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Pedestrian recognition is a key problem for a number of application domains namely autonomous driving, search and rescue, surveillance and robotics. Real-time pedestrian recognition entails determining if a pedestrian is in an image frame. State-of-art pedestrian detection convolution neural networks(CNN) such as Fast R-CNN depend on computationally expensive region detection algorithms to hypothesize...
While all cloud based platforms possess security vulnerabilities, the additional security challenges with container systems stem from the sharing of Host OS among independent containers. If a malicious application was to break into the root of container Daemon, it could gain root access into the host kernel thereby compromising the entire system. It could create Denial-Of-Service attack for other...
High performance computing (HPC) means the aggregation of computational power to increase the ability of processing large problems in science, engineering, and business. HPC on the cloud allows performing on demand HPC tasks by high performance clusters in a cloud environment. The connection structure of the nodes in HPC clusters should provide fast internode communication. It is important that scalability...
Automated, efficient software deployment is essential for today's modern cloud hosting providers. With advances in cloud technology, on demand cloud services offered by public providers are becoming increasingly powerful, anchoring the ecosystem of cloud services. Cloud infrastructure services are appealing in part because they enable customers to acquire and release infrastructure resources on demand...
Mobile devices have limited resources in terms of power and bandwidth. Cloud computing offers a way to reduce the power consumption of mobile devices by offloading computation to the cloud. However, offloading computation means an increase in communication energy consumption. The trade-off between energy and network characteristics (bandwidth/latency) in a mobile device is very important. Therefore...
Prior work has shown that Visual SLAM (VSLAM) algorithms can successfully be used for realtime processing on local robots. As the data processing requirements increase, due to image size or robot velocity constraints, local processing may no longer be practical. Offloading the VSLAM processing to systems running in a cloud deployment of Robot Operating System (ROS) is proposed as a method for managing...
Amazon recently estimated that the cost of energy for its datacenters reached 42% of the total cost of operation. Our previous research proposed an algorithm to predict how much cloud workload is expected during a future time interval. Accurate knowledge of the future workload allows the datacenter operator to place unneeded physical servers in a low-power state to save energy. If more system capacity...
Cloud computing is evolving into the default operational framework running modern data centers. Efficient data center operation is concerned with the total amount of energy consumed as well as assuring adequate resources are available to process all of the incoming work requests. Existing research has demonstrated several algorithms that can be used to determine the optimal number of resources required...
Computer systems configured in Cloud architectures have become more prevalent in the data center. However, a growing concern is the increasing amount of energy required to operate the data center. This has led to an industry-wide effort to look for ways the energy footprint of the datacenter can be reduced. Efficient scaling of the cloud's nodes is one way to manage the energy consumption. This goal...
The increasing demand for cloud computing resources has led to a commensurate increase in the operating power consumption of the systems that comprise the cloud. In this paper, we introduce a novel framework combining load demand prediction and stochastic state transition models. We claim that our model will lead to optimal cloud resource allocation by minimizing energy consumed while maintaining...
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