{"title":"ECO-DNS: DNS预期一致性优化","authors":"Chen Chen, S. Matsumoto, A. Perrig","doi":"10.1109/ICDCS.2015.34","DOIUrl":null,"url":null,"abstract":"The flexibility of the current Domain Name System (DNS) has been stretched to its limits to accommodate new applications such as content delivery networks and dynamic DNS. In particular, maintaining cache consistency has become a much larger problem, as emerging technologies require increasingly-frequent updates to DNS records. Though Time-To-Live (TTL) is the most widely used method of controlling cache consistency, it does not offer the fine-grained control necessary for handling these frequent changes. In addition, TTLs are too static to handle sudden changes in traffic caused by Internet failures or social media trends, demonstrating their inflexibility in the face of unforeseen events. To address these problems, we first propose a metric called Expected Aggregate Inconsistency (EAI), which allows us to consider important factors such as a record's update frequency and popularity when quantitatively measuring inconsistency. We then design ECO-DNS, a lightweight system that leverages the information provided by EAI to optimize a record's TTL. This value can be tuned to individual cache servers' preferences between better consistency and bandwidth overhead. Further-more, our optimization model's flexibility allows us to easily adapt ECO-DNS to handle various caching hierarchies such as multi-level caching while considering the trade off among consistency, overhead, latency, and server load.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"138 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"ECO-DNS: Expected Consistency Optimization for DNS\",\"authors\":\"Chen Chen, S. Matsumoto, A. Perrig\",\"doi\":\"10.1109/ICDCS.2015.34\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The flexibility of the current Domain Name System (DNS) has been stretched to its limits to accommodate new applications such as content delivery networks and dynamic DNS. In particular, maintaining cache consistency has become a much larger problem, as emerging technologies require increasingly-frequent updates to DNS records. Though Time-To-Live (TTL) is the most widely used method of controlling cache consistency, it does not offer the fine-grained control necessary for handling these frequent changes. In addition, TTLs are too static to handle sudden changes in traffic caused by Internet failures or social media trends, demonstrating their inflexibility in the face of unforeseen events. To address these problems, we first propose a metric called Expected Aggregate Inconsistency (EAI), which allows us to consider important factors such as a record's update frequency and popularity when quantitatively measuring inconsistency. We then design ECO-DNS, a lightweight system that leverages the information provided by EAI to optimize a record's TTL. This value can be tuned to individual cache servers' preferences between better consistency and bandwidth overhead. Further-more, our optimization model's flexibility allows us to easily adapt ECO-DNS to handle various caching hierarchies such as multi-level caching while considering the trade off among consistency, overhead, latency, and server load.\",\"PeriodicalId\":129182,\"journal\":{\"name\":\"2015 IEEE 35th International Conference on Distributed Computing Systems\",\"volume\":\"138 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 35th International Conference on Distributed Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDCS.2015.34\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 35th International Conference on Distributed Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCS.2015.34","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ECO-DNS: Expected Consistency Optimization for DNS
The flexibility of the current Domain Name System (DNS) has been stretched to its limits to accommodate new applications such as content delivery networks and dynamic DNS. In particular, maintaining cache consistency has become a much larger problem, as emerging technologies require increasingly-frequent updates to DNS records. Though Time-To-Live (TTL) is the most widely used method of controlling cache consistency, it does not offer the fine-grained control necessary for handling these frequent changes. In addition, TTLs are too static to handle sudden changes in traffic caused by Internet failures or social media trends, demonstrating their inflexibility in the face of unforeseen events. To address these problems, we first propose a metric called Expected Aggregate Inconsistency (EAI), which allows us to consider important factors such as a record's update frequency and popularity when quantitatively measuring inconsistency. We then design ECO-DNS, a lightweight system that leverages the information provided by EAI to optimize a record's TTL. This value can be tuned to individual cache servers' preferences between better consistency and bandwidth overhead. Further-more, our optimization model's flexibility allows us to easily adapt ECO-DNS to handle various caching hierarchies such as multi-level caching while considering the trade off among consistency, overhead, latency, and server load.