Optimal Admission Control for a QoS-Aware Service-Oriented System

Abstract

Abstract. In the service computing paradigm, a service broker can build new applications by composing network-accessible services offered by loosely coupled independent providers. In this paper, we address the admission control problem for a a service broker which offers to prospective users a composite service with a range of different Quality of Service (QoS) classes. We formulate the problem as a Markov Decision Process (MDP) problem with the goal of maximizing the broker revenue while guaranteeing non-functional QoS requirements to its already admitted users. To assess the effectiveness of the MDP-based admission control, we present experimental results where we compare the optimal decisions obtained by the analytical solution of the MDP with other policies.

QoS-aware management of monotonic service orchestrations

“…Orc also has built in sites to track passage of time (Rtime, Rwait), deal with data structures (tuples, lists, records), handle concurrency (semaphores, channels) and define new sites (class). An interested reader is referred to the Orc documentation 6 for details.…”

“…Handling Probabilistic QoS: To handle uncertainty in QoS, probabilistic frameworks have been favored by a number of authors [18,26,27,34,39,40,44,50,51,52,53]. When the workflow of the orchestration is statically defined regardless of data, rules for composing QoS probability distributions of the called services have been proposed for various QoS domains [6,8,9,10,11,19,49]. Optimal service selection among different options has been solved by efficient optimization methods, by using, for example, Markov models [6,18].…”

“…When the workflow of the orchestration is statically defined regardless of data, rules for composing QoS probability distributions of the called services have been proposed for various QoS domains [6,8,9,10,11,19,49]. Optimal service selection among different options has been solved by efficient optimization methods, by using, for example, Markov models [6,18]. For orchestrations exhibiting data-dependent workflow or QoS values, however, such methods do not apply.…”

Towards priority-awareness in autonomous intelligent systems

“…As such, a number of runtime modelling techniques have been developed that take into account NFRs' priorities [1,4,15,17]. These modelling techniques are based on optimisation methods, including decision analysis and utility theory [25,26].…”

“…[Problems] A feature of such AIS is that they typically employ single objective optimization techniques. These techniques use a uni-scalar cumulative utility value to represent a combined priority for all NFRs [1,2,4,15,17]. However, the adaptive actions taken by the AIS can have different impacts, either positive or negative, on the satisfaction levels of individual NFRs.…”