El-Osery, Aly, and Jeff Prevost, eds., Control and Systems Engineering: A Report on Four Decades of Contributions., Vol. 27. Springer, 2015.
Book Chapters
J. Lwowski, P. Benavidez, J. J. PREVOST, and M. Jamshidi, "Task Allocation Using Parallelized Clustering and Auctioning Algorithms for Heterogeneous Robotic Swarms Operating on a Cloud Network,” Chapter 3 in Autonomy and Artificial Intelligence, (W. F. Lawless, et al, eds.), Springer-Verlag, 2017 (In Press).
abstract
In this paper, a novel centralized robotic swarm of heterogeneous unmanned vehicles consisting of autonomous surface vehicles and micro-aerial vehicles is presented. The swarm robots operate in an outdoor environment and are equipped with cameras and Global Positioning Systems (GPS). Manipulations of the swarm demonstrate how aspects of individual robotic platforms can be controlled cooperatively to accomplish a group task in an efficient manner. We demonstrate the use of air-based robots to build a map of important features of the local environment, such as the locations of targets. The map is then sent to a cloud-based cluster on a remote network. The cloud performs clustering algorithms using the map to calculate optimal clusters of the targets. The cloud then performs an auctioning algorithm to assign the clusters to the surface-based robots based on several factors such as relative position and capacities. The surface-robots then travel to their assigned clusters to complete the allocated tasks. Lastly, we present the results of simulating our cooperative swarm in both software and hardware, demonstrating the effectiveness of our proposed algorithm.
J. J. PREVOST, K. Nagothu., B. Kelly and M. Jamshidi, “Energy Aware Load Prediction for Cloud Data Centers”, Control and Systems Engineering, A Report on Four Decades of Contributions, Springer, 2015.
abstract
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.
Journal Publications
B. Kelley, A. Yerrapragada, J. J. PREVOST, N.Bahadori, N. Namvar, Ali Karimoddini, "Advancements in 5G Physical Layer Security", Special Issues of IEEE Access: Phy and Mac Advanced in 5G Wireless, Accepted Jan. 2017.
abstract
The development of 5G cellular will enable pervasive, on-demand, and high bandwidth communication services, such as the Internet of Things and self-driving vehicles. As 5G increasingly becomes critical infrastructure for innovative applications, equally, or perhaps, even more highly advanced ”5G-security” protocols are desirable to avoid major service breaches. Most current methods of telecommunications security rely upon authentication and encryption, generally applied at the session layer of the 7-layer OSI communication model. Our perspective is that 5G security can be advanced by applying quantum information theory and self organizing network (SON) theory. We address here the potential for applying both methods in the context of 5G physical layer security. Our major contribution is the presentation of quantum security for detecting clandestine eavesdropping and self organizing framework for external 5G physical layer jamming attacks.
S. M. A. Karim, J. J. PREVOST, P. Rad, “Efficient Real-Time Mobile Computation in the Cloud using Containers”, International Journal of Computing and Digital Systems, Vol. 5, Issue 1, ISSN 2210-142X, January. 2016.
abstract
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 utilizing containers. 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, and based on the inputs, we decide whether to offload the application to the cloud or not. Our algorithm was implemented using two different approaches, a fuzzy-logic system and a neuro-fuzzy system. These were both simulated using MATLAB®, and the results compared. Our previous work demonstrated that the fuzzy-logic system performed better than the referenced system. This paper demonstrates that the neuro-fuzzy approach, utilizing a container based hosting model, improves on our prior results.
P.Rad, M. Jamshidi, G. Berman, J. J. PREVOST, “A Software Defined Networking Architecture for Software Defined Clouds”, International Journal of Complex Systems – Computing, Sensing and Control, Vol. 3, Issue 2, ISSN 2334-4830, December 2015.
abstract
Multi-tenant clouds with resource virtualization offer elasticity of resources and elimination of initial cluster setup cost and time for applications. However, poor network performance, performance variation and noisy neighbors are some of the challenges for execution of high performance applications on public clouds. Utilizing these virtualized resources for scientific applications, which have complex communication patterns, require low latency communication mechanisms and a rich set of communication constructs. To minimize the virtualization overhead, a novel approach of low latency networking for HPC Clouds is proposed and implemented over a multi-technology software defined network. The efficiency of the proposed low-latency SDN is analyzed and evaluated for high performance applications. The results of the experiments show that the latest Mellanox FDR InfiniBand interconnect and Mellanox OpenStack plugin gives the best performance for implementing virtual machine based high performance clouds with large message sizes.
Peer-reviewed Conference Papers
B. Kelley, J. J. Prevost, P. Rad, A. Fatima, “Securing Cloud Containers using Quantum Networking Channels”, Proc. Smart Cloud 2016, New York, November 2016.
abstract
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 user applications, rejecting service to other applications. In this paper, we propose a quantum network security framework for the cloud. We devise a means by which quantum particles, denoted entangled bell pairs, are routed to network nodes. This enables teleportation of quantum information between source and destination only when root privileges are required by an application. The secure quantum channel works on a use-once only policy, so the key data cannot be easily copied, regenerated or spoofed without detection. A network framework for multiple pre-staged channels is devised and we illustrate that policy for network routing of entangle particles formulated as a multi-tenant teleportation network, capable of disseminating key data to servers hosting Docker container applications. The framework can achieve provably high levels of security and is capable of integration into a cloud data center for securing applications using Docker Containers. We also describe quantum network layer protocols for cloud container security that leverage the unique properties of quantum entanglement. To resolve security concerns, this layer would control access between application and container daemon, thereby facilitating
A. Sahba, J. J. PREVOST, “Hypercube based clusters in Cloud Computing”, Proc. World Automation Congress (WAC) 2016, San Juan, Puerto Rico, July 31-August 4, 2016.
abstract
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.
J. Benson, J. J. Prevost, P. Rad, “Survey of Automated Software Deployment for Computational and Engineering Research”, IEEE Systems Conference (SysCon) 2016, Orlando, Fl., April 18-24, 2016.
abstract
Software deployment is essential in today’s modern cloud systems. 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 for applications in response to load surges. This paper addresses the challenge of building an effective multi-cloud application deployment controller as a customer add-on outside of the cloud utility service itself. Such external controllers must function within the constraints of the cloud providers’ APIs. In this paper, we also describe the different steps necessary to deploy applications using such external controller. Then as a candidate for such external controllers, we use the defined taxonomy to survey several existing management tools such as Chef, SaltStack, and Ansible for application automation on cloud computing services based on the defined model. We use the taxonomy and survey results not only to identify similarities and differences of the architectural approaches of cloud computing, but also to identify areas requiring further research.
S.M.A. Karim, and J. J. PREVOST, “Efficient Mobile Computing using the Cloud”, Proceedings of the 2015 3rd International Conference on Future Internet of Things and Cloud, Rome, Italy, August 24-26 2015.
abstract
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.
M. Muppidi, P. Benavidez, J. J. PREVOST, P. Najafirad, and M. Jamshidi , “Cloud-Based Realtime Robotic Visual SLAM”, Proceedings of the 2015 IEEE International Systems Conference, Vancouver, 2015.
abstract
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J. J PREVOST, K. Nagothu, B. Kelley, and M. Jamshidi, “Optimal Calculation Overhead for Energy Efficient Cloud Workload Prediction”, Proc. World Automation Congress (WAC) 2014, Big Island, Hawaii, August 3-7, 2014.
abstract
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P. Rad, V. Lindberg*, J. J. Prevost, W. Zhang, M. Jamshidi, “ZeroVM: Secure distributed processing for data analytics”, Proc. World Automation Congress (WAC) 2014, Big Island, Hawaii, August 3-7, 2014.
abstract
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J. J. PREVOST; K. Nagothu, B. Kelley, and M. Jamshidi, “Optimal update frequency model for physical machine state change and virtual machine placement in the cloud”, Proc. 2013 8th International Conference on System of Systems Engineering (SoSE), Maui, Hawaii, June 2-6, 2013, EDAS # 1569744689, Best Paper Awardee.
abstract
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J. J. PREVOST, K. Nagothu, B. Kelley, and M. Jamshidi, “Load Prediction Algorithm for Multi-Tenant Virtual Machine Environments”, Proc. World Automation Congress (WAC) 2012, Puerto Vallarta, Mexico, Paper EDS # 1569572457, Best Paper Awardee.
abstract
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K. Nagothu, B. Kelley, J. J. Prevost, M. Jamshidi, M., “On prediction to dynamically assign heterogeneous microprocessors to the minimum joint power state to achieve Ultra Low Power Cloud Computing”, Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), paper # 6542221049, Pacific Grove, California, November 2011.
abstract
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J. J. PREVOST, K. Nagothu, B. Kelley, and M. Jamshidi, “Prediction of cloud data center networks loads using stochastic and neural models”, Proc. 2011 6th International Conference on System of Systems Engineering (SoSE), paper #1569453089, Albuquerque, NM, June 27-31, 2011.
abstract
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K. Nagothu, B. Kelley, J. J. PREVOST, M. Jamshidi, “Ultra low energy cloud computing using adaptive load prediction”, Proc. World Automation Congress (WAC), Kobe, Japan, September 2010.
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T. Shaneyfelt, K. Nagothu, K., J. J. PREVOST, A. Kumar, S. S. M. Ghazi, M. Jamshidi, “Control and simulation of robotic swarms in heterogeneous environments”, Proc. IEEE SMC Conference, Singapore, October 13-15, pp. 1314 – 1319, 2010
abstract
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J. J. PREVOST, “Optimization Model for Low Energy Computing in Cloud Datacenters”, published at UTSA Library, December 20, 2013.
abstract
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