An optimal scheduling algorithm based on task duplication

When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O(v2), where v represents the number of tasks.

Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

Well organized datacentres with interconnected servers constitute the cloud computing infrastructure.User requests are submitted through an interface to these servers that provide service to them in an on-demand basis.The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category.Task scheduling in cloud poses numerous challenges impacting the cloud performance.If not handled properly,user satisfaction becomes questionable.More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment.The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution.An improvised seagull optimization algorithm which combines the features of the Cuckoo search(CS)and seagull optimization algorithm(SOA)had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment.The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment.Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization(MO-ACO),ACO and Min-Min algorithms.The proposed SOA-CS technique had produced an improvement of 1.06%,4.2%,and 2.4%for makespan and had reduced the overall cost to the extent of 1.74%,3.93%and 2.77%when compared with PSO,ACO,IDEA algorithms respectively when 300 vms are considered.The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.

Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing

The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such problems.In this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task sequence.Firstly,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic strategies.Secondly,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities.Besides,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process.Compared with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity.

Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.

Task scheduling for multi-electro-magnetic detection satellite with a combined algorithm

Task scheduling for electro-magnetic detection satellite is a typical combinatorial optimization problem. The count of constraints that need to be taken into account is of large scale. An algorithm combined integer programming with constraint programming is presented. This algorithm is deployed in this problem through two steps. The first step is to decompose the original problem into master and sub-problem using the logic-based Benders decomposition; then a circus combines master and sub-problem solving process together, and the connection between them is general Benders cut. This hybrid algorithm is tested by a set of derived experiments. The result is compared with corresponding outcomes generated by the strength Pareto evolutionary algorithm and the pure constraint programming solver GECODE, which is an open source software. These tests and comparisons yield promising effect.

Task Scheduling Optimization in Cloud Computing Based on Genetic Algorithms

Task scheduling is the main problem in cloud computing that reduces system performance;it is an important way to arrange user needs and perform multiple goals.Cloud computing is the most popular technology nowadays and has many research potential in various areas like resource allocation,task scheduling,security,privacy,etc.To improve system performance,an efficient task-scheduling algorithm is required.Existing task-scheduling algorithms focus on task-resource requirements,CPU memory,execution time,and execution cost.In this paper,a task scheduling algorithm based on a Genetic Algorithm(GA)has been presented for assigning and executing different tasks.The proposed algorithm aims to minimize both the completion time and execution cost of tasks and maximize resource utilization.We evaluate our algorithm’s performance by applying it to two examples with a different number of tasks and processors.The first example contains ten tasks and four processors;the computation costs are generated randomly.The last example has eight processors,and the number of tasks ranges from twenty to seventy;the computation cost of each task on different processors is generated randomly.The achieved results show that the proposed approach significantly succeeded in finding the optimal solutions for the three objectives;completion time,execution cost,and resource utilization.

A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud(DGC)systems for low response time and high cost-effectiveness in recent years.Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption.Many factors in DGCs,e.g.,prices of power grid,and the amount of green energy express strong spatial variations.The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations.This work adopts a G/G/1 queuing system to analyze the performance of servers in DGCs.Based on it,a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm(SBA)to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs,and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications.Realistic databased experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

AI(Artificial Intelligence)workloads are proliferating in modernreal-time systems.As the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be reexamined.In particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline constraints.To cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two observations.First,resource planning for AI workloadsis a complicated search problem that requires much time for optimization.Second,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in advance.Based on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of resources.Instead of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of tasks.Thus,in any case,the workload isimmediately executed according to the resource plan maintained.Specifically,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload changes.The proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its effectiveness.Simulationexperiments show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

基于多种群遗传算法的航天复杂系统测试任务调度

针对航天复杂系统型号较多,传统测试流程与调度设计只能人工定制化排布,效率较低且未有效优化,同时,考虑到航天复杂系统快速测试的迫切需求,提出一种基于多目标遗传算法的航天测试流程自动生成方法。该方法在测试项集合明确的前提下,将测试项抽象为离散事件,以测试总时间和测试资源均衡度为优化目标,充分考虑航天器测试的诸多约束,将其作为遗传算法执行过程中交叉或变异的禁忌项。在初始种群确定后,对测试流程和调度方案进行自动生成和优化。对算例的仿真结果表明,该方法相对于同实验条件下的传统半串行测试方法和单目标优化方法,测试总时间或资源均衡度得到了较大提升。在进一步扩展优化目标和约束项后,该方法可有效提高航天复杂系统测试过程的快速响应能力和可靠性。

面向可持续生产中多任务调度的双重增强模因算法

从经济、环境和社会3个维度,全面提升生产调度方案的可持续性具有重要意义.针对并行机生产场景,建立考虑机器指派、加工顺序、人员安排以及开关机控制等4种决策任务的调度模型.为实现对复杂决策空间的高效寻优,提出一种融合两种局部优化策略的双重增强模因算法(Dual-enhanced memetic algorithm, DMA)求解模型.从随机更新角度,针对不同决策任务,构造单步变邻域搜索(One-step variable neighborhood search, 1S-VNS)策略.从定向优化角度,分析目标和关键任务之间的匹配关系,提出一种可持续目标导向策略(Sustainable goals-oriented strategy, SGS).考虑到两种优化策略的不同特点,单步变邻域搜索策略作用于整个种群,目标导向策略强化种群中的精英个体,实现对输出解集的双重优化.仿真实验结果表明,双重优化策略能有效地增强算法性能,并且所提算法在非支配解的多样性和收敛性上具有优越性.

基于改进型哈里斯鹰算法的云制造服务组合优化方法研究

云制造环境下,云平台以制造服务组合的形式为个性化需求提供按需服务,可显著提升订单响应速度与提高资源利用率。采用哈里斯鹰算法构建制造服务组合,针对制造服务间物流转运问题,建立独特的编码与解码机制。为解决该算法存在早熟收敛问题,引入Logistic一维混沌系统,设计非线性逃逸能量更新机制和3种邻域搜索策略。通过实验验证了改进后的哈里斯鹰算法在解决云制造服务组合优化问题时具有显著优越性。

基于融合任务规则优先级蚁群算法的多AGV路径规划实现研究

蚁群算法所具备的合作搜索能力被广泛用于寻找单台AGV最短路径,却不适用解决现实情况中多台AGV同时使用的问题,为此提出了融合任务规则优先级的蚁群算法实现多AGV路径规划,用于解决现实问题中多台AGV同时使用而且存在多种碰撞冲突的情形。通过将AGV运行的路径环境进行建模等针对性措施,把蚁群算法引入到AGV路径规划的现实问题中,然后考虑多AGV路径规划可能存在的不同碰撞冲突类型,并考虑不同AGV拥有不同的任务优先级的现实情况,提出了避免AGV碰撞的策略,形成了基于融合任务规则优先级蚁群算法的多AGV路径规划算法。通过仿真实验结果,证实所提出的算法可以避免多台AGV之间的路径冲突,同时利用了蚁群算法寻求最优路径的能力,改进后的蚁群算法能够用于多AGV路径规划的实际场景中。

基于改进磷虾群算法的风-光-水-火联合系统优化调度

为了提高可再生能源的利用率,减少弃风弃光量,提出了一种基于改进磷虾群算法的风-光-水-火联合系统优化调度方法。以综合成本最小为目标函数,综合考虑各种约束条件,建立了风-光-水-火联合系统优化调度模型。利用Logistic混沌映射、余弦控制因子和柯西变异等3种策略对磷虾群算法进行改进,提高了其全局搜索性能。采用改进磷虾群算法对风-光-水-火联合系统优化调度模型进行求解。通过算例证明,改进磷虾群算法的迭代次数、收敛时间和求解精度均优于磷虾群算法和PSO算法。

Load balancing in cloud environs:Optimal task scheduling via hybrid algorithm

In cloud computing,a lot of challenges like the server failures,loss of confidentiality,improper workloads,etc.are still bounding the efficiency of cloud systems in real-world scenarios.For this reason,many research works are being performed to overcome the shortcoming of existing systems.Among them,load balancing seems to be the most critical issue that worsen the performance of the cloud sector,and hence there necessitates the optimal load balancing with optimal task scheduling.With the intention of accomplishing optimal load balancing by effectual task deployment,this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms.Usually,handling of tasks in cloud system is an NP-hard problem and moreover,nonpreemptive independent tasks are crucial in cloud computing.This paper goes with the introduction of a new optimal load balancing model by considering three major objectives:minimum makespan,priority,and load balancing,respectively.Moreover,a new single-objective function is also defined that incorporates all the three objectives mentioned above.Furthermore,the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insistedWOA(FM-WOA)is introduced.This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm(WOA)and Firefly(FF)algorithm.Finally,the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan,task completion(priority),and degree of imbalance as well.

基于多阶段的混合云多目标任务调度策略研究

为了解决混合云环境中任务调度问题,本文研究了多阶段任务调度策略以应对混合云架构灵活的特点和用户多样化的服务需求。首先,设计了数据感知资源供应算法,结合任务量和私有云性能判断是否对可用资源池进行成本最小化的动态扩展,保证任务能够在截止时间内顺利完成。其次,为了满足用户多样化的需求,考虑任务完成时间、数据安全性和负载均衡构建了混合云多目标任务调度模型。最后,为了更好地求解模型,提出了改进的NSGA-II算法,该算法通过结合收敛性指标和拥挤度距离进行环境选择和匹配选择,使得算法在非支配解较多的时候仍然能够取得优秀结果。通过仿真模拟,提出的调度算法不仅能花费最小公有云租用成本,还能在保证任务截止时间完成任务的基础上,对数据安全和负载均衡产生显著优化效果,对比初始调度方案,其性能提升了98.2%。

基于粒子群的工业大数据雾计算多目标优化任务调度算法

针对工业生产中基于软件定义网络的雾计算架构大数据处理场景中的独立任务调度问题,提出了基于粒子群的多目标优化任务调度算法。该算法同时考虑优化任务的完成时间与异构雾集群的负载均衡值两个指标,采用一种非线性递减的方法更新惯性权重值,解决基本粒子群算法后期权值较大导致局部收敛能力较弱的问题。通过MATLAB仿真验证了所提算法的有效性。试验结果表明,在前期获得更大搜索空间的同时该算法提高了后期的收敛速率。

融合局部搜索与Pareto支配的多目标任务调度模型

为了解决复杂任务群调度过程中资源利用不均、任务完成时间较长等问题,以最小化资源负载均方差和最小化任务群完成时间为目标构建复杂任务群资源调度模型,提出一种融合局部搜索和Pareto支配的多目标优化算法BRLSN(multi-objective optimization based on boundary range local search and NSGA-Ⅱ,BRLSN)。该算法采用有效的编码方式与交叉变异算子进行迭代寻优,并利用基于边界区域局部搜索的精英保留策略扩大算法搜索范围,保存种群优良个体。实验结果表明,BRLSN相较于其他多目标算法在收敛性和多样性上有显著的提升,同时算法收敛速度更快,种群质量更高,明显优化了最终目标函数的结果值。

改进的粒子群优化算法在云计算任务调度中的应用

针对粒子群优化算法在求解云计算任务调度问题中存在的收敛速度慢、精度低、易陷入局部极值等缺陷,综合考虑最大完成时间最少、任务执行总时间最优两个优化目标,提出一种多策略融合的粒子群优化(multi-strategy particle swarm optimization,MSPSO)算法,并将其应用于求解云计算任务调度问题。该算法融合模拟退火算法、饥饿游戏搜索和双重变异限制策略。首先,通过模拟退火算法动态更新惯性权重,平衡粒子群优化算法的全局搜索和局部搜索,帮助粒子跳出局部极值。其次,引入饥饿游戏搜索算法优化粒子位置更新策略,在算法后期加快粒子收敛速度,提高结果精度。最后,采用双重变异限制策略,同时限制粒子速度和位置,避免粒子发生越界。与其他3种粒子群优化算法进行对比实验,在适应度平均值、最小值、标准差3个方面,MSPSO都有更好的表现。通过仿真,在求解不同任务量的云计算任务调度问题中,MSPSO在总成本、适应度值最小化两方面均表现出明显优势。尤其当任务量为40时,MSPSO总成本比其他算法分别降低了14.4%、15.3%、11.2%,适应度值分别降低了10.5%、10.6%、7.6%,验证了所提算法在求解云计算任务调度问题中的有效性。