Behavior Of Virtual Machines Research Articles

Guaranteed Bang for the Buck: Modeling VDI Applications to Identify Storage Requirements

In the cloud environment, most services are provided by virtual machines (VMs). Identifying storage requirements of VMs is challenging, but it is essential for good user experiences while optimizing use of storage resources. Determining the storage configuration necessary to support and satisfy VMs first requires an accurate description of the VM configurations, and the problem is further exacerbated by the diversity and special characteristics of the VMs. In this paper, we study Virtual Desktop Infrastructure (VDI), a prevalent and complicated VM application, to identify and characterize storage requirements of VMs and determine how to meet such requirements with minimal storage resources and cost. We first create a model to describe the behavior of VDI, and we collect real VDI traces to populate this model. The model allows us to identify the storage requirements of VDI and determine the potential bottlenecks of a given storage configuration. Based on this information, we can tell what capacity and minimum capability a storage configuration needs in order to support and satisfy given VDI configurations. We show that our model can describe more fine-grained VM behavior varying with time and virtual disk types compared with the rules of thumb currently used in industry.

Power-aware performance analysis of self-adaptive resource management in IaaS clouds

In this paper, Stochastic Activity Networks (SANs) are used to model and evaluate the performance and power consumption of an Infrastructure-as-a-Service (IaaS) cloud. The proposed SAN model is scalable and flexible, yet encompasses some details of an IaaS cloud, such as Virtual Machine (VM) provisioning, VM multiplexing, and failure/repair behavior of VMs. Using the proposed SAN, a power-aware self-adaptive resource management scheme is presented for IaaS clouds that automatically adjusts the number of powered-on Physical Machines (PMs) regarding variable workloads in different time intervals. The proposed scheme respects user-oriented metrics by avoiding Service Level Agreement (SLA) violations while taking provider-oriented metrics into consideration. The behavior of the proposed scheme is analyzed when the arriving workload changes, and then its performance is compared with two non-adaptive baselines based on diverse performance and power consumption measures defined on the system. A validation of the proposed SAN model and the resource management scheme against an adapted version of the CloudSim framework is also presented.

Parameter Tuning for Scalable Multi-Resource Server Consolidation in Cloud Systems

Infrastructure as a Service cloud providers are increasingly relying on scalable and efficient Virtual Machines (VMs) placement as the main solution for reducing unnecessarycosts and wastes of physical resources. However, thecontinuous growth of the size of cloud data centers posesscalability challenges to find optimal placement solutions. The use of heuristics and simplified server consolidation models that partially discard information about the VMs behavior represents the typical approach to guarantee scalability, but at the expense of suboptimal placement solutions. A recently proposed alternative approach, namely Class-Based Placement (CBP), divides VMs in classes with similar behavior in terms of resource usage, and addresses scalability by considering a small-scale server consolidation problem that is replicated as a building block for the whole data center. However, the server consolidation model exploited by the CBP technique suffers from two main limitations. First, it considers only one VM resource (CPU) for the consolidation problem. Second, it does not analyze the impact of the number (and size) of building blocks to consider. Many small building blocks may reduce the overall VMs placement solution quality due to fragmentation of the physical server resources over blocks. On the other hand, few large building blocks may become computationally expensive to handle and may be unsolvable due to the problem complexity. This paper extends the CBP server consolidation model to take into account multiple resources. Furthermore, we analyze the impact of block size on the performance of the proposed consolidation model, and we present and compare multiple strategies to estimate the best number of blocks. Our proposal is validated through experimental results based on a real cloud computing data center.

Formal Description of the Virtual Machines System Framework

Virtual resources have the duality of hardware and software, which brings new challenges to virtual resources management and manipulation. There is urgent need for formally describing virtual resources in order to effectively reason about virtual resources allocation, scheduling and management. This paper describes the virtual resources and their dynamic behavior. We first give the formal definition of the resources pool and the virtual machine (VM). Then, we describe the dynamic behavior of VMs. In particular, we present a formal description of how virtual resources are used by VMs. This paper reveals the logical laws of virtual resources and their dynamic behaviors and makes the virtualization theory system more rigorous.

Detecting similarities in virtual machine behavior for cloud monitoring using smoothed histograms

The growing size and complexity of cloud systems determine scalability issues for resource monitoring and management. While most existing solutions consider each Virtual Machine (VM) as a black box with independent characteristics, we embrace a new perspective where VMs with similar behaviors in terms of resource usage are clustered together. We argue that this new approach has the potential to address scalability issues in cloud monitoring and management. In this paper, we propose a technique to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. This innovative technique models VMs behavior exploiting the probability histogram of their resources usage, and performs smoothing-based noise reduction and selection of the most relevant information to consider for the clustering process. Through extensive evaluation, we show that our proposal achieves high and stable performance in terms of automatic VM clustering, and can reduce the monitoring requirements of cloud systems.

Improving Scalability of Cloud Monitoring Through PCA-Based Clustering of Virtual Machines

Cloud computing has recently emerged as a leading paradigm to allow customers to run their applications in virtualized large-scale data centers. Existing solutions for monitoring and management of these infrastructures consider virtual machines (VMs) as independent entities with their own characteristics. However, these approaches suffer from scalability issues due to the increasing number of VMs in modern cloud data centers. We claim that scalability issues can be addressed by leveraging the similarity among VMs behavior in terms of resource usage patterns. In this paper we propose an automated methodology to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. The innovative contribution of the proposed methodology is the use of the statistical technique known as principal component analysis (PCA) to automatically select the most relevant information to cluster similar VMs. We apply the methodology to two case studies, a virtualized testbed and a real enterprise data center. In both case studies, the automatic data selection based on PCA allows us to achieve high performance, with a percentage of correctly clustered VMs between 80% and 100% even for short time series (1 day) of monitored data. Furthermore, we estimate the potential reduction in the amount of collected data to demonstrate how our proposal may address the scalability issues related to monitoring and management in cloud computing data centers.