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Cloud computing applications have grown rapidly in the last decade, where many organizations are migrating their applications to cloud data center as they expected high performance, reliability, and the best quality of service. Data centers deploy a variety of technologies, such as software-defined networks (SDN), to effectively manage their resources. The SDN approach is a highly flexible network architecture that automates network configuration using a centralized controller to overcome traditional network limitations. This paper proposes an SDN-based monitoring algorithm to detect the performance anomaly and identify the bottleneck of the distributed application in the cloud data center using the support vector machine algorithm. It collects the data from the network devices and calculates the performance metrics for the distributed application components that are used to train the SVM algorithm and build a baseline model of the normal behavior of the distributed application. The SVM model detects performance anomaly behavior and identifies the root cause of bottlenecks using one-class support vector machine (OCSVM) and multi-class support vector machine (MCSVM) algorithms. The proposed method does not require any knowledge about the running applications or depends on static threshold values for performance measurements. Simulation results show that the proposed method can detect and locate the failure occurrences efficiently with high precision and low overhead compared to statistical methods, Naive Bayes Classifier and the decision tree machine learning method.

Cloud computing applications have grown rapidly in the last decade, where many organizations are migrating their applications to cloud data center as they expected high performance, reliability, and the best quality of service. Data centers deploy a variety of technologies, such as software-defined networks (SDN), to effectively manage their resources. The SDN approach is a highly flexible network architecture that automates network configuration using a centralized controller to overcome traditional network limitations. This paper proposes an SDN-based monitoring algorithm to detect the performance anomaly and identify the bottleneck of the distributed application in the cloud data center using the support vector machine algorithm. It collects the data from the network devices and calculates the performance metrics for the distributed application components that are used to train the SVM algorithm and build a baseline model of the normal behavior of the distributed application. The SVM model detects performance anomaly behavior and identifies the root cause of bottlenecks using one-class support vector machine (OCSVM) and multi-class support vector machine (MCSVM) algorithms. The proposed method does not require any knowledge about the running applications or depends on static threshold values for performance measurements. Simulation results show that the proposed method can detect and locate the failure occurrences efficiently with high precision and low overhead compared to statistical methods, Naive Bayes Classifier and the decision tree machine learning method. [ABSTRACT FROM AUTHOR]

The unprecedented growth in data volume results in an urgent need for a dramatic increase in the size of data center networks. Accommodating millions to even billions of servers in the structure of data center networks, while providing highly-efficient interconnecting architectures, is of prominent importance nowadays. Generally, high scalability, noticeable fault-tolerance, great network capacity, limited delay, reasonable power consumption, and justifiable construction cost have to be listed as the principal design goals of well-organized cloud data center networks. In this paper, Platonica, a novel dual-centric and recursively defined architecture, is proposed. Platonica is constructed by employing fault-tolerant building blocks inspired by the edge connection pattern of five varieties of Platonic solids that turns the proposed architecture to a flexible scheme. It also escapes the adoption of any leveling policy for the network equipment, which leads to the efficient usage of all the employed resources. Moreover, to ameliorate the routing efficiency and the network fault-tolerance, two exclusively designed routing algorithms are presented. Both theoretical analysis and simulation results demonstrate that Platonica is highly fault-tolerant and can provide notable network capacity, good average path length, and low latency to support delay-sensitive and data-intensive applications. It can also be considered as a cost-effective and power-efficient network architecture. Altogether, Platonica achieves a good balance among all the major goals of designing cloud data center networks. [ABSTRACT FROM AUTHOR]

Being a key computing element in cloud data center, virtual machines should be able to migrate from one location to another to meet the requirements of the cloud users and the defined policies of the cloud computing system. The mobility is an important issue when a virtual machine migrates across IP subnets. This paper focuses on the mobility management in cloud computing systems, and proposes a mobility-oriented cloud data center network architecture based on the identity/locator decoupling method of the mobility-driven networks. In cloud data center network, a mobile node refers to a virtual machine, and the mobility behavior mainly refers to virtual machine migration. In the proposed architecture, a virtual machine could implement live migration between IP subnets without service interruption. The evaluation shows that the proposed scheme can solve mobility issues effectively in virtual machine migration among IP subnets.

The main research contents of the national basic research program of China（973 program）“Fundamental Theory and Key Technologies of Software Defined Cloud Data Center Network”was introduced.The challenges of cloud data center network faces currently were pointed out,and the solutions based on software defined networking were proposed.The four research tasks for software defined cloud data center network research were elaborated,namely,architecture of software defined cloud data center network,optimization of cloud data center network resource utilization,resource management of multi-tenant cloud data center network,and coordinated control of cloud data center network energy.

The extensive use of cloud services and newly-evolved security threats have made most cloud service providers deploy a variety of security devices such as firewalls, Internet Protocol Security, and intrusion detection systems to control resource access based on the security requirements of the data center. Therefore, security requirements are becoming more fine-grained where the control of access depends on heterogeneous partition levels like filtering network traffic, Internet Protocol Security encryption-based traffic forwarding, and payload inspection. However, today, cloud service providers are looking to systematically harden security by incorporating multiple security devices in the network in a cost-effective way. This requires evaluating several alternative security architectures to satisfy both organizational security requirements and business constraints. In this paper, we present an automated framework to synthesize data center security configurations by exploring various security design alternatives to provide better in-depth defense for the cloud infrastructure. The main design alternatives use different patterns of isolation for different segments of the cloud infrastructure. In this work, we take a dummy data center topology, cloud service provider security (connectivity and isolation) requirements and business constraints (usability and cost) as input, and synthesize a correct and optimal data center security strategy by way of determining the optimal placement of different security devices in the data center. [ABSTRACT FROM AUTHOR]