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A new cost-effective mechanism for VM-to-user mapping in cloud data centers

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Abstract

Attracting customers through reward programs is the primary key success for customer-oriented organizations. One of the most famous customer reward programs is applying price discount. In our negotiation-based cloud resource allocation problem, discount price is offered based on both status of a provider and behavior (or loyalty class) of a customer. That is, a resource customer who has appropriate buying behavior and negotiates for resource type instances with high necessity to sell is deserved to receive high price discount from provider. To do this, first, three customer’s loyalty classes are defined and customers are classified into theses classes in terms of their previous buying behavior using fuzzy system in name FCLCDS. Second, another fuzzy system in name FNTSVMTDS is designed to determine the value of necessity to sell resource type. The outputs of FCLCDS and FNTSVMTDS are called Loyalty Class (LC) and Necessity to Sell VM Type (NtSVMT), respectively. Finally, a fuzzy system in name FDCDS is proposed to determine the discount coefficient based on both LC and NtSVMT inputs. Furthermore, appropriate times for calculating/re-calculating the discount coefficients that are applied by a resource provider to relax its counter-offers are calculated. We perform extensive simulation experiments to compare our designed negotiator in name FDMDA with MDA and FNSSA. The results show that our designed FDMDA outperforms MDA and FNSSA.

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Notes

  1. Counter-proposal is the proposal which is made in response to the received proposal from a trading opponent.

  2. Typically three customer classes (or membership levels) called: “Gold”, “Silver” and “Bronze” are defined [10].

  3. Amazon EC2 Instance Types. [Online]. Available: https://aws.amazon.com/ec2/instance-types/.

  4. Each PDA determines the maximum price discount that can be applied for LC of type Gold (i.e., MaxDiscountGold), the maximum price discount that can be applied for LC of type Silver (i.e., MaxDiscountSilver) and the maximum price discount that can be applied for LC of type Bronze (i.e., MaxDiscountBronze) in a way that MaxDiscountGold> MaxDiscountSilver> MaxDiscountBronze..

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Authors and Affiliations

  1. Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, 1651153311, Iran

    Sepideh Adabi, Zahra Hossein-Haje & Sahar Adabi

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  1. Sepideh Adabi
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Appendix: setting the values of MinDDTF and MaxDDTF

Appendix: setting the values of MinDDTF and MaxDDTF

Following, the process of determining the values of MinDDTF and MaxDDTF is discussed. Considering different value ranges of three classes of negotiation deadline in names Loose, Moderate and Tight (see Table 7), various market conditions are experienced. The value of MinDDTF should be determined in a way that safe and acceptable visit time is guaranteed under different market conditions. As the minimum value of a negotiator’s deadline is 20 (see Tight deadline values in Table 7), and consider the two facts: (1) any decision about making price discount is made between the half time of a negotiator’s deadline and negotiator’s deadline, and (2) provider agents make any decision in even rounds of the markets, MinDDTF should be selected from the set MinTF = {2, 4, 6, 8} to provide the possibility of making even market rounds between \( \frac{20}{2} \) (i.e., half of deadline) and 20 (i.e., deadline). In other words, by having ten market rounds between \( \frac{20}{2} \) and 20, the even market rounds in which any decision can be made are \( \left({\frac{20}{2} + 2} \right),\left({\frac{20}{2} + 4} \right),\left({\frac{20}{2} + 6} \right)\;{\text{and}}\;\left({\frac{20}{2} + 8} \right). \) This selection guarantees that the agents who have Tight deadlines are provided with the chance of applying price discount (if necessary) at least one time. In addition, the value of MaxDDTF should be determined in a way that all negotiators with different deadlines benefit from it. Thus, by having a negotiator agent who has the minimum time for renting resources (i.e., negotiator whose deadline value is equal to 20), and considering the two above mentioned facts in making price discount, the value of MaxDDTF should be selected from the set MaxTF = {4, 6, 8} to provide the possibility of making even market rounds between \( \frac{20}{2} \) (i.e., half of deadline) and 20 (i.e., deadline). Logically, in defining the MaxTF set, number 2 is not considered. That is because, any value of MaxDDTF should be greater than the minimum value of MinDDTF. Following, extensive amount of simulations was carried out considering the different feasible combinations from the MinTF and MaxTF sets. Then, the experimental results of these various combinations are reported to decide about the suitable values of MinDDTFand MaxDDTF. It is experimentally determined that MinDDTF = 4 and MaxDDTF = 8 give acceptable results in different market conditions. Obviously, determination of the more exact amounts of MinDDTF and MaxDDTF can be focused in future works through adopting a suitable learning technique.

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Adabi, S., Hossein-Haje, Z. & Adabi, S. A new cost-effective mechanism for VM-to-user mapping in cloud data centers. Cluster Comput 23, 2425–2451 (2020). https://doi.org/10.1007/s10586-019-03017-w

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