{"title":"明智的资源管理","authors":"C. Poellabauer, Timothy Durnan","doi":"10.1145/1095810.1118579","DOIUrl":null,"url":null,"abstract":"Consider the following scenario taken from the mobile and wireless computing domain. Energy has been receiving increasing attention, resulting in a number of different energy management techniques, including Dynamic Voltage Scaling (DVS) [1]. DVS is based on the concept of reducing the speed/voltage of a CPU when it is under-utilized, thereby reducing its power consumption while increasing the task execution times. In real-time systems, DVS algorithms have to compute energy-saving speed/voltage levels while ensuring that task deadlines are met. The figure below visualizes this problem for two devices A and B, where shaded areas indicate times of power consumption caused by the CPU and arrows indicate communication between two devices. The vertical line indicates the end-to-end deadline Td, i.e., the processing and communication steps of both devices A and B have to be concluded before Td. Typical examples for such scenarios are sensor networks with in-network data aggregation or mobile multimedia. For example, device A captures an image, compresses it, and sends it to B, which decompresses and displays it. The figure shows the same scenario twice, once without DVS and once with DVS. In the latter case, both devices reduce their energy overheads, but device B also misses its deadline. As a consequence, either one or both devices have to increase their clock frequencies to ensure that the deadline is met, increasing their energy costs. However, if both devices operate in isolation, A -- unaware of the missed end-to-end deadline -- would continue to operate at its low speed, while B has to increase its speed. Now assume that B is essential to the operation of the distributed system, but at the same time it is also the more energy-constrained device (e.g., the remaining battery lifetime is lower than A's). In this case, it is desirable that A reduces its use of DVS, such that B can continue to fully exploit its DVS capability to prolong its battery life. To achieve that, it is necessary for A and B to negotiate limits to the use of DVS, e.g., by introducing a deadline on A, called virtual deadline Tv (rightmost graph in above figure). This deadline forces A to run faster (limiting the extent to which A can exploit DVS), but allowing B to fully utilize DVS.","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":"1 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2005-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The case for judicious resource management\",\"authors\":\"C. Poellabauer, Timothy Durnan\",\"doi\":\"10.1145/1095810.1118579\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Consider the following scenario taken from the mobile and wireless computing domain. Energy has been receiving increasing attention, resulting in a number of different energy management techniques, including Dynamic Voltage Scaling (DVS) [1]. DVS is based on the concept of reducing the speed/voltage of a CPU when it is under-utilized, thereby reducing its power consumption while increasing the task execution times. In real-time systems, DVS algorithms have to compute energy-saving speed/voltage levels while ensuring that task deadlines are met. The figure below visualizes this problem for two devices A and B, where shaded areas indicate times of power consumption caused by the CPU and arrows indicate communication between two devices. The vertical line indicates the end-to-end deadline Td, i.e., the processing and communication steps of both devices A and B have to be concluded before Td. Typical examples for such scenarios are sensor networks with in-network data aggregation or mobile multimedia. For example, device A captures an image, compresses it, and sends it to B, which decompresses and displays it. The figure shows the same scenario twice, once without DVS and once with DVS. In the latter case, both devices reduce their energy overheads, but device B also misses its deadline. As a consequence, either one or both devices have to increase their clock frequencies to ensure that the deadline is met, increasing their energy costs. However, if both devices operate in isolation, A -- unaware of the missed end-to-end deadline -- would continue to operate at its low speed, while B has to increase its speed. Now assume that B is essential to the operation of the distributed system, but at the same time it is also the more energy-constrained device (e.g., the remaining battery lifetime is lower than A's). In this case, it is desirable that A reduces its use of DVS, such that B can continue to fully exploit its DVS capability to prolong its battery life. To achieve that, it is necessary for A and B to negotiate limits to the use of DVS, e.g., by introducing a deadline on A, called virtual deadline Tv (rightmost graph in above figure). This deadline forces A to run faster (limiting the extent to which A can exploit DVS), but allowing B to fully utilize DVS.\",\"PeriodicalId\":20672,\"journal\":{\"name\":\"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles\",\"volume\":\"1 1\",\"pages\":\"1-2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1095810.1118579\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1095810.1118579","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Consider the following scenario taken from the mobile and wireless computing domain. Energy has been receiving increasing attention, resulting in a number of different energy management techniques, including Dynamic Voltage Scaling (DVS) [1]. DVS is based on the concept of reducing the speed/voltage of a CPU when it is under-utilized, thereby reducing its power consumption while increasing the task execution times. In real-time systems, DVS algorithms have to compute energy-saving speed/voltage levels while ensuring that task deadlines are met. The figure below visualizes this problem for two devices A and B, where shaded areas indicate times of power consumption caused by the CPU and arrows indicate communication between two devices. The vertical line indicates the end-to-end deadline Td, i.e., the processing and communication steps of both devices A and B have to be concluded before Td. Typical examples for such scenarios are sensor networks with in-network data aggregation or mobile multimedia. For example, device A captures an image, compresses it, and sends it to B, which decompresses and displays it. The figure shows the same scenario twice, once without DVS and once with DVS. In the latter case, both devices reduce their energy overheads, but device B also misses its deadline. As a consequence, either one or both devices have to increase their clock frequencies to ensure that the deadline is met, increasing their energy costs. However, if both devices operate in isolation, A -- unaware of the missed end-to-end deadline -- would continue to operate at its low speed, while B has to increase its speed. Now assume that B is essential to the operation of the distributed system, but at the same time it is also the more energy-constrained device (e.g., the remaining battery lifetime is lower than A's). In this case, it is desirable that A reduces its use of DVS, such that B can continue to fully exploit its DVS capability to prolong its battery life. To achieve that, it is necessary for A and B to negotiate limits to the use of DVS, e.g., by introducing a deadline on A, called virtual deadline Tv (rightmost graph in above figure). This deadline forces A to run faster (limiting the extent to which A can exploit DVS), but allowing B to fully utilize DVS.