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The extreme growth in deployment of cloud based services along with applications requiring high computational complexity has had an adverse effect on energy consumption in data centers. When cloud data centers add more computing capacity and increase in size, they generate more heat, require extra cooling. To counter this effect, energy dissipation must be reduced. Servers consume power even when...
Trust management systems provide a means for trustworthy interactions in cloud environments. However, trust establishment could be compromised when malicious cloud users intentionally provide unfair feedbacks to decrease the reputation of some cloud providers or to benefit others. In this paper, we define "Feedback Entropy" as a newmetric to detect unfair rating attacks. As such, we propose...
One of the key enablers of the digital society is a highly reliable information infrastructure that can ensure resiliency to a wide range of failures and attacks. In cloud networks, replicas of various content are located at geographically distributed data centers, thus inherently enhancing cloud network reliability through diversification and redundancy of user accessibility to the content. However,...
In support of Cloud Robotics, Robotics and Automation as a Service (RAaaS) frameworks have the potential to reduce the complexity of software development, simplify software installation and maintenance, and facilitate data sharing for machine learning. In this proof-of-concept paper, we describe Berkeley Robotics and Automation as a Service (Brass), a RAaaS prototype that allows robots to access a...
The volatility of web software systems, for example due to traffic fluctuations, can be addressed through cloud resource elasticity. Cloud providers offer specific services to automate the process of elasticity, so that application developers can efficiently and effectively manage their cloud resources. Current autoscaling methods mostly employ rule-based or threshold-based techniques. In this work,...
This paper proposes a novel and scalable model for automatic anomaly detection on a large system such as a cloud. Anomaly detection issues early warning of unusual behavior in dynamic environments by learning system characteristic from normal operational data. Anomaly detection in large systems is difficult to detect due heterogeneity, dynamicity, scalability, hidden complexity, and time limitation...
This poster shows the performance evaluation of UnaCloud Opportunistic Computing IaaS. We analyze from an HPC perspective, two virtualization frameworks Virtual Box and VMware ESXi and compare them over this particular opportunistic cloud environment. The benchmarks consist of two set of tests, High Performance Linpack and IOzone, that examine the performance and the Input/Output response. The purpose...
Data Center Networks (DCNs) constitute the communication backbone for the cloud computing paradigm. Recently, network connectivity analysis in terms of reliability has received attention from the network research community. The traditional network features are useful; however, they are insufficient to determine how well-connected or well-designed a DCN is against the node or link removals. In this...
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