Pool Of Services Research Articles

Features and Perspectives of the Market of Information and Communication Technologies’ Functioning in Data-Driven Society

The article is intended to define the conceptual prospects for the development of the information and communication technologies market in a data-driven society. According to the results of the study, it was established that the implementation of innovative technological startups for the reorganization of information and communication networks using modern switching equipment, characterized by high performance, remains a relevant and global task for the development of the target market. This will provide potential opportunities for providing a high level of service to several numbering zones at once and maintaining the availability of a wide range of information and communication services in the modern conditions of digitalization of society. It was determined that the sphere of communication and information occupies an important place in the economic and social revival of Ukraine. Broad access to significant amounts of information and database management ensures the efficiency of organization and management of state, production and non-production processes of economic activity, thereby expanding the possibilities of successful business, optimizing costs and ensuring a high level of economic and information security of the business entities. The information and telecommunications market were analyzed and the activity of participating operators was diagnosed. It has been studied that in a data-driven society, the success of achieving goals in the digital development of information and communication enterprises directly depends on global information influence. A significant reduction in the number of telecommunications operators and providers was analyzed. It was determined that small operators of the target market are not able to ensure the appropriate level of quality, availability and continuity of service, in connection with which a significant part of the target audience switches to the services of large providers. Based on the analysis of the main indicators of the development of the information and communication technologies market, the main promising directions of development based on the formation of income from a diverse pool of services are determined.

Learning and Balancing Unknown Loads in Large-Scale Systems

Consider a system of identical server pools where tasks with exponentially distributed service times arrive as a time-inhomogeneous Poisson process. An admission threshold is used in an inner control loop to assign incoming tasks to server pools, while in an outer control loop, a learning scheme adjusts this threshold over time to keep it aligned with the unknown offered load of the system. In a many-server regime, we prove that the learning scheme reaches an equilibrium along intervals of time when the normalized offered load per server pool is suitably bounded and that this results in a balanced distribution of the load. Furthermore, we establish a similar result when tasks with Coxian distributed service times arrive at a constant rate and the threshold is adjusted using only the total number of tasks in the system. The novel proof technique developed in this paper, which differs from a traditional fluid limit analysis, allows us to handle rapid variations of the first learning scheme, triggered by excursions of the occupancy process that have vanishing size. Moreover, our approach allows us to characterize the asymptotic behavior of the system with Coxian distributed service times without relying on a fluid limit of a detailed state descriptor. Funding: The work in this paper was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Gravitation Grant NETWORKS-024.002.003 and Vici Grant 202.068].

Designing Service Menus for Bipartite Queueing Systems

Designing Fair and Incentive-Compatible Matching Systems Many service systems face the challenge of dynamically matching customers who have heterogeneous preferences with an available pool of service providers in a fair and equitable manner. The first come, first served (FCFS) service discipline offers a straightforward and fair approach to allocate arriving customers to available servers and manage congestion. However, FCFS may limit the ability of service providers to reach an efficient allocation based on the individual preferences of customers and the characteristics of servers. In “Designing Service Menus for Bipartite Queuing Systems,” Caldentey, Hillas, and Gupta develop a queueing framework to address this dynamic matching problem. Their proposed methodology enables service providers to offer a menu of different service classes, each served by a distinct pool of servers. By thoughtfully designing this menu, a service provider can encourage self-interested customers to select service classes in a way that optimizes the trade-off between quality of matching and waiting time delays.

Sharing economy models and sustainability: Towards a typology

The sharing economy was initially beckoned as a facilitator of exchanges that would not compromise future needs and held great promise for those at the lower end of the socioeconomic pyramid. However, as the sharing economy expanded, questions about its sustainability emerged. This expansion manifested in two main forms: an influx of new users and providers into existing operations and the emergence of new platforms, resulting in a proliferation of sharing economy models. By categorizing these models based on their resource utilization, this paper establishes a connection between scalability and compromised sustainability, shedding light on the interplay between the two. The paper identifies seven distinct configurations in the sharing economy: co-use, re-use, repeated use, sustainable output, pooling of resources, and products and services created specifically for individual users. These configurations serve as a tool to uncover the tensions between scalability and coordination, as well as between sustainability and provision. The paper contributes to prior research by bringing attention to how the sharing economy is entangled in these tensions and by developing a typology. Understanding how these tensions can be resolved presents a highly significant practical contribution, allowing stakeholders in the sharing economy to navigate the challenges of scalability and sustainability effectively.

Queueing analysis of imbalance between multiple server pools with an application to 3-phase EV charging

We consider systems where multiple servers operate in parallel, with a particular feature: servers are classified into d classes, and we wish to keep approximate balance between the load allocated to each class. We introduce a relevant imbalance metric, and study its behavior under stochastic demands with different task routing policies. For random routing, we analyze two cases of interest, depending on whether capacity constraints are operative: we obtain expressions for the stationary distribution and analyze the scaling behavior of our metric as a function of system size. Subsequently, we analyze active routing to the least loaded class, obtaining sharp bounds for the imbalance metric. As a practical application, we study the problem of imbalance between d = 3 phases, for the service of electrical vehicle charging. We show the engineering relevance of our imbalance metric in this context, and validate the theoretical results with simulations and real traces from EV charging data.

Efficient Decentralized Multi-agent Learning in Asymmetric Bipartite Queueing Systems

New Algorithm Enables Efficient Decentralized Learning in Bipartite Queueing Systems Bipartite queueing systems, where agents with individual job queues request service from a pool of heterogeneous servers, are standard models for service applications like data networks and call centers. Traditionally, a central controller schedules agent requests with full knowledge of system parameters. However, emerging applications require decentralized operation without this central coordination or complete system information. This presents challenges as agents lack the global knowledge needed to efficiently route jobs. Recent research into efficient decentralized learning algorithms for such systems faces limitations in nonoptimal throughput, demanding computations, or degrading efficiency with exponential queue growth. In contrast, this paper introduces an algorithm that enables queues to efficiently learn decentralized scheduling policies while ensuring throughput optimality. The approach is computationally lightweight, achieving queue length bounds that scale polynomially rather than exponentially in system size. Experiments demonstrate faster convergence and robustness of our algorithm compared with prior decentralized algorithms.

The role of the IAEA in establishing international standards for the use of atomic energy (retrospective analysis)

Paradoxically, the peaceful use of nuclear energy and all the prospects it opens up for humanity often coexist with the prospect of nuclear proliferation and nuclear war. This difficult perception is understandable: the materials, knowledge, and expertise needed to produce nuclear weapons are generally no different from those needed to produce nuclear energy and conduct nuclear research.Therefore, ensuring the peaceful and safe use of nuclear energy has always been in the center of attention of the international community. The approach is determined by a complex of national and international measures. It is generally accepted that the primary responsibility for regulating the use of nuclear energy rests with national authorities, but it is also clear that other countries may also be affected. Thus, like many other human activities with potentially transboundary consequences, nuclear energy regulation requires a residual, and in some cases joint, responsibility on the part of the international community to ensure the harmonization of standards, coordination and pooling of resources and services, and regulatory cooperation this activity.In this regard, the IAEA and other international and regional organizations play the role of coordination centers. Article II of the IAEA Statute states that the organization shall seek to accelerate and increase the contribution of atomic energy to world peace, health and prosperity and shall provide, as far as possible, assistance upon request. IAEA supervision or control shall not be used to achieve any military objective.International peration in the field of nuclear energy over the past six decades has resulted in legally binding norms and advisory standards and regulations. This article outlines this global legal framework for the safe and peaceful development of nuclear energy. In particular, it concerns nuclear safety, radiation protection, radioactive waste management, transportation of radioactive materials, emergency assistance and planning, civil liability for nuclear damage, physical protection of nuclear material, armed attack on nuclear facilities, safeguards and controls carried out and provided by the IAEA.

On the Protection of a High Performance Load Balancer Against SYN Attacks** This is an extended journal version of [2

SYN flooding is a simple and effective denial-of-service attack. In this attack, many TCP SYN requests are sent to the targeted server, in an attempt to consume its resources and make it unresponsive to legitimate traffic. While SYN attacks have traditionally targeted web servers, they are also known to be very harmful to intermediate cloud devices, and in particular to stateful load balancers (LBs). Fighting against a SYN attack without negatively affecting legitimate connections is not easy, especially if the LB needs to perform frequent server pool updates during the attack, which is very likely since attacks can often last for many hours or even days. This paper is the first to propose LB schemes that guarantee high throughput of one million connections per second, while supporting a high pool update rate without breaking connections and fighting against a high rate SYN attack. Using an analysis and a proof of concept, we show that the LB can handle up to 10 million fake SYNs per second when the RTT is 10ms, and up to 5 million fake SYNs per second when the RTT is 20ms.

The Generalized c/μ Rule for Queues with Heterogeneous Server Pools

Dynamic Routing of Queues with Heterogeneous Server Pools In “The Generalized c/μ Rule for Queues with Heterogeneous Server Pools,” Long, Zhang, Zhang, and Zhang study the optimal control of queueing systems with heterogeneous server pools and a single customer class. The goal is to balance the holding cost of the queue with the operating costs of the server pools. They introduce the target-allocation policy, the Gc/μ rule, and the fixed priority policy for systems with general, convex, and concave cost functions, respectively. They also consider an extension to minimize operating costs and maintain a service-level target for customers waiting in the queue. Moreover, they show that their asymptotically optimal routing policies coincide with several classic policies in the literature in special cases.

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

<span>The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy.</span>

Dynamic congestion management system for cloud service broker

<span lang="EN-US">The cloud computing model offers a shared pool of resources and services with diverse models presented to the clients through the internet by an on-demand scalable and dynamic pay-per-use model. The developers have identified the need for an automated system (cloud service broker (CSB)) that can contribute to exploiting the cloud capability, enhancing its functionality, and improving its performance. This research presents a dynamic congestion management (DCM) system which can manage the massive amount of cloud requests while considering the required quality for the clients’ requirements as regulated by the service-level policy. In addition, this research introduces a forwarding policy that can be utilized to choose high-priority calls coming from the cloud service requesters and passes them by the broker to the suitable cloud resources. The policy has made use of one of the mechanisms that are used by Cisco to assist the administration of the congestion that might take place at the broker side. Furthermore, the DCM system is used to help in provisioning and monitoring the works of the cloud providers through the job operation. The proposed DCM system was implemented and evaluated by using the CloudSim tool.</span>

A new remote web-based MDSplus data visualization system for EAST

WebScope has been widely used in the Experimental Advanced Superconducting Tokamak (EAST) experiment for many years. The choice of the Java applet technology for its implementation has been successful, which allows for pulse data communication and instant feedback through various web browsers. Accessing MDSplus data with it however brings some inconvenience due to the majority of browsers have dropped support for Java plugin. Recently, a new web-based MDSplus data visualization system with the MVVM (Model–View–ViewModel) architecture has been developed. The flexibility brought by using JavaScript middle components in the architecture reduces the program coupling and improves the system availability. A further availability is proposed here, that is, using reverse proxy mechanism to publish the abstract interfaces provided by MDSplus separately from the visual resources, with the proxy to specify data request relationships. For incoming network traffic in the server pool, effective load balancing is ensured. The slice storage mechanism in MDSplus and thumbnail are now used for storing and reading long pulse data, and exporting data through proxy ports which ensures secure access to experimental data. Pulse access variables are also stored in cloud server for authorized users to share across devices. The system was successfully applied to EAST experiment and achieved good performance. The design details of the system are described in this paper.

Utility Maximizing Load Balancing Policies

Consider a service system where incoming tasks are instantaneously dispatched to one out of many heterogeneous server pools. Associated with each server pool is a concave utility function that depends on the class of the server pool and its current occupancy. We derive an upper bound for the mean normalized aggregate utility in stationarity and introduce two load balancing policies that achieve this upper bound in a large-scale regime. Furthermore, the transient and stationary behavior of these asymptotically optimal load balancing policies is characterized on the scale of the number of server pools in the same large-scale regime. Funding: This work was supported by the Netherlands Organization for Scientific Research (NWO) through [Gravitation Grant NETWORKS-024.002.003] and [Gravitation Grant Vici 202.068]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsy.2022.0103 .

A partially flexible routing strategy for assigning emergency department patients to inpatient wards

Increasing flexibility in assigning emergency department patients to inpatient wards shortens the boarding time, which is known as an important driver for emergency department overcrowding crisis, whereas high levels of flexibility damages quality of care, adversely affects staff satisfaction, and complicates the implementation. We propose a partially flexible routing strategy to balance the trade-offs between positive and negative aspects of flexibility. We consider a partially flexible two-class queue with two heterogeneous server pools as our base model and analyze it by adopting a Matrix Analytic approach and derive a closed-form expression for the performance measures of this system by utilizing probability-generating functions. Using the base model, we develop a partially flexible routing algorithm that forms a bipartite graph that is both connected and cyclic. Connectivity and cyclicity alongside other properties of this algorithm offer comparable boarding times, a high level of quality of care, simplicity in practice, and staff satisfaction. By considering a fully flexible structure, where all patients can be assigned to all inpatient wards, as a benchmark, our simulation study results show that the performance of our proposed policy is similar to a fully flexible design in reducing boarding time under various system settings.

Edge Computing-Enabled Internet of Vehicles: Towards Federated Learning Empowered Scheduling

Classical edge computing algorithms assume that the execution time is always known in resource allocation. However, in practice, the execution time in the edge server is hard to estimate due to the complex environment, especially in Internet of Vehicles (IoV), which makes resource allocation a significant challenge. To address this problem, we propose an optimal resource allocation approach based on Federated Learning (FL). In our proposed approach, we consider both the delay and energy consumption. First, we assume that we have perfect knowledge about the execution time in the edge server, and we obtain the optimal CPU cycles which should be assigned to process 1-bit task. We also verify the Poisson property of the task arrival for each server pool in the edge server. Since we use Delay Energy Product (DEP) as our optimization target for resource allocation, we can derive the optimal balanced delay and energy performance in the scheduling. Next, we propose the Federated Learning based method to estimate the execution time in the edge server. Simulation results obtained demonstrate the effectiveness of our proposed approach.

Uniform stability of some large-scale parallel server networks

In this paper we study the uniform stability properties of two classes of parallel server networks with multiple classes of jobs and multiple server pools of a tree topology. These include a class of networks with a single nonleaf server pool, such as the ‘N’ and ‘M’ models, and networks of any tree topology with class-dependent service rates. We show that with \(\sqrt{n}\) safety staffing, and no abandonment, in the Halfin–Whitt regime, the diffusion-scaled controlled queueing processes are exponentially ergodic and their invariant probability distributions are tight, uniformly over all stationary Markov controls. We use a unified approach in which the same Lyapunov function is used in the study of the prelimit and diffusion limit. A parameter called the spare capacity (safety staffing) of the network plays a central role in characterizing the stability results: the parameter being positive is necessary and sufficient that the limiting diffusion is uniformly exponentially ergodic over all stationary Markov controls. We introduce the concept of “system-wide work conserving policies," which are defined as policies that minimize the number of idle servers at all times. This is stronger than the so-called joint work conservation. We show that, provided the spare capacity parameter is positive, the diffusion-scaled processes are geometrically ergodic and the invariant distributions are tight, uniformly over all “system-wide work conserving policies." In addition, when the spare capacity is negative we show that the diffusion-scaled processes are transient under any stationary Markov control, and when it is zero, they cannot be positive recurrent.

Hybrid eagle strategy for QOS-based cloud service composition

Web Services are the collections of open protocols and standards used for exchanging data between applications and systems. Cloud computing is a prominent computer technology, and technical users are receiving most of the benefits from it as it is dispersing information technology based capabilities to the customers in terms of ‘web services’ with flexibility and ease of use. As the use of cloud computing grows on a daily basis, service deployment also improved on a daily basis. Cloud computing has made significant progress and has advanced to the next stage of growth by adding new technologies to fulfil the current demands of consumers where the consumers can make ease of using resources in the form of platform, software or infrastructure on demand without administrating the underlying intricacy of the technology. Nowadays, multiple services provided by cloud platforms with analogous efficacies are available to the users which can be attributed as Quality of Service (QoS) parameters. Selecting an optimal cloud service which can satisfy the consumers’ requirements is often found to be a tedious task and quiet challenging. Diverse metaheuristic has been put forward in resolving the problem. To address this problem, in this paper we proposed a Multi-Criteria Decision Making methodology with a technique Simple Additive Weighting (SAW) which can help customer in determining the most preferable cloud service among the pool of existing service according to their satisfaction. As the same time we propose an enhanced Eagle Strategy with Whale Optimization (ESWOA) methodology to maintain equilibrium among local as well as global optimum. This system can recommend time-saving dependable trustworthy cloud services from a pool of accessible cloud services.

Hybrid multi criteria decision methods for optimal cloud selection in mobile cloud computing

Cloud computing provides the users with flexibility of using computational systems on demand at nominal cost. Execution of resource intensive task on mobile devices is still a big challenge due to resource constraints of mobile devices. Utilizing the servi ces of resource rich cloud servers for offloading the task from mobile device can overcome the resource constraint issues of mobile devices. When offloading the task, there is a need to choose an optimal cloud server from a pool of available cloud servers offering similar services. The proposed work uses three hybrid multicriteria decision methods for choosing the optimal cloud server and comparative analysis of the three methods are presented. Real time cloud servers located at four different regions are c onsidered with six quality of service ( QoS) attributes. The results indicate that all the three methods are viable solutions in selecting the optimal cloud server with multi - objective optimization on the basis of ratio anal ysis (MOORA) providing a faster response compared to other two methods. Sensitivity analysis performed proves the correctness and effectiveness of the proposed methodology.

Analysis of a priority queueing system with the enhanced fairness of servers scheduling

AbstractA multi-server queueing system with two types of customers is analyzed. Both types of customers have own reserved servers and also there is a pool of servers that can be used by both types of customers. Type 1 customers have almost preemptive priority with flexible use of a priority depending on the current number of customers of this type in an infinite buffer. Type 2 customers do not have an input buffer and are lost in cases of absence of available servers upon arrival or expelling (forced termination) from service by Type 1 customers. A multi-dimensional Markov chain, which defines the dynamics of the considered system under the fixed total number of servers, numbers of reserved servers, a pattern of the correlated arriving processes, service rates, and the thresholds defining the mechanism of expelling Type 2 customers from service, is analyzed. A numerical example of solving an optimization problem based on the obtained results is considered. Results can be used for the elaboration of enhanced protocols of servers scheduling in many real-world systems including cognitive radio networks with channels leasing.

Study of a linear-physical-programming-based approach for web service selection under uncertain service quality

Abstract To promote the practical use of web service technologies, it is critical to select a proper web service that will meet users’ preferences from a pool of web services with similar functions. It is usually a core issue during web service selection to describe and evaluate the quality of these web services. Average value can be employed to describe certain service quality and to evaluate the quality with multiple criteria through the Simple Additive Weight Method. Based on an analysis of various deficiencies in existing approaches proposed in some studies, four indicators were used: Price, Response Time, Reliability and Credibility. On top of that, recent sampling data sets were introduced to describe the uncertain quality of service (QoS) criteria; linear physical programming was used to evaluate QoS with comprehensive criteria to meet the need to objectively describe uncertain QoS criteria, and preference regions were set to adapt to interval characteristics of criteria. Finally, a computational example is provided to demonstrate the feasibility and effectiveness of the method.