Melanie Siebenhaar

Maximizing Cloud Provider Profit from Equilibrium Price Auctions

Auctioning constitutes a market-driven scheme for the allocation of cloud-based computing capacities. It is practically applied today in the context of Infrastructure as a Service offers, specifically, virtual machines. However, the maximization of auction profits poses a challenging task for the cloud provider, because it involves the concurrent determination of equilibrium prices and distribution of virtual machine instances to the underlying physical hosts in the data center. In the work at hand, we propose an optimal approach, based on linear programming, as well as a heuristic approach to tackle this Equilibrium Price Auction Allocation Problem (EPAAP). Through an evaluation based on realistic data, we show the practical applicability and benefits of our contributions. Specifically, we find that the heuristic approach reduces the average computation time to solve an EPAAP by more than 99.9%, but still maintains a favorable average solution quality of 96.7% in terms of cloud provider profit, compared to the optimal approach.

Adaptive matchmaking for RESTful services based on hRESTS and MicroWSMO

Matchmaking -- i.e., the task of finding functionally suitable service offers based on a service request -- has only been addressed in the context of WS-* Web services. However, RESTful services are gaining increasing attraction and have been adopted by major companies, thus increasing the need for suitable matchmaking solutions. This paper introduces XAM4SWS, an adaptive matchmaker for semantic Web services that supports multiple service description formats, including hRESTS and MicroWSMO for RESTful services. XAM4SWS adapts existing methodologies from WS-* matchmaking and extends them through the inclusion of REST-specific service features. A prototypical implementation of the matchmaker is evaluated with respect to multiple information retrieval metrics using an adapted semantic Web service test collection.

Comparison and retrieval of process models using related cluster pairs

Although increasingly IT-supported, effective techniques for process model retrieval and identification of process model differences or changes - needed for a variety of management and conformance purposes - are still challenging problems in business process management. Performing automated process comparison and finding relevant reference processes are not routine procedures for todays operational process repositories. Efficient combinations of similarity measures for various process model characteristics can still improve the performance of process comparison and retrieval. The approach at hand introduces the concept of related cluster pairs, parameterises it with semantic, string-based, and novel hybrid metrics for comparing process models, and defines a novel similarity notion for process model retrieval. Evaluations with process data from the SAP reference model show that our approach outperforms current related work and established text search engines.

Concurrent negotiations in cloud-based systems

Utilizing cloud-based services, consumers gain a high level of flexibility, but they cannot obtain individual Quality of Service guarantees or request service compositions according to their specific business needs. Therefore, appropriate mechanisms for an automated negotiation of Quality of Service parameters are required that do not only consider the individual business objectives and strategies of the negotiation partners involved, but do also account for the dependencies between the different services and service tiers in cloud computing. This enables enterprises to increase the quality and flexibility of their business processes and lays the foundation for market-based complex service provisioning. In this paper, we present one such negotiation approach and evaluate the application of different negotiation strategies.

Let the clouds compute: cost-efficient workload distribution in infrastructure clouds

With cloud computing, a virtually inexhaustible pool of computing capacity has become available to IT users. However, given the large number of Infrastructure as a Service offers with differing pricing options, the cost-efficient distribution of workloads poses a complex challenge. In this work-in-progress paper, we formally describe the Cloud-oriented Workload Distribution Problem and propose an exact as well as a heuristic optimization approach. Through an evaluation that is based on realistic data from the cloud market, we examine the performance and practical applicability of these approaches.

Complex service provisioning in collaborative cloud markets

Todays cloud consumers gain a high level of flexibility by using externally provided cloud-based services. However, they have no means for requesting combined services from different clouds or for enforcing an individual quality level. Laying the foundation for market-based cloud collaborations including the negotiation of individual quality parameters is an important aspect for future cloud computing. Cloud consumers, especially enterprises are then able to request complex services with consumerdriven quality guarantees according to their individual needs and are not concerned with the problem on how to make the different components work together. In this paper, we present an approach for collaborative complex service provisioning in cloud computing and an evaluation of selected mechanisms for the negotiation of quality parameters in such a collaborative market-based scenario.

Process Model Analysis Using Related Cluster Pairs

Due to changing market conditions and resulting flexibility requirements, the reference-conform implementation of processes in companies increasingly gains importance. The internal assessment of the realisation of reference processes (process conformance) is a resource-intensive task in terms of time and cost. The paper at hand presents a process model analysis method to address this issue using a combined structural and semantic comparison and analysis approach. The method provides decision support for process analysts concerning the adjustment of processes to reference processes in IT Governance contexts.

Towards Heuristic Optimization of Complex Service-Based Workflows for Stochastic QoS Attributes

The problem of selecting services from a set of functionally appropriate ones under Quality of Service constraints - the Service Selection Problem - is well-recognized in the literature based on deterministic parameters. However, Quality of Service may rather follow a stochastic distribution and, thus, may change at runtime. In order to cope with differing Quality of Service, we present a heuristic approach for efficiently addressing the Service Selection Problem in conjunction with stochastic Quality of Service attributes. Accounting for penalty cost which accrue due to Quality of Service violations, our approach reduces the impact of stochastic Quality of Service behavior on total cost significantly.

Role-Based Templates for Cloud Monitoring

Cloud computing has evolved in the recent years to a well established computing paradigm. With this evolution, the complexity and requirements for monitoring cloud-based services have also increased. Without a doubt, monitoring for cloud computing is a crucial task which has been addressed in a number of research works. However, monitoring for cloud computing is often designed to be carried out by cloud providers. Monitoring by cloud providers on the one hand offers the flexibility and full control required for monitoring, on the other hand, the trustworthiness of the cloud provider is often questioned. In this work, we present a generic approach that can harmonize both of the aforementioned issues. Our solution abstracts from the complexity by using role-based templates for monitoring in combination with autonomous agents, thus, this approach can be used by both, cloud consumers and cloud providers. With a proof of concept prototype, we show that our approach can be adapted for large scale cloud monitoring scenarios. Furthermore, we discuss the possibility that our monitoring solution can be extended to be applicable for different domains.

Robust Cloud Monitor Placement for Availability Verification

While cloud computing provides a high level of flexibility, it also implies a shift of responsibility to the cloud provider and thus, a loss of control for cloud consumers. Although existing means such as service level agreements or monitoring solutions offered by cloud providers aim to address this issue, there is still a low degree of trust on consumer side that cloud providers properly measure compliance against SLAs. A solution lies in designing reliable means for monitoring cloud-based services from a consumers perspective. We already proposed such a monitoring approach in our former work. However, our experiments revealed that our approach is sensitive to network impairments. Hence, in the work at hand, we introduce the Robust Cloud Monitor Placement Problem and present a formal optimization model. Based on the model, we propose an initial optimization approach, that allows to obtain an exact solution using off-the-shelf algorithms.