Service Selection Algorithm Research Articles

Deep Reinforcement Learning-Based Task Offloading and Load Balancing for Vehicular Edge Computing

Vehicular edge computing (VEC) effectively reduces the computational burden on vehicles by offloading tasks from resource-constrained vehicles to edge nodes. However, non-uniformly distributed vehicles offloading a large number of tasks cause load imbalance problems among edge nodes, resulting in performance degradation. In this paper, we propose a deep reinforcement learning-based decision scheme for task offloading and load balancing with the optimization objective of minimizing the system cost considering the split offloading of tasks and the load dynamics of edge nodes. First, we model the mutual interaction between mobile vehicles and Mobile Edge Computing (MEC) servers using a Markov decision process. Second, the optimal task-offloading and resource allocation decision is obtained by utilizing the twin delayed deep deterministic policy gradient algorithm (TD3), and server load balancing is achieved through edge collaboration using a server selection algorithm based on the technique for order preference by similarity to the ideal solution (TOPSIS). Finally, we have conducted extensive simulation experiments and compared the results with several other baseline schemes. The proposed scheme can more effectively reduce the system cost and increase the system resource utilization.

PRTMNSGA-III: a novel multi-objective algorithm for QoS-aware multi-cloud IoT service selection

pRTMNSGA-III: a novel multi-objective algorithm for QoS-aware multi-cloud IoT service selection

An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty

Currently, the selection of web services with an uncertain quality of service (QoS) is gaining much attention in the service-oriented computing paradigm (SOC). In fact, searching for a service composition that fulfills a complex user’s request is known to be NP-complete. The search time is mainly dependent on the number of requested tasks, the size of the available services, and the size of the QoS realizations (i.e., sample size). To handle this problem, we propose a two-stage approach that reduces the search space using heuristics for ranking the task services and a bat algorithm metaheuristic for selecting the final near-optimal compositions. The fitness used by the metaheuristic aims to fulfil all the global constraints of the user. The experimental study showed that the ranking heuristics, termed “fuzzy Pareto dominance” and “Zero-order stochastic dominance”, are highly effective compared to the other heuristics and most of the existing state-of-the-art methods.

QoS driven cost-efficient resource allocation in edge computing: A distributed game theoretic approach

In edge computing, app vendors provide task-offloading services to small intelligent devices with limited resources via edge servers. Users of these devices aim to utilize a better quality of service at a lower cost, and the app vendor tries to maximize their user base. In this paper, we propose an edge computing resource allocation technique to address the critical issue of maintaining a trade-off between user cost and QoS while increasing the number of users served by the app vendor. In the proposed technique, we formulate the problem as an Edge Server Selection Game (ESSGame), where users can optimize individual objective functions. An Edge Server Selection Algorithm (ESSA) is presented by which the system quickly converges to a pure Nash equilibrium (PNE). It is proved that ESSAGame is a potential game with at least one PNE. The quality of the PNE in ESSGame is also determined in terms of the Price of Stability (PoS). Numerical results are also presented for the performance of the proposed technique.

Two-stage hybrid genetic algorithm for robot cloud service selection

Robot cloud service platform is a combination of cloud computing and robotics, providing intelligent cloud services for many robots. However, to select a cloud service that satisfys the robot’s requirements from the massive services with different QoS indicator in the cloud platform is an NP hard problem. In this paper, based on the cost model between the cloud platform, cloud services and cloud service robotics, we propose a two-stage service selection strategy, namely, candidate services selection stage according to the specific QoS requirements of service robots and final cost optimization stage. Additionally, with respect to optimizing the final cost for the model, we propose a Dynamic Vector Hybrid Genetic Algorithm (DVHGA) that is integrated with local and global search process as well as a three-phase parameter updating policy. Specifically, inspired by momentum optimization in deep learning, dynamic vector is integrated with DVHGA to modify the weights of QoS and ensure the reasonable allocation of resources. Moreover, we suggest a linear evaluation method for the service robots and the cloud platform concerning time and final cost at the same time, which could be expected to be used in the real application environment. Finally, the empirical results demonstrate that the proposed DVHGA outperforms other benchmark algorithms, i.e., DABC, ESWOA, GA, PGA and GA-PSO, in convergence rate, total final cost and evaluation score.

MFLCES: Multi-Level Federated Edge Learning Algorithm Based on Client and Edge Server Selection

This research suggests a multi-level federated edge learning algorithm by leveraging the advantages of Edge Computing Paradigm. Model aggregation is partially moved from a cloud center server to edge servers in this framework, and edge servers are connected hierarchically depending on where they are located and how much computational power they have. At the same time, we considered an important issue: the heterogeneity of different client computing resources (such as device processor computing power) and server communication channels (which may be limited by geography or device). For this situation, a client and edge server selection algorithm (CESA) based on a greedy algorithm is proposed in this paper. Given resource constraints, CESA aims to select as many clients and edge servers as possible to participate in the model computation in order to improve the accuracy of the model. The simulation results show that, when the number of clients is high, the multi-level federated edge learning algorithm can shorten the model training time and improve efficiency compared to the traditional federated learning algorithm. Meanwhile, the CESA is able to aggregate more clients for training in the same amount of time compared to the baseline algorithm, improving model training accuracy.

Resilience and load balancing in Fog networks: A Multi-Criteria Decision Analysis approach

The advent of Cloud Computing enabled the proliferation of Internet-of-Things (IoT) applications for smart environments. However, the distance of these resources makes them unsuitable for delay-sensitive applications. Hence, Fog Computing has emerged to provide such capabilities in proximity to end devices through distributed resources. These limited resources can collaborate to serve distributed IoT application workflows using the concept of stateless micro Fog service replicas, which provides resiliency and maintains service availability in the face of failures. Load balancing supports this collaboration by optimally assigning workloads to appropriate services, i.e., distributing the load among Fog nodes to fairly utilize compute and network resources and minimize execution delays. In this paper, we propose using ELECTRE, a Multi-Criteria Decision Analysis (MCDA) approach, to efficiently balance the load in Fog environments. We considered multiple objectives to make service selection decisions, including compute and network load information. We evaluate our approach in a realistic unbalanced topological setup with heterogeneous workload requirements. To the best of our knowledge, this is the first time ELECTRE-based methods are used to balance the load in Fog environments. Through simulations, we compared the performance of our proposed approach with traditional baseline methods that are commonly used in practice, namely random, Round-Robin, nearest node, and fastest service selection algorithms. In terms of the overall system performance, our approach outperforms these methods with up to 67% improvement.

Performance-Power Tradeoff in Heterogeneous SaaS Clouds With Trustworthiness Guarantee

Software-as-a-service (SaaS) clouds grow dramatically due to cost-effectiveness, availability, and flexibility. Quality of service (QoS) and power, which represent performance and cost, respectively, are conflicting yet critical issues in the service scheduling of SaaS clouds, and some researchers have investigated the tradeoff between them. However, existing works do not involve QoS attacks in which untrusted service providers provide fake QoS values to absorb service requests, resulting in lower user experience and system profits. In this paper, we jointly consider the QoS performance, queue congestion, and energy consumption to formulate the performance-power tradeoff while considering QoS attacks. To address this NP scheduling problem, we propose a Lyapunov-based decomposition strategy that converts the original problem into three equivalent subproblems. By aggregating the solving strategies for the three subproblems, we develop the online service selection and trustworthiness management algorithm that optimizes the performancepower tradeoff while resisting QoS attacks. In addition, a light-weighted trustworthiness management strategy is designed to update trustworthiness values without storing large amounts of past information. Mathematical analyses and simulations demonstrate that our proposed control framework realizes detection and resistance of QoS attacks and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$[O(1 / V), O(V)]$</tex-math></inline-formula> tradeoff between performance and power with a performance-power tradeoff parameter V.

Task offloading and parameters optimization of MAR in multi-access edge computing

With the development of mobile augmented reality (MAR) technology, the demand for MAR applications is increasing. However, MAR is rarely used in mobile devices due to its high computational and energy consumption. In this paper, we study the task offloading and parameters optimization of MAR applied to mobile devices in mobile edge computing. Considering the influence of the MAR client energy consumption, service delay and detection accuracy in the task offloading and parameters optimization process, we design a function to evaluate MAR client energy efficiency. The problem of task offloading and parameters optimization is formulated to minimize energy efficiency function under the limitation of MAR task completion time and wireless bandwidth resources. To solve this problem, we propose a server selection and parameters optimization (SSPO) algorithm to realize client task offloading and parameters optimization. The SSPO algorithm first generates priority queue of tasks. Based on the order of priority queue, tasks are offloaded to appropriate mobile edge server according to the analytic hierarchy process. After that, the parameters are calculated and the tasks are redistributed according to the completion time until the energy efficiency function converges. Simulation results show that the proposed algorithm is better than the comparison algorithm.

Analysis performance of content delivery network by used Rateless Code method

Content Delivery Network (CDN) is a server system network to content contribution in an application/web to various accessors around the world so that data sent and received by users was faster. Server selection algorithm determines the extent to which the quality of server service to client requests. This study to analyze CDN performance, used Geographical algorithm Domain Name Server (GeoDNS), Round Robin (RR), Weigthed Round Robin (WRR), dan Least Connection (LC). In addition, Rateless Code (RLC) is also applied to reduce packet loss, there are two methods used, namely the first method is rateless code 50 and the second method is rateless code 100. Thus, the trial was carried out 20 times. Besides that, it is also applied Rateless Code (RLC) to less packet loss. From the research results, the use of CDN can improve server service performance. CDN managed to reduce the maximum to the average delay about 67,6%. From the studied CDN algorithm, Geo DNS experienced the lowest delay up to about 67.6 ms, however LC experienced the lowest packet loss, namely about 2,84%. The application of rateless code on CDN with LC algorithm succeeded in reducing packet loss to about 2.84%, but delay increased to about 75.3%.

OPTCLOUD: An Optimal Cloud Service Selection Framework Using QoS Correlation Lens.

Cloud computing has grown as a computing paradigm in the last few years. Due to the explosive increase in the number of cloud services, QoS (quality of service) becomes an important factor in service filtering. Moreover, it becomes a nontrivial problem when comparing the functionality of cloud services with different performance metrics. Therefore, optimal cloud service selection is quite challenging and extremely important for users. In the existing approaches of cloud service selection, the user's preferences are offered by the user in a quantitative form. With fuzziness and subjectivity, it is a hurdle task for users to express clear preferences. Moreover, many QoS attributes are not independent but interrelated; therefore, the existing weighted summation method cannot accommodate correlations among QoS attributes and produces inaccurate results. To resolve this problem, we propose a cloud service framework that takes the user's preferences and chooses the optimal cloud service based on the user's QoS constraints. We propose a cloud service selection algorithm, based on principal component analysis (PCA) and the best-worst method (BWM), which eliminates the correlations between QoS and provides the best cloud services with the best QoS values for users. In the end, a numerical example is shown to validate the effectiveness and feasibility of the proposed methodology.

Cost aware service selection in a mobile edge marketplace

A marketplace plays an important role that bridges between Mobile Edge Infrastructure Services (EISs) providers and their customers, also manages relations between actors in a mobile edge ecosystem. One of the key services of the marketplace is service selection where not only a list of EISs matching the customers’ demands is provided but also enables service selection based on the customers’ requirements to fully automate the process. In this paper, we first investigate important attributes of EISs (such as coverage area, latency models, pricing models, etc.), and requirements of edge-based applications (such as latency and reliability) for EIS selection in a marketplace. We then formulate an optimization problem to choose the right set of EISs among available services in the marketplace. We propose two service selection algorithms, i.e., Best Fit (BF) and an Improved version of BF (IBF) to minimize the monetary cost of selected services subject to latency, reliability constraints and customer requirements. The evaluation shows that IBF has 4% improvement in monetary cost as compared to the BF. IBF has only 1% deviation from the optimal solution generated by a brute force algorithm, while it is 189 times faster than the brute force. Accordingly, IBF not only outperforms BF in terms of monetary costs but also achieves the optimal solution as compared to the brute force algorithm in significantly lower execution time. Furthermore, IBM CPLEX Optimizer is also implemented to solve the considered problem to have more concrete evaluation. The results indicate that although CPLEX can also solve the problem with the optimal result, its computing time is still dramatically worse than IBF.

Multi-Criteria Service Selection Agent for Federated Cloud

Federated cloud interconnects small and medium-sized cloud service providers for service enhancement to meet demand spikes. The service bartering technique in the federated cloud enables service providers to exchange their services. Selecting an optimal service provider to share services is challenging in the cloud federation. Agent-based and Reciprocal Resource Fairness (RRF) based models are used in the federated cloud for service selection. The agent-based model selects the best service provider using Quality of Service (quality of service). RRF model chooses fair service providers based on service providers' previous service contribution to the federation. However, the models mentioned above fail to address free rider and poor performer problems during the service provider selection process. To solve the above issue, we propose a Multi-criteria Service Selection (MCSS) algorithm for effectively selecting a service provider using quality of service, Performance-Cost Ratio (PCR), and RRF. Comprehensive case studies are conducted to prove the effectiveness of the proposed algorithm. Extensive simulation experiments are conducted to compare the proposed algorithm performance with the existing algorithm. The evaluation results demonstrated that MCSS provides 10% more services selection efficiency than Cloud Resource Bartering System (CRBS) and provides 16% more service selection efficiency than RPF.

Distributed multi-user QoS-aware service selection

Previous research on service selection in cloud environments often assumes a centralised broker that keeps track of all candidate services and selects the appropriate candidate for each user request based on quality of service (QoS). As the number of users and services increases, the centralised service selection algorithm becomes a bottleneck. We propose a distributed, multi-broker selection algorithm that enables real-time processing of a large number of user requests. Each broker keeps track of a subset of users and a subset of service candidates, and rebalancing is performed to ensure that no broker is overloaded. The selection procedure considers the user QoS requirements, service QoS values and their processing capacities. Simulations demonstrate a clear advantage of the proposed approach over a single-broker approach by up to 96% in terms of QoS satisfaction when using 100 brokers, and especially in terms of response time satisfaction, showing positive impact of user and service balancing, and analysing various selection algorithms run by the brokers.

A decentralized agent‐based semantic service control and self‐adaptation in smart health mobile applications

AbstractIn this article, we propose a decentralized agent‐based autonomic semantic service adaptation controller and reconfiguration (ASSACR) to solve the problem of context‐aware service selection and deployment for distributed healthcare mobile applications, where functional and nonfunctional users' needs are expressed using domain‐independent ontology. The main objective of this approach is to provide optimal personalized services as fast as possible. Indeed, a new ontology for semantic description and parallel management of the contextual services is presented. At first, a new dominance operator‐based constraints violation degree is used to reduce the search space. Then, optimal personalized paths that meet user's needs, preferences and devices' availability are discovered and selected in a smaller search space more effectively and efficiently using dynamic services selection algorithm, based on new ASSACR multiagent strategies. To validate the performance and efficiency of the proposed approach, we compared the proposed approach with existing multiagent strategies of the diabetic follow‐up use cases. Experimental results show that the proposed approach effectively addressed both the semantic service representation and agent aspects in terms of performance and quality service selection.

A hybrid multi-criteria decision making algorithm for cloud service selection

A hybrid multi-criteria decision making algorithm for cloud service selection

Research on Optimization Method of Computer Network Service Quality Based on Feature Matching Algorithm

On the whole, the main basis for strengthening the quality of network service is the construction of network system, reasonable arrangement of network and adjustment of information resources. In order to enhance the quality of network services, we should minimize the problems. In order to improve the quality of network, we should study more new network technologies and make up for network loopholes in time. In this study, in order to solve the efficiency problem brought by mass candidate services to quality-aware service selection, a service selection strategy based on feature matching algorithm is proposed. Based on statistical method, a probability matrix describing the mapping relationship between quality demand pattern and quality-oriented service pattern is established, and the service selection algorithm is improved by using the probability matrix. A series of experiments on artificial simulated data sets generated from real service data sets prove the feasibility and effectiveness of the proposed method.

Genetic algorithm for web services selection supporting quality of service

Genetic algorithm for web services selection supporting quality of service

QoS‐aware big service composition using distributed co‐evolutionary algorithm

AbstractBig services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way.

Service Discovery and Selection Based on Dynamic QoS in the Internet of Things

Web services are self-describing and self-contained modular applications based on the network. With the deepening of web service applications, service consumers have gradually increased their requirements for service functions and service quality. Aiming at how to select the optimal plan from a large number of execution plans with the same function and different QoS characteristics, this paper proposes a web service selection algorithm that supports QoS global optimization and dynamic replanning. The algorithm uses position matrix coding to represent all execution paths and replanning information of the service combination. By calculating the Hamming distance of the service quality between individuals, the quality of the service portfolio is improved. By specifying the total user time limit and implementing a good solution retention strategy, the problem of the impact of algorithm running time on service quality is solved. The experimental results show that the method proposed in this paper is effectively integrated into the development trend of QoS and close to the requester’s needs and can better meet user needs. This algorithm improves the user’s satisfaction with the returned service to a certain extent and improves the efficiency of service invocation.