使用虚拟机进行云计算环境的动态资源分配 [英] Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment

问题描述

我正在寻找上述标题的源代码，使用c＃或任何相关代码（如果有）请提供给我。



摘要：

云计算允许企业客户根据需求扩展和缩减其资源使用。云模型中许多被吹捧的收益来自通过虚拟化技术的资源多路复用。在本文中，我们提出了一个系统，该系统使用虚拟化技术根据应用需求动态分配数据中心资源，并通过优化使用的服务器数量来支持绿色计算。我们引入了偏度的概念来衡量服务器的多维资源利用的不均匀性。通过最小化偏差，我们可以很好地组合不同类型的工作负载，并提高服务器资源的整体利用率。我们开发了一套启发式算法，可有效防止系统过载，同时节省能源消耗。跟踪驱动仿真和实验结果表明我们的算法实现了良好的性能。





现有系统：

虚拟像Xen这样的机器监视器（VMM）提供了一种将虚拟机（VM）映射到物理资源的机制。此映射主要是从云用户隐藏的。例如，具有Amazon EC2服务的用户不知道其VM实例的运行位置。云提供商需要确保底层物理机（PM）有足够的资源来满足他们的需求。 VM实时迁移技术可以在应用程序运行时更改VM和PM之间的映射。 PM的容量也可以是异构的，因为多代硬件在数据中心中共存。





现有系统的缺点：

•政策问题仍然是如何自适应地决定映射，以便在使用的PM数量最小化的同时满足VM的资源需求。

•这对于虚拟机的资源需求是异构的，因为它们运行的​​应用程序集不同，并随着工作负载的增长和缩小而随时间变化。两个主要缺点是过载避免和绿色计算。





建议系统：

在本文中，我们介绍了自动化资源管理系统的设计和实现，以实现两个目标之间的良好平衡。两个目标是过载避免和绿色计算。

1.过载避免：PM的容量应足以满足在其上运行的所有VM的资源需求。否则，PM会过载并导致其VM的性能下降。

2.绿色计算：只要能够满足所有VM的需求，应尽量减少使用的PM数量。空闲PM可以关闭以节省能源。





建议系统的优点：

我们制作以下贡献：

v我们开发了一个资源分配系统，可以有效避免系统过载，同时最大限度地减少使用的服务器数量。

v我们将偏斜的概念引入衡量服务器的不均衡利用率。通过最小化偏度，我们可以在多维资源约束下提高服务器的整体利用率。

v我们设计了一种负载预测算法，可以准确捕获应用程序的未来资源使用情况，而无需查看虚拟机内部。该算法可以捕捉资源使用模式的上升趋势，并有助于显着减少放置流失。



BASE PAPER：http：//zhenxiao.com/papers/tpds2012。 pdf

I am looking for source code for above mentioned title using c# or any related codes if available please provide me.

ABSTRACT:
Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of "skewness" to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.


EXISTING SYSTEM:
Virtual machine monitors (VMMs) like Xen provide a mechanism for mapping virtual machines (VMs) to physical resources. This mapping is largely hidden from the cloud users. Users with the Amazon EC2 service, for example, do not know where their VM instances run. It is up to the cloud provider to make sure the underlying physical machines (PMs) have sufficient resources to meet their needs. VM live migration technology makes it possible to change the mapping between VMs and PMs While applications are running. The capacity of PMs can also be heterogeneous because multiple generations of hardware coexist in a data center.


DISADVANTAGES OF EXISTING SYSTEM:
• A policy issue remains as how to decide the mapping adaptively so that the resource demands of VMs are met while the number of PMs used is minimized.
• This is challenging when the resource needs of VMs are heterogeneous due to the diverse set of applications they run and vary with time as the workloads grow and shrink. The two main disadvantages are overload avoidance and green computing.


PROPOSED SYSTEM:
In this paper, we present the design and implementation of an automated resource management system that achieves a good balance between the two goals. Two goals are overload avoidance andgreen computing.
1. Overload avoidance: The capacity of a PM should be sufficient to satisfy the resource needs of all VMs running on it. Otherwise, the PM is overloaded and can lead to degraded performance of its VMs.
2. Green computing: The number of PMs used should be minimized as long as they can still satisfy the needs of all VMs. Idle PMs can be turned off to save energy.


ADVANTAGES OF PROPOSED SYSTEM:
We make the following contributions:
v We develop a resource allocation system that can avoid overload in the system effectively while minimizing the number of servers used.
v We introduce the concept of "skewness" to measure the uneven utilization of a server. By minimizing skewness, we can improve the overall utilization of servers in the face of multidimensional resource constraints.
v We design a load prediction algorithm that can capture the future resource usages of applications accurately without looking inside the VMs. The algorithm can capture the rising trend of resource usage patterns and help reduce the placement churn significantly.

BASE PAPER: http://zhenxiao.com/papers/tpds2012.pdf

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