Lifting the Haze off the Cloud: A Consumer-Centric Market for Database Computation in the Cloud
by Yue Wang, Alexandra Meliou, Gerome Miklau
Abstract:
The availability of public computing resources in the cloud has revolutionized data analysis, but requesting cloud resources often involves complex decisions for consumers. Estimating the completion time and cost of a computation and requesting the appropriate cloud resources are challenging tasks even for an expert user. We propose a new market-based framework for pricing computational tasks in the cloud. Our framework introduces an agent between consumers and cloud providers. The agent takes data and computational tasks from users, estimates time and cost for evaluating the tasks, and returns to consumers contracts that specify the price and completion time. Our framework can be applied directly to existing cloud markets without altering the way cloud providers offer and price services. In addition, it simplifies cloud use for consumers by allowing them to compare contracts, rather than choose resources directly. We present design, analytical, and algorithmic contributions focusing on pricing computation contracts, analyzing their properties, and optimizing them in complex workflows. We conduct an experimental evaluation of our market framework over a real-world cloud service and demonstrate empirically that our market ensures three key properties: (a) that consumers benefit from using the market due to competitiveness among agents, (b) that agents have incentive to price contracts fairly, and (c) that inaccuracies in estimates do not pose a significant risk to agents’ profits. Finally, we present a fine-grained pricing mechanism for complex workflows and show that it can increase agent profits by more than an order of magnitude in some cases.
Citation:
Yue Wang, Alexandra Meliou, and Gerome Miklau, Lifting the Haze off the Cloud: A Consumer-Centric Market for Database Computation in the Cloud, PVLDB, vol. 10, no. 4, 2016, pp. 373–384.
Bibtex:
@article{WangMM2016,
  author    = {Yue Wang and 
               Alexandra Meliou and
               Gerome Miklau},
  title     = {\href{http://www.vldb.org/pvldb/vol10/p373-wang.pdf}{Lifting the Haze off the Cloud: A Consumer-Centric Market for Database Computation in the Cloud}},
  abstract = {
  The availability of public computing resources in the cloud has revolutionized data analysis, but requesting cloud resources often involves complex decisions for consumers. Estimating the completion time and cost of a computation and requesting the appropriate cloud resources are challenging tasks even for an expert user. We propose a new market-based framework for pricing computational tasks in the cloud. Our framework introduces an agent between consumers and cloud providers. The agent takes data and computational tasks from users, estimates time and cost for evaluating the tasks, and returns to consumers contracts that specify the price and completion time. Our framework can be applied directly to existing cloud markets without altering the way cloud providers offer and price services. In addition, it simplifies cloud use for consumers by allowing them to compare contracts, rather than choose resources directly. We present design, analytical, and algorithmic contributions focusing on pricing computation contracts, analyzing their properties, and optimizing them in complex workflows. We conduct an experimental evaluation of our market framework over a real-world cloud service and demonstrate empirically that our market ensures three key properties: (a) that consumers benefit from using the market due to competitiveness among agents, (b) that agents have incentive to price contracts fairly, and (c) that inaccuracies in estimates do not pose a significant risk to agents’ profits. Finally, we present a fine-grained pricing mechanism for complex workflows and show that it can increase agent profits by more than an order of magnitude in some cases.
  },
  journal   = {PVLDB},
  volume    = {10},
  number    = {4},
  year      = {2016},
  venue     = {PVLDB},
  pages     = {373--384},
}