Service Requests Research Articles

CEC: A Containerized Edge Computing Framework for Dynamic Resource Provisioning

Container has been widely used in application development and management systems. However, there are two major challenges faced in the real deployment at edge servers. The varying workload of service requests and the startup delay of containers force a flexible resource provisioning scheme in containerized edge computing. To this end, we propose CEC, a containerized edge computing framework for dynamic resource provisioning, and especially for the smart connected community where exists multiple intelligent applications. CEC integrates workload prediction and resource pre-provisioning to enable low latency of user service requests and high utilization of edge resources. First, we present an online periodic request prediction algorithm. Then, we designed a control-based resource pre-provisioning algorithm based on the predicted request distribution, which is a self-adaptive controller to tune the resource for containers. We evaluate the performance of CEC by simulation and system experiments. The simulation experiment shows that the prediction accuracy of the proposed algorithm is higher than other two prediction algorithms. The testbed experiments demonstrate that the control-based resource pre-provisioning algorithm has low service latency and high resource utilization compared with baselines.

Development of Machine Learning Based State-of-Charge Prediction Model for Plug-in Electric Vehicle's Relocation in Sharing System

This paper presents a Plug-in electric vehicle (PEV) relocation strategy in a sharing system considering machine learning based state-of-charge (SOC) prediction model. The proposed work involves the machine learning approaches like the Adaboost algorithm and XGBoost algorithm for PEVs SOC prediction in relocation problems for the first time. Also, the low-capacity batteries of PEVs are considered as it is affordable by the common people with less charging time. It is also believed that the large number of low capacities PEVs based taxi owners in metropolitan cities will be attracted towards relocation-based sharing system considering machine learning based SOC prediction model. The customer request for sharing PEVs will be reflected in the management system at the beginning. Assuming all the PEVs with a certain level of SOC during pre-relocation are connected to the management system. The customer request for PEVs service will be assigned to the nearest PEVs depending on the predicted value of SOC by machine learning approaches. The machine learning-based predicted value of PEVs SOC will help the management system to identify the nearest PEVs for the customer sharing system. The present work steps are listed here. Firstly, the PEVs with higher predicted SOC value by machine learning have been shared with the customer from the nearest location considering descending order of their SOC. Further, the rest of the PEVs will be assigned to the nearest charging station by the PEV sharing management system. Secondly, the customer request is assigned from the station to relocate PEVs with higher SOC values in descending order. Different case studies are assumed including one practical scenario of Mumbai, India. The optimization problem of multi-depot vehicle routing problem (MDVRP) with the minimization of relocation cost has been solved by IBM CPLEX optimizer in this work. Finally, the proposed work efficacy has been demonstrated with some validated results including less relocation costs for all the case studies under consideration with lower capacity based PEVs.

Online Service Request Duplicating for Vehicular Applications

Vehicles on roads have increasingly powerful computing capabilities and edge nodes are being widely deployed. They can work together to provide computing services for onboard driving systems, passengers, and pedestrians. Typical applications in vehicular systems have service requirements such as low latency and high reliability. Most studies in vehicular networks concerning latency and reliability focus on vehicular communication at the network level. Based on these fundamental works, an increasing proportion of vehicles boast complex applications that require service-level end-to-end performance guarantees. Several works guarantee service-level latency or reliability while new and innovative applications are demanding a joint optimization of the above two metrics. To address the critical challenges induced by the joint modeling of latency and reliability, system uncertainty, and performance and cost trade-off, we employ service request duplication to ensure both latency and reliability performance at the service level. We propose an online learning-based service request duplication algorithm based on a multi-armed bandit framework and Lyapunov optimization theory. The proposed algorithm achieves an upper-bounded regret compared to the oracle algorithm. Simulations are based on real-world datasets and the results demonstrate that the proposed algorithm outperforms the benchmarks.

Model-Driven Dependability Assessment of Microservice Chains in MEC-Enabled IoT

Multi-access edge <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> omputing (MEC)-enabled Internet of Things (IoT) is considered as a promising paradigm to deliver computation-intensive and delay-sensitive services to users. IoT service requests can be served by multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> icro <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ervices (MSs) that form a chain, called a micro <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ervice <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> hain (MSC). However, the high complexity of MSs and security threats in MEC-enabled IoT pose new challenges to MSC dependability. Proactive rejuvenation techniques can mitigate the impact of resource degradation of MSs and host <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</i> perating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ystems (OSes) executing them. In this paper, we develop a multi-dimensional semi-Markov model to investigate the effectiveness of proactive rejuvenation techniques in improving the dependability (availability and reliability) of a dynamic and heterogeneous MSC. The results of numerical experiments firstly reveal how MSs can be effectively combined, in different deployment configurations, with host OSes to improve MSC dependability, secondly jointly optimize the rejuvenation trigger intervals of host OS and MSs running on it, and finally show the impact of time-varying parameters. We also identify the bottlenecks for MSC dependability improvement by sensitivity analysis, and give the ranges of important parameter values guaranteeing five-nines availability. In addition, the superiority of our model is demonstrated by comparison with the continuous-time Markov chain model.

Application of Crowdsourcing in User Experience collection – a case study of Malayalam mobile applications

PurposeThis paper aims to prove the following hypothesis Problem Statement: HYPOTHESIS (1) User Experience collection of mobile applications can be done using the Crowdsourcing mechanism; (2) User Experience collection of mobile applications are influenced by the mindset of Crowdmembers, culture/ethnicity/social background, ease of interface use and rewards, among other factors.Design/methodology/approachThe authors of this paper, did a literature review first to find if Crowdsourcing was applicable and a used method to solve problems in Software Engineering. This helped us to narrow down the application of Crowdsourcing to the Requirements Engineering-Usability (User Experience) collection. User experience collection of two Malayalam language-based mobile applications, AarogyaSetu and BevQ was done as the next step. Incorporating findings from Study I, another study using AarogyaSetu and Manglish was launched as Study II. The results from both cases were consolidated and analyzed. Significant concerns relating to expectations of Crowd members with User Experience collection were unraveled and the purpose of Study was accomplished.Findings(1) Crowdsourcing is and can be used in Software Engineering activities. (2) Crowd members have expectations (motivating factors) of User Interface and other elements that enable them to be an effective contributor. (3) An individual’s environment and mindset (character) are influential in him becoming a contributor in Crowdsourcing. (4) Culture and social practices of a region strongly affects the crowd-participating decision of an individual.Originality/valueThis is purely self-done work. The value of this research work is two-fold. Crowdsourcing is endorsed significant in Software Engineering tasks, especially in User Experience collection of mobile applications. Two, the Crowd service requesters can be careful about designing the questionnaire for Crowdsourcing. They have to be aware and prepared to meet the expectations of the Crowd. This can ensure the active participation of potential contributors. Future researchers can use the results of this work to base their research on similar purposes.

A secure physical health test data sharing scheme based on token distribution and programmable blockchains

With the rapid development of computers and networks, more and more convenient services are provided relying on the Internet. In this paper, we focus on the data sharing in students’ physical health tests. To reduce the burden of filling in information manually, physical health test service providers have designed a mode of obtaining students’ information, including personal information and test results, from a trusted third-party tester for gathering all physical health items and automatically filling. This mode is called one network unified management. However, if the trusted third-party organization sends too much unnecessary information to service requester, dishonest requester will maliciously use the students’ redundant information, which causes great security risk to students’ information. To solve the problem, this paper designs a token distribution scheme adapted to that mode based on programmable blockchains. Secure and fast data sharing is approached. Both traceability and evidence collection are available. For a network with 500 participants and size of maximum token set is 5×210, data sharing can be completed within 13.04 microseconds while traceability can be completed within 3.61 microseconds of extra time consumption. Ensuring safety and traceability, the time consumption of the scheme is proved to be linear with the increasing population of participants. It is also tested with real physical health test data, proving that the scheme is available in reality.

Towards deploying SFC with parallelized VNFs under resource demand uncertainty in mobile edge computing

The increasing demand in Mobile Edge Computing (MEC) networks has led to the emergence of Network Function Virtualization (NFV) technology. Through the virtualization technique, NFV can decouple each Virtual Network Function (VNF) from the hardware and deploy it flexibly in the MEC. Each service request in NFV assembles a Service Function Chain (SFC) by tying different VNFs and processes the traffic in the chained order. How to allocate resources and deploy SFC in MEC to meet Quality of Service (QoS) requirements is an important challenge for NFV. An increase in the number of VNFs with traffic passing one after the other causes high delay in SFC assembly. Also, the demanded resources may change during SFC assembly, which is clearly neglected in existing works. Therefore, high delay and resource demand uncertainty during SFC assembly affect QoS. With this motivation, we solve the SFC deployment problem by Parallelizing VNFs under Resource Demand Uncertainty (PVRDU) in MEC. We use the dependency between VNFs to transform the original SFC into a parallel SFC and then assemble several sub-SFCs based on a Deep Reinforcement Learning (DRL) approach. Meanwhile, PVRDU uses a Markov-based approximation algorithm to handle resource demand uncertainty. We perform extensive trace-driven simulations to verify the effectiveness of the proposed algorithm. The evaluation results of PVRDU are promising in different aspects compared to state-of-the-art methods. Specifically, for the considered scenario, DPC reduced SFC delay by as much as 8.7%.

The Effects of Social-Withdrawal Characteristics Among Older Academics: An Indonesian Case Study

Indonesia, the world’s fourth-most populous country, sees a significant increase in its aging population each year. This demographic shift is reflected in the country’s public higher-education sector, where the number of older academics has been growing substantially in recent years. However, older people’s social participation is often limited due to certain forms of withdrawal, such as shyness, avoidance, and unsociability. This research report aims to explore the impact that these social-withdrawal characteristics among older people may have on data collection and policy-formulation processes in public universities in Indonesia. To gather data for this study, an online questionnaire was employed. The results of this study suggest that older people’s social-withdrawal characteristics can have two effects. First, older individuals are often reluctant to participate in research, particularly as online questionnaire respondents, which can result in a lower response rate. Second, our study found that most older academic staff were unwilling to request or demand specific services or facilities from university administration, making decision-makers unaware of the needs of their older staff and aging issues in general. As a consequence, university administration may not consider instituting an aging policy at their institution, as they believe that older workers do not require special attention. This lack of awareness can have serious implications for the well-being of older academic staff and the overall effectiveness of university policies.

Survey of mayors cite MH crisis a top concern

An “unprecedented” mental health crisis is overwhelming U.S. cities, which lack adequate resources to address growing challenges, according to a new report released today by the U.S. Conference of Mayors, the Bloomberg news agency reported June 3. In recent years, the COVID‐19 pandemic has exacerbated mental health issues, particularly those involving substance abuse, said a survey of mayors of 117 cities in 39 states. “The report also cited “staggering increases in stress, depression, isolation, loneliness and accompanying mental health hurdles faced by Americans of all ages.” In a survey conducted this spring, 97% of mayors said requests for mental health services increased in their city in the past two years, but 88% lack resources to address the crisis. Substance use disorders topped the list of mental and behavioral health problems in 65% of cities, followed by homelessness stemming from mental illness in 56%. Other challenges included shortages of mental and behavioral health workers, including school counselors, as well as a lack of access to behavioral health services. Although the vast majority of cities reported inadequate mental health resources, 82% have developed new initiatives and/or increased funding to mental health programs. Ninety‐three percent reported that they have improved their emergency response to behavioral health crises. Meanwhile, 94% of cities said their police department provides mental health programs to officers.

HARMONIC: Shapley values in market games for resource allocation in vehicular clouds

Real-time allocation of resources to fulfill service requests from road vehicles is becoming increasingly complex, for two main reasons: the continuous increase in the number of Internet-connected vehicles on roads all over the world, and the emergence of complex and resource-greedy applications that require fast execution, often under limited availability of computational resources. While many resource allocation solutions to this problem have been proposed recently, these solutions rely on unrealistic scenarios and constraints that limit their practical use.This paper presents HARMONIC, a Game Theory-based coalition game that aims to maximize resource utilization and dynamically balance resource usage across multiple Vehicular Clouds (VCs). HARMONIC employs a Shapley value-based strategy to determine the order of task allocation to available resources. It is built upon our proposed Market Game model, specifically designed to address resource allocation challenges in dynamic VCs. We conduct a comparative analysis with existing literature solutions under various scenarios and resource constraints to evaluate HARMONIC’s performance. Our simulation results demonstrate that HARMONIC achieves resource allocation in fewer rounds and with fewer failures. Furthermore, it effectively distributes tasks to more VCs, improving load balancing and overall system efficiency.

Digital Twin Concept, Method and Technical Framework for Smart Meters

Smart meters connect smart grid electricity suppliers and users. Smart meters have become a research hotspot as smart grid applications like demand response, power theft prevention, power quality monitoring, peak valley time of use prices, and peer-to-peer (P2P) energy trading have grown. But, as the carriers of these functions, smart meters have technical problems such as limited computing resources, difficulty in upgrading, and high costs, which to some extent restrict the further development of smart grid applications. To address these issues, this study offers a container-based digital twin (CDT) approach for smart meters, which not only increases the user-facing computing resources of smart meters but also simplifies and lowers the overall cost and technical complexity of meter changes. In order to further validate the effectiveness of this method in real-time applications on the smart grid user side, this article tested and analyzed the communication performance of the digital twin system in three areas: remote application services, peer-to-peer transactions, and real-time user request services. The experimental results show that the CDT method proposed in this paper meets the basic requirements of smart grid user-side applications for real-time communication. The container is deployed in the cloud, and the average time required to complete 100 P2P communications using our smart meter structure is less than 2.4 seconds, while the average time required for existing smart meter structures to complete the same number of P2P communications is 208 seconds. Finally, applications, the future development direction of the digital twin method, and technology architecture are projected.

Smart Meter Development Using Digital Twin Technology for Green Energy Distribution Optimization

This study proposes a digital twin (DT) approach and technical framework for smart meters to solve potential implementation and development problems and adapt to the new energy revolution trend and increase smart grid network security. DT models were deployed in the cloud and edge using a smart meter DT demonstration system. This paper evaluates the DT system's communication performance in real-time smart grid application through three dimensions: remote application service for smart grid user side, P2P transaction on the user side, and user real-time request service. This study's container-based decision tree strategy for smart meters meets the smart grid's real-time communication requirements for user-side applications.

Layer-Aware Containerized Service Orchestration in Edge Networks

Edge computing provides computational resources in the vicinity of end-users to reduce delay compared to traditional remote clouds. However, the capacity of edge resources usually is not sufficient for the required computational demands. Therefore, it is necessary to design methods for employing these resources in an efficient manner. On the other hand, network function virtualization (NFV) is a promising solution to use the network resources in a more flexible way than traditional schemes. Although more focus has been on realization of NFV systems via virtual machines so far, recent studies show that container-based solutions can improve efficiency thanks to lightweight implementation and layered structure of containers. Nonetheless, to the best of our knowledge, there is no comprehensive study on the problem of orchestrating services composed of a chain of containerized network functions in edge networks. In this paper, we consider this scenario when service requests are submitted to the system and address important aspects of this problem such as downloading and sharing container layers and steering traffic among network functions. We present the formulation of the problem as an integer linear program (ILP) and prove its NP-hardness. Then, to handle this problem, we propose RCCO, a polynomial-time algorithm based on ideas from deterministic and randomized rounding framework. Our results from extensive evaluations show that the bandwidth consumption of the proposed algorithm compared to the optimal algorithm is higher by only about 4% while it can outperform baselines from literature by more than 37%.

Deep Q-Network-Based Cloud-Native Network Function Placement in Edge Cloud-Enabled Non-Public Networks

Owing to the advantages of satisfying service requirements and providing strong security, non-public networks (NPNs) are considered as a promising technology in vertical industries. However, to efficiently manage cloud-native network functions (CNFs) in NPNs, a sophisticated control plane management scheme should be designed. In this paper, we propose a deep Q-network-based CNF placement algorithm (DQN-CNFPA) that jointly minimizes the costs incurred by launching and operating CNFs in edge clouds and the backhaul control traffic overhead. In addition, DQN-CNFPA learns the spatiotemporal patterns in service requests and adaptively places CNFs in edge clouds according to the expected incurred costs. The evaluation results demonstrate that DQN-CNFPA can reduce the total cost by up to 26.2% compared with a conventional scheme that does not learn spatiotemporal service request patterns.

Emergence of Social Norms in Metanorms Game With High-Order Interaction Topology

In an autonomous system, rational agents aim to maximize their benefits. Rational agents gradually give up cooperation with others and only request services, which leads to the phenomenon of free-riding. This phenomenon generally exists in the real society and application system. Therefore, how to reduce the behavior, which is similar to free-riding, has become an important research topic. In addition, the traditional pairwise interaction network cannot truly reflect the interaction properties of cooperative events. Social norms provide an effective way to promote cooperation for autonomous systems. Axelrod's metanorm game model achieves the emergence of social norms by conducting punishment to connivance based on punishing defection. However, links in networks only allow for pairwise interactions. Therefore, we introduce hypergraphs, which are more suitable to reflect group interaction to model metanorm games. The establishment efforts of norms are examined on hypergraphs (uniform random hypergraph (URH), hyperdegree-heterogeneous random hypergraphs, and real-world hypergraphs). We show that the difference in group sizes affects norms emergence of agents and realize the establishment of social norms on URH. To a certain extent, the probability of being seen is positively correlated with agents' vengefulness and learning time and negatively correlated with boldness. However, the usage of the boldness and vengefulness learning (BV-learning) algorithm on HRHs cannot make norms emergence because leavers cannot participate in learning and the fixed learning step. Therefore, we propose the dynamic relevance BV-learning algorithm to overcome the aforementioned problems, so hub agents and leavers can jointly establish better social norms. We also verify the evolution process of agents' cooperation rate by BV-learning and the dynamic relevance BV-learning, respectively, which demonstrate that the establishment of social norms can indeed promote agents' cooperation. Finally, in order to illustrate the universality of the dynamic relevance BV-learning clearly, we study norms establishment on real-world hypergraphs by and compare it with BV-learning. It can be found that the dynamic relevance BV-learning can effectively establish social norms on real-world hypergraphs.

FLAME: Trusted Fire Brigade Service and Insurance Claim System Using Blockchain for Enterprises

Smart fire detection and insurance systems have gained considerable attention from researchers and industries. At the same time, automatic requests for fire brigade services to cure fire and instant claims settlement to defend insurance fraud are lacking in literatures. We propose a trusted fire brigade service and insurance claim (FLAME) framework using blockchain for enterprises to provide immediate fire brigade services and prevent insurance frauds. A system model is presented to explain architecture, and overall functionality of FLAME using blockchain. Further, a sensing network and connectivity model is proposed to detect true fire and send an emergency service request to monitoring station. Smart contracts are designed to automate fire brigade service and insurance claim processes. A prototype of the FLAME is implemented on Hyperledger Besu blockchain using Istanbul Byzantine Fault Tolerance 2.0 consensus protocol. Simulation results show that latency and throughput of the FLAME are better compared to state-of-the-art models.

Using an Estimation of Distribution Algorithm to Achieve Multitasking Semantic Web Service Composition

Web service composition composes existing web services to accommodate users’ requests for required functionalities with the best possible quality of services (QoS). Due to the computational complexity of this problem, evolutionary computation (EC) techniques have been employed to efficiently find composite services with near-optimal functional quality (i.e., quality of semantic matchmaking, QoSM for short) or non-functional quality (i.e., QoS) for each composition request individually. With a rapid increase in composition requests from a growing number of users, solving one composition request at a time can hardly meet the efficiency target anymore. Driven by the idea that the solutions obtained from solving one request can be highly useful for tackling other related requests, multitasking service composition approaches have been proposed to efficiently deal with multiple composition requests concurrently. However, existing attempts have not been effective in learning and sharing knowledge among solutions for multiple requests. In this paper, we model the problem of collectively handling multiple service composition requests as a new multi-tasking service composition problem and propose a new Permutation-based Multi-factorial Evolutionary Algorithm based on an Estimation of Distribution Algorithm (EDA), named PMFEA-EDA, to effectively and efficiently solve this problem. In particular, we introduce a novel method for effective knowledge sharing across different service composition requests. For that, we develop a new sampling mechanism to increase the chance of identifying high-quality service compositions in both the single-tasking and multitasking contexts. Our experiment shows that our proposed approach, PMFEA-EDA, takes much less time than existing approaches that process each service request separately, and also outperforms them in terms of both QoSM and QoS.

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.

Admission and Placement Policies for Latency-Compliant Secure Services in 5G Edge–Cloud System

This article proposes an optimal admission and placement stochastic controller that inserts security and latency compliance in the operational aspects of edge–cloud system under a fifth generation (5G) deployment. The proposed mechanism uses the framework of semi-Markov decision making process and seeks for an optimal policy that efficiently allocates the virtual resources to secure and run the services across the cloudified infrastructure. Driven by a new latency-oriented cost structure, the optimal controller achieves a secure and latency compliant operation by optimally balancing the service requests between the edge and the cloud system taking into account the service profile, the workload, and the traffic load. A structural analysis of the optimal policy reveals its implementation friendliness, which is key for its deployment or derivation of suboptimal mechanisms. Numerical results unveil that the admission and placement decision making process does not adversely impact the performance of the admission decision making process. Finally, a cloudnomics analysis shows that the optimal cost can be further optimized by fine tuning the parameters of the proposed cost structure. In this respect, numerical results show a reduction of approximately 162% for some cases of the scenario under analysis.

Dynamic SFC Embedding Algorithm Assisted by Federated Learning in Space–Air–Ground-Integrated Network Resource Allocation Scenario

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart cities. The space-air-ground integrated network (SAGIN) provides a solution to address this challenge. However, SAGIN is heterogeneous, time-varying, and multi-dimensional information sources, making it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. This paper proposes a service provision method based on Service Function Chaining (SFC) to solve this problem. Network Function Virtualization (NFV) is essential for efficient resource allocation in SAGIN to meet the resource requirements of user service requests. We propose a federation learning (FL) based algorithm to solve the embedding problem of SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load to balance resource consumption. Then an SFC scheduling mechanism is proposed that allows SFC reconfiguration to reduce the service blocking rate. Simulation results show that our proposed FL-VNFE algorithm is more advantageous compared to other algorithms, with 12.9%, 2.52%, and 10.5% improvement in long-term average revenue, acceptance rate, and long-term average revenue-cost ratio, respectively.