# ARAAC: A Rational Allocation Approach in Cloud Data Center Networks
## 4 Related Work  
Being overwhelmed by the ideas of virtualized networks and cloud computing triggered new directions for networking and computing resource management. Research proposals that tackle such directions in the literature are numerous. In this context, and in regard to the resource allocation problem we discuss the following. In [12], the authors studied the problem of bandwidth resource reservation in cloud networks. In their work, the authors proposed an auction based mechanism to distribute the bandwidth resource. To motivate the users' revealing the truth about their resource valuation, the proposed auction model deploys the Vickrey-Clarke-Groves (VCG) truth-telling mechanism. According to the amount of required resources, and the effect (i.e., the inconvenience) of this on others, the mechanism calculates additional charge that is added to the bidders' price offers, together with what to be paid for the required resources. The same mechanism was already proposed by [PERSON] et al. in [13] for bandwidth allocation in Virtual Private Networks (VPNs). The mechanism can truly provide a social welfare, economic, and strategy-proof solution. However, deploying the proposal in cloud service allocation problems may get become computationally intractable. Others may claim such a proposal to be greedy, indeed, it only tackles the providers' profit maximization, giving zero weight to the issues of resource price-units and the tenants' utilization behavior. The proposal of [14] comes with another payment strategy that calculates the _Shapley value_[15] incurred by the traffic of each bidder, and accordingly the resource providers decide whether to accept or reject the bid. This is somehow similar to the philosophy of the VCG truth-telling mechanism, however, no charges are added to the bids, as it focusses on comparing the offered bids with the bidder's Shapley value. If the later is larger, the bid is rejected; otherwise the bid is accepted. Again, such a pricing policy does not provide for profit maximization; it follows the average marginal charge for the allocation decisions. Moreover, the issue of resource utilization behavior was also not addressed in their proposal. In [4], the authors propose a model that supports both bandwidth reservation (i.e., ahead reservation for resource guarantees) and allocation (i.e., for real time requirements) scenarios. In their work, resources are distributed among the users on the basis of auction scenarios. Firstly, the model runs in the reservation mode; then, what remains from the available pool of resources after reservation decisions are revealed is auctioned again for allocations. In the reservation mode, the model defines a high price unit value called the _image pricing value_ with the goal of maximizing the provider's profits. In allocation auctions, the selling price-unit is fixed for the same auctioned items, and it is defined according to its market clearing prices. This assumes the offering of fair pricing  
Figure 3: Acceptance rates of the simulated allocation mechanisms. among all bidders; fairness is always required especially in such scenarios. However, fairness does not originate through charged prices only; in global auction markets, such as eBay and others, the process of resource allocation or even reservation, considers the bidders' other attributes, such as their feedback records or history alongside their offered prices. In our context, such feedback and historical behavioral records may match the tenants' willingness to pay and their resource utilization attitudes. Such attributes are of great importance when it comes to environments with _limited_ capacities, such as cloud-DCNs. What is more, the allocation proposal of [4] assumes that as long as there is enough bandwidth to provide, the model chooses those bidders who offer bid prices higher than the predefined market clearing price [16]. However, to accommodate for more requests, those chosen bidders may not receive the full amount of resources they requested.