Dynamic cloud resource management for efficient media applications in mobile computing environments

Single-instruction-set architecture (Single-ISA) heterogeneous multi-core processors (HMP) are superior to Symmetric Multi-core processors in performance per watt. They are popular in many aspects of the Internet of Things, including mobile multimedia cloud computing platforms. One Single-ISA HMP integrates both fast out-of-order cores and slow simpler cores, while all cores are sharing the same ISA. The quality of service (QoS) is most important for virtual machine (VM) resource management in multimedia mobile computing, particularly in Single-ISA heterogeneous multi-core cloud computing platforms. Therefore, in this paper, we propose a dynamic cloud resource management (DCRM) policy to improve the QoS in multimedia mobile computing. DCRM dynamically and optimally partitions shared resources according to service or application requirements. Moreover, DCRM combines resource-aware VM allocation to maximize the effectiveness of the heterogeneous multi-core cloud platform. The basic idea for this performance improvement is to balance the shared resource allocations with these resources requirements. The experimental results show that DCRM behaves better in both response time and QoS, thus proving that DCRM is good at shared resource management in mobile media cloud computing.     

Profile-based dynamic application assignment with a repairing genetic algorithm for greener data centers

Data centers have become essential to modern society by catering to increasing number of Internet users and technologies. This results in significant challenges in terms of escalating energy consumption. Research on green initiatives that reduce energy consumption while maintaining performance levels is exigent for data centers. However, energy efficiency and resource utilization are conflicting in general. Thus, it is imperative to develop an application assignment strategy that maintains a trade-off between energy and quality of service. To address this problem, a profile-based dynamic energy management framework is presented in this paper for dynamic application assignment to virtual machines (VMs). It estimates application finishing times and addresses real-time issues in application resource provisioning. The framework implements a dynamic assignment strategy by a repairing genetic algorithm (RGA), which employs realistic profiles of applications, virtual machines and physical servers. The RGA is integrated into a three-layer energy management system incorporating VM placement to derive actual energy savings. Experiments are conducted to demonstrate the effectiveness of the dynamic approach to application management. The dynamic approach produces up to 48% better energy savings than existing application assignment approaches under investigated scenarios. It also performs better than the static application management approach with 10% higher resource utilization efficiency and lower degree of imbalance.     

Distributed two-level cloud-based multimedia task scheduling

Recently, Multimedia cloud is emerging as a promising technology to effectively process multimedia services. A key problem in multimedia cloud is how to deal with task scheduling and load balancing to satisfy the quality of service demands of users. In this paper, we propose a two levels task scheduling mechanism for multimedia cloud to addresses the problem. The first level scheduling is from the users’ multimedia application to the data centers, and the second is from the data centers to servers. The data centers and virtual machines both are modeled as M/M/1 queuing systems. The algorithm proposed formulates the task-scheduling problem as cooperative game among data centers. Then we allocate the tasks received by a data center to servers using cooperative game again among servers. Various simulations are conducted to validate the efficiency of the proposed task scheduling approaches. The results showed that the proposed solutions provided better performance as compared to the existing approaches.     

An efficient job management of computing service using integrated idle VM resources for high-performance computing based on OpenStack

In recent years, various studies on OpenStack-based high-performance computing have been conducted. OpenStack combines off-the-shelf physical computing devices and creates a resource pool of logical computing. The configuration of the logical computing resource pool provides computing infrastructure according to the user’s request and can be applied to the infrastructure as a service (laaS), which is a cloud computing service model. The OpenStack-based cloud computing can provide various computing services for users using a virtual machine (VM). However, intensive computing service requests from a large number of users during large-scale computing jobs may delay the job execution. Moreover, idle VM resources may occur and computing resources are wasted if users do not employ the cloud computing resources. To resolve the computing job delay and waste of computing resources, a variety of studies are required including computing task allocation, job scheduling, utilization of idle VM resource, and improvements in overall job’s execution speed according to the increase in computing service requests. Thus, this paper proposes an efficient job management of computing service (EJM-CS) by which idle VM resources are utilized in OpenStack and user’s computing services are processed in a distributed manner. EJM-CS logically integrates idle VM resources, which have different performances, for computing services. EJM-CS improves resource wastes by utilizing idle VM resources. EJM-CS takes multiple computing services rather than single computing service into consideration. EJM-CS determines the job execution order considering workloads and waiting time according to job priority of computing service requester and computing service type, thereby providing improved performance of overall job execution when computing service requests increase.

     

Task rescheduling optimization to minimize network resource consumption

An increasing number of big-data services are being deployed in a cloud computing environment, attracted by the on-demand service, rapid elasticity, and low maintenance costs. As a result, ensuring the quality of service has become an important research problem. Traditionally, task rescheduling is used to ensure a consistent quality of service in the event of failure of a virtual machine. However, the network resource consumption of different rescheduling methods varies. To address this problem, we propose a task rescheduling method that minimizes network resource consumption.The method includes three algorithms. The first obtains a set of good virtual machines from the large quantity of service-providing virtual machines using the skyline operation. A ranking algorithm then fuses the data size and the task emergency to identify significant tasks. Finally, we present an algorithm that automatically determines the optimal insertion point for each task. To verify the effectiveness of the proposed method, we extend the renowned simulator CloudSim and conduct a series of experiments. The results show that our method is more efficient than other methods in terms of network resource consumption.     

CyberGuarder: A virtualization security assurance architecture for green cloud computing

As the sizes of IT infrastructure continue to grow, cloud computing is a natural extension of virtualisation technologies that enable scalable management of virtual machines over a plethora of physically connected systems. The so-called virtualisation-based cloud computing paradigm offers a practical approach to green IT/clouds, which emphasise the construction and deployment of scalable, energy-efficient network software applications (NetApp) by virtue of improved utilisation of the underlying resources. The latter is typically achieved through increased sharing of hardware and data in a multi-tenant cloud architecture/environment and, as such, accentuates the critical requirement for enhanced security services as an integrated component of the virtual infrastructure management strategy. This paper analyses the key security challenges faced by contemporary green cloud computing environments, and proposes a virtualisation security assurance architecture, CyberGuarder, which is designed to address several key security problems within the ‘green’ cloud computing context. In particular, CyberGuarder provides three different kinds of services; namely, a virtual machine security service, a virtual network security service and a policy based trust management service. Specifically, the proposed virtual machine security service incorporates a number of new techniques which include (1) a VMM-based integrity measurement approach for NetApp trusted loading, (2) a multi-granularity NetApp isolation mechanism to enable OS user isolation, and (3) a dynamic approach to virtual machine and network isolation for multiple NetApp’s based on energy-efficiency and security requirements. Secondly, a virtual network security service has been developed successfully to provide an adaptive virtual security appliance deployment in a NetApp execution environment, whereby traditional security services such as IDS and firewalls can be encapsulated as VM images and deployed over a virtual security network in accordance with the practical configuration of the virtualised infrastructure. Thirdly, a security service providing policy based trust management is proposed to facilitate access control to the resources pool and a trust federation mechanism to support/optimise task privacy and cost requirements across multiple resource pools. Preliminary studies of these services have been carried out on our iVIC platform, with promising results. As part of our ongoing research in large-scale, energy-efficient/green cloud computing, we are currently developing a virtual laboratory for our campus courses using the virtualisation infrastructure of iVIC, which incorporates the important results and experience of CyberGuarder in a practical context.     

云工作流中基于分时虚拟机的任务层调度算法

云计算是新的一种面向市场的商业计算模式,向用户按需提供服务,云计算的商业特性使其关注向用户提供服务的服务质量。任务调度和资源分配是云计算中两个关键的技术,所使用的虚拟化技术使得其资源分配和任务调度有别于以往的并行分布式计算。目前主要的调度算法是借鉴网格环境下的调度策略,研究基于QoS的调度算法,存在执行效率较低的问题。我们对云工作流任务层调度进行深入研究,分析由底层资源虚拟化形成的虚拟机的特性,结合工作流任务的各类QoS约束,提出了基于虚拟机分时特性的任务层ACS调度算法。经过试验,我们提出的算法相比于文献[1]中的算法在对于较多并行任务的执行上存在较大的优势,能够很好的利用虚拟的分时特性,优化任务到虚拟机的调度。     

Towards an adaptive human-centric computing resource management framework based on resource prediction and multi-objective genetic algorithm

The complexity, scale and dynamic of data source in the human-centric computing bring great challenges to maintainers. It is problem to be solved that how to reduce manual intervention in large scale human-centric computing, such as cloud computing resource management so that system can automatically manage according to configuration strategies. To address the problem, a resource management framework based on resource prediction and multi-objective optimization genetic algorithm resource allocation (RPMGA-RMF) was proposed. It searches for optimal load cluster as training sample based on load similarity. The neural network (NN) algorithm was used to predict resource load. Meanwhile, the model also built virtual machine migration request in accordance with obtained predicted load value. The multi-objective genetic algorithm (GA) based on hybrid group encoding algorithm was introduced for virtual machine (VM) resource management, so as to provide optimal VM migration strategy, thus achieving adaptive optimization configuration management of resource. Experimental resource based on CloudSim platform shows that the RPMGA-RMF can decrease VM migration times while reduce physical node simultaneously. The system energy consumption can be reduced and load balancing can be achieved either.     

基于LP&GR算法的多优先级虚拟机迁移策略研究*

如何对云计算中心的虚拟机(Virtual machine,VM)资源进行合理分配是近年来研究的一个热点问题。针对这一问题,本文提出了一种基于负载预测和灰色关联度(Load Prediction and Gray Relational,LP&GR)的虚拟机资源分配算法,通过预测虚拟机的负载状态防止虚拟机发生过载,并建立了基于虚拟机负载评价函数的决策分配模型。同时为虚拟机的迁移队列设置了多个优先级,结合了抢占式与非抢占式的执行策略,保证了虚拟机的有序迁移,并提高资源利用率。实验结果表明,结合多优先级的LP&GR算法同比其他算法能够有效实现云中心的负载均衡。     

基于深度强化学习的自适应虚拟机整合方法

能耗限制的服务质量优化问题一直以来都是数据中心虚拟机资源管理所面临的巨大挑战之一.尽管现有的工作通过虚拟机整合技术一定程度上降低了能耗和提升了系统服务质量,但这些方法通常难以实现长期最优的管理目标,并且容易受到业务场景变化的影响,面临变更困难以及管理成本高等难题.针对数据中心虚拟机资源管理存在的能耗和服务质量长期最优难保证以及策略调整灵活性差的问题,提出了一种基于深度强化学习的自适应虚拟机整合方法(deep reinforcement learning-based adaptive virtual machine consolidation method, RA-VMC).该方法利用张量化状态表示、确定性动作输出、卷积神经网络和加权奖赏机制构建了从数据中心系统状态到虚拟机迁移策略的端到端决策模型;设计自动化状态生成机制和反向梯度限定机制以改进深度确定性策略梯度算法,加快虚拟机迁移决策模型的收敛速度并且保证近似最优的管理性能.基于真实虚拟机负载数据的仿真实验结果表明：与开源云平台中流行的虚拟机整合方法相比,该方法能够有效地降低能耗和提高系统的服务质量.     

Recent advancement in VM task allocation system for cloud computing: review from 2015 to2021

Cloud computing is new technology that has considerably changed human life at different aspect over the last decade. Especially after the COVID-19 pandemic, almost all life activity shifted into cloud base. Cloud computing is a utility where different hardware and software resources are accessed on pay per user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization.VM used in data center for distribution of resource and application according to benefactor demand. Cloud data center faces different issue in respect of performance and efficiency for improvement of these issues different approaches are used. Virtual machine play important role for improvement of data center performance therefore different approach are used for improvement of virtual machine efficiency (i-e) load balancing of resource and task. For the improvement of this section different parameter of VM improve like makespan, quality of service, energy, data accuracy and network utilization. Improvement of different parameter in VM directly improve the performance of cloud computing. Therefore, we conducting this review paper that we can discuss about various improvements that took place in VM from 2015 to 20,201. This review paper also contain information about various parameter of cloud computing and final section of paper present the role of machine learning algorithm in VM as well load balancing approach along with the future direction of VM in cloud data center.

     

边云协同计算中基于预测的资源部署与任务调度优化

数据集中处理的云计算模式提供交互迅速、绿色高效的多样化应用服务面临新挑战.将云计算能力扩展到边缘设备,提出了边云协同计算框架;设计了基于任务预测的资源部署算法,在云服务中心通过二维时间序列对任务进行预测,结合分类聚合、延迟阈值判定等优化边缘服务器任务运行所需资源部署;提出了基于帕累托优化的任务调度算法,在边缘服务器分2个阶段进行帕累托渐进比较得到用户服务质量和系统服务效应2个目标曲线的相切点或任一相交点以优化任务调度.实验结果表明：结合基于任务预测的资源部署算法与基于帕累托优化的任务调度算法在提高平均用户任务命中率基础上,其用户平均服务完成时间、系统整体服务效应度、总任务延迟率在不同用户任务规模、不同Zipf分布参数α的应用场景下,均优于基于帕累托优化的任务调度算法和基于FIFO(first input first output)的基准任务调度算法.     

云中多媒体应用中基于混合DAG的最优任务调度研究

云计算的平台优势使得它在多媒体应用中得到广泛使用。由于多媒体服务的多样性和异构性,如何将多媒体任务有效地调度至虚拟机进行处理成为当前多媒体应用的研究重点。对此,研究了云中多媒体最优任务调度问题,首先引入有向无环图来模拟任务中的优先级及任务之间的依赖性,分别对串行、并行、混合结构任务调度模型进行任务调度研究,根据有限资源成本将关键路径中任务节点融合,提出一种实用的启发式近似最优调度方法。实验结果表明,所提调度方法能够以最短的执行时间在有限的资源成本下完成最优的任务分配。     

URL: A unified reinforcement learning approach for autonomic cloud management

Cloud computing is emerging as an increasingly important service-oriented computing paradigm. Management is a key to providing accurate service availability and performance data, as well as enabling real-time provisioning that automatically provides the capacity needed to meet service demands. In this paper, we present a unified reinforcement learning approach, namely URL, to automate the configuration processes of virtualized machines and appliances running in the virtual machines. The approach lends itself to the application of real-time autoconfiguration of clouds. It also makes it possible to adapt the VM resource budget and appliance parameter settings to the cloud dynamics and the changing workload to provide service quality assurance. In particular, the approach has the flexibility to make a good trade-off between system-wide utilization objectives and appliance-specific SLA optimization goals. Experimental results on Xen VMs with various workloads demonstrate the effectiveness of the approach. It can drive the system into an optimal or near-optimal configuration setting in a few trial-and-error iterations.     

资源调度等待开销感知的虚拟机整合

近年来,数据中心庞大的能源开销问题引起广泛关注.虚拟化管理平台可以通过虚拟机迁移技术将虚拟机整合到更少的服务器上,从而提高数据中心能源有效性.对面向数据中心节能的虚拟机整合研究工作进行调研,并总结虚拟机整合研究存在的3个挑战.针对已有工作未考虑虚拟机等待资源调度带来的服务器资源额外开销这种现象,开展了资源调度等待开销感知的虚拟机整合研究.从理论和实验上证明了在具有实际意义的约束条件下,存在着虚拟机等待资源调度带来的服务器资源额外开销,且随着整合虚拟机数量的增长保持稳定.基于典型工作负载的实验结果表明,这个额外开销平均占据了11.7%的服务器资源开销.此外,提出了资源预留整合（MRC）算法,用于改进已有的虚拟机整合算法.算法模拟实验结果表明,MRC算法相比于常用的虚拟机整合算法FFD（first fit decreasing）,明显降低了服务器资源溢出概率.     

Power efficient server consolidation for Cloud data center

Cloud computing has become an essential part of the global digital economy due to its extensibility, flexibility and reduced costs of operations. Nowadays, data centers (DCs) contain thousands of different machines running a huge number of diverse applications over an extended period. Resource management in Cloud is an open issue since an efficient resource allocation can reduce the infrastructure running cost. In this paper, we propose a snapshot-based solution for server consolidation problem from Cloud infrastructure provider (CIP) perspective. Our proposed mathematical formulation aims at reducing power cost by employing efficient server consolidation, and also considering the issues such as (i) mapping incoming and failing virtual machines (VMs), (ii) reducing a total number of VM migrations and (iii) consolidating running server workloads. We also compare the performance of our proposed model to the well-known Best Fit heuristics and its extension to include server consolidation via VM migration denoted as Best Fit with Consolidation (BFC). Our proposed mathematical formulation allows us to measure the solution quality in absolute terms, and it can also be applicable in practice. In our simulations, we show that relevant improvements (from 6% to 15%) over the widely adopted Best Fit algorithm achieved in a reasonable computing time.     

基于离散人工蜂群算法的云任务调度优化

针对现今云计算任务调度只考虑单目标和云计算应用对虚拟资源的服务的质量要求高等问题,综合考虑了用户最短等待时间、资源负载均衡和经济原则,提出一种离散人工蜂群(ABC)算法的云任务调度优化策略。首先,从理论上建立了云任务调度的多目标数学模型;然后,结合偏好满意度策略并引入局部搜索算子和改变侦察蜂搜索方式,提出多目标离散型人工蜂群(MDABC)算法的优化策略。通过不同的云任务调度仿真实验,显示了改进离散人工蜂群算法相对于基础离散人工蜂群算法、遗传算法以及经典贪心算法,能够得到较高的综合满意度,表明了改进离散人工蜂群算法能够更好地改善虚拟资源中云任务调度系统的性能,具有一定的普适性。     

LATOC: an enhanced load balancing algorithm based on hybrid AHP-TOPSIS and OPSO algorithms in cloud computing

Providing required level of service quality in cloud computing is one of the most significant cloud computing challenges because of software and hardware complexities, different features of tasks and computing resources and also, lack of appropriate distribution of tasks in cloud computing environments. The recent research in this field show that lack of smart prioritization and ordering of tasks in scheduling (as an NP-hard problem) has been very effective and resulted in lack of load balancing, response time increase, total execution time increase and also, average resource use decrease. In line with this, the proposed method of this research called LATOC considered first the key criteria of an input task like required processing unit, data length of task and execution time. Then, it addressed task prioritization in separate queues using the technique for order preference by similarity to ideal solution (TOPSIS) and analytic hierarchy process (AHP) in figure of a hybrid intelligent algorithm (AHP-TOPSIS). Each ordered task in separate priority queues was placed based on its priority level, and then, to assign each task from each priority queue to virtual machines, optimized particle swarm optimization was used. Many simulations based on various scenarios in Cloudsim simulator show that smart assignment of prioritized tasks by LATOC resulted in improvement of important cloud computing parameters such as total execution time and average resource use comparing similar methods.

     

Dynamic resource demand prediction and allocation in multi‐tenant service clouds

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and resource allocation methodology that can provision resources in advance, thereby minimizing the virtual machine downtime required for resource provisioning. In this paper, we present a dynamic resource demand prediction and allocation framework in multi‐tenant service clouds. The novel contribution of our proposed framework is that it classifies the service tenants as per whether their resource requirements would increase or not; based on this classification, our framework prioritizes prediction for those service tenants in which resource demand would increase, thereby minimizing the time needed for prediction. Furthermore, our approach adds the service tenants to matched virtual machines and allocates the virtual machines to physical host machines using a best‐fit heuristic approach. Performance results demonstrate how our best‐fit heuristic approach could efficiently allocate virtual machines to hosts so that the hosts are utilized to their fullest capacity. Copyright © 2016 John Wiley & Sons, Ltd.