The costs incurred by cloud providers towards operating their data centers are often determined in large part by their peak demands. The pricing schemes currently used by cloud providers to recoup these costs from their tenants, however, do not distinguish tenants based on their contributions to the cloud's overall peak demand. Using the concrete example of peak-based pricing as employed by many electric utility companies, we show that this "gap" may lead to unfair attribution of costs to the tenants. Simple enhancements of existing cloud pricing (e.g., analogous to the coincident peak pricing (CPP) used by some electric utilities) do not adequately address these shortcomings and suffer from short-term unfairness and undesirable oscillatory price vs. demand relationship offered to tenants. To overcome these shortcomings, we define an alternative pricing scheme to more fairly distribute a cloud's costs among its tenants. Our approach to fair attribution of cloud's costs is inspired by the concept of Shapley values used to fairly divide revenue among participants of a financial coalition. We demonstrate the efficacy of our scheme under price-sensitive tenant demand response using a combination of (i) extensive empirical evaluation with recent workloads from commercial data centers operated by IBM, and (ii) analytical modeling through non-cooperative game theory for a special case of tenant demand model. Cloud Tenant; Pricing Design; Game; Fairness Cloud computing is turning information technology (IT) into a utility wherein cloud providers hide the complexity of building and operating data centers from their customers ("tenants") and supply them with virtualized IT resources. Inevitably, this virtualization creates a "gap" between how cloud providers incur costs for operating their data centers and how they recoup these costs from their tenants, the latter typically in terms of virtualized IT resources such as virtual machines (VMs). One important example of this gap is the difference that often exists between pricing schemes underlying costs incurred by cloud providers versus those used by cloud providers themselves to sell virtualized IT resources. The Problem: Several important components of a cloud provider's costs are significantly affected by the peak of its resource usage. Arguably, the most explicit example of this -our focus in this paper -is found in the form of electric bills using "peak-based pricing" that certain electric utility companies employ, e.g., [1], [2] . In this pricing, the consumer's electric bill contains a separate component for its peak power draw over the billing cycle ("peak charge") in addition to the usual component based on energy consumed ("energy charge"). It is well-known that the peak-related component can contribute a lot to the overall electric bill of the cloud provider [3], [4] . [6], [4], we identify a novel and complementary concern that we find useful to think of as a "fairness" problem. Notions of fair pricing of tenants in our problem are more complex than simple notions of proportional or max/min fairness due to a plurality of quantities used (mean and peak) for pricing and the complex statistics of the coincident/aggregate peak itself. Our problem is most clearly understood by posing the following question in a revenue-neutral cloud environment 1 : how should a cloud provider subject to peak-based pricing for its electric power consumption recoup these costs from its tenants? Whereas the answer is straightforward for the energy charge making up the bill (simply charge each tenant for its own total energy consumption), it becomes less clear when one considers recouping the peak charge. It is easily seen that existing pricing schemes employed by cloud providers amount to distributing the peak charge among tenants in proportion to their resource allocations (e.g., VMs) or, as a first-order approximation, in proportion to their energy consumption. We find such a pricing scheme to be "unfair" since it charges two tenants identically even if one of them contributes more to the cloud's overall peak power draw than the other (Figure 2 shows a simple example). In fact, as we show in Section II, even other pricing schemes that do incorporate the tenants' contributions to the cloud's peak draw into their decision-making continue to suffer from such unfairness. This motivates us to explore an alternative pricing scheme that attributes peak-related costs more carefully leading to fairer charging of tenants. Why Study This Problem? We find our problem worth studying for two main reasons. First, energy-related costs are already significant components of overall costs for many data centers, and correspondingly of their tenants. Consider the following excerpt from a recent New York Times article on how certain data centers already bill their tenants: "... electrical capacity is often the central element of lease agreements, and space is secondary. A result, an examination shows, is that the industry has evolved from a purveyor of space to an energy brokermaking tremendous profits by reselling access to electrical power, and in some cases raising questions of whether the industry has become a kind of wildcat power utility ... 75 percent of Resurgens's lease was directly related to power -essentially for access to about 30 power sockets." [7] It is likely that the relative contribution of energy costs will only continue to grow due to increasing energy prices, especially those for peak draw. Second, one may wonder if our fairness concern can be alleviated simply by replacing peak-based pricing citeduke with real-time pricing. In fact, many providers and consumers do find peak-based pricing more appealing due to the lower associated uncertainty compared to spot prices. As evidence that such choices are already being made by existing data centers and their tenants, consider the folowing excerpt from the same New York Times article: "One key to the profit reaped by some data centers is how they sell access to power. ... many data centers charge tenants as if they were using all of that capacity -in other words, full price for power that is available but not consumed." Generally speaking, since spot prices can be highly dynamic, it may be desirable for certain energy consumers to hedge their electricity costs by buying from a third party an energy future whose form may be very similar 1 A revenue-neutral cloud ensures that the costs it recoups from its tenants are exactly what it incurs due to its own operation.
REF fairly distributed a DC's overall electricity cost among its users.
On Fair Attribution of Costs under Peak-Based Pricing to Cloud Tenants
MASCOTS
Computer Science