CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end‐users under a usage‐based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter‐networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter‐networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy‐efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.     

Proactive virtualized resource management for service workflows in the cloud

A Cloud platform offers on-demand provisioning of virtualized resources and pay-per-use charge model to its hosted services to satisfy their fluctuating resource needs. Resource scaling in cloud is often carried out by specifying static rules or thresholds. As business processes and scientific jobs become more intricate and involve more components, traditional reactive or rule-based resource management methods are not able to meet the new requirements. In this paper, we extend our previous work on dynamically managing virtualized resources for service workflows in a cloud environment. Extensive experimental results of an adaptive resource management algorithm are reported. The algorithm makes resource management decisions based on predictive results and high level user specified thresholds. It is also able to coordinate resources among the component services of a workflow so that unnecessary resource allocations and terminations can be avoided. Based on observations from previous experiments, the algorithm is extended with a new resource merge strategy in order to prevent average resource size from shrinking. Simulation results from synthetic workload data demonstrated the effectiveness of the extension.     

Infrastructure Federation Through Virtualized Delegation of Resources and Services

Infrastructure federation is becoming an increasingly important issue for modern Distributed Computing Infrastructures (DCIs): Dynamic elasticity of quasi-static Grid environments, incorporation of special-purpose resources into commoditized Cloud infrastructures, cross-community collaboration for increasingly diverging areas of modern e-Science, and Cloud Bursting pose major challenges on the technical level for many resource and middleware providers. Especially with respect to increasing costs of operating data centers, the intelligent yet automated and secure sharing of resources is a key factor for success. With the D-Grid Scheduler Interoperability (DGSI) project within the German D-Grid Initiative, we provide a strategic technology for the automatically negotiated, SLA-secured, dynamically provisioned federation of resources and services for Grid-and Cloud-type infrastructures. This goal is achieved by complementing current DCI schedulers with the ability to federate infrastructure for the temporary leasing of resources and rechanneling of workloads. In this work, we describe the overall architecture and SLA-secured negotiation protocols within DGSI and depict an advanced mechanism for resource delegation through means of dynamically provisioned, virtualized middleware. Through this methodology, we provide the technological foundation for intelligent capacity planning and workload management in a cross-infrastructure fashion.     

Failure-aware resource provisioning for hybrid Cloud infrastructure

Hybrid Cloud computing is receiving increasing attention in recent days. In order to realize the full potential of the hybrid Cloud platform, an architectural framework for efficiently coupling public and private Clouds is necessary. As resource failures due to the increasing functionality and complexity of hybrid Cloud computing are inevitable, a failure-aware resource provisioning algorithm that is capable of attending to the end-users quality of service (QoS) requirements is paramount. In this paper, we propose a scalable hybrid Cloud infrastructure as well as resource provisioning policies to assure QoS targets of the users. The proposed policies take into account the workload model and the failure correlations to redirect users’ requests to the appropriate Cloud providers. Using real failure traces and a workload model, we evaluate the proposed resource provisioning policies to demonstrate their performance, cost as well as performance–cost efficiency. Simulation results reveal that in a realistic working condition while adopting user estimates for the requests in the provisioning policies, we are able to improve the users’ QoS about 32% in terms of deadline violation rate and 57% in terms of slowdown with a limited cost on a public Cloud.     

SLA-driven dynamic cloud resource management

As the size and complexity of Cloud systems increase, the manual management of these solutions becomes a challenging issue as more personnel, resources and expertise are needed. Service Level Agreement (SLA)-aware autonomic cloud solutions enable managing large scale infrastructure management meanwhile supporting multiple dynamic requirement from users. This paper contributes to these topics by the introduction of Cloudcompaas, a SLA-aware PaaS Cloud platform that manages the complete resource lifecycle. This platform features an extension of the SLA specification WS-Agreement, tailored to the specific needs of Cloud Computing. In particular, Cloudcompaas enables Cloud providers with a generic SLA model to deal with higher-level metrics, closer to end-user perception, and with flexible composition of the requirements of multiple actors in the computational scene. Moreover, Cloudcompaas provides a framework for general Cloud computing applications that could be dynamically adapted to correct the QoS violations by using the elasticity features of Cloud infrastructures. The effectiveness of this solution is demonstrated in this paper through a simulation that considers several realistic workload profiles, where Cloudcompaas achieves minimum cost and maximum efficiency, under highly heterogeneous utilization patterns.     

Towards Automated Provisioning and Emergency Handling in Renewable Energy Powered Datacenters

Designing eco-friendly system has been at the forefront of computing research. Faced with a growing concern about the server energy expenditure and the climate change, both industry and academia start to show high interest in computing systems powered by renewable energy sources. Existing proposals on this issue mainly focus on optimizing resource utilization or workload performance. The key supporting hardware structures for cross-layer power management and emergency handling mechanisms are often left unexplored. This paper presents GreenPod, a research framework for exploring scalable and dependable renewable power management in datacenters. An important feature of GreenPod is that it enables joint management of server power supplies and virtualized server workloads. Its interactive communication portal between servers and power supplies allows dataeenter operators to perform real-time renewable energy driven load migration and power emergency handling. Based on our system prototype, we discuss an important topic： virtual machine （VM） workloads survival when facing extended utility outage and insufficient onsite renewable power budget. We show that whether a VM can survive depends on the operating frequencies and workload characteristics. The proposed framework can greatly encourage and facilitate innovative research in dependable green computing.     

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.     

基于自适应控制方法的云计算服务器性能管理

随着云计算技术的广泛使用,如何对采用虚拟化技术的云计算服务器的性能进行有效管理,是云计算研究的热点问题之一.论文提出了一种基于自适应控制理论的动态资源控制策略（DRC）,该控制策略在保证服务级别协议的前提下,对运行在服务器上的各个虚拟机进行优化配置,使服务器的硬件资源得到最大化的利用.同时设计了一种新型的自适应线性二次高斯控制器,来应对具有Web应用所面对的动态负载.在基于Xen技术搭建的实验平台上,对服务器的性能在不同工作负载的情况下进行了测试,并与未采用DRC策略的服务器性能进行了对比.实验结果表明,在动态工作负载下,与为采用DRC策略的服务器相比,DRC控制策略能够有效保证不同Web应用的响应时间稳定在设定的参考值.     

Optimization of virtual resource management for cloud applications to cope with traffic burst

Being the latest computing paradigm, cloud computing has proliferated as many IT giants started to deliver resources as services. Thus application providers are free from the burden of the low-level implementation and system administration. Meanwhile, the fact that we are in an era of information explosion brings certain challenges. Some websites may encounter a sharp rising workload due to some unexpected social concerns, which make these websites unavailable or even fail to provide services in time. Currently, a post-action method based on human experience and system alarm is widely used to handle this scenario in industry, which has shortcomings like reaction delay. In our paper, we want to solve this problem by deploying such websites on cloud, and use features of the cloud to tackle it. We present a framework of dynamic virtual resource management in clouds, to cope with traffic burst that applications might encounter. The framework implements a whole work-flow from prediction of the sharp rising workload to a customized resource management module which guarantees the high availability of web applications and cost-effectiveness of the cloud service providers. Our experiments show the accuracy of our workload forecasting method by comparing it with other methods. The 1998 World Cup workload dataset used in our experiment reveals the applicability of our model in the specific scenarios of traffic burst. Also, a simulation-based experiment is designed to indicate that the proposed management framework detects changes in workload intensity that occur over time and allocates multiple virtualized IT resources accordingly to achieve high availability and cost-effective targets.     

vMES: Virtualization aware manufacturing execution system

The large scale emergence in the last decade of various cloud solutions, ranging from software-as-a-service (SaaS) based solutions for business process management and implementation to very sophisticated private cloud solutions capable of high performance computing (HPC) and efficient virtualization, constitute the building blocks for engineering the next generation of flexible enterprise systems that can respond with great agility to changes in their environment. These new technologies are adopted at a certain level by manufacturing enterprises in order to advance in a new era of mass customization where flexibility, scalability and agility are the differentiating factors. In this context, this paper introduces the virtualized manufacturing execution system (vMES), an intermediate layer in the manufacturing stack, and discusses the advantages and limitations offered by this approach for manufacturing enterprises. A classification of MES workloads based on the ISA-95 function model is presented, focusing on the virtualization techniques suitable for each workload, considering the algorithms and technologies used and the virtualization overhead. A pilot vMES implementation using a parallel process for smart resource provisioning and automatic scaling is also presented. The pilot implementation using six Adept robots and one IBM CloudBurst 2.1 private cloud and an ISA-95 based MES is described; the virtualization sequence is analyzed in several scenarios of resource workload collocation on physical cloud blades with and without perturbations.