Service Selection Problem Research Articles

Service Selection under Uncertainty

Cloud computing has emerged as a popular computing paradigm, providing scalable and pay-per-use services to execute a variety of tasks. However, selecting the most suitable cloud services for a client can be challenging, as it involves taking into account the service characteristics and a set of client restrictions. In this paper, we formalize the problem of Service Selection under Uncertainty (SSuU) as an optimization problem. Our goal is to allocate tasks to appropriate cloud services while considering the probability of Service Level Agreement (SLA) violations and a set of client restrictions. We introduce an efficient dynamic programming approach to calculate the probability of SLA violations incrementally over time. We prove that the SSuU problem is strongly NP-complete, and propose an Integer Linear Programming (ILP) formulation and an Iterated Local Search-based algorithm for tackling it. To facilitate evaluation, we also introduce a set of benchmark instances for the SSuU problem. We extensively evaluate our proposed solutions on 94 input instances and compare them to the exact method (i.e., which always produces the optimal solution). Our results demonstrate that the metaheuristic approach is significantly faster and leads to exact solutions in the great majority of the evaluation scenarios.

The impact of new western land and sea corridor development on port deep hinterland transport service and route selection

New transportation corridor development aims to provide more convenient transportation conditions for port deep hinterland areas and to enhance the two-way opening of land and sea through the utilization of various transport modes, such as railroad, highway, and water transportation. In this paper, the microcontainer transport service and route selection problem is introduced within a new transportation corridor context, and a decision-making tool is provided for shippers to develop door-to-door service plans. A multiobjective 0–1 planning model is constructed with the objective of minimizing the total transportation cost, transportation time, and carbon emissions. The loading and unloading time windows of railroad and waterway transportation, as well as the time window of consignee receipt, are considered in model construction to obtain the nondominated Pareto optimal front. The validity of both the model and algorithm is demonstrated in this paper by using the Chongqing-Hamburg and Chengdu-Hamburg routes, following the development of the New Western Land and Sea Corridor (NWLSC), as examples. The example results show that the NWLSC is appealing for time-sensitive cargoes, and its economic benefits can be enhanced if relevant policies such as railroad freight reductions are supported. The findings in this paper can provide decision support for western provinces in the implementation of targeted policies to regulate and optimize the regional transportation structure, which can further promote the opening up of western Chinese provinces as well as the integrated and coordinated development of coastal and inland regions, providing practical value for reducing carbon emissions.

Budgeted Edge Service Selection in Mobile Edge Computing Environment

Mobile edge computing allows end-users to retrieve required services from edge servers with low network latency. However, when massive service instances with the same or interchangeable functionalities are available at the network edge, how to select appropriate service instances for use to minimize the overall latency perceived is a critical problem. This is not trivial due to that: 1) usually multiple service instances are involved to collectively fulfil an end-user's ever-increasing complicated need; 2) the overall cost incurred should not exceed end-user's service recruiting budget. In this article, we formally define the budgeted edge service selection (BESS) problem. Then, we model the BESS problem as a constrained optimization problem and theoretically prove its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hardness. Next, to solve the BESS problem effectively, we propose two approaches named BESS-O and BESS-A, respectively. Specifically, BESS-O is an optimal approach based on nonlinear integer programming. It can find out optimal solutions to BESS problems. Moreover, BESS-A is a greedy approach. It can find out approximate BESS solutions with higher efficiency, which is more useful for dealing with large-scale BESS problems. Finally, we conduct comprehensive experiments to evaluate our approaches against five representative approaches. Experimental results demonstrate that given a specific service usage budget, our approaches proposed in this article can achieve the highest completion ratio and the lowest service delay.

DSCPL: A Deep Cloud Manufacturing Service Clustering Method Using Pseudo-Labels

Research on service clustering for manufacturing network by deep learning is an effective method for service discovery and service management in manufacturing industries. Manufacturing service clustering can be used to solve the problem of service selection and composition in cloud manufacturing. After clustering characteristics of graph data, this paper analyzes and studies the manufacturing network structure, and finds the network characteristics formed by clustering. A deep manufacturing cloud service clustering model using pseudo-labels (DSCPL) is proposed, which combines graph topology and node features to cluster nodes with similar attributes. The specific contributions of the paper are as follows: 1) Defining a parametric nonlinear map embedded the graph space containing graph structure (adjacency information) and node information (features) into a low-dimensional feature space. 2)Building an auxiliary target distribution to realize the self-learning mechanism in order to adapt to the clustering task, and 3) Realizing the unsupervised clustering method. Comprehensive experiments show that the clustering effect of this method is better than the current advanced deep clustering methods on public datasets and simulated datasets.

Machine Learning-Based Two-Stage Task Offloading Optimization for Power Distribution Internet of Things

The increase in the number of services in the power distribution grid leads to a massive increase in task data. Power distribution internet of things (PDIoT) is the specific application of internet of things (IoT) in the power distribution grid. By deploying a large number of PDIoT devices, the voltage, active power, reactive power, and harmonic parameters are collected to support distribution grid services such as fault identification and status detection. Therefore, PDIoT utilizes massive devices to collect and offload tasks to the edge server through 5G network for real-time data processing. However, how to dynamically select edge servers and channels to meet the energy-efficient and low-latency task offloading requirements of PDIoT devices still faces several technical challenges such as task offloading decisions coupling among devices, unobtainable global state information, as well as interrelation of various quality of service (QoS) metrics such as energy efficiency and delay. To this end, we firstly construct a joint optimization problem to maximize the weighted difference between energy efficiency and delay of devices in PDIoT. Then, the joint optimization problem is decomposed into a large-timescale server selection problem and a small-timescale channel selection problem. Next, we propose an ML-based two-stage task offloading algorithm, where the large-timescale problem is solved by two-side matching in the first stage, and the small-timescale problem is solved by adaptive ε -greedy learning in the second stage. Finally, simulation results show that compared with the task offloading delay-first matching algorithm and the matching theory-based task offloading strategy, the proposed algorithm performs superior in terms of energy efficiency and delay.

ABAC: Alternative by alternative comparison based multi-criteria decision making method

Decision-making appears as a complex and challenging task when it requires finding the most suitable alternative among the numerous alternatives in the presence of multiple, usually conflicting criteria. At the same time, stakeholders expect a simple, transparent, and traceable decision-making method. Multi-Criteria Decision-Making (MCDM) methods rank the alternatives considering multiple criteria. The rank reversal problem is an important issue in most existing conventional MCDM methods. This paper proposes a new alternative by alternative comparison-based MCDM Method (ABAC) that addresses the rank reversal problem. We prove that ABAC is free from the rank reversal problem. To illustrate and validate ABAC, we have taken the cloud service selection problem as an application. Further, to show the effectiveness of ABAC, we have provided several case studies covering various domains. We perform several experiments by simulating the ABAC method. We have compared ABAC and existing MCDM methods. The experimental results support that the ABAC method is a rank reversal free MCDM method. We also carry out sensitivity analysis for ABAC. Salient features of ABAC over existing MCDM methods are (i) it is simple; (ii) it is rank reversal free; (iii) it is more scalable.

Balancing Privacy Risk and Benefit in Service Selection for Multiprovision Cloud Service Composition

The popularity of cloud computing has fueled the growth in multiprovision cloud service composition (MPCSC), where each cloud service provider (CSP) can fulfill multiple tasks, i.e., offer multiple services, simultaneously. In the MPCSC, users would rather disclose some private data for more benefits (e.g., personalized services). However, the more private data is released, the more serious the privacy risk faced by users. In particular, the multiservice provision characteristic of MPCSC further exacerbates the privacy risk. Therefore, how to balance the privacy risk and benefit in service selection for MPCSC is a challenging research problem. In this paper, firstly we explore the service selection problem of balancing privacy risk and benefit in MPCSC (SSBM), then we propose an improved Kuhn–Munkres (KM) algorithm solution to the SSBM problem. Furthermore, we conduct a series of simulation experiments to evaluate the proposed approach. The experimental results show that the proposed approach is both efficient and effective for solving the SSBM problem.

Empirical Matching-Based Computation Offloading Optimization for 5G and Edge Computing-Integrated EIoT

Electric Internet of things (EIoT) that integrates 5G and edge computing can provide data transmission and processing guarantee for smart grid. However, computation offloading optimization including joint optimization of server selection and computation resource allocation still faces several challenges such as difficulty in tradeoff balance among various quality of service (QoS) parameters, coupling between server selection and computation resource allocation, and multi-device competition. To address these challenges, we propose an empirical matching-based computation offloading optimization algorithm for 5G and edge computing-integrated EIoT. The optimization objective is to minimize the computation offloading delay by jointly optimizing large timescale server selection and small timescale computation resource allocation. We first model the large timescale server selection problem as a many-to-one matching problem, which can be decoupled from small timescale computation resource allocation by establishing a matching preference list based on empirical performance. Then, the large timescale server selection problem is solved by pricing-based matching with a quota algorithm. Furthermore, based on the obtained suboptimal result of large timescale server selection, the small timescale computation resource allocation problem is subsequently solved by Lagrange dual decomposition, the result of which is used to update large timescale empirical performance. Finally, extensive simulations are carried out to demonstrate the superior performance of the proposed algorithm by comparing it with existing algorithms.

Multi-Armed Bandits Learning for Task Offloading in Maritime Edge Intelligence Networks

In the context of complex and dynamic marine environment, the offloading of computing tasks for ships of Internet of Things (IoT) users is a very challenging problem considering the different quality of service (QoS) requirements of maritime applications. Mobile edge computing driven by powerful computing capability and edge intelligence is taken as a promising solution, especially for the resource-constrained and delay-sensitive maritime IoT users. In this paper, we study the optimal edge server selection problem for ship IoT users to jointly minimize the latency and energy consumption for task offloading. Specifically, we first propose a novel space-air-ground-edge (SAGE) integrated maritime network architecture to offload computation-intensive IoT services at sea. Then, the latency and energy consumption of data transmission and processing during offloading are modelled. Based on the models, the edge server selection problem is formulated into a Multi-Armed Bandits learning problem, with considering the task latency requirement and energy budget. To achieve the optimal solution, a novel algorithm, referred to as UCB1-ESSS, is developed, which links the latency, energy consumption, and network constraints by introducing both reward and cost. The simulation results show that the proposed algorithm can achieve considerably lower offloading latency and weighted latency-energy cost compared with the traditional algorithms under different QoS requirements, which proves the efficacy of theproposed algorithm.

Transport service selection and routing with carbon emissions and inventory costs consideration in the context of the Belt and Road Initiative

The Belt and Road Initiative (BRI) entails investments to improve overland (rail) transport between Europe and China. This paper introduces a microscopic Multi-Commodity Flow Service Selection Problem for freight transport under the BRI and provides a decision tool for shippers to make door-to-door service plans. The minimizing objective function considers transportation costs, in-transit inventory costs, and carbon emissions. A series of sampled data of each provincial region of China are collected from Chinese multimodal transport operators. Results show that inland regions are strongly attracted to the rail mode for shipments to Europe. However, the “last mile” (including “first mile”) transport from the shipper to the long-haul transport terminal strongly influences this choice, and carbon emissions are strongly influenced by the available last mile transport links. Under the dual impact of in-transit inventory and carbon emission costs, regions that prefer rail to maritime are much further east than suggested by previous literature.

Using seagull optimisation algorithm to select mobile service in cloud and edge computing environment

With the rapid development of edge computing, more and more services are deployed on edge servers. Compared with traditional cloud computing, services in the edge computing environment are closer to users, which bring benefits of high performance and low latency to the user-service interactions. However, due to the limited resources of edges, services provided by edges alone may fail to meet increasingly complex mobile computing requirements; therefore, services on clouds become an effective supplement. With the massive increment of services in the mobile internet, selecting proper services to fulfil mobile users' requests becomes a key research field. This paper proposes a service selection model for mobile service selection problem in cloud and edge computing environment. The proposed model combines the seagull optimisation algorithm and the simulated annealing algorithm. Through comparative experiments on simulation datasets with referencing to some other service selection models, it can be inferred that the proposed selection model finds a solution with better QoS performance in fewer iterations.

Mobile service selection in edge and cloud computing environment with grey wolf algorithm

The proliferation of mobile devices has resulted in the tremendous development of edge computing. A common mechanism is that once the requests from the users are too complex to be afforded by a single service, then edge computing services ought to step in. Nevertheless, services on edge servers are most commonly resource-constrained and unstable. To this end, we propose a novel solution for the mobile service selection problem with edge and cloud computing. An extended grey wolf algorithm is presented; specifically, crossover operator and roulette wheel are applied in reproduction and selection operations. Comparative experiments are implemented between our approach and other nature-inspired algorithms to verify the effectiveness and efficiency, which demonstrate our method may find a solution with better QoS values.

SERVER SELECTION ON BASE OF Z-NUMBERS

The paper is devoted to the problem of multi criteria decision making under linguistic uncertainty. Information of different approaches for modeling linguistic uncertainty have been analyzed. The concept of z-numbers proposed by L. Zadeh have been presented. Z-number is presented as cortege of two fuzzy number A and B, where A is analyzed factor, B is reliability of A assessment. The method of conversion z-numbers into generalized fuzzy numbers have been applied. As test have been used server selection problem. As decision making model have been used weighted average method. All calculations and results are presented.

Fair Server Selection in Edge Computing With $Q$-Value-Normalized Action-Suppressed Quadruple Q-Learning

Edge computing is a promising paradigm that brings servers closer to users, leading to lower latencies and enabling latency-sensitive applications such as cloud gaming, virtual/augmented reality, telepresence, and telecollaboration. Due to the high number of possible edge servers and incoming user requests, the optimum choice of user-server matching has become a difficult challenge, especially in the 5G era where the network can offer very low latencies. In this article, we introduce the problem of fair server selection as not only complying with an application’s latency threshold but also reducing the variance of the latency among users in the same session. Due to the dynamic and rapidly evolving nature of such an environment and the capacity limitation of the servers, we propose as solution a reinforcement learning (RL) method in the form of a quadruple Q-Learning model with action suppression, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -value normalization, and a reward function that minimizes the variance of the latency. Our evaluations in the context of a cloud gaming application show that, compared to an existing methods, our proposed method not only better meets the application’s latency threshold but is also more fair with a reduction of up to 35% in the standard deviation of the latencies experienced by users.

Deep Reinforcement Learning-Based Server Selection for Mobile Edge Computing

With Mobile Edge Computing (MEC), computational intensive applications can be offloaded to the nearby edge servers to support latency-sensitive applications on mobile devices. Different from the cloud, edge servers usually have limited resources, and then selecting which edge server to run the offloaded computation becomes an important issue. Although server selection has received considerable attention, not much work has been done to consider the limited coverage of the edge server and the frequent user movement, which introduce many dynamic changing factors affecting the workload of the edge server and making it hard to achieve long-term optimum in the edge server selection. To deal with these challenges, we model the problem of continuous server selection as a Markov Decision Process (MDP). The difficulty of this problem is that achieving long-term optimum requires future knowledge, such as user mobility, server workload, etc, which is not known a priori. We do not have such knowledge and thus cannot find the optimal policy through traditional methods. To address this problem, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Reinforcement Learning (DRL)</i> based algorithm to learn the selection policy based on the observed performance of past server selections. Specifically, a Long Short-Term Memory (LSTM) based neural network is exploited to encode the historical information which helps infer future knowledge of the dynamically changing factors. Then the DRL model selects the optimal server automatically based on the extracted system states. Extensive trace-driven evaluations demonstrate that the proposed DRL-based algorithm has the lowest overall cost compared to existing solutions.

How to Leverage Mobile Vehicles to Balance the Workload in Multi-Access Edge Computing Systems

In this correspondence, we investigate the joint relay and server selection problem for a novel vehicle-assisted multi-access edge computing (MEC) system, where vehicles on the road relay data from an end device to appropriate MEC servers for computing in a store-carry-forward manner. To harness the stochastic nature of task arrivals, vehicle availability, and resource availability at MEC servers, we design a relaying scheme to minimize the average task latency based on a Markov decision process. Our study shows that our proposed scheme is highly effective in balancing the communications and computing while minimizing the end-to-end latency for task completion.

QoS aware web service selection using orthogonal array learning on fruit fly optimization approach

PurposeThis paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment (web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue.Design/methodology/approachIn this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE/best/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism.FindingsTo test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability.Originality/valueIn this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.

An Intelligent Web Service Composition and Resource-Optimization Method Using K-Means Clustering and Knapsack Algorithms

Service-oriented architecture (SOA) has emerged as a flexible software design style. SOA focuses on the development, use, and reuse of small, self-contained, independent blocks of code called web services that communicate over the network to perform a certain set of simple tasks. Web services are integrated as composite services to offer complex tasks and to provide the expected services and behavior in addition to fulfilling the clients’ requests according to the service-level agreement (SLA). Web service selection and composition problems have been a significant area of research to provide the expected quality of service (QoS) and to meet the clients’ expectations. This research paper presents a hybrid web service composition model to solve web service selection and composition problems and to optimize web services’ resource utilization using k-means clustering and knapsack algorithms. The proposed model aims to maximize the service compositions’ QoS and minimize the number of web services integrated within the service composition using the knapsack algorithm. Additionally, this paper aims to track the service compositions’ QoS attributes by evaluating and tracking the web services’ QoS using the reward function and, accordingly, use the k-means algorithm to decide to which cluster the web service belongs. The experimental results on a real dataset show the superiority and effectiveness of the proposed algorithm in comparison with the results of the state–action–reward–state–action (SARSA) and multistage forward search (MFS) algorithms. The experimental results show that the proposed model reduces the average time of the web service selection and composition processes to 37.02 s in comparison to 47.03 s for the SARSA algorithm and 42.72 s for the MFS algorithm. Furthermore, the average of web services’ resource utilization results increased by 4.68% using the proposed model in comparison to the resource utilization by the SARSA and MFS algorithms. In addition, the experimental results showed that the average number of service compositions using the proposed model improved by 26.04% compared with the SARSA and MFS algorithms.

Privacy regulation aware service selection for multi-provision cloud service composition

The proliferation of cloud computing is driving the rapid growth of multi-provision cloud service composition (MPCSC), in which each cloud service provider (CSP) can offer multiple services simultaneously. Due to the openness and virtualization of cloud environments, cloud service providers (CSPs) may collect and distribute users’ privacy information without permission or authorization. More seriously, some CSPs have the ability to infer additional privacy information by combining multiple pieces of such data. Therefore, privacy is one of the main concerns of users seeking to take advantage of cloud services (CSs). This paper aims at addressing this problem by constructing a privacy regulation aware cloud service composition under considering the multi-service provision characteristics of CSPs. Firstly, it models user privacy requirements to formulate the problem of privacy regulation aware service selection for MPCSC (PSSM). Then, it proposes a pre-processed Kuhn–Munkres (KM) algorithm-based approach to solving the PSSM problem. Finally, it demonstrates that the proposed approach is effective and efficient for solving the PSSM problem by conducting a series of simulation experiments.

Many-objective cloud manufacturing service selection and scheduling with an evolutionary algorithm based on adaptive environment selection strategy

Cloud manufacturing service selection and scheduling (CMSSS) problem has obtained wide attentions in recent years. However, most existing methods describe this problem as single-, bi-, or tri-objective models. Little work deals with this problem in four or more objectives simultaneously. This paper investigated CMSSS problem in consideration of the interests of users, cloud platform and service providers. An eight-objective CMSSS optimization model is constructed for the problem. Meanwhile, a many-objective evolutionary algorithm with adaptive environment selection (MaOEA-AES) is designed to address the problem. Specifically, diversity-based population partition technology is used to divide the population into multiple subregions to maintain the population diversity, and an adaptive penalty boundary intersection (APBI) distance is designed to select elitist solutions in different stages of evolutionary process. The proposed algorithm is tested on 2 cases with 5 and 8 objectives in CMSSS problems and each of them has sixteen experimental groups with different problem scales. The experiment results show that MaOEA-AES is competitive to resolve the MaO-CMSSS model compared with eight state-of-the-art evolutionary algorithms in convergence and diversity.