Heterogeneous Earliest Finish Time Algorithm Research Articles

Task scheduling using glowworm-based optimal heterogeneous earliest finish time algorithm for mobile grid

Task scheduling using glowworm-based optimal heterogeneous earliest finish time algorithm for mobile grid

QoS-aware resource allocation method for the internet of things using triplet and heterogeneous earliest finish time algorithms

QoS-aware resource allocation method for the internet of things using triplet and heterogeneous earliest finish time algorithms

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. 

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.

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.

Time Optimization in Cloud Computing with the Heterogeneous Earliest Finish Time Algorithm

Time Optimization in Cloud Computing with the Heterogeneous Earliest Finish Time Algorithm

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%.

An improved list-based task scheduling algorithm for fog computing environment

A high-performance execution of programs predominately depends on the efficient scheduling of tasks. An application consists of a sequence of tasks that can be represented as a directed acyclic graph (DAG). The tasks in the DAG have precedence constraints between them and each task has a different timeline on different processors. In this paper, a new list-based scheduling algorithm is proposed which schedules the tasks which are represented as a DAG structure. The main focus of this algorithm is to schedule the tasks to the suitable processing node in fog environment as the fog nodes have limited processing capacity. The assignment of tasks on the fog node should consider both the computation cost of the node and the execution finishing time of the node. The proposed algorithm has three phases. (1) the level sorting phase, where the independent tasks are identified (2) in the Task prioritization phase the proposed algorithm assigns priority to the task which has more successors so that more tasks in the next level can start their execution and (3) in the task selection phase a balanced combination of local optimal and global optimal approach is considered to assign a task to a suitable processor which further enhances the processor selection phase results in minimizing both the makespan and overall computation cost of the processors. Extensive experiments are carried out using randomly generated graphs and graphs from the real-world to analyze the performance of the proposed algorithm. The results show that the proposed algorithm outperforms all other well-known algorithms like predict earliest finish time, heterogeneous earliest finish time algorithm, minimal optimistic processing time, and SDBBATS in terms of performance matrices like average scheduling length ratio, speedup, and makespan.

DRHEFT: Deadline-Constrained Reliability-Aware HEFT Algorithm for Real-Time Heterogeneous MPSoC Systems

Heterogeneous multiprocessor system-on-chips (MPSoCs) are suitable platforms for real-time embedded applications that require powerful parallel processing capability as well as low power consumption. For such applications, soft-error reliability (SER) due to transient faults and lifetime reliability (LTR) due to permanent faults are both key concerns. There have been several efforts in the literature oriented toward related reliability problems. However, most existing techniques only concentrate on improving one of the two reliability metrics, which are not suitable for embedded systems deployed in critical applications in need of a long lifetime as well as a reliable execution. This article develops a novel heterogeneous earliest-finish-time (HEFT)-based algorithm to maximize SER and LTR simultaneously under the real-time constraint for dependent tasks executing on heterogeneous MPSoC systems. More specifically, a new deadline-constrained reliability-aware HEFT algorithm, namely DRHEFT, is proposed, which seeks for the best SER–LTR tradeoff solutions through using fuzzy dominance to evaluate the relative fitness values of candidate solutions. The extensive experiments on real-life benchmarks as well as synthetic applications demonstrate that DRHEFT is capable of achieving better SER–LTR tradeoff solutions with higher hypervolume and less computation cost when compared with the state-of-the-art approaches.

An Adaptive Scheduling Algorithm for Dynamic Jobs for Dealing with the Flexible Job Shop Scheduling Problem

Modern manufacturing systems build on an effective scheduling scheme that makes full use of the system resource to increase the production, in which an important aspect is how to minimize the makespan for a certain production task (i.e., the time that elapses from the start of work to the end) in order to achieve the economic profit. This can be a difficult problem, especially when the production flow is complicated and production tasks may suddenly change. As a consequence, exact approaches are not able to schedule the production in a short time. In this paper, an adaptive scheduling algorithm is proposed to address the makespan minimization in the dynamic job shop scheduling problem. Instead of a linear order, the directed acyclic graph is used to represent the complex precedence constraints among operations in jobs. Inspired by the heterogeneous earliest finish time (HEFT) algorithm, the adaptive scheduling algorithm can make some fast adaptations on the fly to accommodate new jobs which continuously arrive in a manufacturing system. The performance of the proposed adaptive HEFT algorithm is compared with other state-of-the-art algorithms and further heuristic methods for minimizing the makespan. Extensive experimental results demonstrate the high efficiency of the proposed approach.

Dynamic Variant Rank HEFT Task Scheduling Algorithm Toward Exascle Computing

The accomplishment of Exascale systems in the next few years will pose the need for deep hierarchical parallelism and a high degree of heterogeneity, in addition to the expected component failures. Moreover, it will be constrained by energy limitation and power consumption. As a result, Exascale scheduling algorithms need to be robust in balancing the workload consistently, reliably and efficiently amongst the processors. It is also required to achieve the lowest overhead complexity, and, at the same time, decrease the communication costs as low as possible. In this paper, a task scheduler framework is introduced. The framework consists of three parts, the first of which is the resource monitor, the second the task scheduler and the third the dispatcher. The resource monitor explores the resources in the system dynamically, collects the computing resources metadata, and updates the metadata in a continuous manner. The task-scheduler schedules the tasks based on an improved version of the Heterogeneous Earliest Finish Time (HEFT) heuristic, a directed acyclic graph (DAG) scheduling algorithm. The dispatcher receives tasks list from scheduler to allocate tasks on a resources based on HEFT insertion method and constrained. The HEFT algorithm is compatible efficiently for heterogeneous systems and it improves operations without increasing the time complexity. In this paper, our improvement for HEFT algorithm is introduced. The improved algorithm is based on two steps: a static step which uses various schemes to prioritize the nodes (tasks) and a dynamic one. The dynamic step has the ability to allocates tasks that are scheduled statically and is able to receive and allocate new tasks dynamically. The algorithm we named: Dynamic Variant Rank HEFT algorithm (DVR HEFT), is implemented. A Random job generator is implemented to conduct a comparison study to evaluate DVR-HEFT algorithm against some of the state-of-the-art algorithms, specifically HEFT, PEFT, and Lookahead. Our results indicate that DVR-HEFT is able to reduce the scheduler’s makespan in HEFT algorithm without increasing the algorithm’s time complicity.

Modified HEFT Algorithm for Task Scheduling in Cloud Environment

Cloud computing is now dominated in the area of high performance distributing computing and it provides resource polling and on demand services through internet. Therefore task scheduling becomes an important research area in the field of cloud environment because user’s services demand change dynamically. Heterogeneous Earliest Finish Time (HEFT) unable to distribute the task efficiently. We modify HEFT algorithm that distribute the workload among the processor in effective way and reduce the makespan time of applications. Computational results (Fig. 4-5) shows that modify HEFT algorithm perform better than existing HEFT, Heterogeneous Earliest Finish Time (CPOP) algorithm.

Optimal Task Scheduling for Distributed Cluster With Active Storage Devices and Accelerated Nodes

With advancements in compute-intensive and memory-bound applications, the need for faster and more energy-efficient processing platforms continues. In support of these advancements, heterogeneous platforms have been proposed to enhance the performance and efficiency in the cloud. These platforms include field programmable gate arrays and graphical processing units in addition to general-purpose processors. Furthermore, there is a strong interest in advancing active solid-state drives to support both storage and computation. In this paper, we present a generic formulation to support the modeling of such a heterogeneous cloud environment, without being specific to a particular cloud platform such as Spark or Hadoop. We represent the cloud as a collection of clients, middleware control nodes, and high performance compute nodes (HPN), where the HPNs represent the options of advanced compute technologies in a heterogeneous cloud. The objective of the paper is to present a simple and efficient formulation for scheduling applications in such a heterogeneous cloud. Consistent with recent software modeling of artificial intelligence applications, we propose to map applications into directed acyclic graph representations of tasks. The optimization problem is then formulated to infer the best scheduling of tasks on the HPNs, while minimizing the overall execution time and data communication delays between nodes. Unlike existing scheduling algorithms that assume equal performance across nodes, our formulation explicitly takes into account the different compute capabilities of the heterogeneous nodes. The resulting task scheduling is then evaluated to provide insights into the performance gains with the proposed advanced heterogeneous cloud computing environment. The results show improved performance when comparing the proposed task-scheduling algorithm with the genetic algorithm and heterogeneous earliest finish time algorithms. We also show the performance gains achieved with the optimal task scheduling on a heterogeneous cloud system as compared with a conventional CPU-only cloud system.

Applying intelligent data traffic adaptation to high-performance multiple big data analytics platforms

This paper introduces the integration of two data processing platforms, RHhadoop and SparkR, to carry out rapid big data retrieval and analytics using R programming, which can serve as part of business intelligence. Besides, it has developed the job scheduling optimization called Memory-Sensitive Heterogeneous Earliest Finish Time algorithm to enhance system throughput. However, the bottleneck of system throughput is definitely relevant to data traffic problem over network, especially a large amount of data exchange between distributed computing nodes within a cluster. The objective of this paper is to propose an intelligence approach to tackle the crucial problems of inefficient data traffic flow. Adaptive network-based fuzzy inference system along with particle swarm optimization has employed to tune the network-related parameters at computing nodes for improving network QoS and speed up data transportation significantly. In order to examine the computing efficiency, performance index has been evaluated for all of treatments in the experiment.

DAG Scheduling in Heterogeneous Computing and Grid Environments Using Variable Neighborhood Search Algorithm

ABSTRACTDAG scheduling is a process that plans and supervises the execution of interdependent tasks on heterogeneous computing resources. Efficient task scheduling is one of the important factors to improve the performance of heterogeneous computing systems. In this paper, an investigation on implementing Variable Neighborhood Search (VNS) algorithm for scheduling dependent jobs on heterogeneous computing and grid environments is carried out. Hybrid Two PHase VNS (HTPHVNS) DAG scheduling algorithm has been proposed. The performance of the VNS and HTPHVNS algorithm has been evaluated with Genetic Algorithm and Heterogeneous Earliest Finish Time algorithm. Simulation results show that VNS and HTPHVNS algorithm generally perform better than other meta-heuristics methods.

A list scheduling algorithm for heterogeneous systems based on a critical node cost table and pessimistic cost table

SummaryThis paper presents a novel list‐based scheduling algorithm called Improved Predict Earliest Finish Time for static task scheduling in a heterogeneous computing environment. The algorithm calculates the task priority with a pessimistic cost table, implements the feature prediction with a critical node cost table, and assigns the best processor for the node that has at least 1 immediate successor as the critical node, thereby effectively reducing the schedule makespan without increasing the algorithm time complexity. Experiments regarding aspects of randomly generated graphs and real‐world application graphs are performed, and comparisons are made based on the scheduling length ratio, robustness, and frequency of the best result. The results demonstrate that the Improved Predict Earliest Finish Time algorithm outperforms the Predict Earliest Finish Time and Heterogeneous Earliest Finish Time algorithms in terms of the schedule length ratio, frequency of the best result, and robustness while maintaining the same time complexity.

Resource preprocessing and optimal task scheduling in cloud computing environments

SummaryCloud computing came into being and is currently an essential infrastructure of many commerce facilities. To achieve the promising potentials of cloud computing, effective and efficient scheduling algorithms are fundamentally important. However, conventional scheduling methodology encounters a number of challenges. During the tasks scheduling in cloud systems, how to make full use of resources and how to effectively select resources are also important factors. At the same time, communication delay also plays an important role in cloud scheduling, which not only leads to waiting between tasks but also results in much idle interval time between processing units. In this paper, a fuzzy clustering method is used to effectively preprocess the cloud resources. Combining the list scheduling with the task duplication scheduling scheme, a new directed acyclic graph based scheduling algorithm called earliest finish time duplication algorithm for heterogeneous cloud systems is presented. Earliest finish time duplication attempts to insert suitable immediate parent nodes of the current selected node in order to reduce its waiting time on the processor. The case study and experimental results illustrate that the algorithm proposed in this paper is better than the popular heterogeneous earliest finish time algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

Workflow task scheduling algorithm based on resource clustering in cloud computing environment

Focusing on the characteristics of resource under large-scale,heterogeneous and dynamic environment in cloud computing,a workflow task scheduling algorithm based on resource fuzzy clustering was proposed.After quantizing and normalizing the resource characteristics,this algorithm integrated the theory of clustering to divide the resources based on the workflow task model and the resource model constructed in advance.The cluster with better synthetic performance was chosen firstly in scheduling stage.Therefore,it shortened the matching time between the task and the resource,and improved the scheduling performance.By comparing this algorithm with HEFT(Heterogeneous Earliest Finish Time) and DLS(Dynamic Level Scheduling),the experimental results show that the average SLR(Schedule Length Ratio) of this algorithm was smaller than that of HEFT by 3.4%,the DLS by 9.9%,and the average speedup of this algorithm was faster than that of HEFT by 5.9%,the DLS by 10.2% with the increase of tasks in a certain range of [0,100];when the resources were increased in a certain range of [0,100],the average SLR of this algorithm was smaller than that of HEFT by 3.6%,the DLS by 9.7%,and the average speedup of this algorithm was faster than that of HEFT by 4.5%,the DLS by 10.8%.The results indicate that the proposed algorithm realizes the reasonable division of resources,and it surpasses HEFT and DLS algorithms in makespan.

Dynamic task scheduling algorithm with load balancing for heterogeneous computing system

In parallel computation, the scheduling and mapping tasks is considered the most critical problem which needs High Performance Computing (HPC) to solve it by breaking the problem into subtasks and working on those subtasks at the same time. The application sub tasks are assigned to underline machines and ordered for execution according to its proceeding to grantee efficient use of available resources such as minimize execution time and satisfy load balance between processors of the underline machine. The underline infrastructure may be homogeneous or heterogeneous. Homogeneous infrastructure could use the same machines power and performance. While heterogeneous infrastructure include machines differ in its performance, speed, and interconnection. According to work in this paper a new dynamic task scheduling algorithm for Heterogeneous called a Clustering Based HEFT with Duplication (CBHD) have been developed. The CBHD algorithm is considered an amalgamation between the most two important task scheduling in Heterogeneous machine, The Heterogeneous Earliest Finish Time (HEFT) and the Triplet Clustering algorithms. In the CBHD algorithm the duplication is required to improve the performance of algorithm. A comparative study among the developed CBHD, the HEFT, and the Triplet Cluster algorithms has been done. According to the comparative results, it is found that the developed CBHD algorithm satisfies better execution time than both HEFT algorithm and Triplet Cluster algorithm, and in the same time, it achieves the load balancing which considered one of the main performance factors in the dynamic environment.