People often have to queue for a busy service in many places around a city, and knowing the queuetime can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queuetime moni...
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People often have to queue for a busy service in many places around a city, and knowing the queuetime can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queuetime monitoring are based on pre-deployed infrastructures, such as cameras and infrared sensors, which are costly and fail to deliver the queuetime information to scattered citizens. This paper presents CrowdQTE, a mobile crowdsensing system, which utilizes the sensor-enhanced mobile devices and crowd hu- man intelligence to monitor and provide real-timequeuetime information for various queuing scenarios. When people are waiting in a line, we utilize the accelerometer sensor data and ambient contexts to automatically detect the queueing behav- ior and calculate the queuetime. When people are not waiting in a line, it estimates the queuetime based on the information reported manually by participants. We evaluate the perfor- mance of the system with a two-week and 12-person deploy- ment using commercially-available smartphones. The results demonstrate that CrowdQTE is effective in estimating queu- ing status.
Recent development on 3D flash memories largely promotes the wide application of Solid-State Drives (SSDs) by providing larger capacity from vertically-stacked layers. However, there exist speed variations across diff...
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Recent development on 3D flash memories largely promotes the wide application of Solid-State Drives (SSDs) by providing larger capacity from vertically-stacked layers. However, there exist speed variations across different layers because of manufacturing process variations or physical designs, which induces new challenges to fully explore the advantages of existing SSDs, e.g. the parallel structure. This paper investigates the effect of speed variation on the parallel performance of SSDs. To balance chips' workloads, traditional method selects chips in a round-robin way. As chip queuetime can be estimated by some main factors, the chip also can be chosen in a greedy way. However, because of the layer speed variation, queue time estimation model should be modified. This paper first establishes a new queue time estimation model with the awareness of the flash layer information. Then the model is used to estimate the chip queuetime and to direct write requests into the chip with the least queuetime. The key idea is to largely reduce queuetime of each write, thus reducing the average SSD response time. Finally, this new request redirection method is evaluated on SSDsim with real world workloads. Experimental results show that our model can estimate the queuetime more accurately and our new request redirection method can improve 8.3% of write queuetime on average under the situation of 4 times speed variation among 16 layers.
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