Auction Models

The proposed auction models can be studied based on the following attributes:

1. Being direct auction or a reverse auction, 2. Being combinatorial or non-combinatorial, 3. Being a single-shot or an iterative auction,

4. Auction for a single composite service request or multiple ones.

3.8.1.1 Direct / Reverse Auction Model

Many of the researchers have modelled service selection as a reverse auction (Esmaeilsabzali and Larson 2005; Mohabey et al. 2007a; Mohabey et al. 2007b; Prashanth and Narahari 2008; Blau et al. 2010; Watanabe et al. 2012; He et al. 2014). In this approach, service requester or an independent third-party take the role of the auctioneer and service providers bid to sell their services.

Contrary to this trend is the direct auction model proposed in Lamparter (2007). In this auction model, service providers offer their services in bundles. Service requesters can bid to buy these bundles. For each bundle, the requester with the highest bid wins the bundle.

The general problem with modelling composite service selection as a direct auction is that a requester who needs multiple services to create a composite service may have to attend multiple auctions to win all the required services. To have a fully operational composite service, the requester needs to win all the related bundles. With no guarantee for winning all the bundles, the requester might end up winning some services and losing some others. Such situation may incur undesirable cost for the requester.

However, the direct auction model proposed by Lamparter does not have this problem. As the auction is modelled as a combinatorial auction, a composite service requester can prepare a bid for a bundle of services to make sure that they will win the auction only if they get all the required services.

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As this study is the first to consider service selection for multiple composite service requests, we will discuss it in more details in subsection 3.8.1.4 (A Single Request / Multiple Requests).

3.8.1.2 Combinatorial / Non-combinatorial Auction Model

A number of researchers have chosen to model the composite service selection problem based on non-combinatorial auctions, such as (Esmaeilsabzali and Larson 2005; Blau et al. 2010; Watanabe et al. 2012). In this model, a provider can only bid to offer a single service. We have already discussed the implications of such an assumption in subsection 3.3 (Dependencies between Constituent Web Services of a Composition).

Combinatorial auction model has been a popular approach among many researchers from different disciplines. As already discussed in subsection 2.3.3, combinatorial auctions offer many advantages such as increased economic efficiency, increased revenue or in case of a reverse auction, cost savings (Cramton et al. 2006), time efficiency and impacting the market structure (Bichler et al. 2006). At the same time, the complementarity effects that exist between the tasks of a composite service and the dependencies between web services forming a composition have been a major motivation for researchers to consider combinatorial auction models for service selection. Examples include: (Mohabey et al. 2007a; Mohabey et al. 2007b; Lamparter 2007; Prashanth and Narahari 2008; He et al. 2014).

As mentioned in Blau et al. (2010), the main problem with combinatorial auction is the complexity of the winner determination problem. Combinatorial auctions are proved to be NP-complete (Sandholm 2002) and therefore a solution based on these auction models is not scalable to settings with large numbers of bidders and services involved. Therefore, one of the concerns in this area is to evaluate the proposed model in terms of scalability.

3.8.1.3 One-shot / Iterative Auction

Some researchers have modelled the auction for composite service selection as a one-shot auction: the bidders submit the offers and the auctioneer determines the winners based on submitted bids. However, some researchers such as Watanabe et al. (2012) and He et al. (2014) have chosen more complex auction models.

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The proposed non-combinatorial auction model in Watanabe et al. (2012) has two steps. In the first step, for each task in the composite service, the first few providers (number is varied in the experiment) with the best quality maximizing offers are selected (quality includes the price). So far, this approach is similar to the local optimization discussed in subsection 3.5.1. However, in order to address the limitation of local optimization approaches in not being able to support the end-to-end quality constraints for composite services, a second step is proposed. In this step, the global quality constraints are investigated and if violated by the current offers, the providers will be asked to improve their quality while allowing them to have a trade-off tactic. As discussed in subsection 3.6.3.2, a trade-off tactic allows the participants to improve on some quality attributes while decreasing the desirability of some others. The proposed solution does not guarantee to find the optimal (quality maximizing) utility for the composite service. However, the end-to-end quality constraints are satisfied in the second step if negotiation with providers is successful.

An iterative combinatorial auction model is proposed in He et al. (2014) to solve the service selection problem. A number of stop criteria has been defined and if none of the criterion is met at the end of a round of bid submission, the auction proceeds to the next round. One such stop criteria is the quality requirements of the service requester being satisfied by the available service offers. If there is no set of bids to fulfil the quality requirements, the auctioneer sends an Ask-QoS to more competitive providers and asks them to improve their quality and price offers. Application of iterative auctions for composite web service selection implies that service providers are willing to spend enough time attending multiple rounds of auction for the same composite service. This approach may not lead to profitable trades for service providers, for all types of composite service requests, for example composite services which prices are not expected to be high. We will discuss the limitation of the application of iterative auctions for web service selection in more detail in subsection 4.3.1.2.

3.8.1.4 A Single Request / Multiple Requests

Most of the research in this area aims to solve the service selection for one composite service, including (Esmaeilsabzali and Larson 2005; Mohabey et al. 2007a; Mohabey et al. 2007b; Prashanth and Narahari 2008; Blau et al. 2010; Watanabe et al. 2012; He et al. 2014).

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Papazoglou (2003) is one of the first to discuss the presence of multiple composite services in the web services marketplaces. He states that the purpose of these markets is to create the opportunity for service requesters and providers to meet and conduct business as well as fostering the possibility of offering value-added services such as aggregation of the web service supply/demand.

Other researchers have studied the web services markets from a variety of aspects. For example, if they need to be open (Papazoglou 2003) or established privately (Petrie and Bussler 2008); be centralized or decentralized (Yarom et al. 2004); their fundamental structure and players (Geng et al. 2003; Legner 2009; Weinhardt et al. 2011b) and the best strategies of the players (Tang 2004; Gunther et al. 2007); trust establishment (Brehm and Golinska 2009); and the semantic aspect of web services in such marketplaces (Lamparter 2007; Schulte 2010). In these literatures, all researchers agree on composite services being an essential offering in the web services’ markets. However, composite services are considered as already-existing entities that can be traded in the market along single web services. Very limited research exists on how these markets can facilitate the different aspect of creating value-added composite services, including composite service selection or the price determination of a composite service.

Tang (2004) is one of the first to consider the impact of multiple requests for composite service selection. Taking the service provider’s perspective, this study investigates the optimal strategies for offering web services, taking into account the integration cost. Tang has analyzed a setting where two service vendors sell two distinct but functionally complementary services to different groups of potential buyers. Based on the performed analysis, service providers benefit from offering composite services, either through forming strategic alliance with other providers or selling composite services in a marketplace. With the main focus being on providers’ best strategies in offering functionally complementary services, there is no discussion of specific auction models for allocating services to requesters.

In a more recent study, Lamparter (2007) studied the use of semantic technologies to automate contracting in a market for web services. This study includes a conceptual market model to match web service offers to multiple composite service requests. In this direct auction, the service providers offer their services in bundles and service requesters

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bid to buy these bundles. However, the proposed model has limitations in addressing the composite service selection problem.

Firstly, it is assumed that the composite service requester is limited to have only one winning bundle. This means that the service requester should only bid for the bundles that include all the services required to create the composite service. In other words, the bundle is not dividable. With such an assumption, the requests for complex composite services are not very likely to find service bundles which include all the required services.

The second problem is that even if this constraint, requester having one winning bid, is relaxed to allow requesters have multiple winning bids, the service requester faces a challenging problem in bid preparation: they need to decide how to divide the required composition with respect to existing offered bundles so that they can optimally achieve their quality and price requirements.

To reduce the bid preparation complexity for service requesters, it is possible to design a different auction model that instead of the requester checking how to prepare the bids, the auction-based model finds the optimal bundles based on the requester’s requirements. However, extra constraints are required to be added to the matching algorithm to make sure that: (1) the combination of the bundles includes all the required services for the composition, and (2) the requester will not be assigned more services than they require.

Thirdly, the model aims at solving the composite service selection problem for all the requests simultaneously. This means even with one request being unsuccessful in finding all its constituent services, the whole auction will fail; that is, no other requests would be assigned any services. The success rate of such a model in allocating services to requests is likely to be low, especially if the requests are for complex composite services.

Finally, no evaluation has been done on the performance of the proposed model. Therefore, it is not possible to have estimates about the success rate of the auction-based model, the final cost of the composite services or the time required by the model to find service allocation for composite requests.

To the best of our knowledge, our study is the first to consider the impact of presence of multiple requests on the composite service selection approach, while addressing the limitations discussed before, and perform a thorough evaluation of the performance of the different possible service selection approaches in such a setting.

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