John J. Prevost

Prediction of cloud data center networks loads using stochastic and neural models

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 required performance levels. We characterize the ability of neural network and auto-regressive linear prediction algorithms to forecast loads in cloud data center applications. In this paper, the performance of our models against two sets of data at multiple look-ahead times is also presented.

Cloud-based realtime robotic Visual SLAM

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 increasing processing constraints. The traditional bottleneck with VSLAM performing feature identification and matching across a large database. In this paper, we present a system and algorithms to reduce computational time and storage requirements for feature identification and matching components of VSLAM by offloading the processing to a cloud comprised of a cluster of compute nodes. We compare this new approach to our prior approach where only the local resources of the robot were used, and examine the increase in throughput made possible with this new processing architecture.

Optimal update frequency model for physical machine state change and virtual machine placement in the cloud

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 to service these requests. However, a key issue not addressed in these algorithms is determining the frequency of recalculating the number of required resources. Changing the required resources at a rate slower than the optimal update frequency results in lower energy efficiency due to the over allocation of resources. Changing the resources at a rate higher than the optimal frequency results in insufficient time for systems to change state, which results in SLA violations. In this paper, a stochastic optimization model is presented that determines the optimal update frequency for changing the states of the nodes of the cloud as well as determining the proper frequency for recalculating the maximum expected load, which improves the determination of the optimum number of resources required, therefore maximizes energy efficiency and minimizes SLA violations.

Control and simulation of robotic swarms in heterogeneous environments

Simulation provides a low cost method of initial testing of control for robotic swarms. The expansion of robotic swarms to heterogeneous environments drives the need to model cooperative operation in those environments. The Autonomous Control Engineering center at The University of Texas at San Antonio is investigating methods of simulation techniques and simulation environments. This paper presents results from adapting simulation tools for diverse environments.

Simulation of underwater robots using MS Robot Studio

One stage in designing the control for underwater robot swarms is to confirm the control algorithms via simulation. To perform the simulation Microsoftpsilas Robotic Studiocopy was chosen. The problem with this simulator and others like it is that it is set up for land-based robots only. This paper explores one possible way to get around this limitation. This solution cannot only work for underwater vehicles but aerial vehicles as well.

Optimal calculation overhead for energy efficient cloud workload prediction

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 is required, servers in a low-power state are transitioned back to an active state. In this paper, we extend our prior research by presenting a new approach to determining the frequency of calculating the prediction of the expected capacity. We present a dynamic prediction quantization method to determine the optimal number of prediction calculation intervals. These new algorithms allow us to predict future load within required Service Level Agreements while minimizing the number of times the prediction calculations must be performed. We finally test this model by simulating the stochastic time horizon and dynamic quantization algorithms and compare the results with three competing methods. We show that our model provides up to a 20% reduction in the number of calculations required while maintaining the given Service Level Agreement.

Hypercube based clusters in Cloud Computing

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 is preserved as well. This paper proposes a hypercube topology for connecting the nodes in an HPC cluster that facilitates fast communications between nodes. In addition, the proposed hypercube topology provides the ability to scale, which is needed for high performance computing on the cloud.

Efficient Mobile Computation Using the Cloud

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 computation offloading must be done strategically. The optimum utilization of the available mobile device resources needs to be assured. In this paper, we propose an intelligent and dynamic algorithm to offload computation to the cloud. We focus on offloading computation based upon the communication topology, device energy and user inputs. We analyze the cost of offloading computation for different user inputs. Based on the inputs, we decide whether to offload the application to the cloud or not. We have simulated our algorithm in MATLAB®, and compared our result to previous approaches. We have found out that our algorithm saves more time, compared to a previous approach, and also reduces device energy usage by moving energy hungry processes to the cloud.

Energy Aware Load Prediction for Cloud Data Centers

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 at a specific time. This allows physical servers determined not to be needed to be placed in a low-power sleep state to save energy. If more system capacity is required, servers in a sleep state are transitioned back to an active state. In this paper, we extend our prior research by presenting both a stochastic model for state change as well as a new approach to determining the sampling frequency for performing the prediction of the expected capacity. The first result we show is that this allows the optimal prediction time horizon to be chosen. We next present a dynamic prediction quantization method to determine the optimal number of prediction calculation intervals. Both of these new algorithms allow us to predict future load within required Service Level Agreements while minimizing the number of prediction calculations. This effectively optimizes our ability to predict while minimizing the detrimental effect of additional calculations on our cloud resources. Finally, we test this model by simulating the stochastic time horizon and dynamic quantization algorithms and compare the results with three competing methods. We show that our model provides up to a 20% reduction in the number of calculations required while maintaining the given Service Level Agreement.

A Comprehensive Solution for Research-Oriented Cloud Computing.

Cutting edge research today requires researchers to perform computationally intensive calculations and/or create models and simulations using large sums of data in order to reach research-backed conclusions. As datasets, models, and calculations increase in size and scope they present a computational and analytical challenge to the researcher. Advances in cloud computing and the emergence of big data analytic tools are ideal to aid the researcher in tackling this challenge. Although researchers have been using cloud-based software services to propel their research, many institutions have not considered harnessing the Infrastructure as a Service model. The reluctance to adopt Infrastructure as a Service in academia can be attributed to many researchers lacking the high degree of technical experience needed to design, procure, and manage custom cloud-based infrastructure. In this paper, we propose a comprehensive solution consisting of a fully independent cloud automation framework and a modular data analytics platform which will allow researchers to create and utilize domain specific cloud solutions irrespective of their technical knowledge, reducing the overall effort and time required to complete research.