1,721,029 research outputs found
Scalable and automatic virtual machines placement based on behavioral similarities
Full text linkThe success of the cloud computing paradigm is leading to a significant growth in size and complexity of cloud data centers. This growth exacerbates the scalability issues of the Virtual Machines (VMs) placement problem, that assigns VMs to the physical nodes of the infrastructure. This task can be modeled as a multi-dimensional bin-packing problem, with the goal to minimize the number of physical servers (for economic and environmental reasons), while ensuring that each VM can access the resources required in the next future. Unfortunately, the naïve bin packing problem applied to a real data center is not solvable in a reasonable time because the high number of VMs and of physical nodes makes the problem computationally unmanageable. Existing solutions improve scalability at the expense of solution quality, resulting in higher costs and heavier environmental footprint. The Class-Based placement technique (CBP) is a novel approach that exploits existing solutions to automatically group VMs showing similar behaviour. The Class-Based technique solves a placement problem that considers only some representative VMs for each class, and that can be replicated as a building block to solve the global VMs placement problem. Using real traces, we analyse our proposal performance, comparing different alternatives to automatically determine the number of building blocks. Furthermore, we compare our proposal against the existing alternatives and evaluate the results for different workload compositions. We demonstrate that the CBP proposal outperforms existing solutions in terms of scalability and VM placement quality
Detecting Similarities in Virtual Machine Behavior for Cloud Monitoring using Smoothed Histograms
No full textThe growing size and complexity of cloud systems determine scalability issues
for resource monitoring and management. While most existing solutions con-
sider 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
A distributed architecture to support infomoblity services
No full textThe growing popularity of mobile and location aware devices allows the deployment of infomobility systems that provide access to information and services for the support of user mobility. Current systems for infomobility services assume that most information is already available on the mobile device and the device connectivity is used for receiving critical messages from a central server. However, we argue that the next generation of infomobility services will be characterized by collaboration and interaction among the users, provided through real-time bidirectional communication between the client devices and the infomobility system.In this paper we propose an innovative architecture to support next generation infomobility services providing interaction and collaboration among themobile users that can travel by several different transportation means, ranging from cars to trains to foot. We discuss the design issues of the architecture, with particular emphasis on scalability, availability and user data privacy, which are critical in a collaborative infomobility scenario
A comparison of techniques to detect similarities in cloud virtual machines
No full textScalability in monitoring and management of cloud data centres may be improved through the clustering of virtual machines (VMs) exhibiting similar behaviour. However, available solutions for automatic VM clustering present some important drawbacks that hinder their applicability to real cloud scenarios. For example, existing solutions show a clear trade-off between the accuracy of the VMs clustering and the computational cost of the automatic process; moreover, their performance shows a strong dependence on specific technique parameters. To overcome these issues, we propose a novel approach for VM clustering that uses Mixture of Gaussians (MoGs) together with the Kullback-Leiber divergence to model similarity between VMs. Furthermore, we provide a thorough experimental evaluation of our proposal and of existing techniques to identify the most suitable solution for different workload scenarios
Automated clustering of VMs for scalable cloud monitoring and management
No full textThe size of modern datacenters supporting
cloud computing represents a major challenge in terms of
monitoring and management of 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 monitoring re-
source samples, considering in most cases only average CPU
utilization of VMs sampled at a very coarse time granularity.
We claim that better management without compromising
scalability could be achieved by clustering together VMs
that show similar behavior in terms of resource utilization.
In this paper we propose an automated methodology to
cluster VMs depending on the utilization of their resources,
assuming no knowledge of the services executed on them. The
methodology considers several VM resources, both system-
and network-related, and exploits the correlation between the
resource demand to cluster together similar VMs. We apply
the proposed methodology to a case study with data coming
from an enterprise datacenter to evaluate the accuracy of
VMs clustering and to estimate the reduction in the amount
of data collected. The automatic clustering achieved through
our approach may simplify the monitoring requirements and
help administrators to take decisions on the management of
the resources in a cloud computing datacenter
A quantitative methodology based on component analysis to identify key users in social networks
No full textSocial networks are gaining an increasing popularity on the Internet and are becoming a critical media for business and marketing. Hence, it is important to identify the key users that may play a critical role as sources or targets of content dissemination. Existing approaches rely only on users social connections; however, considering a single kind of information does not guarantee satisfactory results for the identification of the key users. On the other hand, considering every possible user attribute is clearly unfeasible due to huge amount of heterogenous user information. In this paper, we propose to select and combine a subset of user attributes with the goal to identify sources and targets for content dissemination in a social network. We develop a quantitative methodology based on the principal component analysis. Experiments on the YouTube and Flickr networks demonstrate that our solution outperforms existing solutions by 15%
Improving scalability of cloud monitoring through PCA-based Clustering of Virtual Machines
No full textCloud 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
Technological solutions to support Mobile Web 2.0 services
No full textThe widespread diffusion and technological improvements of wireless networks and portable devices are facilitating mobile accesses to the Web and Web 2.0 services. The emerging Mobile Web 2.0 scenario still requires appropriate solutions to guarantee user interactions that are comparable with present levels of services. In this chapter we classify the most important services for Mobile Web 2.0, and we identify the key functions that are required to support each category of Mobile Web 2.0 services. We discuss some possible technological solutions to implement these functions at the client and at the server level, and we identify some research issues that are still open
Algorithms for Web Service Selection with Static and Dynamic Requirements
No full textA main feature of Service Oriented Architectures is the capability to support the development of new applications through the composition of existing Web services that are offered by different service providers. The runtime selection of which providers may better satisfy the end-user requirements in terms of quality of service remains an open issue in the context of Web services. The selection of the service providers has to satisfy requirements of different nature: requirements may refer to static qualities of the service providers, which do not change over time or change slowly compared to the service invocation time (for example related to provider reputation), and to dynamic qualities, which may change on a per-invocation basis (typically related to performance, such as the response time). The main contribution of this paper is to propose a family of novel runtime algorithms that select service providers on the basis of requirements involving both static and dynamic qualities, as in a typical Web scenario. We implement the proposed algorithms in a prototype and compare them with the solutions commonly used in service selection, which consider all the service provider qualities as static for the scope of the selection process. Our experiments show that a static management of quality requirements is viable only in the unrealistic case where workload remains stable over time, but it leads to very poor performance in variable environments. On the other hand, the combined management of static and dynamic quality requirements allows us to achieve better user-perceived performance over a wide range of scenarios, with the response time of the proposed algorithms that is reduced up to a 50 % with respect to that of static algorithms
Architectures for scalable and flexible Web personalization services
No full textThe complexity of services provided through theWeb is con-tinuously increasing and issues introduced by both heteroge-neous client devices and Web content personalization are be-coming a major challenge for the Web. Tailoring Web andmultimedia resources tomeet the user and client requirementsopens twomain novel issues in the research area of content de-livery. The content adaptation operations may be computa-tionally expensive, requiring high efficiency and scalability intheWeb architectures.Moreover, personalization services in-troduce security and consistency issues for user profile infor-mation management. In this paper, we propose a novel dis-tributed architecture, with four variants, for the efficient de-livery of personalized service where the nodes are organizedin two levels.We discuss how the architectural choices are af-fected by security and consistency constraints as well as by theaccess to privileged information of the content provider.More-over we discuss performance trade-offs of the various choices
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