Cluster Of Virtual Machines Research Articles

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.

Applying authentication and network security to in-cloud enterprise resource planning system

The service-oriented hosts in enterprises like enterprise resources planning (ERP) system have always encountered the crucial problem of unexpected down-time or system failure that will cause data loss and system termination. Failover is a challenge issue that cannot be done successfully between physical hosts. Traditional information security using demilitarized zone approach costs a lot. Therefore, this paper introduces in-cloud enterprise resources planning (in-cloud ERP) deployed in the virtual machine cluster together with access control authentication and network security which can resolve the three problems mentioned above. Access control authentication and network security have been implemented in the cloud computing system to prevent the service-oriented hosts form external fraud, intrusion, or malicious attacks. As a result of the experiments the number of accessing in-cloud ERP is 5.2 times as many as in-house ERP. The total expenditure of in-cloud ERP has decreased significantly to 48.4 % the cost of in-house ERP. In terms of operational speed, the approach proposed in this paper outperforms two well-known benchmark ERP systems, in-house ECC 6.0 and in-cloud ByDesign.

Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems

Cloud computing has recently emerged as a new paradigm to provide computing services through large-size data centers where customers may run their applications in a virtualized environment. The advantages of cloud in terms of flexibility and economy encourage many enterprises to migrate from local data centers to cloud platforms, thus contributing to the success of such infrastructures. However, as size and complexity of cloud infrastructures grow, scalability issues arise in monitoring and management processes. Scalability issues are exacerbated because available solutions typically consider each virtual machine (VM) as a black box with independent characteristics, which is monitored at a fine-grained granularity level for management purposes, thus generating huge amounts of data to handle. We claim that scalability issues can be addressed by leveraging the similarity between VMs in terms of resource usage patterns. In this paper, we propose an automated methodology to cluster similar VMs starting from their resource usage information, assuming no knowledge of the software executed on them. This is an innovative methodology that combines the Bhattacharyya distance and ensemble techniques to provide a stable evaluation of similarity between probability distributions of multiple VM resource usage, considering both system- and network-related data. We evaluate the methodology through a set of experiments on data coming from an enterprise data center. We show that our proposal achieves high and stable performance in automatic VMs clustering, with a significant reduction in the amount of data collected which allows to lighten the monitoring requirements of a cloud data center.

EScience in the Social Cloud

Social networks offer great potential for fostering collaboration between individuals and amongst groups. This potential collaborative environment is not only applicable for recreation, but can also provide considerable value to diverse research communities. For this reason scientists are increasingly utilizing social networking concepts in projects to form groups, share information, publicize their work and communicate with their peers. This article describes two different approaches to supporting eScience, by providing scientific computing and collaboration within what we term the Social Cloud. In our first approach the social network is used as a collaborative overlay, in combination with the ad hoc creation of infrastructure composed of virtual machine clusters built from resources contributed, by the users, to the Social Cloud. Our second approach is based around the principle of volunteer computing, where the Social Cloud provides researchers with a platform to exploit social networks by reaching out to non technical users who would otherwise be unlikely to donate computational time for scientific and other research. In this article we specifically explore the motivations of users to contribute computational time and examine the various ways these motivations can be catered to through the use of incentives in existing social networks.

A novel Adaptive Cluster Transformation (ACT)-based intrusion tolerant architecture for hybrid information technology

Recently, the building of strong intrusion tolerant systems is in great demand since the openness and the distributed nature of information systems are easily used to compromise the systems by intentional attacks. To achieve intrusion tolerance by enabling the systems to survive various types of intrusions, we suggest a novel approach, Adaptive Cluster Transformation (ACT), in this paper. Instead of using a fixed cluster size as in conventional approaches, ACT adapts a variable cluster size depending on the system status. This is proved to maintain good quality of service (QoS). In addition, the early prediction of incoming massive packets makes ACT possible to replace any damaged clusters with new ones consisting of pristine virtual machines (VMs). This also contributes to defend the system against a Denial of Service (DoS). The performance of ACT is compared with other fixed sizes of VM cluster architectures by CSIM 20. And it is verified that the proposed method is more effective in maintaining the specific level of QoS as well as providing strong security to the targeted system.

A Novel Cloud Load Balancing Mechanism in Premise of Ensuring QoS

In premise of ensuring users' QoS, the paper proposes a novel load balancing mechanism in cloud computing environment. The idea takes into account not only the energy efficiency of cloud providers, also the needs of users' QoS. The main work includes: construct QoS model, resource model of cloud infrastructure and the mapping between low-level resource metrics and QoS attributes; model load status of the virtual machine instance, and estimate the resource utilization ratio of the virtual machine cluster quantitatively; design the task scheduling algorithm and elastic scaling algorithm to achieve the tasks distribution and the elastic scaling of the virtual machines cluster respectively. By simulation using CloudSim platform, the result shows that this novel method has better load balance degree and complement time compared with other common algorithms such as round robin and green load, achieving the purpose of optimal load balancing in premise of ensuring QoS.

Automated Clustering of Virtual Machines based on Correlation of Resource Usage

The recent growth in demand for modern applicationscombined with the shift to the Cloud computing paradigm have led to the establishment of large-scale cloud data centers. The increasing size of these infrastructures represents a major challenge in terms of monitoring and management of the system resources. Available solutions typically consider every Virtual Machine (VM) as a black box each with independent characteristics, and face scalability issues by reducing the number of monitored resource samples, considering in most cases only average CPU usage sampled at a coarse time granularity. We claim that scalability issues can be addressed by leveraging thesimilarity between VMs in terms of resource usage patterns.In this paper we propose an automated methodology to cluster VMs depending on the usage of multiple resources, both systemand network-related, assuming no knowledge of the services executed on them. This is an innovative methodology that exploits the correlation between the resource usage to cluster together similar VMs. We evaluate the methodology through a case study with data coming from an enterprise datacenter, and we show that high performance may be achieved in automatic VMs clustering. Furthermore, we estimate the reduction in the amount of data collected, thus showing that our proposal may simplify the monitoring requirements and help administrators totake decisions on the resource management of cloud computing datacenters.

Optimizing virtual machines using hybrid virtualization

Minimizing virtualization overhead and improving the reliability of virtual machines are challenging when establishing virtual machine cluster. Paravirtualization and hardware-assisted virtualization are two mainstream solutions for modern system virtualization. Hardware-assisted virtualization is superior in CPU and memory virtualization and becoming the leading solution, yet paravirtualization is still valuable in some aspects as it is capable of shortening the disposal path of I/O virtualization. Thus we propose the hybrid virtualization which runs the paravirtualized guest in the hardware-assisted virtual machine container to take advantage of both. Experiment results indicate that our hybrid solution outweighs origin paravirtualization by nearly 30% in memory intensive test and 50% in microbenchmarks. Meanwhile, compared with the origin hardware-assisted virtual machine, hybrid guest owns over 16% improvement in I/O intensive workloads.

Developing resource consolidation frameworks for moldable virtual machines in clouds

This paper considers the scenario where multiple clusters of Virtual Machines (i.e., termed Virtual Clusters) are hosted in a Cloud system consisting of a cluster of physical nodes. Multiple Virtual Clusters (VCs) cohabit in the physical cluster, with each VC offering a particular type of service for the incoming requests. In this context, VM consolidation, which strives to use a minimal number of nodes to accommodate all VMs in the system, plays an important role in saving resource consumption. Most existing consolidation methods proposed in the literature regard VMs as “rigid” during consolidation, i.e., VMs’ resource capacities remain unchanged. In VC environments, QoS is usually delivered by a VC as a single entity. Therefore, there is no reason why VMs’ resource capacity cannot be adjusted as long as the whole VC is still able to maintain the desired QoS. Treating VMs as “moldable” during consolidation may be able to further consolidate VMs into an even fewer number of nodes. This paper investigates this issue and develops a Genetic Algorithm (GA) to consolidate moldable VMs. The GA is able to evolve an optimized system state, which represents the VM-to-node mapping and the resource capacity allocated to each VM. After the new system state is calculated by the GA, the Cloud will transit from the current system state to the new one. The transition time represents overhead and should be minimized. In this paper, a cost model is formalized to capture the transition overhead, and a reconfiguration algorithm is developed to transit the Cloud to the optimized system state with low transition overhead. Experiments have been conducted to evaluate the performance of the GA and the reconfiguration algorithm.

TopCluster: A hybrid cluster model to support dynamic deployment in Grid

Cluster virtualization is a promising approach to construct customized execution environments for Grid users. However, Virtual-Machine Cluster (VCluster) comes with the cost of the overhead caused by virtual machines, which therefore degrades system performance. In this paper, we propose a novel hybrid cluster model, namely TopCluster. By exploiting a Hybrid Batch System (HyBS) mechanism, TopCluster can flexibly allocate both physical or virtualized resources via a unified front-end. To reduce the overhead caused by the dynamic deployment of virtual machines and applications, we also propose a value-based deployment strategy along with TopCluster. An XML-based Application Deployment Description Language (ADDL) is also newly proposed to describe properties, installation procedure, and configuration operations of applications. A prototype system has been implemented and a series of experiments was conducted to evaluate TopCluster by replaying a real world trace. Results show that TopCluster can averagely generate 24.6% and 46.8% higher system throughput, 26.3% and 110.6% higher resource utility, and 35.1% and 56.1% lower waiting time of jobs than two commonly applied cluster models in Grid: Traditional Physical Cluster (PCluster) and VCluster.

Multi-Layered Performance Monitoring for Cloud Computing Applications

Abstract Cloud computing revolutionizes the way large amounts of data are processed and offers a compelling paradigm to organizations. An increasing number of data-intensive scientific applications are being ported to cloud environments such as virtualized clusters, in order to take advantage of increased cost-efficiency, flexibility, scalability, improved hardware utilization and reduced carbon footprint, among others. However, due to the complexity of the application execution environment, routine tasks such as monitoring, performance analysis and debugging of applications deployed on the cloud become cumbersome and complex. These tasks often require close interaction and inspection of multiple layers in the application and system software stack. For example, when analyzing a distributed application that has been provisioned on a cluster of virtual machines, a researcher might need to monitor the execution of his program on the VMs, or the availability of physical resources to the VMs. This would require the researcher to use different sets of tools to collect and analyze performance data from each level. Otus is a tool that enables resource attribution in clusters and currently reports only the virtual resource utilization and not the physical resource utilization on virtualized clusters. This is insufficient to fully understand application behavior on a cloud platform; it would fail to account for the state of the physical infrastructure, its availability or the variation in load by other VMs on the same physical host, for example. We are extending Otus to collect metrics from multiple layers, starting with the Hypervisor. Otus can now collect information from both the VM level as well as the Hypervisor level; and this information is collected in an OpenTSDB database, which is scalable to large clusters. A web-based application allows the researcher to selectively visualize these metrics in real-time or for a particular time range in the past. We have tested our multi-layered monitoring technique on several Hadoop Mapreduce applications and clearly identified the causes of several performance problems that would otherwise not be clear using existing methods. Apart from helping researchers understand application needs, our technique could also help accelerate the development and testing of new platforms for cloud researchers.

Behavior analysis of neural network ensemble algorithm on a virtual machine cluster

Ensemble learning has gained considerable attention in different learning tasks including regression, classification, and clustering problems. One of the drawbacks of the ensemble is the high computational cost of training stages. Resampling local negative correlation (RLNC) is a technique that combines two well-known methods to generate ensemble diversity—resampling and error negative correlation—and a fine-grain parallel approach that allows us to achieve a satisfactory balance between accuracy and efficiency. In this paper, we introduce a structure of the virtual machine aimed to test diverse selection strategies of parameters in neural ensemble designs, such as RLNC. We assess the parallel performance of this approach on a virtual machine cluster based on the full virtualization paradigm, using speedup and efficiency as performance metrics, for different numbers of processors and training data sizes.

Remote Automation Laboratory Using a Cluster of Virtual Machines

This paper presents a remote laboratory for industrial automation comprising different programmable logic controller (PLC) manufacturers. This facility provides an environment for remote users to learn many automation topics while using different PLCs together with several types of sensors, actuators, and industrial communication networks. The laboratory architecture that makes up the remote automation laboratory (RAL) is based on a Moodle-server master PC (MPC), which also manages the booking system of PLC benches available in the laboratory. There exist a cluster of virtual machines (VMs) running on several slave PCs (SPCs), four VMs per SPC. Each VM can manage a type of PLC bench of the automation laboratory. The MPC server balances the load of SPCs allowing the implementation and easy upgrading of the RAL offering different PLCs at the same time within the same remote interface. The remote laboratory has Internet protocol cameras providing a view of the real environment of each PLC bench to the remote user. The authentication system and management of remote users and PLC benches are made via Moodle plug-ins and hypertext preprocessor (PHP) scripts. Two external/internal routers dynamically configure the remote users' access to a particular PLC bench. RAL architecture can be applied to other e-learning areas.

CKAP4/p63 Is a Receptor for the Frizzled-8 Protein-related Antiproliferative Factor from Interstitial Cystitis Patients

Antiproliferative factor (APF) is a low molecular weight sialoglycopeptide that is secreted by bladder cells from interstitial cystitis patients and is a potent inhibitor of both normal bladder epithelial and bladder carcinoma cell proliferation. We hypothesized that APF may produce its antiproliferative effects by binding to a transmembrane receptor. This study demonstrates that cytoskeleton-associated protein 4/p63 (CKAP4/p63), a type II transmembrane receptor, binds with high affinity to APF. The antiproliferative activity of APF is effectively inhibited by preincubation with anti-CKAP4/p63-specific antibodies, as well as by short interfering RNA knockdown of CKAP4/p63. Immunofluorescent confocal microscopy showed co-localization of anti-CKAP4/p63 and rhodamine-labeled synthetic APF binding in both cell membrane and perinuclear areas. APF also inhibits the proliferation of HeLa cervical carcinoma cells that are known to express CKAP4/p63. These data indicate that CKAP4/p63 is an important epithelial cell receptor for APF.