Efficient Service Composition Research Articles

Designing a resilient cloud network fulfilled by reinforcement learning

Cloud manufacturing networks operate in stochastic environments, making them susceptible to various disruptions. Designing a resilient cloud network that can withstand and recover from disruptions while maintaining an efficient service composition is crucial. This paper emphasizes the importance of developing resilient cloud manufacturing networks, particularly in the context of unprecedented challenges. It explores the complexities of service composition using reinforcement learning algorithms, specifically Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). SAC has emerged as a standout performer, demonstrating superior capabilities in enhancing network resilience. The model’s verification is conducted by investigating and comparing the convergence of the results. This research not only examines these reinforcement learning methodologies but also broadens its scope to provide ongoing development and evaluation of a robust network. The model is applied to a COVID-19 case study to validate the results. Beyond theoretical frameworks, this research highlights the need for adaptive and resilient manufacturing systems, offering insights into real-world challenges. The results emphasize the significance of using RL algorithms, particularly when customer satisfaction is the top priority. Moreover, when logistic costs and costs of changing servers are lower than customer dissatisfaction, the use of RL algorithms becomes essential for addressing disruption scenarios. The findings highlight the critical role of collaboration in intertwined supply networks in helping a disrupted network withstand and recover from a disruption.

Proactive Resource Allocation for Cloud Manufacturing: A Dynamic Approach with DNN-Based Hybrid Many Objective Leopard Seal Algorithm

In the domain of cloud manufacturing, diverse services and the escalating demand for efficient and cost-effective service composition selection in cloud manufacturing have spurred day by day. Traditional methods face several challenges such as the risk of convergence issues and suboptimal solutions, high dimensionality of decision space, increased computational complexity, and scalability issues. To overcome these complexities, this paper proposed a novel method named the Deep Neural Network (DNN) based Hybrid Many Objective Leopard Seal (HMOLS) algorithm. In this study, DNN is employed to predict future Quality of Service (QoS) for each service based on historical data and integrated features. These predictions are fed back into the optimization model, influencing the dynamic weight calculation for each objective function. In this work, the Leopard Seal Optimization is implemented for hyperparameter optimization to enhance the performance of the HMOLS algorithm. Various evaluation metrics namely accuracy, throughput, reliability, resource utilization, and scalability are utilized to evaluate the performance of the HMOLS method and compare its performance with existing methods. The experimental outcomes depict the effectiveness of the HMOLS approach for the effective selection of services and fusion for cloud manufacturing. DNN-based predictions utilize deep neural networks to predict resource demand or utilization patterns and enable more accurate allocation decisions. These predictions optimize resource allocation by reallocating resources accordingly and address the challenges of resource utilization. Meanwhile, this method is required to overcome the computational overhead for applicability in cloud manufacturing.

Mobility and energy efficient services composition algorithm with QoS guarantee for large scale Cyber–Physical–Social Systems

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user’s movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user’s mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user’s mobility traces, avoiding the random generation of users’ traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices’ batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider.

Toward an efficient web service composition based on an improved BTLBO algorithm

Toward an efficient web service composition based on an improved BTLBO algorithm

Hybrid Integrated Aquila Optimizer for Efficient Service Composition with Quality of Service Guarantees in Cloud Computing

Hybrid Integrated Aquila Optimizer for Efficient Service Composition with Quality of Service Guarantees in Cloud Computing

A service composition method using improved hybrid teaching learning optimization algorithm in cloud manufacturing

In the cloud manufacturing process, service composition can combine a single service into a complex service to meet the task requirements. An efficient service composition strategy is crucial, as it affects the efficiency of resource and capacity sharing in the cloud manufacturing system. However, in the face of a large-scale environment, the existing methods have the problems of slow convergence and instability. To solve the problems above, we propose an improved optimization method, named improved-TC. Specifically, we are inspired by the horizontal crossover of CSO in the hybrid-TC teaching phase, the Hybrid-TC is proposed in our previous work, which is a hybrid of the teaching-learning-based optimization algorithm (TLBO) and the crisscross optimization algorithm (CSO). Improved-TC is an improvement on the learning phase of hybrid-TC algorithm, we change the search method of hybrid-TC in the learning phase to a one-dimensional search, thereby some dimensions in the population that are trapped in the local optimum have the chance to jump out of the iteration. Experiments show that our proposed method has a faster convergence speed and more stability in the face of service composition in large-scale environments.

Truly Scalable and Reusable SOA Services

Abstract: Web Services are materialized as a major technology carried out for automated interaction between distributed and miscellaneous applications. It is defined as a software service that provides business solutions consumed by different service requester. It can be accessed by a standard web protocol. Service composition is the mechanism used for selecting, reusing and combining existing web services to build new web services. With immense increase in web services, quality assessment plays an essential role in the selection approach. QoS is defined as the ability to guarantee the requirements like latency, reliability, bandwidth, etc. in order to satisfy a service level agreement between an application provider and end-user. QoS based dynamic service composition leads to the upward growth of an organization that implements multiple services to provide its business solution. The proposed research work elaborates the research work on different methodologies of QoS prediction with its implications to provide a proper road map for future research on efficient service composition. Keywords: SOA, service reuse, scalable, web services, QoS.

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

With the advent of Service-Oriented Architecture (SOA), services can be registered, invoked, and combined by their identical Quality of Services (QoS) attributes to create a new value-added application that fulfils user requirements. Efficient QoS-aware service composition has been a challenging task in cloud computing. This challenge becomes more formidable in emerging resource-constrained computing paradigms, such as the Internet of Things and Fog. Service composition has regarded as a multi-objective combinatorial optimization problem that falls in the category of NP-hard. Historically, the proliferation of services added to problem complexity and navigated solutions from exact (none-heuristics) approaches to near-optimal heuristics and metaheuristics. Although metaheuristics have fulfilled some expectations, the quest for finding a high-quality, near-optimal solution has led researchers to devise hybrid methods. As a result, research on service composition shifts towards the hybridization of metaheuristics. Hybrid metaheuristics have been promising efforts to transcend the boundaries of metaheuristics by leveraging the strength of complementary methods to overcome base algorithm shortcomings. Despite the significance and frontier position of hybrid metaheuristics, to the best of our knowledge, there is no systematic research and survey in this field with a particular focus on strategies to hybridize traditional metaheuristics. This study’s core contribution is to infer a framework for hybridization strategies by conducting a mapping study that analyses 71 papers between 2008 and 2020. Moreover, it provides a panoramic view of hybrid methods and their experiment setting in respect to the problem domain as the main outcome of this mapping study. Finally, research trends, directions and challenges are discussed to benefit future endeavours.

Web Service Composition for Executing Workflows: Open Issues, Challenges, Survey and Future Directions

Cloud computing has nowadays become a dominant technology to reduce the computation cost by elastically providing resources to users on a pay-per-use basis. More and more data intensive business and scientific web service applications represented by workflows have been moved or are in active transition to cloud platforms. Therefore, efficient web service compositing for executing workflow are in high demand. This paper conduct extensive survey of various existing web service composition technique for executing workflow. From survey it is identified very limited work is done for minimizing workflow execution processing time with minimal energy dissipation considering task deadline requirement under heterogeneous cloud computational environment. This paper identifies open issues and future challenges and presented possible solution in building efficient web service composition for executing workflows under heterogeneous cloud computational environment

Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching.

Recently, manufacturing firms and logistics service providers have been encouraged to deploy the most recent features of Information Technology (IT) to prevail in the competitive circumstances of manufacturing industries. Industry 4.0 and Cloud manufacturing (CMfg), accompanied by a service-oriented architecture model, have been regarded as renowned approaches to enable and facilitate the transition of conventional manufacturing business models into more efficient and productive ones. Furthermore, there is an aptness among the manufacturing and logistics businesses as service providers to synergize and cut down the investment and operational costs via sharing logistics fleet and production facilities in the form of outsourcing and consequently increase their profitability. Therefore, due to the Everything as a Service (XaaS) paradigm, efficient service composition is known to be a remarkable issue in the cloud manufacturing paradigm. This issue is challenging due to the service composition problem’s large size and complicated computational characteristics. This paper has focused on the considerable number of continually received service requests, which must be prioritized and handled in the minimum possible time while fulfilling the Quality of Service (QoS) parameters. Considering the NP-hard nature and dynamicity of the allocation problem in the Cloud composition problem, heuristic and metaheuristic solving approaches are strongly preferred to obtain optimal or nearly optimal solutions. This study has presented an innovative, time-efficient approach for mutual manufacturing and logistical service composition with the QoS considerations. The method presented in this paper is highly competent in solving large-scale service composition problems time-efficiently while satisfying the optimality gap. A sample dataset has been synthesized to evaluate the outcomes of the developed model compared to earlier research studies. The results show the proposed algorithm can be applied to fulfill the dynamic behavior of manufacturing and logistics service composition due to its efficiency in solving time. The paper has embedded the relation of task and logistic services for cloud service composition in solving algorithm and enhanced the efficiency of resulted matched services. Moreover, considering the possibility of arrival of new services and demands into cloud, the proposed algorithm adapts the service composition algorithm.

An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment

By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms.

QoS Constrained Large Scale Web Service Composition Using Abstraction Refinement

Efficient service composition in real time while providing necessary Quality of Service (QoS) guarantees has been a challenging research problem with ever growing complexity. Several heuristic based approaches with diverse proposals for taming the scale and complexity of web service composition, have been proposed in literature. In this paper, we present a new approach for efficient service composition based on abstraction refinement. Instead of considering individual services during composition, we propose several abstractions to form service groups and the composition is done on these abstract services. Abstraction reduces the search space significantly and thereby can be done reasonably fast. While this can expedite solution construction to a great extent, this also entails a possibility that it may fail to generate any solution satisfying the QoS constraints, though the individual services construct a valid solution. Hence, we propose to refine an abstraction to generate the composite solution with desired QoS values. A QoS satisfying solution, if one exists, can be constructed with multiple iterations of abstraction refinement. While in the worst case, this approach may end up exploring the complete composition graph constructed on individual services, on an average, the solution can be achieved on the abstract graph. The abstraction refinement techniques give a significant speed-up compared to the traditional composition techniques. Experimental results on real benchmarks show the efficiency of our proposed mechanism in terms of time and the number of services considered for composition.

Efficient Device-to-Device Service Invocation Using Arrowhead Orchestration

The Internet of Things (IoT) enables interaction from real-world physical objects using sensors to the virtual world of computers and the Internet. The use of service-oriented architecture (SOA) is one step in the creation of basic and complex interactions between several sensors and actuators. However, the use of SOA-enabled technologies alone does not meet all requirements of how sensor and actuator systems could be integrated to create distributed monitoring and control applications. The centralized, traditional method of communication in wireless sensor networks via a gateway presents drawbacks that have to be addressed; device-to-cloud communication adds higher latency and higher power consumption and is less robust than the device-to-device (D2D) communication approach. Moreover, all these characteristics reduce the scalability of the network, thus limiting the use of IoT in the industry. In this article, the proposed method utilizes the arrowhead framework orchestration system to generate service composition within a (wireless) network formed by IoT devices. The aim is to achieve efficient D2D service invocation to reduce the drawbacks of today’s widely used device-to-cloud approach. The method in this article performs efficient service composition for industrial IoT, including mapping SOA service composition in very small resource-constrained devices using the arrowhead orchestration. The results presented in this article at the service level can increase performance and robustness in fog computing on resource-constrained devices.

An Agent-Based Integrated Self-Evolving Service Composition Approach in Networked Environments

Service composition is an important research problem in service computing systems, which combines simple and individual services into composite services to fulfill users' complex requirements. Service composition usually consists of four stages, i.e., service discovery, candidate selection, service negotiation and task execution. In self-organising systems, there is the fifth stage of service composition: self-evolution. Most of existing works study only some of the five stages. However, these five stages should be systematically studied so as to develop an integrated and efficient service composition approach. Against this background, this paper proposes an agent-based integrated self-evolving service composition approach. This approach systematically takes the five stages of service composition into consideration. It is also decentralised and self-evolvable. Experimental results demonstrate that the proposed approach can achieve almost the same success rates while uses much less communication overhead and time consumption in comparison with three existing representative approaches.

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.

A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments

Mobile cloud computing has the features of resource constraints, openness, and uncertainty which leads to the high uncertainty on its quality of service (QoS) provision and serious security risks. Therefore, when faced with complex service requirements, an efficient and reliable service composition approach is extremely important. In addition, preference learning is also a key factor to improve user experiences. In order to address them, this paper introduces a three-layered trust-enabled service composition model for the mobile cloud computing systems. Based on the fuzzy comprehensive evaluation method, we design a novel and integrated trust management model. Service brokers are equipped with a learning module enabling them to better analyze customers’ service preferences, especially in cases when the details of a service request are not totally disclosed. Because traditional methods cannot totally reflect the autonomous collaboration between the mobile cloud entities, a prototype system based on the multi-agent platform JADE is implemented to evaluate the efficiency of the proposed strategies. The experimental results show that our approach improves the transaction success rate and user satisfaction.

A Genetic-Based Adaptive Approach for Reliable and Efficient Service Composition

Composite services often run in dynamically changing environments where new composition requirements can be added and Web services may disappear or their quality of service may decline. The service composition requirements are classified into three groups: functional, nonfunctional, and transactional requirements. The functional computing requirements concern functional capabilities of Web services. The nonfunctional computing requirements are defined as service-level agreements (SLA) constraints. The transactional computing requirements define behavioral constraints and are expressed by designers using the acceptable termination states (ATS) concept. In this work, we proposed an adaptive service composition (ASC) approach. Such an approach is adaptive as it can be used to either define or reconfigure composite services according to SLA and ATS constraints. It introduces a new recovery capability (RC) concept for capturing the recoverability level supported by Web services. We adopted a genetic-based algorithm to model the ASC approach. The choice of such an algorithm is justified by the nature of the problem of our ASC process which belongs to the NP-hard class. We experimentally showed the effectiveness of our ASC approach and empirically studied the service composition RC in particular.

Cloud-SEnergy: A bin-packing based multi-cloud service broker for energy efficient composition and execution of data-intensive applications

The over-reliance of today's world on information and communication technologies (ICT) has led to an exponential increase in data production, network traffic, and energy consumption. To mitigate the ecological impact of this increase on the environment, a major challenge that this paper tackles is how to best select the most energy efficient services from cross-continental competing cloud-based datacenters. This selection is addressed by our Cloud-SEnergy, a system that uses a bin-packing technique to generate the most efficient service composition plans. Experiments were conducted to compare Cloud-SEnergy's efficiency with 5 established techniques in multi-cloud environments (All clouds, Base cloud, Smart cloud, COM2, and DC-Cloud). The results gained from the experiments demonstrate a superior performance of Cloud-SEnergy which ranged from an average energy consumption reduction of 4.3% when compared to Based Cloud technique, to an average reduction of 43.3% when compared to All Clouds technique. Furthermore, the percentage reduction in the number of examined services achieved by Cloud-SEnergy ranged from 50% when compared to Smart Cloud and average of 82.4% when compared to Base Cloud. In term of run-time, Cloud-SEnergy resulted in average reduction which ranged from 8.5% when compared to DC-Cloud, to 28.2% run-time reduction when compared to All Clouds.

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

SummaryMany sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP‐hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature‐inspired meta‐heuristic techniques are proposed for solving this problem. The nature‐inspired meta‐heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide‐ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature‐inspired meta‐heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature‐inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years.

Towards an understanding of cloud services under uncertainty: A possibilistic approach

With the development of Web technologies and the increasing use of the Internet, more and more web services are being deployed. This gave birth to what called cloud services, which are widely used for building distributed cloud applications. With cloud-based service delivery, it seems hard for users to find the right service for their needs. However, if a single cloud service cannot meet all user requirements, a cloud service composition is required. With the rise of Web services on the Internet, the quality of response has been considered as an important criterion to choose the most relevant answer. The performance of web services may vary depending on the dynamic Internet environment, which makes the quality of service uncertain. Then, the need to deal with uncertainty in the operation of cloud services. Over the past decade, several approaches to service composition have been proposed to cope with this challenge. In this article, we provide a flexible and efficient cloud services composition framework that can respond to user queries and improve cloud services composition results by exploiting data uncertainty. We then introduce an effective strategy that allows modeling, invocation, and composition of services by dealing with data uncertainty. The experimental evaluation of a real case study demonstrates the effectiveness of our proposed strategy.