Summary Service-Oriented Computing (SOC) enables the composition of loosely coupled service agents provided with varying Quality of Service (QoS) levels, effectively forming a multiagent system (MAS). Selecting a (near-)optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. As the number of distributed services, especially in the cloud, is rising rapidly, the impact of the network on the QoS keeps increasing. Despite this and opposed to most MAS approaches, current service approaches depend on a centralized architecture which cannot adapt to the network. Thus, we propose a scalable distributed architecture composed of a flexible number of distributed control nodes. Our architecture requires no changes to existing services and adapts from a centralized to a completely distributed realization by adding control nodes as needed. Also, we propose an extended QoS aggregation algorithm that allows to accurately estimate network QoS. Finally, we evaluate the benefits and optimality of our architecture in a distributed environment.
Many approaches, such as @cite_13 @cite_10 , deal with point-to-point network QoS, but they do not consider services and compositions from SOC. One of the few examples that combines this with SOC is @cite_18 which looks at service compositions in cloud computing. The difference is that instead of the normal composition problem a scheduling problem is solved where services can be deployed on virtual machines at will. Also no QoS algorithm is given, so it is unclear, if that approach can compute input-dependent QoS and network transfer times.
@cite_10 @cite_18 @cite_13
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Service-Oriented Computing (SOC) enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. Selecting a (near-)optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. With the advent of Cloud Computing, both the number of such services and their distribution across the network are rising rapidly, increasing the impact of the network on the QoS of such compositions. Despite this, current approaches do not differentiate between the QoS of services themselves and the QoS of the network. Therefore, the computed latency differs substantially from the actual latency, resulting in suboptimal QoS for service compositions in the cloud. Thus, we propose a network-aware approach that handles the QoS of services and the QoS of the network independently. First, we build a network model in order to estimate the network latency between arbitrary services and potential users. Our selection algorithm then leverages this model to find compositions that will result in a low latency given an employed execution policy. In our evaluation, we show that our approach efficiently computes compositions with much lower latency than current approaches. Services in cloud computing can be categorized into two groups: Application services and Utility Computing Services. Compositions in the application level are similar to the Web service compositions in SOC (Service-Oriented Computing). Compositions in the utility level are similar to the task matching and scheduling in grid computing. Contributions of this paper include: 1) An extensible QoS model is proposed to calculate the QoS values of services in cloud computing. 2) A genetic-algorithm-based approach is proposed to compose services in cloud computing. 3) A comparison is presented between the proposed approach and other algorithms, i.e., exhaustive search algorithms and random selection algorithms. This paper focuses on service deployment optimization in cloud computing environments. In a cloud, an application is assumed to consist of multiple services. Each service in an application can be deployed as one or more service instances. Different service instances operate at different quality of service (QoS) levels depending on the amount of computing resources assigned to them. In order to satisfy given performance requirements, i.e. service level agreements (SLAs), each application is required to optimize its deployment configuration such as the number of service instances, the amount of computing resources to assign and the locations of service instances. Since this problem is NP-hard and often faces trade-offs among conflicting QoS objectives in SLAs, existing optimization methods often fail to solve it. mathrmE3-R is a multiobjective genetic algorithm that seeks a set of Pareto-optimal deployment configurations that satisfy SLAs and exhibit the trade-offs among conflicting QoS objectives. By leveraging queueing theory, E3-R estimates the performance of an application and aids defining SLAs in a probabilistic manner. Moreover, E3-R automatically reduces the number of QoS objectives and improves the quality of solutions further. Experimental studies demonstrate that E3-R efficiently obtains quality deployment configurations that satisfy given SLAs. Copyright © 2011 John Wiley & Sons, Ltd. Motivated by the fact that most of the existing QoS service composition solutions have limited scalability, we develop a hierarchical-based solution framework to achieve scalability by means of topology abstraction and routing state aggregation. The paper presents and solves several unique challenges associated with the hierarchical-based QoS service composition solution in overlay networks, including topology formation (cluster detection and dynamic reclustering), QoS and service state aggregation and distribution, and QoS service path computation in a hierarchically structured network topology. In our framework, we (1) cluster network nodes based on their Internet distances and maintain clustering optimality at low cost by means of local reclustering operations when dealing with dynamic membership; (2) use data clustering and Bloom filter techniques to jointly reduce complexity of data representation associated with services within a cluster; and (3) investigate a top-down approach for computing QoS service paths in a hierarchical topology.