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HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm 时间:2018-09-23 23:59   来源:未知   作者:admin   点击: 次 Abstract:Handwritten character recognition (HCR) is a mainstream mobile device input method that has attracted significant research interest. Although previous studies have delivered reasonable recognition accuracy, it remains difficult to directly embed the advanced HCR service into mobile device software and obtain excellent but fast results. Cloud computing is a relatively new online computational resource provider which can satisfy the elastic resource requirements of the advanced HCR service with high-recognition accuracy. However, owing to the delay sensitivity of the character recognition service, the performance loss in the traditional cloud virtualization technology (e.g., kernel-based virtual machine (KVM)) may impair the performance. In addition, the improper computational resource scheduling in cloud computing impairs not only the performance but also the resource utilization. Thus, the HCR online service is required to guarantee the performance and improve the resource utilization of the HCR service in cloud computing. To address these problems, in this paper, we propose an HCR container as a service (HCRCaaS) in cloud computing. We address several key contributions: (1) designing an HCR engine on the basis of deep convolution neutral networks as a demo for an advanced HCR engine with better recognition accuracy, (2) providing an isolated lightweight runtime environment for high performance and easy expansion, and (3) designing a greedy resource scheduling algorithm based on the performance evaluation to optimize the resource utilization under a quality of service (QoS) guaranteeing. Experimental results show that our system not only reduces the performance loss compared with traditional cloud computing under the advanced HCR algorithm but also improves the resource utilization appropriately under the QoS guaranteeing. This study also provides a valuable reference for other related studies.
1. Introduction
  With the increasing number of mobile devices (e.g., smartphones, tablet computers, and laptops), the input method has become one of the most important applications. Thus, handwritten character recognition (HCR) technology, one of the main input methods for the smartphone, has received considerable research attention and has consequently improved in quality [1, 2]. Nevertheless, some advanced HCR algorithms are difficult to embed in mobile devices because of their resource capacity limitations and the time complexity of the algorithms. Furthermore, embedding bespoke HCR engines into applications is resource and effort intensive, limiting advanced HCR algorithm use and research by individual enterprises. Now, cloud computing [3, 4] provides an innovative networking application model with supercomputing resource capacity. This provides parallel framework to achieve high performance and also supports cross-platform clients [5], freeing clients from the limitations of the computational power and resources in local devices. Furthermore, cloud computing can always provide an elastic distributed resource that can be dynamically allocated to meet varying computing needs. Hence, offloading the HCR task to cloud computing is an effective way to address the conflict between resource capacity limitations and the time complexity of HCR in mobile devices.
  However, the task offloading to cloud computing also brings a new challenge: using the pay-per-use [6, 7] model to adjust the resource size according to different workloads, which may cause an impairment in the quality of service (QoS) and resource utilization [8]. Especially for the delay sensitivity of HCR tasks, the complexity distribution architecture in cloud computing (e.g., Hadoop and Spark) is insufficient. In addition, the key technologies of cloud computing (e.g., computational resource virtualization and resource scheduling) are also important elements that impact the performance.
With these in mind, here, we propose an HCR container as a service (HCRCaaS) based on QoS guarantee policy, which not only provides an advanced HCR algorithm (e.g., a deep convolution neutral network (DCNN) [9]) to provide better recognition accuracy but also reduces the performance loss with container technology for the delay-sensitive requirement. To guarantee the QoS as well as high resource utilization, we propose a resource scheduling algorithm based on a performance evaluation under the resource scheduling greedy policy. Our main contributions are as follows: