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.     

Time-Cost Efficient Scheduling Algorithms for Executing Workflow in Infrastructure as a Service Clouds

Cloud Computing enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. The cloud infrastructures can be used as an appropriate environment for executing of workflow applications. To execute workflow applications in this environment, it is necessary to develop the workflow scheduling algorithms that consider different QoS parameters such as execution time and cost. Therefore, in this paper we focus on two criteria: total completion time (makespan) and execution cost of workflow, and propose two heuristic algorithms: MTDC (Minimum Time and Decreased Cost) which aims to create a schedule that minimizes the makespan and decreases execution cost, and CTDC (Constrained Time and Decreased Cost) which is based on the first algorithm (MTDC) and aims to create a schedule that decreases the execution cost while satisfying the deadline constraint of the workflow application. The proposed algorithms are evaluated by a simulation process using WorkflowSim. To evaluate the proposed algorithms, the results of MTDC are compared with the results of HEFT (Heterogeneous Earliest Finish Time), and the results of CTDC are compared with the results of heuristic based algorithms [such as IC-PCP (IaaS Cloud Partial Critical Paths), IC-PCPD2 (Deadline Distribution) and BDHEFT (Budget and Deadline HEFT)] and meta-heuristic based algorithms [such as PSO (Particle Swarm Optimization) and CGA2 (Coevolutionary Genetic Algorithm with Adaptive penalty function)]. The results show that the proposed algorithms perform better than the mentioned algorithms in most cases.     

Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

     

A fault‐tolerant workflow management system with Quality‐of‐Service‐aware scheduling for scientific workflows in cloud computing

Cloud computing provides solutions to many scientific and business applications. Large‐scale scientific applications, which are structured as scientific workflows, are evaluated through cloud computing. In this paper, we proposed a Quality‐of‐Service‐aware fault‐tolerant workflow management system (QFWMS) for scientific workflows in cloud computing. We have considered two real‐time scientific workflows, i.e., Montage and CyberShake, for an evaluation of the proposed QFWMS. The results of the proposed QFWMS scheduling were evaluated through simulation environment WorkflowSim and compared with three well‐known heuristic scheduling policies: (a) minimum completion time (MCT), (b) Maximum‐minimum (Max‐min), and (c) Minimum‐minimum (Min‐min). By considering Montage scientific workflow, the proposed QFWMS reduces the make‐span 8.86%, 8.94%, and 5.53% compared with existing three heuristic policies. Similarly, the proposed QFWMS reduces the cost 6.19%, 3.52%, and 3.60% compared with existing three heuristic policies. Likewise, by considering CyberShake scientific workflow, the proposed QFWMS reduces the make‐span 19.54%, 21.41%, and 25.71% compared with existing three heuristic policies. Similarly, the proposed QFWMS reduces the cost 8.78%, 8.40%, and 8.61% compared with existing three heuristic policies. More so, for QFWMS, SLA is neither violated for time constraints nor for cost constraints. While for MCT, Max‐min and Min‐min scheduling policies, SLA is violated 32, 37, and 23 times, respectively. Conclusively, the proposed QFWMS scheduling and management system is one of the significant workflow management systems for execution and management of scientific workflows in cloud computing.     

Scheduling strategy for science workflow with deadline constraint on multi-cloud

In view of the deadline-constrained scientific workflow scheduling on multi-cloud,an adaptive discrete particle swarm optimization with genetic algorithm (ADPSOGA) was proposed,which aimed to minimize the execution cost of workflow while meeting its deadline constrains.Firstly,the data transfer cost,the shutdown and boot time of virtual machines,and the bandwidth fluctuations among different cloud providers were considered by this method.Secondly,in order to avoid the premature convergence of traditional particle swarm optimization (PSO),the randomly two-point crossover operator and randomly one-point mutation operator of the genetic algorithm (GA) was introduced.It could effectively improve the diversity of the population in the process of evolution.Finally,a cost-driven strategy for the deadline-constrained workflow was designed.It both considered the data transfer cost and the computing cost.Experimental results show that the ADPSOGA has better performance in terms of deadline and cost reducing in the fluctuant environment.     

Agent在工作流管理系统中的应用研究

当前,大多数工作流管理系统都是独立地管理单个工作流,而忽视了工作流之间的资源约束关系,基于ａｇｅｎｔ 的工作流管理系统能够有效地解决这个问题。本文主要讨论基于 ａｇｅｎｔ 的工作流管理系统包括系统配置、工作流执行的动态调度以及多 ａｇｅｎｔ 系统的组织和通信问题。     

带准备时间和截止期约束的云服务工作流调度算法

带准备时间和截止期约束的云服务工作流费用优化是一个新的云计算资源优化分配问题。分析该NP-hard问题特征,建立相应的整数规划数学模型。构建有效的变量取值概率模型和更新机制,提出高质量初始群体的启发式生成方法;提出混合的分布估计算法（HEDA）,引入个体向全局最优解学习的策略,提高算法的全局搜索和局部优化能力。模拟实验结果表明此提出的方法在合理的CPU时间内可有效减少工作流费用。     

Dynamic scheduling and analysis of real time systems with multiprocessors

This research work considers a scenario of cloud computing job-shop scheduling problems. We consider m realtime jobs with various lengths and n machines with different computational speeds and costs. Each job has a deadline to be met, and the profit of processing a packet of a job differs from other jobs. Moreover, considered deadlines are either hard or soft and a penalty is applied if a deadline is missed where the penalty is considered as an exponential function of time. The scheduling problem has been formulated as a mixed integer non-linear programming problem whose objective is to maximize net-profit. The formulated problem is computationally hard and not solvable in deterministic polynomial time. This research work proposes an algorithm named the Tube-tap algorithm as a solution to this scheduling optimization problem. Extensive simulation shows that the proposed algorithm outperforms existing solutions in terms of maximizing net-profit and preserving deadlines.     

基于机制设计理论的云计算SLA响应时间优化

针对云计算SLA中响应时间这一衡量云服务质量的重要指标,本文提出了一种DSIC(占优策略激励兼容)机制.在所有云资源提供商都是理性的这一共同知识假定下,DISC机制能保证云资源提供商显示真实的资源成本信息,云服务提供商以此为前提选择预算约束下能够在最短时间内完成用户任务的云资源提供商,从而达到优化用户服务响应时间的目的.本文对该机制的性能进行了严格证明,最后提出了一种寻找最优资源提供商组合的算法.     

截止期限约束的实例密集型云服务流调度算法

针对商业云计算中存在大量实例密集型服务流的问题,提出一种新的云环境下两阶段服务流调度算法。该算法先将用户自定义的全局截止期限分配到系统中的每个实例,再将每个实例的截止期限分配到实例中的每个任务中,最后在服务流执行阶段,动态调整后续任务的截止期限,解决了任务可能存在的未能在其截止期限内完成的时间异常问题。CloudSim仿真结果表明,与现有的算法相比,该算法能满足用户定义的截止期限,节约了执行成本,并减少了资源的竞争率,提高了调度的成功率。     

A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing

The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.     

A workflow task scheduling algorithm based on the resources' fuzzy clustering in cloud computing environment

Cloud computing is the key and frontier field of the current domestic and international computer technology, workflow task scheduling plays an important part of cloud computing, which is a policy that maps tasks to appropriate resources to execute. Effective task scheduling is essential for obtaining high performance in cloud environment. In this paper, we present a workflow task scheduling algorithm based on the resources' fuzzy clustering named FCBWTS. The major objective of scheduling is to minimize makespan of the precedence constrained applications, which can be modeled as a directed acyclic graph. In FCBWTS, the resource characteristics of cloud computing are considered, a group of characteristics, which describe the synthetic performance of processing units in the resource system, are defined in this paper. With these characteristics and the execution time influence of the ready task in the critical path, processing unit network is pretreated by fuzzy clustering method in order to realize the reasonable partition of processor network. Therefore, it largely reduces the cost in deciding which processor to execute the current task. Comparison on performance evaluation using both the case data in the recent literature and randomly generated directed acyclic graphs shows that this algorithm has outperformed the HEFT, DLS algorithms both in makespan and scheduling time consumed. Copyright © 2014 John Wiley & Sons, Ltd.     

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

With the rapid development of cloud computing, the number of cloud users is growing exponentially. Data centers have come under great pressure, and the problem of power consumption has become increasingly prominent. However, many idle resources that are geographically distributed in the network can be used as resource providers for cloud tasks. These distributed resources may not be able to support the resource‐intensive applications alone because of their limited capacity; however, the capacity will be considerably increased if they can cooperate with each other and share resources. Therefore, in this paper, a new resource‐providing model called “crowd‐funding” is proposed. In the crowd‐funding model, idle resources can be collected to form a virtual resource pool for providing cloud services. Based on this model, a new task scheduling algorithm is proposed, RC‐GA (genetic algorithm for task scheduling based on a resource crowd‐funding model). For crowd‐funding, the resources come from different heterogeneous devices, so the resource stability should be considered different. The scheduling targets of the RC‐GA are designed to increase the stability of task execution and reduce power consumption at the same time. In addition, to reduce random errors in the evolution process, the roulette wheel selection operator of the genetic algorithm is improved. The experiment shows that the RC‐GA can achieve good results.     

Multi-objective Energy Aware Scheduling of Deadline Constrained Workflows in Clouds using Hybrid Approach

Cloud computing is undoubtedly one of the most significant advances in the domain of information technology. It facilitates elastic and on-demand provisioning of high performance computing capabilities employing pay-per-use model that has snowballed its adoption by scientists and engineers over the past few years. They often exploit workflows to represent their massive applications. Workflow scheduling in cloud has been devoted considerable investigation by researchers owing to its NP-complete nature of problem. Most of the previous studies targeted optimization of schedule length and execution cost within given deadlines/budget restrictions, or both. However, enormous energy consumption in the cloud data centers is not only negatively impacting the environment but also resulting in increased operational costs and thus cannot be ignored. Efficient scheduling strategies can significantly lessen the energy usage while complying with the user’s Quality of Service limitations. This research study proposes a Hybrid Approach for Energy aware scheduling of Deadline constrained workflows (HAED) using Intelligent Water Drops algorithm and Genetic Algorithm, which provides non-dominated solutions to the user. In particular, it focuses on multiple objectives i.e. reduction of schedule length, execution cost and energy usage within deadlines specified by the user. Its performance has been assessed on four scientific workflows from diverse domains using hypervolume and set coverage. The results achieved with the simulations demonstrate that the solutions produced by HAED are of better quality in terms of accuracy and diversity than non-dominated sorting genetic algorithm and hybrid particle swarm optimization.

     

Jitter-approximation tradeoff for periodic scheduling

We consider an asymmetric wireless communication setting, where a server periodically broadcasts data items to different mobile clients. The goal is to serve items into a prescribed rate, while minimizing the energy consumption of the mobile users. Abstractly, we are presented with a set of jobs, each with a known execution time and a requested period, and the task is to design a schedule for these jobs over a single shared resource without preemption. Given any solution schedule, its period approximation is the maximal factor by which the average period of a job in the schedule is blown up w.r.t. its requested period, and the jitter is roughly the maximal variability of times between two consecutive occurrences of the same job. Schedules with low jitter allow the mobile devices to save power by having their receivers switched off longer. In this paper we consider a scenario where clients may be willing to settle for non-optimal period approximation so that the jitter is improved. We present a parametric jitter-approximation tradeoff algorithm that allows us to choose various combinations between jitter optimality and period optimality for any given set of jobs. Zvika Brakerski was born in 1981. He received a masters’ degree from Tel-Aviv University in 2002 and is currently employed as an Electric Engineer. Boaz Patt-Shamir received his PhD from MIT in 1995. He was an assistant professor in Northeastern University until 1997, and then he joined the Dept. of Electrical Engineering in Tel Aviv University, where he directs the Computer Communication and Multimedia Laboratory. He held visiting positions at MIT, Boston University, Bellcore, and HP Labs.     

DIV:一种针对虚拟机云服务的动态、实时完整性验证框架(英文)

with the increasing popularity of cloud services,attacks on the cloud infrastructure also increase dramatically.Especially,how to monitor the integrity of cloud execution environments is still a difficult task.In this paper,a real-time dynamic integrity validation(DIV) framework is proposed to monitor the integrity of virtual machine based execution environments in the cloud.DIV can detect the integrity of the whole architecture stack from the cloud servers up to the VM OS by extending the current trusted chain into virtual machine's architecture stack.DIV introduces a trusted third party(TTP) to collect the integrity information and detect remotely the integrity violations on VMs periodically to avoid the heavy involvement of cloud tenants and unnecessary information leakage of the cloud providers.To evaluate the effectiveness and efficiency of DIV framework,a prototype on KVM/QEMU is implemented,and extensive analysis and experimental evaluation are performed.Experimental results show that the DIV can efficiently validate the integrity of files and loaded programs in real-time,with minor performance overhead.     

Energy‐aware cross‐layer resource allocation in mobile cloud

This paper proposed an energy‐aware cross‐layer mobile cloud resource allocation approach. In this paper, a hybrid cloud architecture is adopted for provisioning mobile service to mobile device users, which include nearby local cloud and remote public cloud. The computation‐intensive tasks can be processed by the remote public cloud, while the delay‐sensitive computation can be processed by the nearby local cloud. On the basis of the system context and mobile user preferences, the energy‐aware cross‐layer mobile cloud resource allocation approach can optimize the consumption of cloud resource and system performance. The cooperation and collaboration among local cloud agent, public cloud supplier, and mobile cloud user are regulated through the economic approach. The energy‐aware cross‐layer mobile cloud resource allocation is performed on the local cloud level and the public cloud level, which comprehensively considers the benefits of all participants. The energy‐aware cross‐layer mobile cloud resource allocation algorithm is proposed, which is evaluated in the experiment environment, and comparison results and analysis are discussed.     

Adaptive VM Management with Two Phase Power Consumption Cost Models in Cloud Datacenter

As cloud computing models have evolved from clusters to large-scale data centers, reducing the energy consumption, which is a large part of the overall operating expense of data centers, has received much attention lately. From a cluster-level viewpoint, the most popular method for an energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers that do not have any virtual resources running. To maximize the energy efficiency with DRS, one of the primary adaptive resource management strategies is a Virtual Machine (VM) migration which consolidates VM instances into as few servers as possible. In this paper, we propose a Two Phase based Adaptive Resource Management (TP-ARM) scheme that migrates VM instances from under-utilized servers that are supposed to be turned off to sustainable ones based on their monitored resource utilizations in real time. In addition, we designed a Self-Adjusting Workload Prediction (SAWP) method to improve the forecasting accuracy of resource utilization even under irregular demand patterns. From the experimental results using real cloud servers, we show that our proposed schemes provide the superior performance of energy consumption, resource utilization and job completion time over existing resource allocation schemes.     

A web service‐based internet of things framework for mobile resource augmentation

Mobile devices are the primary communication tool in day to day life of the people. Nowadays, the enhancement of the mobile applications namely IoTApps and their exploitation in various domains like healthcare monitoring, home automation, smart farming, smart grid, and smart city are crucial. Though mobile devices are providing seamless user experience anywhere, anytime, and anyplace, their restricted resources such as limited battery capacity, constrained processor speed, inadequate storage, and memory are hindering the development of resource‐intensive mobile applications and internet of things (IoT)‐based mobile applications. To solve this resource constraint problem, a web service‐based IoT framework is proposed by exploiting fuzzy logic methodologies. This framework augments the resources of mobile devices by offloading the resource‐intensive subtasks from mobile devices to the service providing entities like Arduino, Raspberry PI controller, edge cloud, and distant cloud. Based on the recommended framework, an online Repository of Instructional Talk (RIoTalk) is successfully implemented to store and analyze the classroom lectures given by faculty in our study site. Simulation results show that there is a significant reduction in energy consumption, execution time, bandwidth utilization, and latency. The proposed research work significantly increases the resources of mobile devices by offloading the resource‐intensive subtasks from the mobile device to the service provider computing entities thereby providing Quality of Service (QoS) and Quality of Experience (QoE) to mobile users.     

Research on DDoS detection in multi-tenant cloud based on entropy change

An attacker compromised a number of VMs in the cloud to form his own network to launch a powerful distrib-uted denial of service (DDoS) attack.DDoS attack is a serious threat to multi-tenant cloud.It is difficult to detect which VM in the cloud are compromised and what is the attack target,especially when the VM in the cloud is the victim.A DDoS detection method was presented suitable for multi-tenant cloud environment by identifying the malicious VM at-tack sources first and then the victims.A distributed detection framework was proposed.The distributed agent detects the suspicious VM which generate the potential DDoS attack traffic flows on the source side.A central server confirms the real attack flows.The feasibility and effectiveness of the proposed detection method are verified by experiments in the multi-tenant cloud environment.