An Improved Genetic Algorithm for Service Selection under Temporal Constraints in Cloud Computing

2015 IEEE International Conference on Web Services

Guermouche

Chaari

et al. 2015

QoS-based service selection is one of the important requirements in Service Oriented Computing (SOC). A challenging task towards this purpose is the selection of the best combination of services that fulfils user's requirements while meeting quality of service (QoS) constraints. This challenge becomes more complex when dealing with time-dependent QoS values and temporal properties. Indeed, during the selection, mutual dependencies between the different temporal constraints may arise so that the selection of each service may influence or be influenced by the selection of other services. On other side, to find the best solution, all potential combinations must be compared. However, the number of these combinations may be very high, which can present a barrier for enabling effective service selection. In this paper, we present a heuristic based timeaware service selection approach to efficiently select a close-tooptimal combination of services. First, pruning techniques are adopted to reduce the search space. Second, a novel heuristic approach is proposed based on service clustering, constraints decomposition and local selection while considering both QoS and temporal constraints. Finally, experiments which confirm the feasibility and effectiveness of the proposed approach in terms of its timeliness and optimality, are conducted.

“…Finally, Constraints (15) and (16) deal with temporal constraints. For simplicity, in this model, we only consider endto-start inter-task temporal constraints.…”

Section: Ef T Mmentioning

confidence: 99%

“…Some works deal with temporal properties in the service selection problem. In [15], Liang et al use genetic algorithm to select services under temporal constraints while dealing with static QoS values. In [16], Wagner et al propose a multiobjective optimization based approach while considering timedependent QoS values.…”

Section: Related Workmentioning

confidence: 99%

Heuristic Based Time-Aware Service Selection Approach

2015 IEEE International Conference on Web Services

Guermouche

Chaari

et al. 2015

“…Zhang et al (Zhang, Ma et al, 2013) take into consideration the dynamic aspect of QoS attributes to compose services in multi-domain environments. In (Liang, Du et al, 2013), Liang et al propose a penalty-based genetic algorithm to select services under temporal constraints. However, authors assume that QoS values do not depend on the time of the execution and only upper bound constraints between activities are considered.…”

Section: Related Workmentioning

confidence: 99%

Time-Dependent QoS Aware Best Service Combination Selection

Jmaïel

International Journal of Web Services Research

Guermouche

et al. 2015

Service Oriented Architecture allows developing complex business applications from existing services. Given that many services are available with the same functionality and with different Quality of Service (QoS) attributes, one common challenge is to select the best service combination regarding user's requirements. Existing solutions often consider static QoS values for candidate services. Nevertheless, in real world applications, QoS values can change during time. In addition, besides structural constraints, several QoS and temporal constraints can also be specified at the business level. Considering time-dependent QoS values associated with business level constraints makes the selection process a very complex and time consuming decision problem given the large number of service combinations to be compared. To deal with this issue, in this paper, the authors propose a novel service selection approach based on QoS and temporal pruning techniques to reduce the number of candidate services. The proposed approach allows pruning uninteresting services based on a set of local thresholds. These latter are measured using constraint optimization models while dealing with general flow structures including sequential, parallel, choice and loop patterns and different types of QoS and temporal constraints. Experimental studies show the benefits of the proposed approach in particular in terms of computational time.

“…Temporal properties have been considered by some works when selecting the best service composition. In [15], Liang et al propose a penalty-based genetic algorithm to select services under temporal constraints. However, authors assume that QoS values do not depend on the time of the execution.…”

Section: Related Workmentioning

confidence: 99%

“…In fact, the service instance SC 21 is valid in a time span before that of the service SA 31 and thus these two service instances can not be part of the same solution. However, the combination C ′ = (SA 11 , SB 21 , SC 11 , SD 21 ) where availability intervals are respectively [10,15], [15,19], [15,19] and [19,22] is a valid solution.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Service Selection Approach with Time-Dependent QoS

2014 IEEE 23rd International WETICE Conference

Chaari²,

Jmaïel³

2014

In advanced service-oriented computing, complex applications are usually specified as abstract business processes. The execution of these applications requires the selection of a set of services to invoke abstract business tasks. With the growing number of alternative services of each business task that differ in their QoS, the selection of the best combination of services that satisfies business process constraints and end-to-end users' requirements becomes a complex decision problem. Current selection approaches consider only static QoS and ignore the fact that QoS values can depend on temporal properties. In this paper, we propose a novel service selection approach considering time-dependent QoS attributes. The proposed approach introduces two search space reduction mechanisms that combine QoS and temporal constraints. The application of these mechanisms improves the performance of the selection process which is demonstrated by experimental results.