Service level agreement based energy-efficient resource management in cloud data centers

As cloud computing has become a popular computing paradigm, many companies have begun to build increasing numbers of energy hungry data centers for hosting cloud computing applications. Thus, energy consumption is increasingly becoming a critical issue in cloud data centers. In this paper, we propose a dynamic resource management scheme which takes advantage of both dynamic voltage/frequency scaling and server consolidation to achieve energy efficiency and desired service level agreements in cloud data centers. The novelty of the proposed scheme is to integrate timing analysis, queuing theory, integer programming, and control theory techniques. Our experimental results indicate that, compared to a statically provisioned data center that runs at the maximum processor speed without utilizing the sleep state, the proposed resource management scheme can achieve up to 50.3% energy savings while satisfying response-time-based service level agreements with rapidly changing dynamic workloads.     

面向云计算数据中心的弹性资源分配算法

巨大规模的数据资源与实时多变的应用请求,增加了云计算数据中心的资源分配难度,为此提出一种弹性资源分配算法。通过分析云计算数据中心下弹性资源分配问题,利用带宽资源与中间变量,将分配问题转换为整数线性规划问题,在各链路上逐级划分数据中心后得到不同分区,并按照从下到上的顺序逐层展开运算,完成动态规划阶段,基于树状数据中心的遍历过程,根据极大允许负载占比,明确可行的分配策略,依据带宽需求按序分配,实现云计算数据中心弹性最大化与资源最佳分配。选取不同的基准测试作业作为检测数据,经过对比分析实验数据,验证所提算法在弹性方面具有显著的优越性,有效降低应用请求响应时长,执行效果较为理想。     

基于改进多目标布谷鸟搜索的资源调度算法

针对IaaS(Infrastructure as a Service)云计算中资源调度的多目标优化问题,提出一种基于改进多目标布谷鸟搜索的资源调度算法。在多目标布谷鸟搜索算法的基础上,通过改进随机游走策略和丢弃概率策略提高了算法的局部搜索能力和收敛速度。以最大限度地减少完成时间和成本为主要目标,将任务分配特定的VM(Virtual Manufacturing)满足云用户对云提供商的资源利用的需求,从而减少延迟,提高资源利用率和服务质量。实验结果表明,该算法可以有效地解决IaaS云计算环境中资源调度的多目标问题,与其他算法相比,具有一定的优势。     

Network-aware virtual machine migration in an overcommitted cloud

Virtualization, which acts as the underlying technology for cloud computing, enables large amounts of third-party applications to be packed into virtual machines (VMs). VM migration enables servers to be reconsolidated or reshuffled to reduce the operational costs of data centers. The network traffic costs for VM migration currently attract limited attention.However, traffic and bandwidth demands among VMs in a data center account for considerable total traffic. VM migration also causes additional data transfer overhead, which would also increase the network cost of the data center.This study considers a network-aware VM migration (NetVMM) problem in an overcommitted cloud and formulates it into a non-deterministic polynomial time-complete problem. This study aims to minimize network traffic costs by considering the inherent dependencies among VMs that comprise a multi-tier application and the underlying topology of physical machines and to ensure a good trade-off between network communication and VM migration costs.The mechanism that the swarm intelligence algorithm aims to find is an approximate optimal solution through repeated iterations to make it a good solution for the VM migration problem. In this study, genetic algorithm (GA) and artificial bee colony (ABC) are adopted and changed to suit the VM migration problem to minimize the network cost. Experimental results show that GA has low network costs when VM instances are small. However, when the problem size increases, ABC is advantageous to GA. The running time of ABC is also nearly half than that of GA. To the best of our knowledge, we are the first to use ABC to solve the NetVMM problem.     

移动边缘计算环境下的动态资源分配策略

在通讯设备爆炸式增长的时代,移动边缘计算作为5G通讯技术的核心技术之一,对其进行合理的资源分配显得尤为重要。移动边缘计算的思想是把云计算中心下沉到基站部署(边缘云),使云计算中心更加靠近用户,以快速解决计算资源分配问题。但是,相对于大型的云计算中心,边缘云的计算资源有限,传统的虚拟机分配方式不足以灵活应对边缘云的计算资源分配问题。为解决此问题,提出一种根据用户综合需求变化的动态计算资源和频谱分配算法(DRFAA),采用"分治"策略,并将资源模拟成"流体"资源进行分配,以寻求较大的吞吐量和较低的传输时延。实验仿真结果显示,动态计算资源和频谱分配算法可以有效地降低用户与边缘云之间的传输时延,也可以提高边缘云的吞吐量。     

基于雾计算的计算资源分配方案

雾计算可以为用户提供近距离的数据存储、计算和其他服务，因此雾计算中的任务调度和资源分配已经成为一个新的研究热点。考虑终端用户和雾设备通常处于一种相对开放的状态，扩展了雾计算的体系结构，提出一种开放式雾计算环境中基于稳定匹配的计算资源分配方案，利用雾网络中动态的计算资源协同为用户提供计算服务并收取计算收益，同时终端用户向雾服务器提交任务请求并支付一定的费用。基于稳定匹配的思想，利用子任务的优先级列表、子任务和计算服务设备的偏好列表解决子任务与计算服务设备的分配问题，保证任务的完成时间和计算服务设备的收益。通过实验对方案性能进行了分析，实验结果表明该方案的资源分配时间相对稳定，且在执行雾计算任务时延以及任务违规率上都优于SGA算法和ACOSA算法。     

Decentralized cloud datacenter reconsolidation through emergent and topology-aware behavior

Consolidation of multiple applications on a single Physical Machine (PM) within a cloud data center can increase utilization, minimize energy consumption, and reduce operational costs. However, these benefits come at the cost of increasing the complexity of the scheduling problem.In this paper, we present a topology-aware resource management framework. As part of this framework, we introduce a Reconsolidating PlaceMent scheduler (RPM) that provides and maintains durable allocations with low maintenance costs for data centers with dynamic workloads. We focus on workloads featuring both short-lived batch jobs and latency-sensitive services such as interactive web applications. The scheduler assigns resources to Virtual Machines (VMs) and maintains packing efficiency while taking into account migration costs, topological constraints, and the risk of resource contention, as well as the variability of the background load and its complementarity to the new VM.We evaluate the model by simulating a data center with over 65,000 PMs, structured as a three-level multi-rooted tree topology. We investigate trade-offs between factors that affect the durability and operational cost of maintaining a near-optimal packing. The results show that the proposed scheduler can scale to the number of PMs in the simulation and maintain efficient utilization with low migration costs.     

A Review of Dynamic Resource Management in Cloud Computing Environments

In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used to address the issues of workload fluctuations. VM consolidation aims to move the VMs from one host to another in order to reduce the number of active hosts and save power. Whereas resource provisioning attempts to provide additional resource capacity to the VMs as needed in order to meet Quality of Service (QoS) requirements. However, these techniques have a set of limitations in terms of the additional costs related to migration and scaling time, and energy overhead that need further consideration. Therefore, this paper presents a comprehensive literature review on the subject of dynamic resource management (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely related works. The outcomes of this research can be used to enhance the development of predictive resource management techniques, by considering the awareness of performance variation, energy consumption and cost to efficiently manage the cloud resources.     

容器云环境虚拟资源配置策略的优化

针对容器化云环境中数据中心能耗较高的问题，提出了一种基于最佳能耗优先（Power Full，PF）物理机选择算法的虚拟资源配置策略。首先，提出容器云虚拟资源的配置和迁移方案，发现物理机选择策略对数据中心能耗有重要影响；其次，通过研究主机利用率与容器利用率，主机利用率与虚拟机利用率，主机利用率与数据中心能耗之间的数学关系，建立容器云数据中心能耗的数学模型，定义出优化目标函数；最后，通过对物理机的能耗函数使用线性插值进行模拟，依据邻近事物相类似的特性，提出改进的最佳能耗优先物理机选择算法。仿真实验将此算法与先来先得（First Fit，FF）、最低利用率优先（Least Fit，LF）、最高利用率优先（Most Full，MF）进行比较，实验结果表明，在有规律不同物理机群的计算服务中，其能耗比FF、LF、MF分别平均降低45%、53%和49%；在有规律相同物理机群的计算服务中，其能耗比FF、LF、MF分别平均降低56%、46%和58%；在无规律不同物理机群的计算服务中，其能耗比FF、LF、MF分别平均降低32%、24%和12%。所提算法实现了对容器云虚拟资源的合理配置，且在数据中心节能方面具有优越性。     

A novel resource allocation mechanism for live cloud-based video streaming service

With the recent emergence of cloud computing, growing numbers of clients are using online cloud services through the Internet such as video streaming service. The rent costs of cloud service providers increase when the resource utilizations of the cloud-servers are not well. Therefore, resource allocation is a crucial problem for cloud data centers. The resource allocation problem is an NP-hard problem. This paper proposes a novel cloud resource allocation mechanism based on a winning strategy for a Nim game. This mechanism offers all clients an effective number of running cloud servers, and allocates cloud resources rapidly and effectively by using a pre-pairing approach. The proposed mechanism does not require searching for remaining resources of the running cloud server; hence, it can reduce the time taken to arrange resources. The experimental results show that the proposed mechanism can improve utilization of cloud servers and reduce the rent costs of the cloud service providers. The proposed mechanism can reach the utilization of cloud servers by as much as 99.96 %. The proposed mechanism is approximately 9 % more efficient than the market-based grid resource allocation algorithm, and 19 % more efficient than the modified best fit decreasing algorithm.

     

采用随机规划模型的云资源分配算法

针对云资源提供问题,为了降低云消费者的资源使用成本,提出了一种采用随机规划模型的云资源分配算法.同时考虑按需实例和预留实例,采用两阶段随机整数规划对云资源提供问题进行建模,在资源预留阶段,根据长期的工作负载情况,确定预留实例的类型和数量,在按需分配阶段,根据当前的工作负载,确定动态分配的按需实例的类型和数量.采用抽样平均近似方法减少资源提供问题的场景数量,降低求解复杂度,并提出了一种基于阶段分解的混合进化算法求解资源提供问题.仿真实验结果表明,采用随机规划模型的云资源分配算法能够在较短时间内获得近似最优的云资源预留方案,有效降低了云消费者的资源使用成本.     

面向多目标的云计算资源调度算法

在传统的虚拟机资源调度中,仅仅考虑当前负载,对虚拟机历史数据没有充分考虑,在处理云计算资源调度的时候出现负载失衡的状况,为了解决上述问题,本文提出了基于启发式遗传算法的资源调度算法,满足多目标规划的情况下实现云计算资源的调度.算法在为用户提供服务的同时充分考虑虚拟机的各种开销和因素,使提供云计算资源的服务器达到负载均衡.对目前的负载情况和历史数据进行分析,经过搜索和计算,计算得到同时满足负载变化数据约束和最小动态迁移开销的最好的云计算资源调度方案.最后,通过仿真实验,对算法进行验证,通过引入负载变化率和平均负载距离二个性能参数来比较和衡量虚拟机负载.实验数据证明,所提出的算法具有很好的全局收敛性和资源利用率,有效解决在资源调度中出现负载失衡和较大动态迁移开销的问题,因此,算法是可行和有效的.     

A two-stage approach for task and resource management in multimedia cloud environment

In recent years, multimedia cloud computing is becoming a promising technology that can effectively process multimedia services and provide quality of service (QoS) provisioning for multimedia applications from anywhere, at any time and on any device at lower costs. However, there are two major challenges exist in this emerging computing paradigm: one is task management, which maps multimedia tasks to virtual machines, and the other is resource management, which maps virtual machines (VMs) to physical servers. In this study, we aim at providing an efficient solution that jointly addresses these challenges. In particular, a queuing based approach for task management and a heuristic algorithm for resource management are proposed. By adopting allocation deadline in each VM request, both task manager and VM allocator receive better chances to optimize the cost while satisfying the constraints on the quality of multimedia service. Various simulations were conducted to validate the efficiency of the proposed task and resource management approaches. The results showed that the proposed solutions provided better performance as compared to the existing state-of-the-art approaches.     

面向云计算的数据中心网络体系结构设计

近年来,云计算技术的蓬勃发展为整个IT行业带来了巨大变革.传统数据中心网络拓扑构建方式及网络层控制平面的运行机制存在固化性,已经难以满足新形势下日益增长的高性能及高性价比需求,并且无法支持云环境下更加灵活的按带宽租赁数据中心网络的运营方式.因此,提出了一种通过低造价的可编程交换机来构建具有高连通性的非树状数据中心网络的方式,并设计了可编程交换机与服务器2.5层代理协同工作的基于凸优化的虚拟网络带宽控制管理机制,从而提供足够的灵活性以对资源虚拟化技术提供更好的支持.实验表明,新型体系结构在降低构建成本的同时大幅提高了数据中心网络的吞吐量并提供了更加灵活的网络带宽分配机制.     

云数据中心资源调度机制研究

云数据中心包含大量计算机,运作成本很高。有效整合资源、提高资源利用率、节约能源、降低运行成本是云数据中心关注的热点。云数据中心通过虚拟化技术将计算资源、存储资源和网络资源构建成动态的虚拟资源池;使用虚拟资源管理技术实现云计算资源自动部署、动态扩展、按需分配;用户采用按需和即付即用的方式获取资源。因此,数据中心对提高资源利用率的迫切需求,促使人们寻求新的方式以建设下一代数据中心。     

Integrating QoS awareness with virtualization in cloud computing systems for delay-sensitive applications

Cloud computing provides scalable computing and storage resources over the Internet. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. The required resources of a VM are sliced from a physical machine (PM) in the cloud computing system. A PM may hold one or more VMs. When a cloud provider would like to create a number of VMs, the main concerned issue is the VM placement problem, such that how to place these VMs at appropriate PMs to provision their required resources of VMs. However, if two or more VMs are placed at the same PM, there exists certain degree of interference between these VMs due to sharing non-sliceable resources, e.g. I/O resources. This phenomenon is called as the VM interference. The VM interference will affect the performance of applications running in VMs, especially the delay-sensitive applications. The delay-sensitive applications have quality of service (QoS) requirements in their data access delays. This paper investigates how to integrate QoS awareness with virtualization in cloud computing systems, such as the QoS-aware VM placement (QAVMP) problem. In addition to fully exploiting the resources of PMs, the QAVMP problem considers the QoS requirements of user applications and the VM interference reduction. Therefore, in the QAVMP problem, there are following three factors: resource utilization, application QoS, and VM interference. We first formulate the QAVMP problem as an Integer Linear Programming (ILP) model by integrating the three factors as the profit of cloud provider. Due to the computation complexity of the ILP model, we propose a polynomial-time heuristic algorithm to efficiently solve the QAVMP problem. In the heuristic algorithm, a bipartite graph is modeled to represent all the possible placement relationships between VMs and PMs. Then, the VMs are gradually placed at their preferable PMs to maximize the profit of cloud provider as much as possible. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.     

基于李雅普诺夫随机优化的车辆边缘计算资源管理

针对车辆边缘计算系统中的计算资源管理问题,提出一种基于李雅普诺夫随机优化的计算卸载与资源分配方案.构建在保证任务量及长期能耗约束下的车辆用户服务时延最小化优化问题,利用李雅普诺夫随机优化理论将优化问题分解.在本地计算资源分配子问题中,通过求解线性问题的方法,得到最优本地计算CPU频率;在计算卸载子问题中,利用数值优化求...     

云环境下基于SLA的优化资源分配研究

针对云计算环境下如何高效分配资源,实现资源供应者利润最大化这一难题,提出了一种基于服务级别协议（SLA）的动态云资源分配策略。该策略通过将SLA中的计算力、网络带宽、数据存储等属性作为优化参数,构造了一种服务请求与资源的映射模型,同时设计相应的效用函数,并结合改进的与模拟退火算法相融合的混合粒子群算法（SA-PSO）,实现云环境下的优化资源分配。实验分析结果表明,基于SLA参数的SA-PSO算法具有更好的全局最优值,在给定虚拟资源相同情况下,调用该算法完成用户任务实现的利润更高。     

Novel resource allocation algorithms to performance and energy efficiency in cloud computing

The rapid growth in demand for computational power has led to a shift to the cloud computing model established by large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy. Cloud providers must ensure that their service delivery is flexible to meet various consumer requirements. However, to support green computing, cloud providers also need to minimize the cloud infrastructure energy consumption while conducting the service delivery. In this paper, for cloud environments, a novel QoS-aware VMs consolidation approach is proposed that adopts a method based on resource utilization history of virtual machines. Proposed algorithms have been implemented and evaluated using CloudSim simulator. Simulation results show improvement in QoS metrics and energy consumption as well as demonstrate that there is a trade-off between energy consumption and quality of service in the cloud environment.     

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.