1,570 research outputs found
Study, development and characterization of aluminum based materials by additive manufacturing
No full textThis PhD thesis work was pursued in a context of investigation and development of different kind of materials by DMLS, in particular aluminum alloys. Among these, AlSi10Mg0.3 casting alloy was investigated and characterized from both mechanical and microstructural (through optical and electron microscopy) point of view. It was previously fundamental to study the process parameters (in particular scan speed, laser power and hatching distance) in order to obtain samples with density near to the theoretical one for the different characterizations. In addition, processing of composite materials has attracted interest due to the potential of the process in freeform fabrication of intricate articles in a reduced supply chain. For this reason, the studied alloy was used as a matrix to fabricate two different "ex-situ" Aluminun Matrix Composites (AMCs): starting from AlSi10Mg powders and mixing them with submicrometric SiC and nanometric MgAl2O4 . It was fundamental to investigate the effect of the main process parameters to obtain composites with the highest density, to be then characterize
A Correlation-based Methodology to Infer Communication Patterns between Cloud Virtual Machines
No full textThe VMs allocation over the servers of a cloud data center is becoming a critical task to guarantee energy savings and high performance.
Only recently network-aware techniques for VMs allocation have been proposed. However, a network-aware placement requires the knowledge of data transfer patterns between VMs, so that
VMs exchanging significant amount of information can be placed on low cost communication paths (e.g. on the same server). The knowledge of this information is not easy to obtain unless a specialized monitoring function is deployed over the data center infrastructure.
In this paper, we propose a correlation-based methodology that aims to infer communication patterns starting from the network traffic time series of each VM without relaying on a special
purpose monitoring. Our study focuses on the case where a data center hosts a multi-tier application deployed using horizontal replication. This typical case of application deployment makes
particularly challenging the identification of VMs communications because the traffic patterns are similar in every VM belonging to the same application tier. In the evaluation of the proposed methodology, we compare different correlation indexes and we consider different time granularities for the monitoring of network traffic. Our study demonstrates the feasibility of the proposed approach, that can identify which VMs are interacting among themselves even in the challenging scenario considered in our experiments
Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems
No full textCloud 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
Scalable and automatic virtual machines placement based on behavioral similarities
No full textThe 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
AGATE: Adaptive Gray Area-based TEchnique to Cluster Virtual Machines with Similar Behavior
No full textAs cloud computing data centers grow in size and complexity to accommodate an increasing number of virtual machines, the scalability of monitoring and management processes becomes a major challenge. Recent research studies show that automatically clustering virtual machines that are similar in terms of resource usage may address the scalability issues of IaaS clouds. Existing solutions provides high clustering accuracy at the cost of very long observation periods, that are not compatible with dynamic cloud scenarios where VMs may frequently join and leave. We propose a novel technique, namely AGATE (Adaptive Gray Area-based TEchnique), that provides accurate clustering results for a subset of VMs after a very short time. This result is achieved by introducing elements of fuzzy logic into the clustering process to identify the VMs with undecided clustering assignment (the so-called gray area), that should be monitored for longer periods. To evaluate the performance of the proposed solution, we apply the technique to multiple case studies with real and synthetic workloads. We demonstrate that our solution can correctly identify the behavior of a high percentage of VMs after few hours of observations, and significantly reduce the data required for monitoring with respect to state-of-the-art solutions
Identifying Communication Patterns between Virtual Machines in Software-Defined Data Centers
No full textModern cloud data centers typically exploit management strategies to reduce the overall energy consumption. While most of the solutions focus on the energy consumption due to computational elements, the advent of the Software-Defined Network paradigm opens the possibility for more complex strategies taking into account the network traffic exchange within the data center. However, a network-aware Virtual Machine (VM) allocation requires the knowledge of data communication patterns, so that VMs exchanging significant amount of data can be placed on the same physical host or on low cost communication paths. In Infrastructure as a Service data centers, the information about VMs traffic exchange is not easily available unless a specialized monitoring function is deployed over the data center infrastructure. The main contribution of this paper is a methodology to infer VMs communication patterns starting from input/output network traffic time series of each VM and without relaying on a special purpose monitoring. Our reference scenario is a software-defined data center hosting a multi-tier application deployed using horizontal replication. The proposed methodology has two main goals to support a network-aware VMs allocation: first, to identify couples of intensively communicating VMs through correlation-based analysis of the time series; second, to identify VMs belonging to the same vertical stack of a multi-tier application. We evaluate the methodology by comparing different correlation indexes, clustering algorithms and time granularities to monitor the network traffic. The experimental results demonstrate the capability of the proposed approach to identify interacting VMs, even in a challenging scenario where the traffic patterns are similar in every VM belonging to the same application tier
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
Automated Clustering of Virtual Machines based on Correlation of Resource Usage
No full textThe recent growth in demand for modern applications combined 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 the similarity 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 system- and 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 to take decisions on the resource management of cloud computing datacenters
Parameter tuning for scalable multi-resource server consolidation in cloud systems
No full textInfrastructure as a Service cloud providers are increasingly relying on scalable and efficient Virtual Machines (VMs) placement as the main solution for reducing unnecessary costs and wastes of physical resources. However, the continuous growth of the size of cloud data centers poses scalability 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
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