Gemini のマルチモーダル機能の 7 つの実例

Here are the tables you requested:
 
**1. Total Revenue by Quarter (in millions USD)**
 
| Quarter Ended       | Year | Total Revenue |
|-------------------|------|--------------|
| March 31          | 2021 | 55,314        |
| June 30           | 2021 | 61,880        |
| September 30      | 2021 | 65,118        |
| December 31       | 2021 | 75,325        |
| March 31          | 2022 | 68,011        |
| June 30           | 2022 | 69,685        |
| September 30      | 2022 | 69,092        |
| December 31       | 2022 | 76,048        |
| March 31          | 2023 | 69,787        |
| June 30           | 2023 | 74,604        |
| September 30      | 2023 | 76,693        |
| December 31       | 2023 | 86,310        |
| March 31          | 2024 | 80,539        |
| June 30           | 2024 | 84,742        |
| September 30      | 2024 | 88,268        |
 
 
 
**2. Revenue by Google Product/Service (in millions USD)**
 
**2021 Q1 - Q4**
 
| Quarter Ended  | Google Search | YouTube Ads | Google Network | Google Other | Google Cloud | Other Bets |
|----------------|--------------|------------|---------------|-------------|-------------|-----------|
| March 31, 2021 | 31,879       | 6,005      | 6,800        | 6,494       | 4,047       | 198       |
| June 30, 2021  | 35,845       | 7,002      | 7,597        | 6,623       | 4,628       | 192       |
| Sept 30, 2021 | 37,926       | 7,205      | 7,999        | 6,754       | 4,990       | 182       |
| Dec 31, 2021  | 43,301       | 8,633      | 9,305        | 8,161       | 5,541       | 181       |
 
**2022 Q1 - Q4**
 
| Quarter Ended  | Google Search | YouTube Ads | Google Network | Google Other | Google Cloud | Other Bets |
|----------------|--------------|------------|---------------|-------------|-------------|-----------|
| March 31, 2022 | 39,618       | 6,869      | 8,174        | 6,811       | 5,821       | 440       |
| June 30, 2022  | 40,689       | 7,340      | 8,259        | 6,553       | 6,276       | 193       |
| Sept 30, 2022 | 39,539       | 7,071      | 7,872        | 6,895       | 6,868       | 209       |
| Dec 31, 2022  | 42,604       | 7,963      | 8,475        | 8,796       | 7,315       | 226       |
 
 
**2023 Q1 - Q4**
 
| Quarter Ended  | Google Search | YouTube Ads | Google Network | Google Other | Google Cloud | Other Bets |
|----------------|--------------|------------|---------------|-------------|-------------|-----------|
| March 31, 2023 | 40,359       | 6,693      | 7,496        | 7,413       | 7,454       | 288       |
| June 30, 2023  | 42,628       | 7,665      | 7,850        | 8,142       | 8,031       | 285       |
| Sept 30, 2023 | 44,026       | 7,952      | 7,669        | 8,339       | 8,411       | 297       |
| Dec 31, 2023  | 48,020       | 9,200      | 8,297        | 10,794      | 9,192       | 657       |
 
**2024 Q1 - Q3**
 
| Quarter Ended  | Google Search | YouTube Ads | Google Network | Subscriptions, Platforms, & Devices | Google Cloud | Other Bets |
|----------------|--------------|------------|---------------|-----------------------------------|-------------|-----------|
| March 31, 2024 | 46,156       | 8,090       | 7,413        | 8,739      | 9,574       | 495       |
| June 30, 2024  | 48,509       | 8,663       | 7,444        | 9,312      | 10,347      | 365       |
| Sept 30, 2024 | 49,385       | 8,921       | 7,548        | 10,656     | 11,353      | 388       |

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Here are the technical lecture notes you requested:
 
# **Achieving Rapid Response Times in Large Online Services**
Jeff Dean, Google
 
## **Introduction**
Rapid response times of web applications are important for making them more interactive, fluid, and easy to use for the user. It’s often challenging to keep web apps performing quickly when there’s a large fanout, or the number of servers that need to be contacted in order to fulfill the user’s request. This can be seen in Google Search, where the results page often requires information from thousands of servers to create. This is made even more challenging when Google services are run in a shared environment, or on a large cluster of servers where many different services can be performed. This allows for an array of network issues like traffic congestion, background activity, and spikes in foreground activity for other services being carried out on the same server cluster. 
 
When the latency of applications under these conditions is inspected, they exhibit what's referred to as long-tail latencies. Jeff used an amusing picture of himself on an African safari getting his shirt pulled by a cheetah to illustrate this concept. As he explained, long tail latency means that when you measure the latency of your application and find that it performs quickly on average, the 99th percentile latency could be very long. For example, if your server has a 10 ms average response time and a 99th percentile latency of 1 second, then if you have to get data from only one of those servers, 1% of requests will take more than a second. But when you have to get a response from 100 servers, 63% of your requests will take over a second, since at least one of the servers is likely to be experiencing this high latency.
 
## **Basic Latency Reduction Techniques**
 
Some common ways of dealing with these issues are:
 
* Differentiated service classes: Prioritizing interactive requests and their network traffic much higher than background requests, as this is less likely to affect the user experience if these requests lag.
* Reduce head-of-line blocking: Dividing large requests into many smaller requests to prevent lag for higher-priority requests waiting behind them. 
* Manage expensive background activities: Rate-limiting background activities or delaying them until there’s less traffic on the servers, as these are usually not directly associated with a user request.
 
## **Fault Tolerance vs. Tolerating Variability**
 
The speaker drew an analogy to fault tolerance, a common hardware technique where unreliable parts of the system, like hard drives or a computer's power supply, are used to create a whole reliable system. By analogy, he wants to use unpredictable components that vary greatly in performance to create a predictable and high-performing system. Jeff pointed out that while both fault tolerance and tolerating variability use extra resources, the difference between the two is in the timescale of their variability. The issues that fault tolerance measures are on a scale of tens or hundreds of events per day, while latency tolerance measures thousands of events per second.  
 
## **Latency Tolerating Techniques**
 
Here are two techniques that Jeff describes for minimizing variability in latency:
 
### Cross Request Adaptation
 
* Collect statistics on the system. This could include latency rates, performance of backends, etc.
* Take action to improve the latency of future requests, for example, by load balancing.
* Timescale for these kinds of actions are generally on the order of tens of seconds to minutes. 
 
### Within-Request Adaptation
 
* Within a single high-level request, cope with slow subsystems.
* Timescale for these kinds of actions are generally immediate, while the user is waiting for a request to be fulfilled. 
 
## **Fine-Grained Dynamic Partitioning**
 
One cross request adaption technique that Jeff discussed was fine-grained dynamic partitioning. Normally, if you have ‘n’ servers, you could simply divide the workload into ‘n’ equal pieces, and each server can deal with one piece each, assuming that there’s no shared environment where other things can happen. But once you have a shared environment, the load becomes unpredictable and can result in a server getting overloaded. In the case of a shared environment, it’s recommended to have a server dealing with 10–100 different pieces of work. This allows for very fine-grain load balancing, because if one server is overloaded, one of those pieces of work can be assigned to another server. Another reason for doing this is that it speeds up failure recovery, because when a server dies, whatever it was responsible for is distributed to other machines, and if the workload has been divided into ‘n’ smaller tasks, this recovery process can happen in ‘n’ separate ways simultaneously.
 
## **Selective Replication**
 
Another technique often used by Google is called selective replication, where heavily-used pieces of information in the system are copied to other server clusters. This can be static, where the number of copies is fixed, or dynamic, where the number of copies of a piece of information is increased or decreased depending on the amount of traffic there is in requests associated with that information.
 
## **Latency-Induced Probation**
 
A third technique that Jeff described for dealing with unpredictable latency and interference effects from shared services was what he calls latency-induced probation, or the concept of removing capacity under load to improve latency. The steps for this are:
 
* Recognize that a server is slow to respond, even if it is a high priority server.
* Make a copy of the data in question on another server.
* Send a “shadow stream” of requests to the slow server. These requests are similar to “canary requests” in that they serve as a check to make sure the server is functioning. 
* Once the latency of the slow server has gone down and the “canary” checks show it working, return it to service.
 
## **Backup Requests**
 
Another technique for minimizing latency variability is the use of backup requests, where a client sends a copy of the same request to two or more server clusters in order to improve latency. If one of the servers selected returns the data faster, the client sends a cancellation request for the duplicate request in the other server queue, if it’s possible to maintain information about where the original request was sent. However, the disadvantage of this is that it can double the processing required if two servers begin processing the request at about the same time. In the latter case, the client needs to check if the issue of simultaneous processing occurred, and if so, to send only one copy of the requested data. 
 
The speaker then measured the improvement in latency using two different systems. The first was a loaded server cluster where data was replicated in two in-memory servers, and 1000 requests were spread across 100 tablets. The speaker measured the time it took for all 1000 keys to be retrieved. The second measurement used an almost completely idle system, but with the same parameters: data was replicated in two in-memory servers, 1000 requests were sent across 100 tablets, and the total retrieval time for all 1000 keys was measured.  In both cases, backup requests reduced latency dramatically. The results for both loaded and idle servers, respectively, are shown in the tables below:
 
### Loaded cluster results:
 
| Policy        | Avg      | Std Dev | 95%ile | 99%ile | 99.9%ile |
| ------------- |:--------:|:-------:|:------:|:------:|:--------:|
| No backups    | 33 ms   | 1524 ms| 24 ms  | 52 ms  | 994 ms   |
| Backup after 10 ms | 14 ms   | 4 ms    | 20 ms  | 23 ms  | 50 ms    |
| Backup after 50 ms | 16 ms   | 12 ms   | 57 ms  | 63 ms  | 68 ms    |
 
### Idle cluster results:
 
| Policy        | 50%ile  | 90%ile | 99%ile | 99.9%ile |
| ------------- |:--------:|:-------:|:------:|:--------:|
| No backups    | 19 ms   | 38 ms  | 67 ms  | 98 ms    |
| Backup after 2 ms | 16 ms   | 28 ms  | 38 ms  | 51 ms    |
 
## Conclusion
 
These techniques can make online services more responsive and can dramatically cut down on processing time and costs.