Cloud computing services for resource accounting establish, host, and manage services rented out to a remote consumer (individual or business). The scope of services ranges from offering essential computational assets like bandwidth, storage, and computational power to sophisticated enterprise applications such as SaaS.
The commonly used business model to charge for these services is a pay-per-use base, where the consumers use resources according to their needs and are then billed for the used resources at the end of a defined period. Idyllically, the consumers should be offered an explicit resource accounting model to identify all factors considered for calculating the consumption charges.
Giving the consumer access to their resource usage data can help them in IT budget planning, making them aware of billing applications and automating the assortment of services according to their specific needs. Also, it is to the benefit of the cloud service provider to make this data available to consumers. It would help them come up with specifically designed user-centric services. Most providers already offer their clients their usage data. However, there are concerns regarding accountability.
Accountability Concerns
An issue that concerns the resource accounting model for such services is the accountability of the resource usage data, whether it is the provider who does the measurements of this data, the consumer, or some other trusted third party.
A combination of these players to measure accountability is also an option. However, like other traditional utility services such as gas and electricity, provider-side accountability is the norm in the cloud services industry. But unlike the consumer-trusted metering for traditional utilities, there is no such metric for consumers to measure data usage. Therefore, they have no choice but to accept what the provider offers them.
Consumer Side Proposition
Continuing the above discussion, we propose a customer-centric resource accounting model to resolve the concerns of all parties. A customer-centric accounting model will allow the customer to programmatically calculate billing charges from the service provider. They will be obliged to respond to the request.
Subsequently, a customer-centric solid accounting model will measure the cloud services billing charges independently by the customer or a trusted third party. This will allow consumers to measure their billing charges, which is only fair. It is proposed that the providers adopt the customer-centric models to perform regular checks and raise alarms when there are apparent contradictions in the consumption data.
Inspecting Consumer Centricity of the Resource Accounting Model
Many service providers provide or publish details of their billing information. However, it must be examined if the detailed information is based on a customer-centric notion. We will start with a basic example and see if there is room for improvement.
S3 Amazon
We take a simple S3 cloud storage service from Amazon to investigate the notion. It is an essential pay-per-use base service. It is marketed as a fast, reliable, and low-cost service, yet, upon examination, researchers found a few ambiguities in the model that cannot be programmatically queried from Amazon.
This suggests that even the simplest resource accounting models cannot match a robust consumer-centric model’s standards, which are relatively loosely coherent with the weak model. However, specifically for S3, the uncertainties can be easily fixed internally by paying little attention. However, it is recommended that a cloud service provider opts for assembling the third party as a measurement service to access and measure their data usage efficiently.
Limitations
Time Zone
One of the ambiguities was the lack of reference to the time zone upon listing the price concerning geographical location. The start and end days of the billing cycle cannot be determined for sure. However, it can be inferred from the provided Amazon developer’s guide that S3 servers are synchronized to GMT.
Data and Metadata
Another concern of S3 is regarding the measurement of user data. In addition to its data, the S3 resource accounting model has system metadata and user metadata. System metadata is only used by S3, while user metadata is only used by the consumer and has a maximum size of 2KB. However, Amazon fails to clarify the storage space acquired by data and metadata.
Checkpoints
Amazon states that they check the user’s storage space at least twice daily. However, they fail to mention the exact time these checkpoints take place. Upon further investigation, it has been observed that they do not check the cloud storage usage twice every day, and the event randomly takes place anytime in a Zulu time zone.
Therefore, it is concluded that the resource accounting model for the cloud still needs to define specific operation parameters before they become trusted consumer utilities.
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