移动数据库协同缓存查询过程的服务差异化

M. Elfaki, H. Ibrahim, A. Mamat, M. Othman
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引用次数: 1

摘要

随着实时移动应用的迅猛发展,移动计算正经历着从数据通信基础设施向全球信息实用工具的转变。可以将无线电话、pda、笔记本电脑和高端工作站视为需要插件到此实用程序以访问信息的设备。然而,由于移动资源有限,如无线带宽不足、断连和客户端移动性等,协同缓存管理面临巨大挑战。其中一个挑战是,所有请求都以相同的方式处理,并且这些请求在发送到主数据库服务器的途中一跳一跳地转发,而所请求的数据项在本地丢失。但是,这些转发数据请求可能会导致延迟,并增加为请求提供服务的跳数。本文旨在研究支持差异化协同缓存和内容分发服务的资源管理问题。为了支持基于优先级的缓存服务,我们重点研究了相邻节点间数据交换的优先性。这样,协作的邻居缓存增加了,系统内的缓存命中率间接增加,而大多数请求都是由协作的邻居节点来应答,而不是依赖于主数据库服务器。与现有的协作缓存模型相比,所提出的模型证明了它能够提供可量化的服务差异,从而提高本地响应数据项请求的百分比,并且效率损失最小。
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Service differentiation for collaborative caching query process in mobile database
With the dramatic explosion of the real time mobile application, mobile computing is undergoing a transition from a data communication infrastructure to a global information utility. Wireless phones, PDAs, laptop, and high-end workstation can be viewed as appliances that need to be plug-in to this utility for accessing the information. However, due to mobile limited resources, such as insufficient wireless bandwidth, disconnection and client's mobility, collaborative cache management faces vast challenges. One of these challenges is that all requests are processed in the same manner, as well as these requests are forwarded from hop to hop along the way towards the main database server while the requested data items are missed locally. However, these forward data requests might cause delay and increase the number of hops to serve the requests. This paper aims to study the resource management problem to support differentiated collaborative caching and content distribution services. We focus on the preferential of data exchange among neighbor's nodes to support priority-based caching services. Thus, the collaborative neighbors caching increased, and the cache hit within the system is increased indirectly while most of the requests are answered from the neighbor nodes collaboration instead of relying much on the main database server. The proposed model demonstrates its ability to provide quantifiable service differentiation in terms of increasing the percentage of answering the data item requests locally, with minimal efficiency penalties compared to existence collaborative caching models.
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