Heterogeneous Earliest Finish Time Research Articles

A bi-objective workflow scheduling in virtualized fog-cloud computing using NSGA-II with semi-greedy initialization

Virtualized Fog-Cloud Computing (VFCC) has emerged as a promising computing model in both research and industry. Its inherent characteristics, such as real-time service provisioning, resource heterogeneity, flexibility, and scalable computational resources, present new opportunities and challenges in the scheduling of computational workflows. Efficient workflow scheduling is crucial in optimizing resource utilization and improving service delivery in VFCC environments. In this paper, we first present a bi-objective optimization model for the workflow scheduling problem in VFCC systems with the aim of minimizing both the system’s makespan and energy consumption. We then propose an efficient list-based method to solve the model. The proposed method consists of two distinct phases. In the first phase, we employ the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine task priorities, establishing the task execution sequence. To ensure both convergence speed and diversity, we implement an intelligent semi-greedy approach when generating the initial population. In the second phase, we introduce an energy and makespan-aware heuristic algorithm to efficiently allocate virtual machines to the prioritized tasks. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using both synthetic and real datasets, including the Epigenomics, Montage, and LIGO graphs. We compare the proposed method with the Heterogeneous Earliest Finish Time (HEFT) and Green-HEFT algorithms in terms of makespan and energy consumption, respectively. Furthermore, we evaluate our proposed method by comparing it to two other algorithms, Multi-objective HEFT (MOHEFT) and Multi-objective ACO (MOACO), using various quality indicators such as generation distance (GD), inverse generation distance (IGD), goal programming approach (GPA), and Hyper volume (HV). The simulation results demonstrate that our method effectively reduces the makespan by 6.38% to 51.19% and energy consumption by 3.52% to 16.49%. Moreover, on average, our method outperforms the other algorithms with an improvement of 2.3%, 5.7%, 9.8%, and 9.34% for the GD, IGD, GPA, and HV metrics, respectively.

A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm

The Internet of Multimedia Things (IoMT) has recently experienced a considerable surge in multimedia-based services. Due to the fast proliferation and transfer of massive data, the IoMT has service quality challenges. This paper proposes a novel fog-based multimedia transmission scheme for IoMT using the Sugano interference system with a quantum genetic optimization algorithm. The fuzzy system devises a mathematically organized strategy for generating fuzzy rules from input and output variables. The Quantum Genetic Algorithm (QGA) is a metaheuristic algorithm that combines genetic algorithms and quantum computing theory. It combines many critical elements of quantum computing, such as quantum superposition and entanglement. This provides a robust representation of population diversity and the capacity to achieve rapid convergence and high accuracy. As a result of the simulations and computational analysis, the proposed fuzzy-based QGA scheme improves packet delivery ratio and throughput by reducing end-to-end latency and delay when compared to traditional algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Heterogeneous Earliest-Finish-Time (HEFT) and Ant Colony Optimization (ACO). Consequently, it provides a more efficient scheme for multimedia transmission in IoMT.

MOCAB/HEFT algorithm of multi radio wireless communication improved achievement assessment

Network-wide conveying is vital in remote associations, and the great part of these broadcasts are built on single-channel single-radio (SC-SR) network frameworks. The problem of the current work is divided into two parts. The first part shows that increasing broadcast and redundancy lead to an increase in time consumption. The second problem is solving complexity problems when tasks are scheduled in a heterogeneous manner in a computing system, where the processors in the network may not be identical and take different time periods to carry out the same task. The goals of this work are to reduce the total cost of network-wide broadcasting to minimize the search space and to solve the complexity problem when tasks are scheduled in a heterogeneous way in the computing system. The MOCAB algorithm is used to select the best transmission path over the network in the first stage. Then, the tasks will be scheduled using the heterogeneous earliest finish time (HEFT) algorithm to extract the values of actual finish time (AFT), earliest start time (EST), and earliest finish time (EFT). The performance of the MOCAB algorithm was evaluated with that of the HEFT algorithm in terms of the delivery ratio of packets delivered. The results showed that the MOCAB algorithm outperformed the HEFT.

Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes

Cloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm produces the maximum outcomes for scheduling tasks in a heterogeneous environment in a reduced time. Reviewed literature proves that HEFT is efficient in terms of execution time and quality of schedule. The HEFT algorithm makes use of average communication and computation costs as weights in the DAG. In some cases, however, the average cost of computation and selecting the first empty slot may not be enough for a good solution to be produced. In this paper, we propose different HEFT algorithm versions altered to produce improved results. In the first stage (rank generation), we execute several methodologies to calculate the ranks, and in the second stage, we alter how the empty slots are selected for the task scheduling. These alterations do not add any cost to the primary HEFT algorithm, and reduce the makespan of the virtual machines’ workflow submissions. Our findings suggest that the altered versions of the HEFT algorithm have a better performance than the basic HEFT algorithm regarding decreased schedule length of the workflow problems. 

EMCS: An Energy-Efficient Makespan Cost-Aware Scheduling Algorithm Using Evolutionary Learning Approach for Cloud-Fog-Based IoT Applications

The tremendous expansion of the Internet of Things (IoTs) has generated an enormous volume of near and remote sensing data, which is increasing with the emergence of new solutions for sustainable environments. Cloud computing is typically used to help resource-constrained IoT sensing devices. However, the cloud servers are placed deep within the core network, a long way from the IoT, introducing immense data transactions. These transactions require heavy electricity consumption and release harmful CO2 to the environment. A distributed computing environment located at the edge of the network named fog computing has been promoted to reduce the limitation of cloud computing for IoT applications. Fog computing potentially processes real-time and delay-sensitive data, and it reduces the traffic, which minimizes the energy consumption. The additional energy consumption can be reduced by implementing an energy-aware task scheduling, which decides on the execution of tasks at cloud or fog nodes on the basis of minimum completion time, cost, and energy consumption. In this paper, an algorithm called energy-efficient makespan cost-aware scheduling (EMCS) is proposed using an evolutionary strategy to optimize the execution time, cost, and energy consumption. The performance of this work is evaluated using extensive simulations. Results show that EMCS is 67.1% better than cost makespan-aware scheduling (CMaS), 58.79% better than Heterogeneous Earliest Finish Time (HEFT), 54.68% better than Bees Life Algorithm (BLA) and 47.81% better than Evolutionary Task Scheduling (ETS) in terms of makespan. Comparing the cost of the EMCS model, it uses 62.4% less cost than CMaS, 26.41% less than BLA, and 6.7% less than ETS. When comparing energy consumption, EMCS consumes 11.55% less than CMaS, 4.75% less than BLA and 3.19% less than ETS. Results also show that with an increase in the number of fog and cloud nodes, the balance between cloud and fog nodes gives better performance in terms of makespan, cost, and energy consumption.

Enhanced hybrid multi-objective workflow scheduling approach based artificial bee colony in cloud computing

This paper presents a hybrid approach based Binary Artificial Bee Colony (BABC) and Pareto Dominance strategy for scheduling workflow applications considering different Quality of Services (QoS) requirements in cloud computing. The main purpose is to schedule a given application onto the available machines in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The proposed approach is called Enhanced Binary Artificial Bee Colony based Pareto Front (EBABC-PF). Our proposed approach starts by listing the tasks according to priority defined by Heterogeneous Earliest Finish Time (HEFT) algorithm, then gets an initial solution by applying Greedy Randomized Adaptive Search Procedure (GRASP) and finally schedules tasks onto machines by applying Enhanced Binary Artificial Bee Colony (BABC). Further, several modifications are considered with BABC to improve the local searching process by applying circular shift operator then mutation operator on the food sources of the population considering the improvement rate. The proposed approach is simulated and implemented in the WorkflowSim which extends the existing CloudSim tool. The performance of the proposed approach is compared with Heterogeneous Earliest Finish Time (HEFT) algorithm, Deadline Heterogeneous Earliest Finish Time (DHEFT), Non-dominated Sort Genetic Algorithm (NSGA-II) and standard Binary Artificial Bee Colony (BABC) algorithm using different sizes of tasks and various benchmark workflows. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

Performant, Multi-Objective Scheduling of Highly Interleaved Task Graphs on Heterogeneous System on Chip Devices

Performance-, power-, and energy-aware scheduling techniques play an essential role in optimally utilizing processing elements (PEs) of heterogeneous systems. List schedulers, a class of low-complexity static schedulers, have commonly been used in static execution scenarios. However, list schedulers are not suitable for runtime decision making, particularly when multiple concurrent applications are interleaved dynamically. For such cases, the static task execution times and expectation of idle PEs assumed by list schedulers lead to inefficient system utilization and poor performance. To address this problem, we present techniques for optimizing execution of list scheduling algorithms in dynamic runtime scenarios via a family of algorithms inspired by the well-known heterogeneous earliest finish time (HEFT) list scheduler. Through dynamically arriving, realistic workload scenarios that are simulated in an open-source discrete event heterogeneous SoC simulator, we exhaustively evaluate each of the proposed algorithms across two SoCs modeled after the Xilinx Zynq Ultrascale+ ZCU102 and O-Droid XU3 development boards. Altogether, depending on the chosen variant in this family of algorithms, we are able to achieve an up to 39% execution time improvement, up to 7.24x algorithmic speedup, or up to 30% energy consumption improvement compared to the baseline HEFT implementation.

Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment

Cloud computing is one of the most commonly used infrastructures for carrying out activities using virtual machines known as processing units. One of the most fundamental issues with cloud computing is task scheduling. The optimal determination of scheduling criteria in cloud computing is a non-deterministic polynomial-time (NP)-complete optimization problem, and several procedures to manage this problem have been suggested by researchers in the past. Among these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm is recognized to produce optimal outcomes in a shorter time period for scheduling tasks in a heterogeneous environment. Literature shows that HEFT gives extraordinary results in terms of quality of schedule and execution time. However, in some cases, the average computation cost and selection of the first idle slot may not produce a good solution. Therefore, here we propose modified versions of the HEFT algorithm that can obtain improved results. In the rank generation phase, we implement different methodologies for calculating ranks, while in the processor selection phase, we modify the way of selecting idle slots for scheduling the tasks. This paper suggests enhanced versions of the HEFT algorithm under user-required financial constraints to minimize the makespan of a specified workflow submission on virtual machines. Our findings also suggest that enhanced versions of the HEFT algorithm perform better than the basic HEFT method in terms of lesser schedule length of the workflow problems running on various virtual machines.

Multi‐objectivetask scheduling in fog computing using improved gaining sharing knowledge based algorithm

SummaryIn the modern Internet of Things (IoT) era, several applications generate a vast amount of data and that needs to be handled appropriately. The conventional cloud computing system delivers us with enormous resources to manage such voluminous data. Despite that, the growing demands of IoT applications on minimal energy consumption, minimal latency, the privacy of data, data processing based on location, and maximum Quality of Service(QoS) impels the advent of fog computing. As the devices in the fog layer are heterogeneous, distributed, and resource‐constrained, how the fog resources are effectively utilized for executing latency and time‐sensitive applications is a primary challenge. The data generated by the IoT devices are voluminous and produce overhead in network bandwidth during transmission and slow down the response time. This article addresses the task scheduling problem in fog computing to minimize the makespan and energy consumption. The proposed work consists of two phases. In the first phase, the tasks are ordered based on the heuristic method, heterogeneous earliest finish time (HEFT). Then the ordered tasks are scheduled by applying the improved gaining sharing knowledge (IGSK) based algorithm. To reduce the energy consumption dynamic voltage frequency scaling (DVFS) is applied in the proposed scheme. To evaluate the proposed scheduling method the simulations are performed on different workflows with varying sizes. The performance evaluation results exhibit that the proposed work outperforms the task scheduling with similar methods in terms of makespan and energy consumption.

Energy and cost aware workflow scheduling in clouds with deadline constraint

AbstractCloud computing is a promising platform for executing scientific workflow applications. Commonly, the scientific workflow applications are complex in size and computation intensive. Task scheduling in clouds is a familiar NP‐complete problem. Efficient workflow application scheduling is critical for meeting multi‐objectives in cloud environments. By reason of its pivotal role, this problem has been widely studied, and many approaches have been developed. Most of the algorithms focus on schedule makespan and execution cost. The cloud data centers consume a large amount of energy while running workflow applications due to a lack of efficient scheduling algorithms to perform the task to a virtual machine (VM) mapping. Hence, there is a significant need to address energy utilization in the cloud data center (CDC) for two reasons such as data center operational cost optimization and to improve the environment. This article presents an energy and cost‐aware scheduling (ECWS) approach with the objectives of decreasing energy utilization, execution cost, and maximizing utilization of the resources. The ECWS algorithm is a heterogeneous earliest finish time (HEFT) based energy‐efficient heuristic for cloud scheduler. The ECWS algorithm includes three sub‐algorithms like RE calculation, RE Threshold selection, and slack algorithm. The effectiveness of the proposed algorithm is evaluated on the WorkflowSim tool using distinct workloads from various scientific areas. The experimental outcome exhibit that the proposed algorithm achieved significant energy conservation, maximized resource utilization, and cost‐saving when compared with related well‐known algorithms.

Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

In recent years, Cloud computing has been developed and become the foundation of a wide range of applications. It allows users to access a catalog of standardized services and respond to their business needs flexibly and adaptively, in the event of unforeseen demands, paying solely for the consumption they have made. Task scheduling problem is considered one of the most critical cloud computing challenges. The problem refers to how to reasonably order and allocate the applications tasks provided by the users to be executed on virtual machines. Furthermore, the quality of scheduling performance has a direct effect on customer satisfaction. The task scheduling problem in cloud computing must be more accurately described in order to improve scheduling performance. In this paper, a multi-objective task scheduling algorithm is proposed based on the decision tree in a heterogenous environment. We introduce a new Task Scheduling-Decision Tree (TS-DT) algorithm for allocating and executing an application’s task. To evaluate the performance of the proposed TS-DT algorithm, a comparative study was conducted among the existing algorithms; Heterogeneous Earliest Finish Time (HEFT), Technique for Order of Preference by Similarity to Ideal Solution that incorporates the Entropy Weight Method (TOPSIS-EWM), and combining Q-Learning with the Heterogeneous Earliest Finish Time (QL-HEFT). Our results show that the proposed TS-DT algorithm outperforms the existing HEFT, TOPSIS-EWM, and QL-HEFT algorithms by reducing makespan by 5.21%, 2.54%, and 3.32%, respectively, improving resource utilization by 4.69%, 6.81%, and 8.27%, respectively, and improving load balancing by 33.36%, 19.69%, and 59.06%, respectively in average.

Are task mappings with the highest frequency of servers so good? A case study on Heterogeneous Earliest Finish Time (HEFT) algorithm

In the current heterogeneous computing environment, scheduling strategies play a key role in achieving high performance under current heterogeneous computing environment. Various algorithms have been proposed to generate high-quality schedules. Nonetheless, all the existing methods initialize task mapping with the highest frequencies of servers, creating significant effects on the results. Does mapping with the highest frequencies of servers generate the best schedule? In this paper, an attempt has been made to answer this question by investigating HEFT, which is a commonly used initial schedule method in scheduling the literature under DVFS environments. In this work, the task mapping is initialized with different server frequency level, along with investigating its impacts on the schedule results. Based on this concept, a corresponding Frequency Scaling Algorithm (FSA) has been proposed. By applying FSA combined with three frequency levels mappings, it has been observed that initial task mapping with middle server frequencies can generate much better results. When the initial frequency is the highest, the scheduling result is not optimal. The proposed algorithm requires less execution time and generates stable performance. Also, the proposed frequency scaling method is found to mitigate the impacts of initial mappings to a certain extent.

An optimized workflow scheduling algorithm on CPU-GPU heterogeneous systems

Aiming at the workflow scheduling problem on CPU-GPU heterogeneous systems, this article proposes a workflow scheduling algorithm that optimizes task priority and processor selection phase. This article focuses on the relationship between processor processing time and task acceleration ratio, and uses optimistic finish time table. This article estimates each task’s communication cost and computation cost on the basis of each task’s acceleration ratio ri. Then the algorithm calculates the task’s priority ranku based on communication cost and computation cost. In processor selection phase, this article takes the Earliest Finish Time (EFT) difference between the processor with the fastest task and the processor selected based on Heterogeneous Earliest Finish Time (HEFT) algorithm as the judgment condition k. If the difference between the earliest completion time of all child tasks is greater than k, the algorithm selects the processor selected by the HEFT algorithm; otherwise, the algorithm selects another one. This article uses the computational shared facility’s kernel timer for simulation experiment, which bases on the University of Manchester’s high performance computing cluster. Based on the simulation results, the proposed algorithm can reduce the maximum completion time and energy consumption, and improve the acceleration ratio. Compared with the HEFT algorithm, the task maximum completion time and energy consumption are reduced by 10% and 5% respectively, and the acceleration ratio is improved by 3%.

EHEFT-R: multi-objective task scheduling scheme in cloud computing

In cloud computing, task scheduling and resource allocation are the two core issues of the IaaS layer. Efficient task scheduling algorithm can improve the matching efficiency between tasks and resources. In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. In EHEFT-R, ordering rules based on priority constraints are used to optimize the quality of the initial solution, and the enhanced heterogeneous earliest finish time (HEFT) algorithm is used to ensure the global performance of the solution space. Simulation experiments verify the effectiveness and superiority of EHEFT-R.

Using differential evolution and Moth–Flame optimization for scientific workflow scheduling in fog computing

Fog computing is an interesting technology aimed at providing various processing and storage resources at the IoT networks’ edge. Energy consumption is one of the essential factors that can directly impact the maintenance cost and CO2 emissions of fog environments. Energy consumption can be mitigated by effective scheduling approaches, in which tasks are going to be mapped on the best possible resources regarding some conflicting objectives. To deal with these issues, we introduce an opposition-based hybrid discrete optimization algorithm, called DMFO-DE. For this purpose, first, a discrete and Opposition-Based Learning (OBL) version of the Moth–Flame Optimization (MFO) algorithm is provided, and it then is combined with the Differential Evolution (DE) algorithm to improve the convergence speed and prevent local optima problem. The DMFO-DE is then employed for scientific workflow scheduling in fog computing environments using the Dynamic Voltage and Frequency Scaling (DVFS) method. The Heterogeneous Earliest Finish Time (HEFT) algorithm is used to find the tasks execution order in the scientific workflows. Our workflow scheduling approach mainly tries to decrease the scheduling process’s energy consumption by minimizing the applied Virtual Machines (VMs), makespan, and communication between dependent tasks. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on the scientific workflows with four different sizes. The experimental results indicate that scheduling using the DMFO-DE algorithm can outperform other metrics such as the number of applied VMs, and energy consumption.

Scheduling energy‐conscious tasks in distributed heterogeneous computing systems

AbstractDistributed heterogeneous systems have been widely adopted in industrial applications by providing high scalability and performance while keeping complexity and energy consumption under control. However, along with the increase in the number of computing nodes, the energy consumption of distributed heterogeneous systems dramatically grows and is extremely hard to predict. Energy‐conscious task scheduling, which tries to assign appropriate priorities and processors to tasks such that the system energy requirement would be met, has received extensive attention in recent years. However, many approaches reduce energy consumption by extending the completion time. In this article, we focus on the scheduling problem of energy‐conscious tasks in distributed heterogeneous computing systems and provide an efficient approach to mitigate energy consumption while minimizing the overall makespan of parallel applications. First, based on the heterogeneous earliest finish time, a fitness function is proposed to balance the makespan and energy consumption. Then, by improving the crossover and mutation operations of the traditional genetic algorithm, we proposed an efficient scheduling approach named energy‐conscious genetic algorithm to optimize the priorities and processor allocation of tasks, with objectives of minimizing the system energy and makespan. Experiment results on real‐world applications and simulations with randomly generated task graphs demonstrate that the proposed approach outperforms in energy‐saving and makespan reducing.

A heuristic-based task scheduling algorithm for scientific workflows in heterogeneous cloud computing platforms

Task scheduling problem in a cloud computing environment is one of the most important issues in this field. Because task schedulers should be aware of underlying platform heterogeneity, task interdependencies, and virtual machine (VM) variable configurations. An efficient task scheduling algorithm can potentially increase the efficiency of cloud computing, by choosing the appropriate virtual machine to do a specific task-scheduling problem, for dependent tasks on heterogeneous resources which is a well-known NP-Hard problem. To reduce minimum total execution time, makespan, in cloud computing many heuristics were presented in literature but lots of them suffer from low efficiency. In this article, a new task priority strategy and applying task duplication methods are proposed, for solving the task scheduling problem of the dependent tasks in heterogeneous cloud computing systems. The novelty of the current paper is to present a new list scheduling algorithm with a new task priority strategy and applying pertinent task duplication techniques. This paper uses optimistic cost table downward (OCTd) and optimistic cost table upward (OCTu) procedures to prioritize tasks in an efficient ordered list; then, it utilizes Heterogeneous Earliest Finish Time (HEFT)-duplication method for performing task duplication technique which significantly reduces makespan. To validate the proposal, we experimentally analyzed the proposed scheduling algorithm with different scientific workflows such as Molecular, LU-Like, FFT, and Montage datasets. The performance comparison of the novel heuristic scheduling algorithm against other existing approaches proved the superiority of proposed algorithm in terms of makespan, speedup, SLR, and efficiency which are prominent scheduling evaluation metrics.

A hybrid list-based task scheduling scheme for heterogeneous computing

Efficient task scheduling is required to attain high performance in both homogeneous and heterogeneous computing systems. An application can be considered as a task grid represented using a Directed Acyclic Graph (DAG). Solving such DAG representing a scheduling problem is an NP-complete task. The primary concern in this problem domain is to reduce the schedule length with minimum complexity. This work presents a Hybrid List-based Task Scheduling using Duplication Scheme (HLTSD) algorithm for heterogeneous processors. The proposed HLTSD algorithm has the same time complexity as that of the recent state-of-the-art algorithms. However, it produces a minimum cost schedule in comparison with other related methods. This work also presents a mathematical formulation to find task priorities. The processor selection phase is improved by utilizing the techniques, like entry task duplication, insertion-based policy, duplication of parent task on other levels, and balancing the load on each processor. The current proposal minimizes the overall makespan of execution by reasonable levels. Performance of the proposed algorithm is evaluated using DAGs adopted from various state-of-the-art algorithms, real-world problems, like Gaussian elimination (GE) and fast Fourier transformation (FFT) task graph and randomly generated graphs with diverse characteristics. The proposed scheme is compared with four state-of-the-art list-based scheduling algorithms, namely Heterogeneous Earliest Finish Time (HEFT), Predict Earliest Finish Time (PEFT), Heterogeneous Scheduling with Improved task Priority (HSIP), and Task Scheduling for Heterogeneous Computing Systems (TSHCS). Based on the best quality schedule, the obtained results suggest that HLTSD has better results in 87% cases.

An Evolutionary Computing-Based Efficient Hybrid Task Scheduling Approach for Heterogeneous Computing Environment

Task schedule optimization enables to attain high performance in both homogeneous and heterogeneous computing environments. The primary objective of task scheduling is to minimize the execution time of an application graph. However, this is an NP-complete (non-deterministic polynomial) undertaking. Additionally, task scheduling is a challenging problem due to the heterogeneity in the modern computing systems in terms of both computation and communication costs. An application can be considered as a task graph represented using Directed Acyclic Graphs (DAG). Due to the heterogeneous system, each task has different execution time on different processors. The primary concern in this problem domain is to reduce the schedule length with minimum complexity of the scheduling procedure. This work presents a couple of hybrid heuristics, based on a list and guided random search to address this concern. The proposed heuristic, i.e., Hybrid Heuristic and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HHG) uses Genetic Algorithm and a list-based approach. This work also presents another heuristic, namely, Hybrid Task Duplication, and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HTDG). The present work improves the quality of initial GA population by inducing two diverse guided chromosomes. The proposal is compared with four state-of-the-art methods, including two evolutionary algorithms for the same task, i.e., New Genetic Algorithm (NGA) and Enhanced Genetic Algorithm for Task Scheduling (EGA-TS), and two list-based algorithms, i.e., Heterogeneous Earliest Finish Time (HEFT), and Predict Earliest Finish Time (PEFT). Results show that the proposed solution performs better than its counterparts based on occurrences of the best result, average makespan, average schedule length ratio, average speedup, and the average running time. HTDG yields 89% better results and HHG demonstrates 56% better results in comparisons to the four state-of-the-art task scheduling algorithms.

Task Mapping and Scheduling for OpenVX Applications on Heterogeneous Multi/Many-Core Architectures

Computer vision applications have stringent performance constraints that must be satisfied when they are run at the edge on programmable low-power embedded devices. OpenVX has emerged as the de-facto reference standard to develop such applications. OpenVX uses a primitive-based programming model that results in a directed-acyclic graph (DAG) representation of the application, which can then be used for automatic system-level optimizations and synthesis to heterogeneous multi- and many-core platforms. Although OpenVX has been standardized, its state-of-the-art algorithm for task mapping and scheduling does not deliver the performance necessary for such applications to be deployed on heterogeneous multi-/many-core platforms. This article focuses on addressing this challenge with three main contributions: First, we implemented a static task scheduling and mapping approach for OpenVX using the heterogeneous earliest finish time (HEFT) heuristic. We show that HEFT allows us to improve the system performance up to 70 percent on one of the most widespread smart systems for applying computer vision and intelligent video analytics in general at the edge (i.e., NVIDIA VisionWorks on NVIDIA Jetson TX2). Second, we show that HEFT, in the context of a vision application for edge computing where some primitives may have multiple implementations (e.g., for CPU and GPU), can lead to load imbalance amongst heterogeneous computing elements (CEs), thus suffering from degraded performance. Third, we present an algorithm called exclusive earliest finish time (XEFT) that introduces the notion of exclusive overlap between single implementation primitives to improve the load balancing. We show that XEFT can further improve the system performance up to 33 percent over HEFT, and 82 percent over the native OpenVX scheduler. We present the results on a large set of benchmarks, including a real-world localization and mapping application (ORB-SLAM) combined with an NVIDIA inference application based on convolutional neural networks (CNNs) for object detection.