1. A method, comprising:
performing, by one or more computing devices:
collecting traffic load data as historical traffic load data for each of a plurality of routes in a network environment comprising a plurality of distinct networks, wherein each route carries traffic between two of the networks over one or more physical connections, wherein each physical connection connects a router of one of the networks to a router of another one of the networks;
generating a topology model of the network environment, wherein the topology model includes the networks, the physical connections between the networks, and the routes between the networks over the physical connections;
analyzing the topology model to determine an extreme-case break for each route in the topology model in the network environment, wherein a break for a given route is a break in one or more of the physical connections that causes traffic to be diverted onto the given route from one or more other routes affected by the break, and wherein the extreme-case break for the given route is a particular break for which an objective function characterizing one or more route performance metrics is optimized; and
generating, for each route, a prediction of extreme-case traffic load on the respective route given the extreme-case break for the respective route according to the collected historical traffic load data for the routes and the topology model.
2. The method as recited in claim 1, further comprising generating a prediction of normal traffic load on each route according to the collected historical traffic load data for the respective route.
3. The method as recited in claim 1, further comprising, prior to said generating, for each route, a prediction of extreme-case traffic load on the respective route, performing a statistical analysis of the collected historical traffic load data for each route to generate historical 99th percentile (p99) data for the respective route, wherein the predictions of extreme-case traffic load for the routes are generated according to the historical p99 data for the routes.
4. The method as recited in claim 1, wherein generating a prediction of extreme-case traffic load on a route given the extreme-case break for the respective route comprises:
determining, from the topology model and the collected historical traffic load data for the routes, an amount of historical traffic load that would be diverted from one or more other routes to the route given the extreme-case break for the route;
combining the historical traffic load data for the route with the determined amount of historical traffic load that would be diverted to the route given the extreme-case break for the route; and
generating the prediction of extreme-case traffic load on the route from the combined historical traffic load for the route.
5. The method as recited in claim 4, wherein said generating the prediction of extreme-case traffic load on the route comprises applying a smoothing function according to an autoregressive integrated moving average (ARIMA) model to the combined historical traffic load for the route.
6. The method as recited in claim 1, wherein said generating a topology model of the network environment comprises determining one or more locations in the topology of the network environment at which two or more of the physical connections are co-located, and wherein the extreme-case break for at least one route is a break at a location in the topology of the network environment at which two or more of the physical connections are co-located, wherein the break affects the two or more co-located physical connections.
7. The method as recited in claim 1, wherein said analyzing the topology model to determine an extreme-case break in the topology model for each route in the network environment comprises, for each route:
simulating two or more breaks at different locations in the topology model that would cause traffic to be diverted onto the route;
for each simulated break:
determining an amount of traffic that would be diverted onto the route from one or more other routes given the break; and
determining values for the one or more route performance metrics according to the determined amount of traffic that would be diverted onto the route given the break.
8. The method as recited in claim 1, wherein the one or more route performance metrics include total traffic load on a route given a break, and wherein the extreme-case break for a given route is a break that causes a highest total traffic load on the given route according to the objective function.
9. A non-transitory computer-accessible storage medium storing program instructions, wherein the program instructions are computer-executable to implement:
obtaining historical traffic load data for each of a plurality of routes between a plurality of devices, wherein each route carries traffic between two of the devices over one or more physical connections between the devices;
obtaining a topology model of the devices and the routes between the devices, wherein the topology model includes, for each route, an indication of an extreme-case break according to the topology model, wherein an extreme-case break for a given route is a particular break in one or more of the physical connections for which an objective function characterizing one or more route performance metrics is optimized; and
generating, for each route, a prediction of extreme-case traffic load on the respective route given the extreme-case break for the respective route according to the historical traffic load data for the routes and the topology model.
10. The non-transitory computer-accessible storage medium as recited in claim 9, wherein at least one of the devices is a router of a distinct network in a network environment comprising a plurality of networks.
11. The non-transitory computer-accessible storage medium as recited in claim 9, wherein the program instructions are further computer-executable to implement generating a prediction of normal traffic load on each route according to the historical traffic load data for the respective route.
12. The non-transitory computer-accessible storage medium as recited in claim 9, wherein the program instructions are further computer-executable to implement generating 99th percentile (p99) data for each route according to the historical traffic load data for the respective route, wherein the predictions of extreme-case traffic load for the routes are generated according to the p99 data for the routes.
13. The non-transitory computer-accessible storage medium as recited in claim 9, wherein, in said generating a prediction of extreme-case traffic load on a route given the extreme-case break for the respective route, the program instructions are computer-executable to implement generating the prediction of extreme-case traffic load on the route from historical traffic load for the route combined with an amount of historical traffic load that would be diverted to the route from one or more other routes given the extreme-case break for the route.
14. The non-transitory computer-accessible storage medium as recited in claim 13, wherein, in said generating a prediction of extreme-case traffic load on a route given the extreme-case break for the respective route, the program instructions are computer-executable to implement applying a smoothing function to the combined historical traffic load for the route.
15. The non-transitory computer-accessible storage medium as recited in claim 9, wherein the topology model further indicates one or more locations at which two or more of the physical connections are co-located, and wherein the extreme-case break for at least one route is a break at a location at which two or more of the physical connections are co-located according to the topology model, wherein the break affects the two or more co-located physical connections.
16. The non-transitory computer-accessible storage medium as recited in claim 9, wherein the extreme-case break is one of a worst-case break determined according to a maximization of the objective function and a best-case break determined according to a minimization of the objective function.
17. A system, comprising:
one or more processors; and
a memory comprising program instructions, wherein the program instructions are executable by at least one of the one or more processors to implement a network analysis module operable to:
collect historical traffic load data for each of a plurality of routes between a plurality of networks, wherein each route carries traffic between two of the networks over one or more physical connections;
generate a topology model of the plurality of networks and the plurality of routes, wherein the topology model includes, for each route, an indication of an extreme-case break according to the topology model, wherein an extreme-case break for a given route is a break in one or more of the physical connections for which an objective function characterizing one or more route performance metrics is optimized; and
generate, for each route, a prediction of extreme-case traffic load on the respective route given the extreme-case break for the respective route according to the historical traffic load data for the routes and the topology model; and
output, for each route, a report indicating the prediction of extreme-case traffic load on the respective route given the extreme-case break for the respective route.
18. The system as recited in claim 17, wherein at least one of the physical connections is a fiber optic connection.
19. The system as recited in claim 17, wherein each physical connection connects a router of one of the networks to a router of another one of the networks.
20. The system as recited in claim 17, wherein the network analysis module is further operable to generate a prediction of normal traffic load on each route according to 99th percentile (p99) data calculated from the historical traffic load data for the respective route.
21. The system as recited in claim 17, wherein the network analysis module is further operable to generate 99th percentile (p99) data for each route according to the historical traffic load data for the respective route, wherein the predictions of extreme-case traffic load for the routes are generated according to the p99 data for the routes.
22. The system as recited in claim 21, wherein, to generate the predictions of extreme-case traffic load on the routes given the extreme-case breaks for the routes, the program instructions are executable by at least one of the one or more processors to apply a smoothing function according to an autoregressive integrated moving average (ARIMA) model to the historical p99 data for the routes.
23. The system as recited in claim 17, wherein, to generate a prediction of extreme-case traffic load on a route given the extreme-case break for the respective route, the network analysis module is operable to generate the prediction of extreme-case traffic load on the route from historical traffic load for the route combined with an amount of historical traffic load that would be diverted to the route from one or more other routes given the extreme-case break for the route.
24. The system as recited in claim 17, wherein the topology model further indicates one or more locations at which two or more of the physical connections are co-located, and wherein the extreme-case break for at least one route is a break at a location at which two or more of the physical connections are co-located according to the topology model, wherein the break affects the two or more co-located physical connections.
25. The system as recited in claim 17, wherein the extreme-case break is one of a worst-case break determined according to a maximization of the objective function and a best-case break determined according to a minimization of the objective function.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A micro-mirror for deflecting an incident light, comprising:
a mirror section for reflecting an incident light at a relative angle;
a hinge section including a fixed section and a movable section each having a flat surface, said movable section having an end, said mirror section and said hinge section being an integral member such that said mirror section extends from the end of said movable section of the hinge section and is formed slanted at a pre-set angle to said flat surface of the movable section of the hinge section; and
a drive means having a bi-morph structure made of two or more of materials having different heat expansion coefficients for deflecting said mirror section to change the relative angle to said incident light while substantially maintaining the pre-set angle between said mirror section and said flat surface of the movable section.
2. The micro-mirror of claim 1, wherein said mirror section is formed slanted at the pre-set angle of approximately 55 degrees to said flat surface of the movable section of the hinge section.
3. The micro-mirror of claim 1, wherein
said drive means includes:
a first drive film provided on one of surfaces of said moving section of the hinge section, and
a second drive film provided on another of the surfaces of said moving section and having larger thermal coefficient than said first drive film.
4. The micro-mirror of claim 3, wherein said first drive film and second drive film are made from different types of conductive materials to each other.
5. The micro-mirror of claim 4, wherein
said first drive film is a poly-crystal silicon film including impurities, and
said second drive film is an aluminum film.
6. The micro-mirror of claim 3, wherein
said first drive film and second drive film are made from the same types of materials having different resistance to each other.
7. The micro-mirror of claim 1, wherein
said hinge section and said mirror section are integrally constructed on a structured film formed on a semiconductor substrate.
8. The micro-mirror of claim 7, wherein
said semiconductor substrate is a silicon substrate.
9. The micro-mirror of claim 7, wherein
said fixed section and movable section of the hinge section are formed on a first crystalline surface of a silicon substrate respectively, and
said mirror section is formed on a second crystalline surface of said silicon substrate.
10. The micro-mirror of claim 9, wherein
said hinge section is fixed to said silicon substrate by said fixed section.
11. The micro-mirror of claim 7, wherein
said structured film includes a nitride film.
12. The micro-mirror of claim 11, wherein
said movable section and said mirror section of said hinge section are made only by a thin film of said nitride film.
13. A scanner device comprising:
a light emitting device;
a mirror section for reflecting an input incident light from said light emitting device at a relative angle;
a hinge section including a fixed section and a movable section each having a flat surface, said movable section having an end, said mirror section and said hinge section being integrally formed such that said mirror section extends from the end of said movable section of the hinge section and is formed slanted at a pre-set angle to said flat surface of the movable section of the hinge section; and
a micro-mirror equipped with a drive means having a bi-morph structure made of two or more of materials having different heat expansion coefficients for deflecting said mirror section to change the relative angle to said incident light while substantially maintaining the pre-set angle between said mirror section and said flat surface of the movable section; and
an optical detector for detecting a return light of a light irradiated by reflecting at said mirror section.
14. The scanner device of claim 13, wherein
said hinge section and said mirror section are integrally constructed on a structured film formed on a semiconductor substrate; and
said optical detector is formed on said silicon substrate.
15. A method for fabricating a micro-mirror which comprises:
a mirror section for reflecting an incident light at a relative angle;
a hinge section including a fixed section and a movable section each having a flat surface, said movable section having an end; and
a drive means having a bi-morph structure made of two or more of materials having different heat expansion coefficients for deflecting said mirror section of the relative angle to said incident light; wherein
said hinge section and the mirror section are integrally constructed by a structured film formed on a semiconductor substrate by utilizing crystal anisotropy of said semiconductor substrate such that said mirror section extends from the end of said movable section of the hinge section and is formed slanted at a pre-set angle to said flat surface of the movable section of the hinge section, the pre-set angle being substantially maintained between said mirror section and said flat surface of the movable section when said mirror section is deflected by said drive means.
16. The method for fabricating the micro-mirror of claim 15, wherein;
said movable section of the hinge section is so formed as to be continuous from said fixed section of the hinge section and is formed so as to construct a bent slanting surface at an extended section of the fixed section of the hinge section.
17. The method for fabricating the micro-mirror of claim 16, further comprising the steps of:
forming a first groove having a first skewed surface at a side wall section on a front surface of said semiconductor substrate, and a second groove having a second skewed surface substantially parallel to said first skewed surface of the first groove at a position and opposite to a flat surface section around said first groove on a back surface of said semiconductor substrate;
forming structured films at said first skewed surface of the first groove and said flat surface section around said first groove;
forming a first drive film at one surface of said structured film;
forming said mirror section and said hinge section made of the structured film by removing said semiconductor substrate with etching process after performing a through-hole etching of said semiconductor substrate to make one end of said structured film to be a free end at a bottom section of said first groove; and
forming a second drive film on another surface of the structured film constructing said hinge section.
18. The method for fabricating the micro-mirror of claim 17, wherein
an-isotropic etching is performed to said first groove and said second groove after forming said first groove on the front surface of the semiconductor substrate and said second groove on the back surface of the semiconductor substrate.
19. The method for fabricating the micro-mirror of claim 18, wherein
said an-isotropic etching is performed using a mask formed by patterning a photo-resist film by UV ray projection exposure method, wherein said photo-resist film is uniformly formed in thickness by a spray method.