Supporting remote dynamic update on wireless sensor networks (WSNs) allows software of sensor nodes to be dynamically updated or enhanced to adapt to changed environmental conditions or application requirements. Many researches use diff-based approaches to transmit only the code difference to sensor nodes for updating. For the resourcelimited WSNs, we have proposed a new diff-based approach named Two-Stage Diff to dynamically update software components effectively in sensor nodes. By using some carefully designed methods to increase the similarities between the old and the new program images, our mechanism can obtain much smaller code difference than traditional diffbased approaches. Besides, flash memory characteristics in erasing and writing data are considered in designing the file format of transmitted update data. Thus, the transmission size of the updated data can be greatly reduced. Especially, our sensor nodes only need to perform simple work instead of complicated dynamic linking in updating program image. As a result, our mechanism can incur less resource consumption and be more suitable for operating in resource-limited WSNs. Experiments with TinyOS show that our Two-Stage Diff can effectively reduce the size of transmitted update data and the update processing time can be significantly reduced as well.
In this paper, we have designed and implemented a kernel-level Web-based QoS (WQoS) mechanism that could efficiently support differentiated services when serving multiple diverse types of Web requests in a cluster-based Web server system. Our mechanism is implemented at kernel level to effectively reduce the number of protection domain switches and data copying between kernel space and user space to accelerate response time. We also use a new content-aware request distribution policy to dispatch requests efficiently in Web clusters providing multiple types of services. Experimental results demonstrate that the Web cluster with our proposed WQoS mechanism can not only efficiently serve multiple types of service requests but also ensure that all requests with high priority can conform to Service Level Agreement (SLA) by dropping acceptable percentage of requests with minor importance during system overload.
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