Service Requests Research Articles

A REVIEW OF THE IMPACT OF COVID-19 ON SERVERLESS COMPUTING TECHNOLOGY

The Covid-19 pandemic had a profound effect on technology in general, and serverless computing is no exception. Covid-19 pandemic has shaped the field of serverless computing, and how technology has evolved in response. Serverless computing technology have been adapted to meet the need for remote working, and how the technology has changed in terms of scalability and cost-effectiveness. This pandemic has affected virtually every aspect of daily life as significant measures are being taken to limit the spread of the virus. The pandemic has changed not only the way companies operate, but also the way they have been able to survive. Studies indicate increased requests for cloud services ranging from resident users, particularly for telecommuting, entertainment, commerce, to education, and as a result, causing traffic shifts at the core of the Internet. Covid-19 had such a significant impact on cloud services that there is an unprecedented amount of demand for cloud service providers like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. This study used data from a variety of sources to analyse the impact of serverless computing during the pandemic and to justify its significance for a pandemic-affected business. It also reviewed the pre-Covid, Covid and post-Covid-19 era. Two survey reports were used in this study and the effect of Covid-19 on Serverless computing. This paper emphasizes the benefits and adoption of Serverless computing during the pandemic, in contrast to other studies that concentrated on the impact of the Covid-19 epidemic on the cloud computing environment.

Handling Efficient VNF Placement with Graph-Based Reinforcement Learning for SFC Fault Tolerance

Network functions virtualization (NFV) has become the platform for decomposing the sequence of virtual network functions (VNFs), which can be grouped as a forwarding graph of service function chaining (SFC) to serve multi-service slice requirements. NFV-enabled SFC consists of several challenges in reaching the reliability and efficiency of key performance indicators (KPIs) in management and orchestration (MANO) decision-making control. The problem of SFC fault tolerance is one of the most critical challenges for provisioning service requests, and it needs resource availability. In this article, we proposed graph neural network (GNN)-based deep reinforcement learning (DRL) to enhance SFC fault tolerance (GRL-SFT), which targets the chain graph representation, long-term approximation, and self-organizing service orchestration for future massive Internet of Everything applications. We formulate the problem as the Markov decision process (MDP). DRL seeks to maximize the cumulative rewards by maximizing the service request acceptance ratios and minimizing the average completion delays. The proposed model solves the VNF management problem in a short time and configures the node allocation reliably for real-time restoration. Our simulation result demonstrates the effectiveness of the proposed scheme and indicates better performance in terms of total rewards, delays, acceptances, failures, and restoration ratios in different network topologies compared to reference schemes.

A multi-UAV assisted task offloading and path optimization for mobile edge computing via multi-agent deep reinforcement learning

To tackle task offloading and path planning challenges in multi-UAV-assisted mobile edge computing, this paper proposes a task offloading and path optimization approach via multi-agent deep reinforcement learning. The primary goal is to minimize the overall energy consumption of the system and improve computational performance. Initially, we established a model for a multi-UAV-assisted mobile edge computing system that centrally manages the UAV network through software-defined networking technology. Subsequently, considering UAV load and fairness in user equipment-related services, we employ the multi-agent deep deterministic policy gradient algorithm to optimize task offloading and UAV path management, aiming at load balancing and reducing overall system energy consumption. Simulation results demonstrate our method’s effectiveness in reducing energy consumption and computation latency compared to benchmark algorithms. It ensures system efficiency, stability, and reliability, meeting mobile edge users’ service requests while utilizing computing resources efficiently.

A game-theoretic approach to mitigate charging anxiety for electric vehicle users through multi-parameter dynamic pricing and real-time traffic flow

While price-based demand response is universally recognized as crucial for managing electric vehicle charging load, the academic literature has explored diverse mechanisms for its implementation. Prior research has revealed that applying load management schemes based on price-based demand response programs results in higher scheduling costs for low or constant-energy consumers. In addition, the inherent uncertainties involved in arrival, service requirements, market pricing and user behaviours have posed serious challenges in managing real-time charging operations. To handle these challenges, this work has addressed the charging problem at the scheduling level by analysing arrival, waiting, service and departure requests. This work proposed a mathematical model to coordinate charging to minimize the total charging cost and time for a given number of vehicles. We have developed an advanced charging algorithm and introduced a multi-leader multi-follower Stackelberg game. This strategic model ensures a Nash equilibrium, aligning the interests of consumers (vehicles) and providers (charging stations), ensuring a fair and balanced charging system. Where, charging load demand, real-time pricing, charging location, and vehicle types are used as input parameters. In the first stage, the real-time pricing obtained from the Spanish Electricity network is used to schedule the charging load, whereas the second stage is dedicated to using the proposed multi-parameter pricing for customer satisfaction and reduced cost. The results demonstrate the superior performance of our proposed scheme compared to existing approaches. Finally, we present a real-life case study to illustrate the practical effectiveness of the proposed techniques. The study underscores the potential of our approach in real-world scenarios, offering a promising avenue for enhancing efficiency and user experience in charging systems.

Advancing orchestration synthesis for contract automata

Contract automata allow to formally define the behaviour of service contracts in terms of service offers and requests, some of which are moreover optional and some of which are necessary. A composition of contracts is said to be in agreement if all service requests are matched by corresponding offers. Whenever a composition of contracts is not in agreement, it can be refined to reach an agreement using the orchestration synthesis algorithm. This algorithm is a variant of the synthesis algorithm used in supervisory control theory and it is based on the fact that optional transitions are controllable, whereas necessary transitions are at most semi-controllable and cannot always be controlled. In this paper, we present advancements of the orchestration synthesis for contract automata. Notably, we identify the existing limits of the orchestration synthesis and propose a novel orchestration synthesis along with additional constructs to enhance the expressiveness and scalability of contract automata. The proposed advancements have been implemented and experimented on two case studies, one of which originates from the railway domain and the other is a card game.

Overcoming Communication Barriers in Clinical Care: A Digital Translation Platform.

Language barriers in healthcare can lead to misdiagnosis, inappropriate treatment, and increased medical errors. Efforts to mitigate these include using interpreters and translation tools, but these measures often fall short, particularly when cultural nuances are overlooked. Consequently, medical professionals may have to rely on their staff or patients' relatives for interpretation, compromising the quality of care. This formative pilot study aims to assess the feasibility of Translatly, a digital translation platform, in clinical practice. Specifically, the study focuses on evaluating: 1. how healthcare professionals overcome language barriers and their acceptance of an on-demand video telephony platform, 2. the feasibility of the platform during medical consultations, and 3. identifying potential challenges for future development. The study included ethnographic interviews with healthcare professionals and an observational pilot to assess the use of the Tranlatly platform in clinical practice. Translatly was developed to make real-time translation easy and accessible on both Android and iOS devices. The system's backend architecture uses Java-based services hosted on DigitalOcean. The app securely exchanges data between mobile devices and servers, with user information and call records stored in a MySQL database. An admin panel helps manage the system, and Firebase integration enables fast push notifications to ensure that healthcare professionals can connect with translators whenever they need to. The platform was piloted in a German university hospital with 170 volunteer non-professional translators, mainly medical students, supporting translation in over 20 languages, including Farsi, Dari and Arabic. Ethnographic research conducted by interviewing healthcare professionals in Frankfurt am Main and other German cities revealed that current practices for overcoming language barriers often rely on family members or digital tools like Google Translate, raising concerns about accuracy and emotional distress. Respondents preferred an on-demand translation service staffed by medically experienced translators, such as medical students, who understand medical terminology and can empathize with patients. The observational pilot study recorded 39 requests for translation services, 16 (41%) of which were successfully completed. The translations covered 6 different languages and were carried out by a team of 10 translators. Most requests came from departments such as infectious diseases (5, 31.25%) and emergency (4, 25%). Challenges were identified around translator availability, with 23 (59%) of requests going unanswered, which was further evidenced by user feedback. The pilot study demonstrates the feasibility of the Translatly platform in real-world healthcare settings. It shows potential to improve communication and patient outcomes by addressing language barriers. Despite its potential, challenges such as translator availability highlight the need for further development.

Enhancing intelligent transport systems: A cutting-edge framework for context-aware service management with hybrid deep learning

This study presents a comprehensive framework for optimizing intelligent transport systems (ITS) by integrating advanced communication and information technologies into vehicles, roads, and infrastructure. The primary goal is to enhance transportation efficiency, safety, and environmental sustainability while improving overall mobility for people and goods. Leveraging contextual information, the framework offers personalized, proactive services such as real-time traffic updates, route recommendations, and parking availability. Additionally, it enhances safety and security by providing early hazard warnings and adapting to changing road conditions. Our proposed framework utilizes the enhanced coral reef optimization (ECRO) algorithm to efficiently group vehicles for energy-saving data collection, maximizing information gathering efficiency. Collected data is then transmitted to a central data gathering center via a sink node optimized through the modified pelican optimization (MPO) algorithm, considering various vehicle node design constraints. An incident detection module accurately classifies and detects road incidents, enabling timely emergency service requests and alternate route recommendations. To facilitate incident detection, we introduce the deep Rigdelet neural network (DRNN), a novel deep learning technique tailored for decision-making in incident classification. We validate our framework's performance through NS-2 simulations using the SUMO traffic generator, demonstrating its effectiveness in meeting quality of service (QoS) metrics. Through comparative analysis with existing frameworks, our proposed approach stands out for its superior performance and ability to optimize ITS operations.

Prioritization-based delay sensitive task offloading in SDN-integrated mobile IoT network

Due to enormous growth of Internet of Things (IoT) in the last decade, the amount of data generated through smart devices is increasing exponentially. Fog computing has emerged as a potential technology to deal such a huge volume of data in which task offloading is the most important aspect which has attracted significant attention. Many research works have been carried out, however, task offloading with latency sensitivity, reliability and result migration over a mobile user environment is still not widely addressed. In this paper, we propose a method for delay-sensitive and fault minimized task offloading for service requests made through a mobile/vehicular end user environment implemented via Software Defined Network (SDN) controllers integrated with the fog layer. This is a novel multi-phased model involving determining the optimal number of SDN controllers, clustering of the fog nodes (FNs) on the basis of SDN proximities, task prioritization and Gravitational Search Algorithm (GSA) based target FN selection. The simulation outcomes of our proposed approach show that there is a reduction in delay by around 23%–30% and around 60%–80% lesser number of tasks unassigned in each round as compared to two base algorithms.

EC-NTD: Efficient countermeasure against DrDoS attacks with NAPT and two-stage detection in SDN-based networks

Distributed reflection denial of service (DrDoS) attacks are a prevalent and troublesome form of DDoS attack. Fake service requests trigger a flood of services responses, typically in large packet sizes, that are sent to targeted hosts via public servers. As public servers are legitimate, and their services are necessary for targeted hosts, DrDoS attacks cannot be effectively blocked through firewalls or most solutions to DDoS floods. This paper leverages the software-defined networking (SDN) technique and proposes an efficient countermeasure with NAPT and two-stage detection (EC-NTD) scheme to safeguard against DrDoS attacks, where NAPT refers to network address port translation. We consider that attack sources (i.e., botnet members that send fake requests to public servers) may be outside or inside an SDN-based network where DrDoS targeted hosts reside. To guard against external attacks, filtering rules and NAPT are used in gateways to distinguish normal responses from those caused by attacks. To recognize internal attacks, the controller detects anomalies in the quantity of requests and their source IP addresses. Additionally, the adaptive adjustment of the attack detection period length helps alleviate the controller’s load while maintaining effective defense. Simulation results reveal that the EC-NTD scheme can efficiently safeguard against DrDoS attacks with different services.

Towards a quantum-safe 5G: Quantum Key Distribution in core networks

The Service-Based Architecture (SBA) of the fifth generation (5G) of cellular networks introduced great advancements in flexibility, efficiency, and performance of modern networks, thanks to the distribution and softwarization of core network entities called Network Functions (NFs). Due to the distributed nature of 5G systems, these NFs are usually located in different sites to provide better Quality of Service (QoS) or specific services, which poses a great challenge regarding security. However, even though the 3GPP standard identifies security requirements by leveraging Transport Layer Security (TLS) authentication and OAuth2.0 authorization, current experimental and commercial deployments often disregard these aspects for the sake of simplicity and performance. In fact, even though data and communication protection is crucial, the implementation of security protocols and mechanisms complicates the deployments and may reduce the efficiency of 5G systems. This paper presents a novel solution that implements TLS for authentication and encryption leveraging entanglement-based Quantum Key Distribution (QKD), ensuring that all the communications between NFs in the 5G core network are secured by QKD keys, and are therefore quantum-safe. Additionally, this paper presents a QKD key repository using the Network Repository Function (NRF), which stores and relays TLS session keys to authenticated consumer NFs to access producer NFs in a fast but quantum-safe process. The solution was validated in a real hardware environment, and obtained results demonstrate that the proposed solution enhances the security of the SBA communications in an efficient way, reducing an 85% the service request time compared to the traditional TLS-based procedure, as well as 29.96% fewer bytes transmitted throughout the process.

A distance adaptive dual-hop routing algorithm for underwater optical wireless networks

Three main kinds of underwater wireless communication, which employ acoustic waves, radio frequency and optical waves, have attracted intensive research interests in recently years. Among them, the underwater optical wireless communication (UOWC) is characterized by high propagation speed and large transmission bandwidth. But, the optical waves in underwater environment are significantly affected by absorption and scattering effects, which limit their transmission range. In order to enhance the performance of UOWC, designing a transmission and energy efficiency routing algorithm has become a non-ignorable issue in UOWC. In this paper, a transmission distance adaptive dual-hop (TDAD) routing algorithm is proposed for underwater optical wireless networks (UOWNs) to improve their efficiency in packet-delivery and energy-consumption. Unlike the existing routing algorithms designed for UOWNs, which pre-set the transmission range of network nodes, the proposed TDAD algorithm adaptively selects the transmission range for each node according to the diversity of heterogeneous service requests and employs location and energy information in its dual-hop based routing procedure. Simulation results indicate that the proposed TDAD algorithm remarkably improves packet delivery rate with more balanced energy consumption when compared to the deviation angle-based single-hop (DAS) algorithm and the distributed sector-based (DS) routing algorithm.

Effectiveness of distributed stateless network server selection under strict latency constraints

We consider a set of network users (nodes), each generating latency-constrained service requests corresponding to the execution of computational tasks on servers positioned either within a cloud infrastructure or at the network edge. Within this framework, we systematically assess the efficacy of a distributed stateless server selection strategy, strategically performed by individual nodes. Leveraging principles from game theory, our study allows for a comparative analysis between the optimality achieved through globally orchestrated stateless allocation and a decentralized stateless server selection mechanism driven by the self-interested objectives of individual nodes.Our emphasis on stateless server allocation, rooted in a probabilistic selection framework between edge and cloud servers, stems from prior empirical revelations demonstrating the advantageous outcomes of determining the optimal distribution of edge and cloud tasks based on static network characteristics. Importantly, this determination occurs irrespective of the real-time network state.The suboptimal nature of the selfish allocation is quantified by the so called “price of anarchy,” a metric shown to approximate unity closely. This observation substantiates the justification for a distributed strategic implementation of stateless policies. This elucidation serves as a pivotal guide for crafting algorithms governing server selection, providing a quantitative validation of the efficacy inherent in distributed self-interested approaches.

A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption.

Optimized provisioning technique of future services with different QoS requirements in multi-access edge computing

With the present trend towards the use of microservices instead of monolithic software, future applications with diverse computational and delay-sensitive requirements, such as virtual reality (VR) or holographic communications (HC), will be redesigned based on loosely coupled microservices due to their more efficient deployment according to available resources. However, it remains a big challenge to provide such services under stringent quality of service (QoS) requirements at minimum time and cost and with an efficient utilization of the network. Presently, multi-access edge computing (MEC) has been proposed as a promising alternative to deploy future microservice-based applications to achieve faster response times while reducing the overall network load. However, network conditions, edge nodes state and service requests are dynamic, increasing the difficulty for the provisioning of services by MECs. In this context, we propose an optimization deployment heuristic (ODA) to obtain sub-optimal deployments in reasonable time, meeting the requirements of some representative future applications under study. In particular, according to latency requirements, there will be delay-tolerant and delay-sensitive applications, playing a major role regarding the alternatives for the deployment onto single or multiple MEC systems. The evaluation analysis shows that our proposal performs significantly better than other benchmark solutions in most of the metrics used for the experiments, especially in minimizing deployment costs in minimal time.

Car Rental System Authors:

The Car Rental System enables customers to reserve vehicles from anywhere globally. By providing personal information, customers can create accounts on the website and proceed to reserve cars according to their preferences. This online system automates manual procedures effectively, aiding customers in filling specific details as per their needs. The goal is to establish a website for customers to book vehicles and request services worldwide KEYWORDS: Car Rental System, Online Booking, PHP, PHPMYADMIN, XAMPP, Database Management, System Analysis, Feasibility Analysis, User Interface Design, Data Flow Diagram, Sequence Diagram, ER Diagram, Database Connectivity

The Medical Writing Center Model in an Academic Teaching Hospital.

Editing services within academic health centers are uncommon, and few studies have reported on their impact. In this article, we describe our medical writing center's editing service for faculty and trainees at a pediatric teaching hospital and associated outcomes of scholarly products (e.g., manuscripts and grants) over an 8-year period. Data for manuscripts and grant proposals edited by the writing center from 2015 through 2022 were collected electronically from our service request database. Outcome data on publications and grant proposals were regularly collected up to 12 months post-submission. Users were also asked if the writing center edits were helpful, improved readability, and if they planned to use the service in the future. From 2015 through 2022, the writing center received 697 requests, 88.4% to edit a document. Of the documents edited, 81.3% of manuscripts and 44.4% of grant proposals were successfully published or funded. When rating their experience, 97.8% of respondents rated the edits "helpful," 96.7% indicated the edits "improved readability," and 99.3% stated they planned to use the writing center in the future. Our results showed steady use of the writing center and high satisfaction with services. A writing center can be an effective tool to support psychology faculty development.

Migration-aware slot-based memory request scheduler to guarantee QoS in DRAM-PCM hybrid memories

Emerging hybrid memory technologies composed of non-volatile memories (NVM) and DRAMs exhibit significant access speeds and capacity improvement. High application performance is feasible by dynamic migration (or relocation) of pages (data) between the memory types. While NVM is used for its density during memory allocation, moving write-intensive pages from NVM to DRAM helps to improve the execution and response time of applications. Existing techniques propose solutions to dynamically identify the pages that need to be moved immediately or in regular intervals. Such an immediate or interval-based rigid migration regime may hamper the service of the regular memory requests, which in turn affects the memory service rate.To alleviate the impact on service rate and improve the quality-of-service (QoS) of the device, this paper proposes a scheduling method to identify the instant at which to migrate the eligible page. Along with regular memory requests, these eligible migration candidate pages are given reserved time slots by taking into account the current memory request rate. Our proposed methods aim to optimize the migration overheads by avoiding unnecessary migrations and at the same time guaranteeing future accesses to the migrated pages. This results in improved execution time and memory response time for the applications.

Makespan minimization for workflows with multiple privacy levels

With the advanced development of Metaverse, various service requests are required to be processed as soon as possible which contains a series of tasks with topology structure (workflow) and different privacy constraints. Due to the diversity and complex hierarchical structure of resources, how to rent suitable resources from cloud, edge, and local nodes to minimize the makespan of workflows is currently one of the key issues in service computing scenarios. Due to the limited computing resources of local devices, tasks need to be offloaded to edge or cloud servers. Based on task privacy constraints, it is challenging to determine the appropriate offloading nodes for each task in the workflow. In this paper, for workflows with multi-privacy level tasks, a privacy-preserving workflow scheduling algorithm based on clustering (PWCSA) is proposed to minimize the workflow makespan with limited bandwidth. Multiple tasks of different privacy levels are clustered into one task pipeline to reduce the data transmission time caused by limited bandwidth, thereby reducing workflow makespan. After statistically calibrating algorithm parameters over a comprehensive set of workflow instances, the proposed algorithms are compared with the state-of-the-art algorithms over five workflow-type instances. The results indicate that the proposed PWCSA algorithm outperforms other algorithms with acceptable computational time. Through a number of workflow instances, the system parameters are verified by analysis of variance methods.

Online Courses Aggregator by the Eyes of Users

The Internet space is replete with platforms that aggregate information about online courses and make ratings for them according to various parameters. There is a request for such services, because due to the growth of the online education market and the undeveloped culture of choosing high-quality products of this market, course aggregators seem to be a good opportunity not to make a mistake in deciding to buy a particular course. However, they are not always an effective help due to the opacity of their tools, a small set of characteristics for comparison and other reasons. The article presents theoretical approaches to the selection of educational products, provides an overview of some aggregators of online courses in order to develop an assumption about what properties this platform should have in order to meet the needs of users to choose an online course suitable for them. The practical significance of the study lies in the fact that the presented results will help in the development of such an aggregator that could take into account the main demands of consumers of the online education market and would be free from the disadvantages inherent in existing tools.

Predictive Service Provisioning With Online Learning in Wireless Edge Networks

Mobile Edge Computing (MEC) technology can be implemented at cellular base stations, enabling flexible and configurable provisions of services for mobile users to access. Nevertheless, the conventional solutions mainly focus on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">statical</i> service provisioning, which ignores the dynamic nature of the arriving service requests. In this work, we first conduct comprehensive data-driven observations on over 4 million service requests throughout 9,800 base stations. Our key findings suggest that users' demands intrinsically exhibit spatial and temporal patterns, which inevitably lead to performance degradation in statical service provisioning. Motivated by that, we design and implement MobiEdge, a predictive service provisioning system with online learning in wireless edge networks. We propose a graph embedding learning-based model for representation learning, thus to achieve accurate request prediction at different base stations. Then, based on the prediction of incoming service requests, we study the service provisioning reconfiguration problem, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , how to jointly optimize service placement and corresponding request scheduling across dual timescales, under constraints of network resources and the total budget. By leveraging the submodular technique, we transform the research issue into a submodular function maximization problem under the <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> -independence system constraint, where <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> is a positive constant related to the ratio of coefficients in constraint conditions. On this basis, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1/(1+q)$</tex-math></inline-formula> approximation algorithm with rigorous theoretical analysis on the bounded maximum utility. Extensive trace-driven evaluations are conducted over networks of different scales, and MobiEdge shows remarkable performance enhancements by achieving the accuracy of up to 98% in service prediction and an average utility of 92.9% to the optimal solution in service provisioning.