Multicriteria decision‐making techniques for avoiding similar task scheduling conflict in cloud computing

An efficient task scheduling approach shows promising way to achieve better resource utilization in cloud computing. Various task scheduling approaches with optimization and decision‐making techniques have been discussed up to now. These approaches ignored scheduling conflict among the similar tasks. The conflict often leads to miss the deadlines of the tasks. The work studies the implementation of the MCDM (multicriteria decision‐making) techniques in backfilling algorithm to execute deadline‐based tasks in cloud computing. In general, the tasks are selected as backfill tasks, whose role is to provide ideal resources to other tasks in the backfilling approach. The selection of the backfill task is challenging one, when there are similar tasks. It creates conflict in the scheduling. In cloud computing, the deadline‐based tasks have multiple parameters such as arrival time, number of VMs (virtual machines), start time, duration of execution, and deadline. In this work, we present the deadline‐based task scheduling algorithm as an MCDM problem and discuss the MCDM techniques: AHP (Analytical Hierarchy Process), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to avoid similar task scheduling conflicts. We simulate the backfilling algorithm along with three MCDM mechanisms to avoid scheduling conflicts among the similar tasks. The synthetic workloads are considered to study the performance of the proposed scheduling algorithm. The mechanism suggests an efficient VM allocation and its utilization for deadline‐based tasks in the cloud environment.     

Task scheduling mechanisms in cloud computing: A systematic review

Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.     

基于粒子群算法的嵌入式云计算资源调度

随着移动互联网的发展,基于嵌入式设备的云计算服务成为研究热点。在国内,嵌入式云计算目前正处于探索研究阶段,云资源管理调度是嵌入式云计算的核心技术之一,其效率直接影响嵌入式云计算系统的性能。为了提高云计算性能,本文提出一种基于粒子群优化算法的云计算任务调度模型。粒子群算法中粒子位置代表可行的资源调度方案,以云计算任务完成时间及资源负载均衡度作为目标函数,通过粒子群优化算法,找出最优资源调度方案。在matlab实验平台进行了仿真,通过大量数据模拟实验表明,该模型可以快速找到最优调度方案,提高资源利用率,具有较好的实用性和可行性。     

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA). Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well‐known NP‐hard problem. To resolve this problem, this paper proposes an energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization.     

基于混合遗传算法的云计算任务节能调度算法

云计算中主机和任务的数量都是十分庞大的,如何通过任务分配调度来减少成本开销和降低能耗是当前云计算和绿色计算领域研究的热点问题。根据云计算任务以及运行环境的特点,将云计算任务分配问题抽象为多维多背包求解问题,并采用改进的混合遗传算法对该问题进行求解。实验结果表明,改进的混合遗传算法能够在较短的时间内找到问题的优化解,并且根据该算法实现的任务分配策略能够有效地减少任务执行的成本开销和能耗。     

云计算环境下任务调度算法的研究

在云计算环境中存在庞大的任务数,为了能更加高效地完成任务请求,如何进行有效地任务调度是云计算环境下实现按需分配资源的关键。针对调度问题提出了一种基于蚁群优化的任务调度算法,该算法能适应云计算环境下的动态特性,且集成了蚁群算法在处理NP-Hard问题时的优点。该算法旨在减少任务调度完成时间。通过在CloudSim平台进行仿真实验,实验结果表明,改进后的算法能减少任务平均完成时间、并能在云计算环境下有效提高调度效率。     

基于改进混合遗传算法的云资源调度算法

在云计算中,系统规模和虚拟机迁移数量都是十分庞大的,需要高效的调度策略对其进行优化。将云计算的任务分配抽象为背包求解问题,可通过遗传算法进行求解。传统的遗传算法具有局部搜索能力差以及早熟现象的缺点,本文采用遗传和贪婪相结合的混合遗传算法。针对混合遗传算法在资源利用率与能源消耗的收敛速度较慢问题,本文通过改进适应度函数,改变了适应度函数在不同染色体间的差异度,从而提高了染色体在选择算子中的择优性能。仿真结果表明,该方法能够有效提高混合遗传算法在云计算资源优化中的收敛速度。     

云环境下任务调度算法对比研究

云计算是完全基于互联网的新兴技术。云计算环境中的任务调度问题一直都是该领域的研究热点。合理高效的任务调度算法在云环境中能有效的缩短任务完成时间,提高系统负载均衡,更好的满足用户与云提供商的需求。本文研究了云平台的任务调度机制,探究了任务调度过程中的关键性指标。通过云仿真平台CloudSim实现并分析了顺序调度算法、Min-Min算法和Max-Min算法,对比其在随机生成用户任务负载与虚拟机计算资源的情况下的任务完成时间,实验证明Min-Min算法与Max-Min算法均优于顺序调度算法。以此为未来研究提供实验支撑和方向。     

Multidimensional QoS cloud computing resource scheduling method based on stakeholder perspective

A multidimensional cloud computing architecture is designed and a multidimensional cloud resource scheduling model is constructed based on the stakeholder perspective of cloud users and cloud service providers to meet the high QoS requirements of cloud users (such as task execution time and task completion time) with low computing costs (such as energy consumption,economic costs and system availability).For the second-level cloud resource scheduling,an MQoS cloud resource scheduling algorithm based on multiple Greedy algorithm is proposed.The experimental results show that under the four cloud computing application scenarios with no aftereffects,the MQoS cloud resource scheduling algorithm has an overall increase of 206.42%~228.99% and 34.26%~56.93 in terms of multidimensional QoS degree compared with FIFO and M2EC algorithms.It has an average overall reduction of 0.48~0.49 and 0.20~0.27 in terms of cloud data center load balance difference.     

Research on optimal two element exchange algorithm for large scale cloud computing server scheduling problem

With the flourishing of cloud computing industry,the rational management and scientific scheduling of cloud computing servers has become an important issue.In terms of model,a new mixed integer programming (MIP) model with affinity constraints and anti-affinity constraints was proposed to describe the scheduling problem of large scale cloud computing server.Considering the time cost of solving large-scale MIP problems,an optimal two element exchange algorithm was designed with the basics of branch and bound method and local search algorithm.By constantly extracting MIP sub-problems from completing scheduling problems and using branch and bound method to solve the sub-problems,the algorithm continuously optimized the server scheduling schemes,so that the scheduling schemes approached the optimal solution.The experimental results show that the algorithm has great advantages over the other methods in testing data set ALISS,and can reduce the resource consumption of cloud computing center by more than 4% when the same task is completed.     

基于云计算平台的智慧城市管理系统设计

为了解决智慧城市管理过程中常出现资源调度速度过慢问题,设计了云计算平台的智慧城市管理系统。该系统采用云管理模块下监控各硬件设备,并构建云计算资源调度目标函数,利用文化粒子群算法对目标函数求解,得到云计算资源调度方案,最后测试结果表明,该系统能够实现智慧城市有效管理,并能实时监测城市情况,在实行资源调度时,任务完成时间较短且系统利用率较高,能够实现资源最大化利用。     

A Multi-objective Optimal Task Scheduling in Cloud Environment Using Cuckoo Particle Swarm Optimization

In cloud computing, varied demands are placed on the constantly changing resources. The task scheduling place very vital role in cloud computing environments, this scheduling process needs to schedule the tasks to virtual machine while reducing the makespan and cost. The task scheduling problem comes under NP hard category. Efficient scheduling method makes cloud computing services better and faster. In general, optimization algorithms are used to solve the scheduling issues in cloud. So, in this paper we combined two optimization algorithms namely called as Cuckoo Search (CS) and Particle Swarm Optimization (PSO).The new proposed hybrid algorithm is called as, CS and particle swarm optimization (CPSO). Our main purpose of the proposed paper is to reduce the makespan, cost and deadline violation rate. The performance of the proposed CPSO algorithm is evaluated using cloudsim toolkit. From the simulation results our proposed works minimize the makespan, cost, deadline violation rate, when compared to PBACO, ACO, MIN–MIN, and FCFS.

     

云计算中基于Shapley值改进遗传算法的虚拟机调度模型

云计算系统具有服务器规模大、用户范围广的特点,但同时也消耗了大量的能源,导致云供应商的高运营成本和高碳排放等问题。云计算高度虚拟化,如何分配和管理其虚拟资源,从而保证高效的物理资源利用和能耗控制,是一个多参数博弈过程,同时也是该领域的一个研究热点。提出了一种虚拟机调度模型及基于Shapley 值的遗传算法（SV-GA）,可通过经济学概念Shapley 值计算出参与工作的物理机贡献值,并通过该贡献值修正遗传算法中变异步骤的概率参数,从而完成虚拟机调度的任务。实验结果表明,与Max-Min、LrMmt及DE算法相比,SV-GA在虚拟机调度过程中的迁移时间、次数、SLA违背率、能耗等多参数博弈中具有优异的表现。     

云计算环境下能量感知的任务调度算法

云计算环境下传统独立任务调度算法容易导致较高资源能耗或较大任务时间跨度.针对该问题,文中提出了两种能量感知的任务调度算法,并利用遗传算法并行化搜索合理调度方案.两种算法在搜索过程中,分别通过能耗时间归一和能耗时间双适应度方法定义适应度函数并进行个体选择.仿真结果表明,与单独考虑时间或能耗相比,这两种算法能够更有效地缩短任务执行时间跨度,降低资源能耗.     

K均值聚类在云计算的应用

相对于传统的应用部署方式,云计算是基于互联网的一种并行处理技术,提供了一个高度可扩展和按需处理的服务。任务调度一直是云计算环境中的研究热点,在云计算环境中具有重要作用。能否合理分配任务到虚拟机资源上是重要问题之一。本文通过对任务请求的资源进行分析,对不同类型的任务进行聚类,将不同类型任务通过改进贪心调度算法合理分配到虚拟机资源上。通过Cloudsim平台模拟实验表明,该算法相对于Min-Min算法在节省能耗方面有较好的效果。     

Combining Task Scheduling in Power Adaptive Dynamic Reconfigurable System

Supplying the electronic equipment by exploiting ambient energy sources is a hot spot. In order to achieve the match between power supply and demands under the variance of environments at real time, a reconfigurable technique is taken. In this paper, a dynamic power consumption model by using a lookup table as a unit is proposed. Then, we establish a system-level task scheduling model according to the task type. Based on single instruction multiple data (SIMD) architecture which contains a processing system and a control system with a Nios Ⅱ processor, a practical dynamic reconfigurable system is built. The approach is evaluated on a hardware platform. The test results show that the system can automatically adjust the power consumption in case of external energy input changing. The utilization of the system dynamic power of their portion is from 80.05% to 91.75% during the first task assignment. During the entire processing cycle, the total energy efficiency is 97.67%.     

Phased Scheduling for Resource-Constrained Mobile Devices in Mobile Cloud Computing

Mobile cloud computing combines wireless access service and cloud computing to improve the performance of mobile applications. Mobile cloud computing can balance the application distribution between the mobile device and the cloud, in order to achieve faster interactions, battery savings and better resource utilization. To support mobile cloud computing, the paper proposes a phased scheduling model of mobile cloud such that mobile device’s users experience lower interaction times and extended battery life. The phased scheduling optimization is solved by two subproblems: mobile device’s batch application optimization and mobile device’s job level optimization. At the first stage, the mobile cloud global scheduling optimization implements the allocation of the cloud resources to the mobile device’s batch applications. At the second stage, mobile device’s job level optimization adjusts the cloud resource usages to optimize the utility of single mobile device’s application. In the simulations, compared with other algorithm, our proposed mobile cloud phased scheduling algorithms achieve the better performance with acceptable overhead.     

EA-DFPSO: An intelligent energy-efficient scheduling algorithm for mobile edge networks

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals. Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays. Compute nodes are usually distributed in edge environments, enabling crucially efficient task scheduling among those nodes to achieve reduced processing time. Moreover, it is imperative to conserve edge server energy, enhancing their lifetimes. To this end, this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization (EA-DFPSO) that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time. The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks. Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.     

A non‐cooperative game model for reliability‐based task scheduling in cloud computing

Cloud computing is a newly emerging distributed system. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non‐cooperative game model for reliability‐based task scheduling approach. This model takes the steady‐state availability that computing nodes provide as the target, takes the task slicing strategy of the schedulers as the game strategy, then finds the Nash equilibrium solution. We also design a task scheduling algorithm based on this model. It can be seen from the experiments that our task scheduling algorithm is better than the so‐called balanced scheduling algorithm.     

P2BED-C: A Novel Peer to Peer Load Balancing and Energy Efficient Technique for Data-Centers Over Cloud

In recent days, cloud computing data centres are considerably involved in performing operations. It accounts for the enormous energy consumption, which increases with an increase in computing capacity. Thinking with respect for the environment, reducing operating costs and energy consumption can prove to be beneficial. Previous works in data-centre energy optimization only involved scheduling the job between the servers based on thermal profiles or workload parameters. Dynamic power management by shutting down the free accessories of data centres was also considered in many models to reduce energy consumption. Further, the role of the communication fabric focused on energy consumption. The proposed work focuses on the minimization of energy consumption at both computing servers and communicating devices. Here, a parameter is defined named config to initialize the configuration of a system in a current state. The parameter will assist the existing Dynamic Voltage Frequency Scheduling (DVFS) scheme for assigning the tasks to a virtual machine to minimize energy consumption at computing servers. Moreover, it extends the Data-centre Energy-efficient Network-aware Scheduling (DENS) with the peer-to-peer load balancer to reduce energy consumption from networking components. The proposed system uses a scheduling algorithm for the cloud data centre, which reduces the energy consumption both at the server and the communication fabric level. Based on the number of samples for the energy consumption, 95% confidence achieve. Energy consumed by the proposed P2BED-C model is 1610.22 Wxh, while other existing approaches FCFS and Round Robin consumed 1684.32 and 1678.35, respectively. The results show considerable improvement in the power utilization of the server resulting in more power savings.