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- Ahammed, Farhan, et al.
(författare)
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Finding lower bounds of localization with noisy measurements using genetic algorithms
- 2011
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Ingår i: Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications (DIVANet '11). - Miami, Florida, USA : Association for Computing Machinery (ACM). - 9781450309042 ; , s. 47-54
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Konferensbidrag (refereegranskat)abstract
- Vehicular Ad-Hoc Networks (VANETs) are wireless networks with mobile nodes (vehicles) which connect in an ad-hoc manner. Many vehicles use the Global Positioning System (GPS) to provide their locations. However the inaccuracy of GPS devices leads to some vehicles incorrectly assuming they are located at different positions and sometimes on different roads. VANETs can be used to increase the accuracy of each vehicle's computed location by allowing vehicles to share information regarding the measured distances to neighbouring vehicles. This paper looks at finding how much improvement can be made given the erroneous measurements present in the system. An evolutionary algorithm is used to evolve instances of parameters used by the VLOCI2 algorithm, also presented in this paper, to find instances which minimises the inaccuracy in computed locations. Simulation results show a definite improvement in location accuracy and lower bounds on how much improvement is possible is inferred.
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- Al-Dulaimy, Auday, et al.
(författare)
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MultiScaler : A Multi-Loop Auto-Scaling Approach for Cloud-Based Applications
- 2022
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Ingår i: IEEE Transactions on Cloud Computing. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-7161. ; 10:4, s. 2769-2786
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Tidskriftsartikel (refereegranskat)abstract
- Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, called MultiScaler , to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions. MultiScaler is mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that the MultiScaler approach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines, MultiScaler produces significantly better reaction to changes in workloads even under the presence of noisy neighbors.
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- Anwar, Adnan, et al.
(författare)
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HPC-Based Intelligent Volt/VAr Control of Unbalanced Distribution Smart Grid in the Presence of Noise
- 2017
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Ingår i: IEEE Transactions on Smart Grid. - : IEEE. - 1949-3053 .- 1949-3061. ; 8:3, s. 1446-1459
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Tidskriftsartikel (refereegranskat)abstract
- The performance of Volt/VAr optimization has been significantly improved due to the integration of measurement data obtained from the advanced metering infrastructure of a smart grid. However, most of the existing works lack: 1) realistic unbalanced multi-phase distribution system modeling; 2) scalability of the Volt/VAr algorithm for larger test system; and 3) ability to handle gross errors and noise in data processing. In this paper, we consider realistic distribution system models that include unbalanced loadings and multi-phased feeders and the presence of gross errors such as communication errors and device malfunction, as well as random noise. At the core of the optimization process is an intelligent particle swarm optimization-based technique that is parallelized using high performance computing technique to solve Volt/VAr-based power loss minimization problem. Extensive experiments covering the different aspects of the proposed framework show significant improvement over existing Volt/VAr approaches in terms of both the accuracy and scalability on IEEE 123 node and a larger IEEE 8500 node benchmark test systems.
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