Alireza Ghaffarkhah

Q-clouds: managing performance interference effects for QoS-aware clouds

Cloud computing offers users the ability to access large pools of computational and storage resources on demand. Multiple commercial clouds already allow businesses to replace, or supplement, privately owned IT assets, alleviating them from the burden of managing and maintaining these facilities. However, there are issues that must be addressed before this vision of utility computing can be fully realized. In existing systems, customers are charged based upon the amount of resources used or reserved, but no guarantees are made regarding the application level performance or quality-of-service (QoS) that the given resources will provide. As cloud providers continue to utilize virtualization technologies in their systems, this can become problematic. In particular, the consolidation of multiple customer applications onto multicore servers introduces performance interference between collocated workloads, significantly impacting application QoS. To address this challenge, we advocate that the cloud should transparently provision additional resources as necessary to achieve the performance that customers would have realized if they were running in isolation. Accordingly, we have developed Q-Clouds, a QoS-aware control framework that tunes resource allocations to mitigate performance interference effects. Q-Clouds uses online feedback to build a multi-input multi-output (MIMO) model that captures performance interference interactions, and uses it to perform closed loop resource management. In addition, we utilize this functionality to allow applications to specify multiple levels of QoS as application Q-states. For such applications, Q-Clouds dynamically provisions underutilized resources to enable elevated QoS levels, thereby improving system efficiency. Experimental evaluations of our solution using benchmark applications illustrate the benefits: performance interference is mitigated completely when feasible, and system utilization is improved by up to 35% using Q-states.

Estimation of communication signal strength in robotic networks

In this paper we consider estimating the spatial variations of a wireless channel based on a small number of measurements in a robotic network. We use a multi-scale probabilistic model in order to characterize the channel and develop an estimator based on this model. We show that our model-based approach can estimate the channel well for several scenarios, with only a small number of gathered measurements. We furthermore consider a sparsity-based channel estimation approach, in which we utilize the compressibility of the channel in the frequency domain. Our results show that this approach can also be effective in several scenarios. We then discuss the underlying tradeoffs between the two approaches. For the model-based approach, we show the impact of the error in the underlying model as well as the error in the estimation of the parameters of the model on the overall performance. For the sparsity-based approach, we show the impact of channel compressibility on the performance. Overall, the proposed framework can be utilized for communication-aware motion planning in robotic networks, where a prediction of the link qualities is needed.

Communication-Aware Motion Planning in Mobile Networks

In this technical note, we propose a communication-aware motion planning framework to increase the probability that a robot maintains its connectivity to a fixed station, while accomplishing a sensing task, in realistic communication environments. We use a probabilistic multi-scale model for channel characterization. Using this model, we propose a probabilistic framework for assessing the spatial variations of a wireless channel, based on a small number of measurements. We then show how our channel learning framework can be utilized for devising communication-aware motion planning strategies. We first present communication-aware objective functions that can plan the trajectory of the robot in order to improve its online channel assessment in an environment. We then propose a communication-aware target tracking approach for the case where a fixed station utilizes a robot (or a number of them) to keep track of the position of a moving target. In this approach, probabilistic channel assessment metrics are combined with sensing goals, when controlling the motion, in order to increase the amount of information that the fixed station receives about the target. Finally, we show the performance of our framework, using both real and simulated channel measurements. Overall, our results indicate that networked robotic operations can benefit considerably from our probabilistic channel assessment and its integration with sensing/motion planning.

A Comprehensive Overview and Characterization of Wireless Channels for Networked Robotic and Control Systems

The goal of this overview paper is to serve as a reference for researchers that are interested in the realistic modeling of wireless channels for the purpose of analysis and optimization of networked robotic systems. By utilizing the knowledge available in the wireless communication literature, we first summarize a probabilistic framework for the characterization of the underlying multiscale dynamics of a wireless link. We furthermore confirm this framework with our robotic testbed, by making an extensive number of channel measurements. To show the usefulness of this framework for networked robotic applications, we then discuss a few recent examples where this probabilistic channel characterization has been utilized for the theoretical analysis and communication-aware design of networked robotic systems. Finally, we show how to develop a realistic yet simple channel simulator, which can be used to verify cooperative robotic operations in the presence of realistic communication links.

Communication-aware target tracking using navigation functions - Centralized case

In this paper we consider a team of robots that are tasked with tracking a moving target cooperatively, while maintaining their connectivity to a base station and avoiding collision. We propose a novel extension of the classical navigation function framework in order to ensure task completion. More specifically, we modify the classical definition of the navigation functions to (1) incorporate measures of link qualities and (2) include the impact of a time-varying objective. Our proposed communication-aware navigation function framework is aimed at maintaining robot connectivity in realistic communication environments, while avoiding collision with both fixed and moving obstacles. We consider both packet-dropping and communication noise based receivers. We furthermore prove the convergence of the proposed framework under certain conditions. Finally, our simulation results show the performance of the proposed navigation framework.

An Integrated Framework for Obstacle Mapping With See-Through Capabilities Using Laser and Wireless Channel Measurements

In this paper, we consider a team of mobile robots that are tasked with building a map of the obstacles, including occluded ones, in a given environment. We propose an integrated framework for mapping with see-through capabilities using laser and wireless channel measurements, which can provide mapping capabilities beyond existing methods in the literature. Our approach leverages the laser measurements to map the visible parts of the environment (the parts that can be sensed directly by the laser scanners) using occupancy grid mapping. The parts that cannot be properly mapped by laser scanners (e.g., the occluded parts) are then identified and mapped based on wireless channel measurements. For the latter, we extend our recently-proposed wireless-based obstacle mapping framework to a probabilistic approach using Bayesian Compressive Sensing. We further consider an integrated approach based on using total variation minimization. We compare the performance of our two integrated methods, using both simulated and real data, and show the underlying tradeoffs. Finally, we propose an adaptive path planning strategy that uses the current estimate of uncertainty to collect wireless measurements that are more informative for obstacle mapping. Overall, our framework enables mapping occluded structures that cannot be mapped with laser scanners alone or a small number of wireless measurements. Our experimental robotic testbed further confirms that the proposed integrated framework can map a more complex real occluded structure that cannot be mapped with existing strategies in the literature.

Path Planning for Networked Robotic Surveillance

In this paper, we consider a robotic surveillance problem where a fixed remote station deploys a team of mobile robots to spatially explore a given workspace, detect an unknown number of static targets, and inform the remote station of their findings. We are interested in designing trajectories (local motion decisions) for the robots that minimize the probability of target detection error at the remote station, while satisfying the requirements on the connectivity of the robots to the remote station. We show how such a design is possible by co-optimization of sensing (information gathering) and communication (information exchange) when motion planning. We start by considering the case where the robots need to constantly update the remote station on the locations of the targets as they learn about the environment. For this case, we propose a communication-constrained motion planning approach for the robots. We next consider the case where the remote station only needs to be informed of the locations of the targets at the end of a given operation time. By building on our communication-constrained results, we propose a hybrid motion planning approach for this case. We consider realistic communication channels that experience path loss, shadowing and multipath fading in the paper. Then, our proposed communication-aware motion planning approaches evaluate the probability of connectivity at unvisited locations and integrate it with the sensing objectives of the robots. We mathematically characterize the asymptotic behavior of our motion planning approaches and discuss the underlying tradeoffs. We finally devise strategies to increase their robustness to multipath fading and other channel estimation uncertainties.

Channel learning and communication-aware motion planning in mobile networks

In this paper we propose a communication-aware motion planning framework to ensure robust cooperative operation of a mobile network in realistic communication environments. We use a probabilistic multi-scale model for channel characterization. We then utilize our previously proposed model-based channel prediction framework in order to devise communication-aware motion-planning approaches. We first propose a motion generation strategy that optimally plans the trajectory of the robot in order to improve its channel learning in an environment. We then propose a communication-aware navigation approach in which link quality predictions are combined with sensing goals in order to ensure cooperative and networked task accomplishment. Our simulation results show the superior performance of our proposed communication-aware motion planning framework.

Dynamic Networked Coverage of Time-Varying Environments in the Presence of Fading Communication Channels

In this article, we study the problem of dynamic coverage of a set of points of interest (POIs) in a time-varying environment. We consider the scenario where a physical quantity is constantly growing at certain rates at the POIs. A number of mobile agents are then deployed to periodically cover (sense or service) the POIs and keep the physical quantity under control bounded at all the POIs. We assume a communication-constrained operation, where the mobile agents need to communicate to a fixed remote station over realistic wireless links to complete their coverage task. We then propose novel mixed-integer linear programs (MILPs) to design periodic trajectories and TX power policies for the mobile agents that minimize the total energy (the summation of motion and communication energy) consumption of the mobile agents in each period, while (1) guaranteeing the boundedness of the quantity of interest at all the POIs, and (2) meeting the constraints on the connectivity of the mobile agents, the frequency of covering the POIs, and the total energy budget of the mobile agents. We furthermore provide a probabilistic analysis of the problem. Our results show the superior performance of the proposed framework for dynamic coverage in realistic fading environments.

Dynamic coverage of time-varying environments using a mobile robot — A communication-aware perspective

In this paper, we study the problem of dynamic coverage of a number of points of interest, in a time-varying environment, using a mobile robot. We consider the scenario where the uncertainty at any point of interest, that is not being sensed by the onboard sensor of the robot, is continuously growing. The robot is then tasked with moving along periodic trajectories, continuously sensing the points of interest and transmitting its gathered sensory data to a remote base station. We assume realistic fading channels between the robot and the base station. We then consider a piecewise linear periodic trajectory for the robot and propose a novel approach, based on sequentially solving a mixed integer program and a nonlinear program, to optimally design the trajectory and the TX power profile of the robot. We consider two different cases of a passive and an active robot. Our results show how sensing and communication objectives can be combined to prevent the instability of the coverage task, in realistic fading environments.