YOM proposes a peer-to-peer cloud infrastructure for the metaverse, offering numerous advantages compared to traditional centralized cloud solutions. By introducing streaming rewards, the YOM network aims to provide a profitable, environmentally-friendly, and meaningful proposition for miners, creators, and gamers alike.
Cloud gaming is likely to replace client-side gaming due to 1) devices requiring increasingly smaller chips as they get more integrated into our bodies, 2) the increasing demand for a premium, engaging and continuous metaverse and 3) the need to cost-efficiently deploy optimized metaverse experiences for a wide range of different types of devices.
Despite these benefits, current cloud solutions are still inefficient and costly for general everyday usage and do not scale effectively. Furthermore, a data center needs to be cooled and maintained, making it overall an unsustainable solution with a large carbon footprint. For this reason, YOM proposes a peer-to-peer cloud infrastructure for the metaverse whereby gamers get rewarded for streaming the metaverse. This way, we combat both the lack of available low-latency machines at scale as well as saving expenses that would otherwise be used for cooling.
In 2022, a typical cloud gaming machine contains a graphics card equivalent to a RTX 2070. To host one hour of content for four users on this machine, costs ~ €2 per hour. To compare, mining on an RTX 2070 rewards miners ~ €0.01 per hour - which is even lower than the energy costs it takes to power the machine (assuming 500 watt energy consumption). Consequently, reallocating the computing jobs to a peer-to-peer streaming network is an incredible opportunity to reduce both the bottom-line operating costs of cloud-based Metaverses by a factor of ~ 5-20x as well as providing miners and gamers ~ 5-20x higher rewards compared to mining PoW blockchains.
The introduction of streaming rewards is a profitable, environmentally-friendly and meaningful proposition for miners and gamers to migrate their resources to the YOM network, who will compete with the pricing of services like AWS, Google and Azure. The algorithm for determining rewards proposed here, liquidity pools and market principles optimize the peer-to-peer mesh network for a healthy circulating economy, overall cost-efficiency and performance.
The YOM Unit Economics for streaming after > 0.25% of tokens are circulating as daily hours streamed / total circulating supply. *The rewards ceiling is the price set by cloud providers and therefore the mesh need to be more efficient than this ceiling.
As the YOM infrastructure streams metaverses, it will auto-benchmark the performance of its metaverses running on the infrastructure and list this data on-chain. The rendering rewards are
$YOM/hrfor each hosted user/connection (45fps@1080p) for streaming/mining an average (benchmarked) metaverse.
Most gaming machines, we call them beacons, will be able to host more connections concurrently, thereby providing an incentive for gamers to allocate heavier gaming machines to the network. A strong beacon may be able to handle 8 connections concurrently (receiving a total of ~
$YOM/hr) while a weaker pc is just able to run less connections. In the end it comes down to how many instances (= users/u) you can stream on the lowest possible electricity costs. We therefore rewrite the formula for calculating rewards as
The x in
$YOM/u/hris determined based on a gradual evolution between two approaches:
- 1.Margin-based approach. This will provide rewards based on a 2-20x profit margin compared to the global average electricity costs to stream metaverses expressed in dollars and the price of YOM expressed in dollars. This approach is used to ensure that early speculative fluctuations in the price do not disrupt the inititially weak internal economy and prevent initial beacons or projects from joining the network due to market inefficiency.
- 2.Market-based approach. This approach provides rewards based on free market economics, which is expressed by the increased proportion of streaming tokens in circulation (i.e. the strength of the internal economy due to metaverse demand) compared to its own increasingly more scarce total tokens in circulation (due to burning). With an increased proportion of tokens circulating due to product demand we increase the market efficiency. With increased market efficiency there is less need for a margin-based approach to correct for speculative price movements.
Initially the weight is stronger on the margin-based approach. As more minutes get streamed and the price of $YOM gets to be more heavily influenced by its internal economy instead of outside speculation, we allocate a stronger weight to the ciculation-based approach for deciding rewards.
For the market-based approach we define its weight by finding the ratio between the streaming tokens in circulation in relationship to the total circulating token supply. For deciding the weighted average between approaches we define the weight for the margin-based approach as
0.1%. This means that the more tokens are being used for streaming, the more dominant the market-based approach becomes.
Next to benchmarking the performance of metaverses, YOM benchmarks the power usage of its machines and infers the consumption of individual connections as mWh (meta Watt hour). We then use the global average costs of electricity to calculate the electricity costs required to host a single average benchmarked metaverse.
Finally we add in a variable profit margin multiplier which is automatically scaled dependent on the supply/demand for machines in a particular region (-> <15ms ping, 45+ fps @ 1080p). We created the following table that demonstrates some example reward schemes:
In order to sustain artifical price movements due to token hoarding and release, we need to determine a threshold percentage of tokens to be used in YOM's circulating streaming economy that forms a sustainable base of value onto which a pricepoint for $YOM can be anchored. We take a range of at least 0.1% of the total circulating supply which may grow towards ~ 5% of the total circulating supply in correspondance with increased demand for streaming minutes. The higher the percentage, the better $YOM represents the underlying value of metaverse streaming.
In order to peg the price of $YOM to the demand for metaverse streaming, we need to build a function that initially optimizes for streaming (product usage) as percentage (t) of the total circulating supply (s) and then increases the price of $YOM in correspondance with the demand for metaverse. For this reason, we created a power function:
The output of this power function determines the $YOM hourly rewards. The maximum rewards a beacon can get from this approach is set at a maximum of 2 $YOM per hour and cannot exceed the USDC rewards ceiling (see diagram). We set this reward to protect the ecosystem from allocating extremely high rewards (preventing outage or other scenarios that could lead to near-zero streaming minutes). As an example we get:
To determine the final streaming rewards, we need the following input:
- 1.Base rewards ->
- The total circulating supply
- The number of tokens circulating for streaming purposes
- The price of $YOM (-> expressed in USDC)
- The power usage of an avg. metaverse (-> expressed in mWh)
- A demand multiplier (-> 45+ fps @ 1080p, <15ms ping)
- 2.Beacon performance ->
- The amount of connected users (-> 45+ fps @ 1080p, <15ms ping)
- 3.Rendering difficulty ->
- Metaverse performance benchmark
After applying the distribution algorithm, the output returns
$YOM/u/hr, which is multiplied by the rendering difficulty and amount of connected users then distributed among all streaming stakeholders according to the following distribution table:
In order to ensure evolving towards a state where most of the tokens in circulation are due to streaming rather than to other purposes (e.g. speculating/trading), we implemented a burning mechanic that over time gradually decreases the total cap of tokens in the supply. When the treshold of >0.25% (see above) is achieved, the burning mechanic halts.
As an example we assume that .8% tokens are circulating for the purposes of metaverse streaming, we assume the power consumption of an average metaverse in a particular region is €0.02, we estimate a relative high demand in a region where the beacon is streaming so we set the demand multiplier at 8, and the amount of connections this particular beacon can stream we set as 2, the graphical requirements are slightly more demanding than average (+10%). We also take into consideration a future scenario where streaming minutes have pushed the $YOM price to $2.8.
Margin-based rewards: $ 0.02 * 8 = $ 0.16 -> 0.06 $YOM /u/hr
Market-based rewards: 0.12 $YOM /u/hr
Normalized rewards : (0.1 * 0.06 + 0.8 * 0.11) / 0.9 = 0.11 $YOM /u/hr
Total rewards (2 users at + 10% difficulty): 0.11 * 2 * 1.1 = 0.24 $YOM/hr:
As you may have noticed, there is a gap between the margin-based and the circulation based approach. This situation may represent an inefficient market where speculative forces outperform the actual value of the demand (which in this example could have been roughly $1.37 instead of $2,80). To create market efficiency, we can close the gap via liquidity providers.
For this reason, YOM will create incentives (liquidity pools) to attract additional liquidity to reinforce the demand/supply of metaverse streaming and reward traders and investors to create market efficiency. The result is a price that converges to a stable state of growth that represents the market for the metaverse.
The liquidity pool allows users to collect rewards on their own terms based on the amount liquidity they provide to the market. Simply put, the more liquidity you provide to enable market efficiency, the more rewards you will receive (difference between speculative and actual/real value). It is up to the community to invent better mechanisms than the margin-based approach, as is proposed in this article.