{"title":"TinyTricia -一个空间优化的Patricia Trie,用于透明访问边缘计算服务","authors":"Josef Hammer, H. Hellwagner","doi":"10.1109/UCC56403.2022.00060","DOIUrl":null,"url":null,"abstract":"Multi-access Edge Computing (MEC) is an essential piece of 5G telecommunication systems to satisfy the challenging low-latency demands of future applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking and virtual IP+port addresses for registered edge services. A core component of our architecture is a Patricia Trie, which stores all our virtual IP+port addresses. Unfortunately, most implementations of Patricia Tries are not geared toward use cases with millions of keys where a low memory footprint becomes essential. In this paper, we present TinyTric$\\dot{w}$, a space-efficient open-source implementation of a Patricia Trie for keys up to 256 bits. TinyTricia can keep track of up to half a billion (229) keys $\\leq$57 bits and up to a quarter of a billion (228-1) keys $\\leq$256 bits with tiny memory requirements. In the latter case, each key can have a data value of any type. Keys $\\leq$57 bits require only 8 to 16 bytes per key (i.e., only 0 to 8 bytes overhead per 57-bit key); for keys $\\geq$58 bits, add the key size (in whole bytes) to these values. Thus, for 16.78 million (224)57-bit keys, our solution requires between 128 and 256 MiB of memory. Unlike some other spaceefficient implementations, TinyTriia allows adding and removing keys at runtime.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"TinyTricia – A Space-Optimized Patricia Trie For Transparent Access to Edge Computing Services\",\"authors\":\"Josef Hammer, H. Hellwagner\",\"doi\":\"10.1109/UCC56403.2022.00060\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-access Edge Computing (MEC) is an essential piece of 5G telecommunication systems to satisfy the challenging low-latency demands of future applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking and virtual IP+port addresses for registered edge services. A core component of our architecture is a Patricia Trie, which stores all our virtual IP+port addresses. Unfortunately, most implementations of Patricia Tries are not geared toward use cases with millions of keys where a low memory footprint becomes essential. In this paper, we present TinyTric$\\\\dot{w}$, a space-efficient open-source implementation of a Patricia Trie for keys up to 256 bits. TinyTricia can keep track of up to half a billion (229) keys $\\\\leq$57 bits and up to a quarter of a billion (228-1) keys $\\\\leq$256 bits with tiny memory requirements. In the latter case, each key can have a data value of any type. Keys $\\\\leq$57 bits require only 8 to 16 bytes per key (i.e., only 0 to 8 bytes overhead per 57-bit key); for keys $\\\\geq$58 bits, add the key size (in whole bytes) to these values. Thus, for 16.78 million (224)57-bit keys, our solution requires between 128 and 256 MiB of memory. Unlike some other spaceefficient implementations, TinyTriia allows adding and removing keys at runtime.\",\"PeriodicalId\":203244,\"journal\":{\"name\":\"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UCC56403.2022.00060\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UCC56403.2022.00060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
TinyTricia – A Space-Optimized Patricia Trie For Transparent Access to Edge Computing Services
Multi-access Edge Computing (MEC) is an essential piece of 5G telecommunication systems to satisfy the challenging low-latency demands of future applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking and virtual IP+port addresses for registered edge services. A core component of our architecture is a Patricia Trie, which stores all our virtual IP+port addresses. Unfortunately, most implementations of Patricia Tries are not geared toward use cases with millions of keys where a low memory footprint becomes essential. In this paper, we present TinyTric$\dot{w}$, a space-efficient open-source implementation of a Patricia Trie for keys up to 256 bits. TinyTricia can keep track of up to half a billion (229) keys $\leq$57 bits and up to a quarter of a billion (228-1) keys $\leq$256 bits with tiny memory requirements. In the latter case, each key can have a data value of any type. Keys $\leq$57 bits require only 8 to 16 bytes per key (i.e., only 0 to 8 bytes overhead per 57-bit key); for keys $\geq$58 bits, add the key size (in whole bytes) to these values. Thus, for 16.78 million (224)57-bit keys, our solution requires between 128 and 256 MiB of memory. Unlike some other spaceefficient implementations, TinyTriia allows adding and removing keys at runtime.