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.
The previously mentioned approaches all simply aggregate static QoS values defined in SLAs. Time-dependent QoS evaluated depending on the execution time are given in @cite_16 . As we will see, our algorithm computes when the execution of each service starts, so we can also compute time-dependent QoS. SLAs with conditionally defined QoS are given in @cite_14 , which can be considered a special case of input-dependent QoS, and, thus, can be handled by our approach, as well.
@cite_14 @cite_16
2003131531 2082201484 1543328822 2136340918
Run-time service discovery and late-binding constitute some of the most challenging issues of service-oriented software engineering. For late-binding to be effective in the case of composite services, a QoS-aware composition mechanism is needed. This means determining the set of services that, once composed, not only will perform the required functionality, but also will best contribute to achieve the level of QoS promised in service level agreements (SLAs). However, QoS-aware composition relies on estimated QoS values and workflow execution paths previously obtained using a monitoring mechanism. At run-time, the actual QoS values may deviate from the estimations, or the execution path may not be the one foreseen. These changes could increase the risk of breaking SLAs and obtaining a poor QoS. Such a risk could be avoided by replanning the service bindings of the workflow slice still to be executed. This paper proposes an approach to trigger and perform composite service replanning during execution. An evaluation has been performed simulating execution and replanning on a set of composite service workflows. 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. We extend the service abstraction in the Open Grid Services Architecture ogsa for Quality of Service (QoS) properties. The realization of QoS often requires mechanisms such as advance or on-demand reservation of resources, varying in type and implementation, and independently controlled and monitored. propose the GARA FostKessl99 architecture. The GARA library provides a restricted representation scheme for encoding resource properties and the associated monitoring of Service Level Agreements (SLAs). Our focus is on the application layer, whereby a given service may indicate the QoS properties it can offer, or where a service may search for other services based on particular QoS properties. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users.