For the outline, first I will introduce you what cloud computing is. Also cloud computing market will be presented. Next, the modeling of cloud computing environment including private cloud will be addressed. Supply strategies which are the main study in the paper will be discussed. In each model, I’ll together present numerical studies. Finally, the conclusion will be discussed.
Cloud computing is a large scale distributed computing which consists of large pool of resources. Those resources can be served a large number of concurrent users. The users can self-service provision resources as they need. Self-service in this sense means users can specify what kind of resources they need. For example in cloud computing, you can specify storage capacity, computing power, even software requirement including operating system to run your IT system. Those resources can be charged to user by pay-per-use basis. So the users can control their budgets. Cost of risk could be reduced for this control. For the virtualization here, cloud computing providers normally apply virtualization technologies to deliver resources to the users. Nowadays there are many cloud computing providers such as …
Now I’m introducing what’s inside cloud computing market. We call this market “Cloud Resource Market”. Same as traditional markets, it has buyers and sellers. In cloud resource market, buyers are those who want to buy resources/services which are available in cloud computing. In this paper, we focus on private clouds. Actually we have used this private cloud for very long history already, they have existed before cloud computing. Usually when talk about cloud computing, we’re referring to public cloud. Private clouds are those who own in-house computing resources in their sites. They can fully manage those resources. They must maximize utilization of those resources as they can because they did invest those resources with high cost. Some data cannot be stored in the cloud because of their sensitivity/security issues. So people still need private clouds. They cannot bring everything to host on public cloud. But in some situations, buyers can pay for additional resources in public cloud. For example, SME business has just small private cloud. SME can rent some resources from service providers in public cloud. In this paper, we focus on VM hosting service. It’s a service that private cloud can host virtual machines in public cloud. In this way, private clouds can spill over some IT workloads to public cloud. For example, in-house resources cannot serve peak workloads/demands.
Workload outsourcing is something presented here. A private cloud owns in-house resources. The private cloud maximizes utilization of the resources. When workload increases such as peak demand, if private cloud wants to avoid performance degradation and also reduce total cost of ownership, some workloads are computed locally / in in-house resources. Some other workloads are outsourced to public cloud. Private cloud can prepare virtual machines to host in public cloud.
Before deriving the supply strategies of service providers, we model a utility function of private cloud. I would not show you all formulations. Also the detail of them is not deeply presented here but in the paper. This utility represents job processing throughput of private cloud. The private cloud maximizes the utility. From this utility function, we can obtain the number of VMs required by private cloud. In this paper, the number of VMs represents “demand”. Next we derive a demand function which can be obtained from utility maximization. We observe the demand is non-increasing staircase function as presented here (the aggregate demand function of all private clouds). Big I is the number of steps (or staircases). Theta is boundary at step i given price p. The sum of these steps, we obtain demand. Next we can derive an inverse demand function also called price function in economics which is the willingness to pay. This function will be used by service providers for defining supply strategies. This function is also a non-increasing staircase function. At each step, given n or supplied VMs, we obtain price. (in this case for the provider n is the num of supplied VMs but actually demand for the private cloud.
This slide presents a numerical study of private cloud. Suppose we have only one private cloud. This private cloud has 2 physical machines. Queue size = 100. With fixed job arrival rate, as the number of jobs increases, the utility first increases as well, then decreases. Why? First the number of VMs is small, so waiting time and loss probability are high, cost due to performance degradation is high as well. But when the number of VMs increases, cost due outsourcing is high. Then we find the optimal number of VMs which maximizes utility of private cloud. [The other figure here] obtained from the demand function. For high job arrival rate, VMM requires more number of VMs to serve more jobs to and to minimize the performance degradation. However if the price is high, the cost of outsourcing increases.
Now let’s see how the single one service provider in monopoly market can obtain optimal supply strategy. Let me talk about one fact a bit. It’s the fact that the monopoly market in cloud computing can exist. Because there could be only one service provider in the market who can offer a special service required by private clouds. For example, in one country, the country government says all banking companies must use cloud services from the only one public cloud certified by the government. The only one service provider fully control the market. The provider can maximize profit by this function (n*). The optimal suuply strat can be obtained.
This above figure shows the non-staircase inverse demand function. Given this function, the service provider can look for the optimal number of supplied VMs that the profit is maximized.
Let’s see the supply strategies in competitive oligopoly market. This market consists of multiple service providers. And service providers compete to supply VMs and to gain more market share. We use noncopoperative/competitive game to obtain the supply strategy. By using the noncooperative game, Nash equilibrium will be found. To reach this Nash equilibrium, we use an iterative algorithm presented in the paper.
In this numerical study, we evaluate only 2 providers. Here… X-axis is the supply strategy of service provider 1, and here strategy of service provider 2. Each provider uses its best response to maximize its profit. Like in this case, given an initial point, service provider 1 uses this strategy. Then 1 -> 2 -> 3 -> 4. Until Nash equilibrium is reached. We observe that there’re many Nash equilibria. At these equilibria, a service provider cannot increase its profit while the other providers still keep unchanged strategies.
Let’s consider the coop market. All service providers can cooperate to improve their profits. Each provider finally supplies a small number of VMs to private clouds, but price and profit are increased.
Economic Analysis of Resource Market in Cloud Computing Environment
Economic Analysis of Resource Market in Cloud Computing Environment Dusit Niyato, Sivadon Chaisiri , and Bu-Sung Lee School of Computer Engineering Nanyang Technological University, Singapore Tuesday, December 8, 2009 Presented in IEEE Asia-Pacific Services Computing Conference (APSCC), Singapore
Introduction: Cloud Computing <ul><ul><li>Large Internet scale computing </li></ul></ul><ul><ul><li>Large pool of resources </li></ul></ul><ul><ul><li>Large number of concurrent users </li></ul></ul><ul><ul><li>On-demand self-service provisioning </li></ul></ul><ul><ul><li>Utility service and pay-per-use </li></ul></ul><ul><ul><li>Virtualization </li></ul></ul><ul><ul><li>Some cloud computing providers – Amazon, Microsoft, Google, Salesforce, GoGrid, Flexiscale, Redplaid, … </li></ul></ul>Cloud Computing
Introduction: Cloud Resource Market <ul><li>Buyers: Private clouds </li></ul><ul><li>Sellers: Service providers in public cloud </li></ul><ul><li>VM hosting service is supplied by service providers </li></ul><ul><li>Workload outsourcing – private clouds spill over their IT workloads to public cloud </li></ul>
Introduction: Workload Outsourcing In-house computing resource (e.g., physical machines and SAN) Public cloud Service provider Private cloud Virtual environment Service provider In-house computing resource Virtual machine
Introduction: This Paper Math models to investigate different types of markets Optimization model for monopoly market Noncooperative game and bargaining game models for competitive and cooperative oligopoly markets Repeated game to analyze the collusion among providers
Cloud Computing Environment Private cloud owns in-house computing resource Job from users is stored in a queue A set of VMs is prepared for processing jobs and stored in VM repository A single VM can execute multiple jobs concurrently (e.g., VM with multiple virtual cores) VMM assigns VM stored in VM repository to available in-house machines / service providers If physical machines are all occupied, VMM outsources VMs to service provider in public cloud
Private Cloud <ul><li>Utility function </li></ul><ul><li>Aggregate demand function of all private clouds </li></ul><ul><li>Willingness to pay (inverse demand function) </li></ul>
Numerical Studies: Private Cloud Optimal number of VMs to be outsourced Utility of private cloud
Supply Strategies: Monopoly Market <ul><li>The only single service provider in the market </li></ul><ul><li>Profit maximization </li></ul><ul><li>Optimal supply strategy obtained from </li></ul><ul><li>Profit of monopolistic service provider </li></ul>
Supply Strategies: Competitive Oligopoly Market <ul><li>Service providers compete to supply VMs </li></ul><ul><li>Noncooperative game is formulated </li></ul><ul><li>Best response of service provider o </li></ul><ul><li>Profit of service provider o at Nash equilibrium </li></ul><ul><li>Nash equilibrium is the solution </li></ul>
Numerical Studies: Competitive Oligopoly Market No provider can increase its profit given the supplied VMs from the other providers
Supply Strategies: Cooperative Oligopoly Market <ul><li>All service providers can cooperate to improve their profits </li></ul><ul><li>Bargaining game is formulated </li></ul><ul><li>Bargaining solution is characterized by the Pareto optimality </li></ul>
Numerical Studies: Comparison of Profits The provider can fully control the market In co-market, a provider reduces num of VMs
Supply Strategies: Collusion among Providers <ul><li>The bargaining solution of cooperative provider may not be the best response of the other ones </li></ul><ul><li>Some providers can deviate from bargaining solution to the best response to gain higher profits </li></ul><ul><li>Pareto optimal – profits of non-deviating providers will be decreased! They must punish the deviating providers! </li></ul><ul><ul><li>Nash equilibrium, now all providers receive worse profits </li></ul></ul><ul><li>Providers can maintain the collusion if they’re aware of such a punishment </li></ul>
Numerical Studies: Collusion among Providers Profit at bargaining solution Profit at Nash equilibrium after being punished Profit of deviated service provider Minimum discount factor to maintain collusion used to weight the future profit
Conclusion <ul><li>An economic analysis of the resource market in cloud computing environment is presented </li></ul><ul><li>Three types of resource markets </li></ul><ul><ul><li>Monopoly market </li></ul></ul><ul><ul><li>Competitive oligopoly market </li></ul></ul><ul><ul><li>Cooperative oligopoly market </li></ul></ul><ul><li>Our main contribution </li></ul><ul><ul><li>Tractable mathematical models for cloud computing environment </li></ul></ul><ul><ul><li>This economic analysis will be useful for planning and optimizing market decision in the cloud </li></ul></ul>