| 1. |
- Berntorp, Karl, et al.
(författare)
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Mobile Manipulation with a Kinematically Redundant Manipulator for a Pick-and-Place Scenario
- 2012
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Ingår i: [Host publication title missing]. - 1085-1992. ; , s. 1596-1602
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Konferensbidrag (refereegranskat)abstract
- Mobile robots and robotic manipulators have traditionally been used separately performing different types of tasks. For example, industrial robots have typically been programmed to follow trajectories using position sensors. If combining the two types of robots and adding sensors new possibilities emerge. This enables new applications, but it also raises the question of how to combine the sensors and the added kinematic complexity. An omni-directional mobile robot together with a new type of kinematically redundant manipulator for future use as a service robot for grocery stores is proposed. The scenario is that of distributing groceries on refilling shelves, and a constraint- based task specification methodology to incorporate sensors and geometric uncertainties into the task is employed. Sensor fusion is used to estimate the pose of the mobile base online. Force sensors are utilized to resolve remaining uncertainties. The approach is verified with experiments.
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| 2. |
- Berntorp, Karl, et al.
(författare)
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Rao-Blackwellized Out-of-Sequence Processing for Mixed Linear/Nonlinear State-Space Models
- 2013
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Ingår i: [Host publication title missing]. ; , s. 805-812
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Konferensbidrag (refereegranskat)abstract
- We investigate the out-of-sequence measurements particle filtering problem for a set of conditionally linear Gaussian state-space models, known as mixed linear/nonlinear state-space models. Two different algorithms are proposed, which both exploit the conditionally linear substructure. The first approach is based on storing only a subset of the particles and their weights, which implies low memory and computation requirements. The second approach is based on a recently reported Rao-Blackwellized forward filter/backward simulator, adapted to the out-of-sequence filtering task with computational considerations for enabling online implementations. Simulation studies on two examples show that both approaches outperform recently reported particle filters, with the second approach being superior in terms of tracking performance.
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| 3. |
- Berntorp, Karl, et al.
(författare)
-
Rao-Blackwellized Particle Filters with Out-of-Sequence Measurement Processing
- 2014
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Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 62:24, s. 6454-6467
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the out-of-sequence measurement (OOSM) problem for mixed linear/nonlinear state-space models, which is a class of nonlinear models with a tractable, conditionally linear substructure. We develop two novel algorithms that utilize the linear substructure. The first algorithm effectively employs the Rao-Blackwellized particle filtering framework for updating with the OOSMs, and is based on storing only a subset of the particles and their weights over an arbitrary, predefined interval. The second algorithm adapts a backward simulation approach to update with the delayed (out-of-sequence) measurements, resulting in superior tracking performance. Extensive simulation studies show the efficacy of our approaches in terms of computation time and tracking performance. Both algorithms yield estimation improvements when compared with recent particle filter algorithms for OOSM processing; in the considered examples they achieve up to 10% enhancements in estimation accuracy. In some cases the proposed algorithms even deliver accuracy that is similar to the lower performance bounds. Because the considered setup is common in various estimation scenarios, the developed algorithms enable improvements in different types of filtering applications.
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| 4. |
- Berntorp, Karl, et al.
(författare)
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Sensor Fusion for Motion Estimation of Mobile Robots with Compensation for Out-of-Sequence Measurements
- 2011
-
Ingår i: ; , s. 211-216
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Konferensbidrag (refereegranskat)abstract
- The position and orientation estimation problem for mobile robots is approached by fusing measurements from inertial sensors, wheel encoders, and a camera. The sensor fusion approach is based on the standard extended Kalman filter, which is modified to handle measurements from the camera with unknown prior delay. A real-time implementation is done on a four-wheeled omni-directional mobile robot, using a dynamic model with 11 states. The algorithm is analyzed and validated with simulations and experiments.
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| 5. |
- Berntorp, Karl, et al.
(författare)
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Storage Efficient Particle Filters with Multiple Out-of-Sequence Measurements
- 2012
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Ingår i: 15th International Conference on Information Fusion (FUSION), 2012. - 9781467304177 ; , s. 471-478
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Konferensbidrag (refereegranskat)abstract
- A particle filter based solution to the out-of-sequence measurement (OOSM) problem is proposed. The solution is storage efficient, while being computationally fast. The filter approaches the multi-OOSM problem by not only updating the estimate at the most recent time, but also for all times between the OOSM time and the most recent time. This is done by exploiting the complete in-sequence information approach and extending it to nonlinear systems. Simulation experiments on a challenging nonlinear tracking scenario show that the new approach outperforms recent state-of-the-art particle filter algorithms in some respects, despite demanding less storage requirements.
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| 6. |
- Dürango, Jonas, et al.
(författare)
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Control-theoretical load-balancing for cloud applications with brownout
- 2014
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Ingår i: 2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). - 9781467360906 - 9781479977468 ; , s. 5320-5327
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Konferensbidrag (refereegranskat)abstract
- Cloud applications are often subject to unexpected events like flash crowds and hardware failures. Without a predictable behaviour, users may abandon an unresponsive application. This problem has been partially solved on two separate fronts: first, by adding a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience, and, second, by introducing replicas - copies of the applications having the same functionalities - for redundancy and adding a load-balancer to direct incoming traffic. However, existing load-balancing strategies interfere with brownout self-adaptivity. Load-balancers are often based on response times, that are already controlled by the self-adaptive features of the application, hence they are not a good indicator of how well a replica is performing. In this paper, we present novel load-balancing strategies, specifically designed to support brownout applications. They base their decision not on response time, but on user experience degradation. We implemented our strategies in a self-adaptive application simulator, together with some state-of-the-art solutions. Results obtained in multiple scenarios show that the proposed strategies bring significant improvements when compared to the state-of-the-art ones.
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| 7. |
- Kihl, Maria, et al.
(författare)
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The Challenge of Cloud Control
- 2013
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Ingår i: The 8th International Workshop on Feedback Computing (Feedback Computing '13).
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Konferensbidrag (refereegranskat)abstract
- Today’s cloud data center infrastructures are not even near being able to cope with the enormous and rapidly vary-ing capacity demands that will be reality in a near future. So far, very little is understood about how to transform today’s data centers (being large, power-hungry facilities, and operated through heroic efforts by numerous adminis-trators) into a self-managed, dynamic, and dependable infrastructure, constantly delivering expected QoS with rea-sonable operation costs and acceptable carbon footprint for large-scale services with sometimes dramatic variations in capacity demands. In this paper, we discuss some of the major challenges for resource-optimized cloud data cen-ter. We propose a new research area called Cloud Control, which is a control theoretic approach to a range of cloud management problems, aiming to transform today´s static and energy consuming cloud data centers into self-managed, dynamic, and dependable infrastructures, constantly delivering expected quality of service with acceptable operation costs and carbon footprint for large-scale services with varying capacity demands.
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| 8. |
- Klein, Cristian, 1985-, et al.
(författare)
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Improving Cloud Service Resilience using Brownout-Aware Load-Balancing
- 2014
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Ingår i: 2014 IEEE 33rd International Symposium On Reliable Distributed Systems (SRDS). - : IEEE Computer Society. - 9781479955848 ; , s. 31-40, s. 31-40
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Konferensbidrag (refereegranskat)abstract
- We focus on improving resilience of cloud services (e.g., e-commerce website), when correlated or cascading failures lead to computing capacity shortage. We study how to extend the classical cloud service architecture composed of a load-balancer and replicas with a recently proposed self-adaptive paradigm called brownout. Such services are able to reduce their capacity requirements by degrading user experience (e.g., disabling recommendations).Combining resilience with the brownout paradigm is to date an open practical problem. The issue is to ensure that replica self-adaptivity would not confuse the load-balancing algorithm, overloading replicas that are already struggling with capacity shortage. For example, load-balancing strategies based on response times are not able to decide which replicas should be selected, since the response times are already controlled by the brownout paradigm.In this paper we propose two novel brownout-aware load-balancing algorithms. To test their practical applicability, we extended the popular lighttpd web server and load-balancer, thus obtaining a production-ready implementation. Experimental evaluation shows that the approach enables cloud services to remain responsive despite cascading failures. Moreover, when compared to Shortest Queue First (SQF), believed to be near-optimal in the non-adaptive case, our algorithms improve user experience by 5%, with high statistical significance, while preserving response time predictability.
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| 9. |
- Papadopoulos, Alessandro, et al.
(författare)
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Control-based load-balancing techniques : Analysis and performance evaluation via a randomized optimization approach
- 2016
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Ingår i: Control Engineering Practice. - : Elsevier. - 0967-0661 .- 1873-6939. ; 52, s. 24-34
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Tidskriftsartikel (refereegranskat)abstract
- Cloud applications are often subject to unexpected events like flashcrowds and hardware failures. Users that expect a predictable behavior may abandon an unresponsive application when these events occur. Researchers and engineers addressed this problem on two separate fronts: first, they introduced replicas - copies of the application with the same functionality - for redundancy and scalability; second, they added a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience. The presence of multiple replicas requires a dedicated component to direct incoming traffic: a load-balancer. Existing load-balancing strategies based on response times interfere with the response time controller developed for brownout-compliant applications. In fact, the brownout approach bounds response times using a control action. Hence, the response time, that was used to aid load-balancing decision, is not a good indicator of how well a replica is performing. To fix this issue, this paper reviews some proposal for brownout-aware load-balancing and provides a comprehensive experimental evaluation that compares them. To provide formal guarantees on the load balancing performance, we use a randomized optimization approach and apply the scenario theory. We perform an extensive set of experiments on a real machine, extending the popular lighttpd web server and load-balancer, and obtaining a production-ready implementation. Experimental results show an improvement of the user experience over Shortest Queue First (SQF)-believed to be near-optimal in the non-adaptive case. The improved user experience is obtained preserving the response time predictability.
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| 10. |
- Papadopoulos, Alessandro, et al.
(författare)
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PEAS : A Performance Evaluation framework for Auto-Scaling strategies in cloud applications
- 2016
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Ingår i: ACM Transactions on Modeling and Performance Evaluation of Computing Systems. - United States : Association for Computing Machinery (ACM). - 2376-3639 .- 2376-3647. ; :4
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Tidskriftsartikel (refereegranskat)abstract
- Numerous auto-scaling strategies have been proposed in the last few years for improving various Quality of Service (QoS)indicators of cloud applications, e.g., response time and throughput, by adapting the amount of resources assigned to theapplication to meet the workload demand. However, the evaluation of a proposed auto-scaler is usually achieved throughexperiments under specific conditions, and seldom includes extensive testing to account for uncertainties in the workloads, andunexpected behaviors of the system. These tests by no means can provide guarantees about the behavior of the system in generalconditions. In this paper, we present PEAS, a Performance Evaluation framework for Auto-Scaling strategies in the presenceof uncertainties. The evaluation is formulated as a chance constrained optimization problem, which is solved using scenariotheory. The adoption of such a technique allows one to give probabilistic guarantees of the obtainable performance. Six differentauto-scaling strategies have been selected from the literature for extensive test evaluation, and compared using the proposedframework. We build a discrete event simulator and parameterize it based on real experiments. Using the simulator, each auto-scaler’s performance is evaluated using 796 distinct real workload traces from projects hosted on the Wikimedia foundations’servers, and their performance is compared using PEAS. The evaluation is carried out using different performance metrics,highlighting the flexibility of the framework, while providing probabilistic bounds on the evaluation and the performance of thealgorithms. Our results highlight the problem of generalizing the conclusions of the original published studies and show thatbased on the evaluation criteria, a controller can be shown to be better than other controllers.
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