high-performance computing is becoming a challenging facet for streaming applications at edge devices in Internet-of-Things (IoT) due to the high computational complexity involved. Therefore, the number of processors has increased significantly on the multiprocessor system subsequently, Voltage Frequency Island (VFI) recently adopted for an effective energy management mechanism in the large scale multiprocessor chip designs. In this paper, energy-aware scheduling of real-time streaming applications on edge-devices is investigated. First, an innovative re-timing based technique is developed to transform the dependent workload into an independent task model to avail resources and the wasted slack in the processors with a possible minimal prologue. Moreover, unlike the existing population-based optimization algorithms, a novel population-based algorithm, ARSH-FATI is proposed that can dynamically switch between explorative and exploitative search modes at run-time for performance trade-off. Finally, a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm is presented. Our static scheduler ARHS-FATI collectively performs task mapping and ordering. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI based MPSoCs

Energy optimization of streaming applications in IoT on NoC based heterogeneous MPSoCs using re-timing and DVFS

The Multiprocessor System-on-Chip (MPSoC) computing architectures are widely adopted in modern embedded systems for real-time applications due to their high performance, reliability, and Quality-of-Service (QoS). Green computing or energy-efficient task scheduling is a critical technological challenging facet in an energy constrained embedded systems because higher energy consumption limits the lifetime of the computing platform and causes an increased carbon footprint. In this paper, we investigate energy-aware task scheduling on Dynamic Voltage and Frequency Scaling (DVFS) enabled Network-on-Chip (NoC) based Heterogeneous MPSoCs (HMPSoCs). We transform the intra-data dependencies into inter-data dependencies of the tasks with precedence constraints represented by Directed Acyclic Graph (DAG). We further implement Energy-efficient Task Scheduling Heuristic (ETSH) algorithm embedded with a list scheduler to perform energy-aware task scheduling while considering the energy performance profiles of the processors and task deadlines. The observed results on 5 real-world and 5 synthetic Task Graphs (TGs) adopted from Embedded Systems Synthesis (E3S) benchmarks suit demonstrate that ETSH outperforms state-of-the-art technique. Concisely, it achieves 20% and 38% average energy-efficiency with and without using coarse-grained software pipelining respectively

Energy-efficient static task scheduling on VFI-based NoC-HMPSoCs for intelligent edge devices in cyber-physical systems

The interlinked processing units in modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoC) architecture is becoming a de facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS; therefore, Voltage Frequency Island (VFI) has been recently adopted for effective energy management mechanism in the large-scale multiprocessor chip designs. In this article, we investigated energy-efficient and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI-based NoC-MPSoCs (VFI-NoC-HMPSoC) with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we proposed a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI-based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent-inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We introduced a notion of Energy Gradient (EG) that guides EGD in its search for island voltage settings and minimize the total energy consumption. We conducted the experiments on eight real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ∼24% and ∼30% over CA-TMES-Search and CA-TMES-Quick, respectively. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM

ARSH-FATI a novel metaheuristic for cluster head selection in wireless sensor networks

IEEE Wireless sensor network (WSN) consists of a large number of sensor nodes distributed over a certain target area. The WSN plays a vital role in surveillance, advanced healthcare, and commercialized industrial automation. Enhancing energy-efficiency of the WSN is a prime concern because higher energy consumption restricts the lifetime (LT) of the network. Clustering is a powerful technique widely adopted to increase LT of the network and reduce the transmission energy consumption. In this article (LT) we develop a novel ARSH-FATI-based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called novel ranked-based clustering (NRC) to reduce the communication energy consumption of the sensor nodes while efficiently enhancing LT of the network. Unlike other population-based algorithms ARSH-FATI-CHS dynamically switches between exploration and exploitation of the search process during run-time to achieve higher performance trade-off and significantly increase LT of the network. ARSH-FATI-CHS considers the residual energy, communication distance parameters, and workload during cluster heads (CHs) selection. We simulate our proposed ARSH-FATI-CHS and generate various results to determine the performance of the WSN in terms of LT. We compare our results with state-of-the-art particle swarm optimization (PSO) and prove that ARSH-FATI-CHS approach improves the LT of the network by $\sim \text{25}\%$