Cloud Service Composition Research Articles

Composition modeling for manufacturing resource cloud service

The lack of semantic information of description model and quality of service (QoS) constraints in manufacturing resource cloud service (MRCS) has caused problems such as uneven distribution, small sharing scope and low standardization of manufacturing resources. The paper studies composition modeling of MRCS for this problem. Firstly, a modeling method for MRCS based on semantic extension is proposed. The formalization method is used to classify manufacturing resources, and the 6-layer semantic information of function, interface, execution, state, QoS and physical-virtual resource mapping is attached to the MRCS, and a data model based on eXtensible Markup Language and a semantic model based on ontology are constructed. Then a composition method for MRCS with multi-constraint of QoS is proposed. A composition framework and process for cloud services with multi-constraint of QoS is planned. A planning method based on hierarchical task network (HTN) is used to decompose manufacturing tasks into atomic tasks. The manufacturing task is decomposed into atomic tasks and used as service requirements by a planning method based on HTN, and multiple independent and related cloud services are composed into a new cloud service according to application needs through interface semantic matching. The attribute weight of the scheme for MRCS is comprehensively calculated based on quality function deployment. The composite cloud service corresponding to each atomic task is used as an independent component service, and the interface semantic matching operation is repeated according to the workflow of the atomic task to form a complete composite cloud service with more powerful functions to realize the manufacturing task.

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

SummaryOne of the most critical issues in using service‐oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP‐hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust‐based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest‐guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large‐scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.

New trends in adaptive smart systems, services, and architectures

Smart Systems are composed of interconnected nodes (devices, services, components, etc.) that can be intelligent, mobile, embedded, cloud-based, or/and adaptive. These nodes interact together through new communication protocols. Architectures of such system needs to adapt its behavior and deals with change in the environment. An adaptive and reconfigurable architecture can repair itself if any execution problems occur, in order to successfully complete its own execution. In the design of an adaptive and reconfigurable software system, several aspects have to be considered. Also other emerging areas such as Big data can also be used to solve traditional problems of Smart Systems, like scalability and performance. This special issue addresses these issues and is composed of seven papers. The paper of1 entitled “Efficient monitoring for intrusion detection in wireless sensor networks” addresses the design of a reliable and energy-efficient monitoring system that resists internal security attacks based on a distributed smart monitoring protocol. The paper of2 entitled “Cloud SLA negotiation and re-negotiation: An ontology-based context-aware approach” defines a context-aware system that automates the negotiation of dynamically generated and adapted SLAs using reasoning techniques and semantic mapping between client requests and provider offers. The paper of3 entitled “Cloud service composition using minimal unsatisfiability and genetic algorithm” targets the run-time phase of adaptive systems. It addresses the dynamic properties of service-oriented adaptive systems by provide a hybrid approach for automatic WS composition based on formal methods and genetic algorithms. The paper of4 entitled “A knowledge-based approach to manage configurable business processes” targets both run-time and design-time as well as structural and behavioral properties of enterprise systems modelled and enacted as Business Processes (BP). It proposes a knowledge-based approach for capturing the different versions of a BP into a single configurable BP and splitting this process into fragments as an alternative of handling multiple BP versions. The paper of5 entitled “Avoiding resource misallocations in business processes” presents an approach for avoiding resource misallocations, considered as an improper combination of resources, in Business Processes (BPs). The paper of6 entitled “Simulating systems of systems using situation/reaction paradigm” targets the designs-time phase of adaptive systems. It considers the paradigm of Systems of Systems that can be applied to different smart and adaptive systems. The paper sees these systems from the mission point of view to characterise their evolving rules with formal modelling and simulation. The paper of7 entitled “A comprehensive survey on modeling of cyber-physical systems” provides a survey of existing approaches to modeling CPS. It aims to study and to classify the CPS properties and to discuss their importance in different application domains. We would like to thank CCPE Editor-in-Chief Dr. Geoffrey C. Fox for his strong support. A special thank goes to all authors for their valuable contributions to this special issue. Special thanks go to all the guest eidtorial board members for their comments that helped in enhancing the quality of the papers.

Evolutionary many-objective assembly of cloud services via angle and adversarial direction driven search

Cloud service composition (CSC) is an effective way to carry out large-scale complicated applications by the ensemble of existing individual services. Each service typically involves several Quality of Service (QoS) criteria contracted for non-functional aspects like time or price, among others, which greatly influence the overall performance of the resulting applications. Service composition approaches have emerged as an important technique in leveraging the quality of composite service efficiently and have attracted significant attention. However, most existing proposals ignore the many-objective nature of CSC and consider up to three objectives, the optimization of diverse QoS aspects of CSC from a many-objective perspective (at least four) still lacks. On another aspect, due to the rapid growth of nondominated solutions in high-dimensional objective spaces, the traditional multi-objective optimization algorithms are usually not capable of handling problems possessing many objectives. To address the above issue, we develop an angle and adversarial direction based optimizer for many-objective CSC scenarios, which evolves a number of subpopulations with adversarial search directions in a parallel paradigm. Additionally, vector angle based selection criterion, which adaptively captures beacon individuals, is utilized to diversify the population. Extensive experiments are carried out on a series of CSC instances utilizing synthetic datasets and the results show that our proposition is competitive and has better versatility compared with the state-of-the-art.

Evolutionary algorithms for many-objective cloud service composition: Performance assessments and comparisons

Service composition and optimal selection (SCOS) concerns the building of optimal composite service by integrating existing services with the aim of performing complex task. Due to a plethora of affordable cloud services providing similar functionalities while differing in quality of service (QoS), how to determine suitable candidates to orchestrate the best composite service, also known as QoS-aware SCOS problem, becomes more complicated. A number of evolutionary optimizers have been developed to resolve SCOS. Unfortunately, a large majority of these optimizers carry out the optimization by aggregating many diverse QoS attributes into a single objective or simply considering two or three representative QoS attributes. SCOS, particularly, from the perspective of many-objective optimization, has not received an appropriate attention. As more factors come into play, SCOS is strictly a many-objective problem. This study explores the scalability of recently state-of-the-art evolutionary many-objective optimization (EMaO) algorithms in addressing SCOS. Comparative results reveal that these EMaO algorithms, never before applied to many-objective SCOS, exhibit distinct search abilities with respect to the objective space dimensionality and problem scale. Based on the empirical observation, useful suggestions and insights for choosing suitable EMaO algorithms pertaining to different SCOS problems are given.

Cost-effective deployment of certified cloud composite services

The advent of cloud computing has radically changed the concept of distributed environments, where services can now be composed and reused at high rates. Today, service composition in the cloud is driven by the need of providing stable QoS, where non-functional properties of composite services are proven over time and composite services continuously adapt to both functional and non-functional changes of the component services. This scenario introduces substantial costs on the cloud providers that go beyond the cost of deploying component services, and require to consider the costs of continuously verifying non-functional properties of composite and component services. In this paper, we propose a cost-effective approach to certification-based cloud service composition. This approach is based, on one side, on a portable certification process for the cloud evaluating non-functional properties of composite services and, on the other side, on a cost-evaluation methodology aimed to produce the service composition that minimizes the total cost paid by the cloud providers, taking into account both deployment and certification/verification costs. Our service composition approach is driven by certificates awarded to single services and by a fuzzy-based cost evaluation methodology, and assumes certified properties as must-have requirements for service selection and composition.

Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm

Aiming at the problems in which there exists collocation between services and manufacturing tasks, multiobjective cloud manufacturing service composition optimization seldom considers the synergy degree of composite cloud services and the complexity of service composition, so a novel service composition optimization approach, called improved genetic algorithm based on entropy (IGABE), is put forward. First, the mathematical expressions of service collocation degree, composition synergy degree, composition entropy, and their related influence factors of the service composition are analyzed, and their definitions and calculation methods are given. Then, a multiobjective cloud manufacturing service composition optimization mathematical model is established. Moreover, crossover and mutation operators are improved by introducing normal cloud model theory and piecewise function, and improved roulette selection method is used to perform the selection operation. And the fitness function of the proposed IGABE is designed by combining Euclidean deviation with angular deviation. Finally, the manufacturing task of a wheeled cleaning robot is exemplified to verify the correctness of the proposed multiobjective optimization model for cloud manufacturing service composition and the effectiveness of the proposed algorithm, compared with Standard Genetic Algorithm (SGA), Hybrid Genetic Algorithm (HGA), and Cloud-entropy Enhanced Genetic Algorithm (CEGA). The studied results show that IGABE converges faster than SGA and HGA and can analyze and reflect the content difference expressed by the objective functions of service composition scheme and its approximation degree to the corresponding dimensions of the ideal point vector more comprehensively than CEGA. As such, the optimal service composition obtained by IGABE algorithm can better meet the complex needs of users.

Collaborative Optimization of Service Composition for Data-Intensive Applications in a Hybrid Cloud

The multi-valued evaluations of quality of service (QoS), the complicated constraints between cloud services (CSs) and the collaborative resource assignments add many difficulties to the problem of CS composition for data-intensive applications (DiA) in a hybrid cloud (CSCD-HC). Solving the CSCD-HC problem has become a challenging task due to the uncertain QoS, the diverse hardware configurations and the flexible pricing about CSs. This paper proposes a collaborative optimization approach for CSCD-HC. This approach models a DiA as a role-based collaboration (RBC) system and employs the environments-classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD-HC problem with complicated constraints. To deal with the multi-valued QoS evaluations, this paper exploits the cloud model theory to analyze the performance of CSs, and presents a new method utilizing the Mahalanobis distance to improve the similarity calculation of QoS cloud models. Based on it, the qualification of candidate CSs can be precisely measured for supporting CS composition. A solution via the IBM ILOG CPLEX optimization package is put forward to solve the CSCD-HC problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing CSCD-HC.

Cloud service composition using minimal unsatisfiability and genetic algorithm

SummaryThe software‐as‐a‐service layer of cloud computing has evolved much interest among the researchers in the academic and industry worlds. It is based on service‐oriented architectures and web service (WS) technology, which are used to offer a remotely accessible application. When a single WS is unable to satisfy all the customer's requirements, a WS composition (WSC) method is applied to connect together various available WSs for building distributed application, using discovery, compatibility checking, selection, and deployment steps. This paper has two‐fold contributions: The major one is the proposition of a new algorithm based on minimally unsatisfiable subset formal algorithm to compose WSs. This contribution ensures a faster WSC, which is prominent in an online context. The second contribution is the proposition of a new penalty‐based genetic algorithm to identify near‐optimal solutions whenever an exact one is not identified using our formal algorithm. Indeed, in some cases, many available WSs provide overlapping/identical functionality; in this context, the choice of participated services is given using quality of service. This work proposes a WSC ensuring to find out an exact or a near‐optimal solution according to the user need. A set of experimentations is applied to validate our contributions.

SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition

Cloud manufacturing (CMfg) is a new service-oriented manufacturing paradigm in which shared resources are integrated and encapsulated as manufacturing services. When a single service is not able to meet some manufacturing requirement, a composition of multiple services is then required via CMfg. Service composition and optimal selection (SCOS) is a key technique for creating an on-demand quality of service (QoS)-optimal efficient manufacturing service composition to satisfy various user requirements. Given the number of services with the same functionality and a similar level of QoS, SCOS has been seen as a key challenge in CMfg research. One effective approach to solving SCOS problems is to use service domain features (SDF) through investigating the probability of services being used for a specific requirement from multiple perspectives. The approach can result in a division of the service space and then help streamline the service space with large-scale candidate services. The approach can also search for optimal subspaces that most likely contribute to an overall optimal solution. Accordingly, this paper develops an SDF-oriented genetic algorithm to effectively create a manufacturing service composition with large-scale candidate services. Fine-grained SDF definitions are developed to divide the service space. SDF-based optimization strategies are adopted. The novelty of the proposed algorithm is presented based on Bayes’ theorem. The effectiveness of the proposed algorithm is validated by solving three real-world SCOS problems in a private CMfg.

Trust level estimation for cloud service composition with inter-service constraints

Many cloud services with similar functionalities but different quality of service (QoS) have emerged in the past few years. Thus, the composition of a set of services in order to perform a particular functionality becomes a challenge. Organizations are more and more interested in moving their information systems (IS) with their various components (data, services, business processes, etc.) to the cloud. The composition of services in a similar case is more difficult since the components of the IS have different levels of complexity. This process should not be based only on the QoS, but other factors must be considered, such as the constraints that may exist between services as well as the trustworthiness of the providers. In this paper, we introduce an approach for the composition of cloud services to deal with the above issues. We compared our approach with other algorithms that treat the same problem, in order to evaluate it. The evaluation results show that our approach returns better results that meet the non-functional requirements and respect the constraints between services while maximizing the trust level of service providers.

A dynamic ant-colony genetic algorithm for cloud service composition optimization

At present, as the candidate services in the cloud service pool increase, the scale of the service composition increases rapidly. When the existing intelligent optimization algorithms are used to solve the large-scale cloud service composition and optimization (CSCO) problem, it is difficult to ensure the high precision and stability of the optimization results. To overcome such drawbacks, a new dynamic ant-colony genetic hybrid algorithm (DAAGA) is proposed in this paper. The best fusion evaluation strategy is used to determine the invoking timing of genetic and ant-colony algorithms, so the executive time of the two algorithms can be controlled dynamically based on the current solution quality, then the optimization ability is maximized and the overall convergence speed is accelerated. An iterative adjustment threshold is introduced to control the genetic operation and population size in later iterations, in which the effect of genetic algorithm is reduced when the population closes to optimal solution, only the mutation operation is implemented to reduce the calculation, and the population size is increased to find the optimal solution more quickly. A series of comparison experiments are carried out and the results show that the accuracy and stability of DAAGA are significantly improved for the large-scale CSCO problem, and the time consumption of the algorithm is also optimized.

A three-phase process for SLA negotiation of composite cloud services

Negotiation is used when two or more parties with conflicting goals pursue their own interests. Service trading in cloud computing is a case in which automated negotiation system (ANS) can be used to achieve high satisfaction levels for both contract parties. An ANS allows the parties negotiate automatically on some parameters that are important for them. Multiple services are combined together to provide a composite service that delivers a value-added service to consumers. With the increasing tendency to use composite cloud services, there is a need for designing ANSs capable of fulfilling requirements of consumers of these services. Unlike existing ANSs which support negotiation of single service, our proposed ANS supports negotiation of composite service. This work also proposes a novel negotiation strategy to enhance satisfaction of both trading parties. Some simulations were carried out to evaluate the performance of proposed negotiation strategy in terms of negotiating agents’ utility and negotiation speed. Analysis of the results of simulations shows that our recommended strategy provides more performance than the others.

Enhancing Service Composition by Discovering Cloud Services Community

In the cloud service community, cloud service providers typically limit component cloud services to perform a specific and limited function to maximize the versatility of cloud services. However, the business needs of consumers are often very complex, requiring multiple component cloud services to be combined to complete the business. In the process of cloud service composition, the question of how to find the right combination of partner cloud services is very challenging. This paper starts with a brief history of cloud service execution and it then analyzes the historical information of the combined cloud service execution. Based on the invocation relationship of the cloud service, the community discovery algorithm is used to divide the component cloud service into multiple cooperative communities. This paper will provide appropriate guidance for subsequent combinations of cloud services to improve the efficiency of cloud service portfolios and improve user satisfaction for combined cloud service execution. The experimental results show that the proposed method can divide the combined cloud service cooperation community in effective time and can effectively improve the success rate of the combined cloud service execution.

IHDETBO: A Novel Optimization Method of Multi‐Batch Subtasks Parallel‐Hybrid Execution Cloud Service Composition for Cloud Manufacturing

Cloud manufacturing (CMfg) is a new service‐oriented smart manufacturing paradigm, and it provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing service on‐demand. Because of the massive manufacturing resources, various users with individualized demands, heterogeneous manufacturing system or platform, and different data type or file type, CMfg is fully recognized as a kind of complex manufacturing system in complex environment and has received considerable attention in recent years. In practical scenarios of CMfg, the amount of manufacturing task may be very large, and there are always quite a lot of candidate manufacturing services in cloud pool for corresponding subtasks. These candidate services will be selected and composed together to complete a complex manufacturing task. Obviously, manufacturing service composition plays a very important role in CMfg lifecycle and thus enables complex manufacturing system to be stable, safe, reliable, and efficient and effective. In this paper, a new manufacturing service composition scheme named as Multi‐Batch Subtasks Parallel‐Hybrid Execution Cloud Service Composition for Cloud Manufacturing (MBSPHE‐CSCCM) is proposed, and such composition is one of the most difficult combination optimization problems with NP‐hard complexity. To address the problem, a novel optimization method named as Improved Hybrid Differential Evolution and Teaching Based Optimization (IHDETBO) is proposed and introduced in detail. The results obtained by simulation experiments and case study validate the effectiveness and feasibility of the proposed algorithm.

Security Incident Information Exchange for Cloud Service Provisioning Chains

Online services are increasingly becoming a composition of different cloud services, making incident-handling difficult, as Cloud Service Providers (CSPs) with end-user customers need information from other providers about incidents that occur at upstream CSPs to inform their users. In this paper, we argue the need for commonly agreed-upon incident information exchanges between providers to improve accountability of CSPs, and present both such a format and a prototype implementing it. The solution can handle simple incident information natively as well as embed standard representation formats for incident-sharing, such as IODEF and STIX. Preliminary interviews show a desire for such a solution. The discussion considers both technical challenges and non-technical aspects related to improving the situation for incident response in cloud-computing scenarios. Our solution holds the potential of making incident-sharing more efficient.

Optimal Fitness Aware Cloud Service Composition using an Adaptive Genotypes Evolution based Genetic Algorithm

With the seamless proliferation of cloud services, it becomes challenging to select and compose cloud services that satisfy the requirements of users. A service may be connected with another service(s) for satisfying a workflow/function in a service composition. Further, the service assessment based on one or two QoS parameters is not accurate enough to achieve the desired optimality in a cloud service composition. Most of the existing methods in the literature consider either a single QoS parameter or two QoS parameters for QoS-aware composition and do not consider the balancing of QoS parameters and/or the connectivity constraints between two compositions. In this paper, we present an Optimal Fitness Aware Cloud Service Composition (OFASC) using an Adaptive Genotype Evolution based Genetic Algorithm (AGEGA) dealing with multiple QoS parameters and providing the solutions that satisfy the balancing QoS parameters and connectivity constraints of service composition. Experimental results show that our approach enhances the efficiency of cloud service composition by converging quickly and obtains better composition when compared to other approaches.

Optimal fitness aware cloud service composition using modified invasive weed optimization

Abstract Quality of Service (QoS)-aware cloud service composition is one of the pivotal problems in cloud computing. With the seamless proliferation of cloud services, it becomes challenging to obtain an optimal cloud service for composition that satisfies a user's requirements. Many composition models available in the literature compose cloud services based on one or two QoS parameters of the candidate services without considering the complete set. These composition models do not consider the connectivity constraints between the candidate cloud services for satisfying a workflow/function in a service composition. In this paper, we present a novel Optimal Fitness Aware Cloud Service Composition using Modified Invasive Weed Optimization dealing with multiple QoS parameters and satisfying the balancing of QoS parameters and the connectivity constraints of cloud service composition. We evaluate the performance of our approach on a data set of real world cloud services, to select the best optimal fitness aware cloud service composition. By performing the parametric and non-parametric test at 1% level of significance, our proposed method is statistically more accurate than the other methods compared.

Evaluating the QoS Cognizance in Composition of Cloud Services: A Systematic Literature Review

Enterprise level computing constantly investigates novel approaches that maximize their profits and minimize their expenses. With the rapid growth of cloud computing XaaS – ‘anything as a service’, service providers are enabled with the rapid deployment of virtual services to service requestors. Because of the enormous growth in the variety of the services and based on the demand of the virtualized resources, cloud service providers are facing tough competition to facilitate the composite service requests made by the service requestors. QoS (Quality of Service) is considered to be a preliminary factor while composing a new cloud service out of heterogeneous and distributed atomic services. Therefore service composition is promising area that focuses on the design and development of the automated approaches to deal with diverse phases of service composition techniques that include service discovery, negotiation, service selection and optimization of the atomic services. This paper provides anatomy of existing studies addressing the problem of cloud service composition that enable to identify intended objectives of the technique along with diverse QoS aware problem solving approaches. Furthermore, the key areas of the improvement in cloud service composition are identified for future research.

Transportation Cloud Service Composition Based on Fuzzy Programming and Genetic Algorithm

The push toward smarter transportation management and decision-making has increased significantly in recent years. Toward this end, it is constructive to establish a traffic intelligence platform leveraging web services, big data analytics, and cloud computing. The developers of any such platform always face the challenge of selecting or creating composition plans from among numerous possible plans, often with unclear requirements, that satisfy their quality-of-service (QoS) requirements. Typical QoS properties associated with a web service are execution cost and time, availability, successful evaluation, usage frequency, and accuracy. Most of these factors are vague and/or difficult to quantity. To address the need to handle vague inputs, a constraint satisfaction-based web service composition algorithm is proposed, which is based on fuzzy multi-objective linear programming. A genetic algorithm with Pareto optimization evaluation with weighted standardized Euclidean distance is used that demonstrates good performance as the quantity of service candidate increases. The algorithm is capable of finding a satisfactory solution under input of vague QoS requirements, which shows better capability than multiple-objective linear programming. The applicability and performance of the proposed algorithm are validated in an online transportation analytics platform environment.