Large Number Of Concurrent Requests Research Articles

Designing a Multi-Level Caching Forward Index for Recommendation System Services

This paper introduces a forward index based on microservices, which effectively addresses the issue of forward read amplification in recommendation systems through a multi-layer caching mechanism. This allows the forward service to handle a large number of concurrent requests with limited resources while ensuring service stability and final value rate. The paper provides a detailed description of the forward index's data structure design, traffic penetration calculation, and overall service architecture design, dividing the system into three parts: SDK, proxy service, and storage, corresponding to data query, data processing, and data storage respectively. In the SDK design, three components are employed: Cache Slot, synchronous queue, and timer, supporting synchronous, asynchronous, and flexible request forms. The proxy service design handles heterogeneous data and online computation, supporting batch and asynchronous designs, and provides a dynamic data protocol to adapt to the characteristic needs of different microservices. The storage layer design considers performance, cost, and maintainability, utilizing a Cache & SSD model to cope with high-concurrency scenarios.

APR-QKDN: A Quantum Key Distribution Network Routing Scheme Based on Application Priority Ranking.

As the foundation of quantum secure communication, the quantum key distribution (QKD) network is impossible to construct by using the operation mechanism of traditional networks. In the meantime, most of the existing QKD network routing schemes do not fit some specific quantum key practicality scenarios. Aiming at the special scenario of high concurrency and large differences in application requirements, we propose a new quantum key distribution network routing scheme based on application priority ranking (APR-QKDN). Firstly, the proposed APR-QKDN scheme comprehensively uses the application's priority, the total amount of key requirements, and the key update rate for prioritizing a large number of concurrent requests. The resource utilization and service efficiency of the network are improved by adjusting the processing order of requests. Secondly, the queuing strategy of the request comprehensively considers the current network resource situation. This means the same key request may adopt different evaluation strategies based on different network resource environments. Finally, the performance of the APR-QKDN routing scheme is compared with the existing schemes through simulation experiments. The results show that the success rate of application key requests of the APR-QKDN routing scheme is improved by at least 5% in the scenario of high concurrency.

Research on Edge Cloud Load Balancing Strategy Based on Chaotic Hierarchical Gene Replication

Edge cloud is used to handle latency-sensitive services. However, due to the large number of concurrent requests for edge intensive tasks, the resource allocation strategy affects the stability of nodes. In addition to an adaptive resource allocation model based on chaotic hierarchical gene replication (CRPSO model), the concept of chaotic replication ratio is proposed. This study is divided into two parts. The first is to verify the algorithm verification of the simulation platform. By comparison, it is found that CRPSO reduces the CPU and bandwidth utilization by 43.7% and 62.7% on average, respectively, and the memory usage is also lower than other algorithms. Thereafter, we compared the CRPSO algorithm with the Kubernetes clustering algorithm. Experiments showed that the fitness of the CRPSO model is 33.7% higher than that of the comparison algorithm on average. The algorithm is superior to the cluster scheduling algorithm in terms of CPU utilization and memory utilization. Furthermore, the total variance of the two resources involved in this model improved significantly, reaching 69.8% on average. In addition, CRPSO also has great advantages in other aspects of CPU and memory. It is indicated that the model in this study is suitable for the scenario of edge large-scale requests.

XDBAuth: Blockchain Based Cross Domain Authentication and Authorization Framework for Internet of Things

The innovation of ubiquitous and pervasive computing helps service-oriented organizations in the realization of a virtual coalition. The virtual coalition is a set of IoT domains i.e., smart homes and smart hospitals that are linked together through communication lines to share resources. Such virtual coalitions need secure cross-domain permission delegation and access control mechanisms. In existing approaches, permission delegation and access control are performed at the resource owner domain or by a single trusted third party. This single trusted third party may fail to work or compromise. Therefore, it will collapse either the whole system or the security of the system. We propose xDBAuth, a decentralized Blockchain (BC) based permission delegation and access control framework for the Internet of Things (IoT). Also, we proposed a hierarchy of local and global smart contracts that perform permission delegation and access control for both internal and external user/IoT devices. Additionally, the proposed framework preserves an external user's privacy by allowing them to get authentication in their parent IoT domains. During authentication, Proof-of-Authenticity/Integrity (PoAI) mechanism is used to find and retrieve user/IoT device platform hashes stored on local BC. After successful authentication, BC authorizes the user/IoT device based on the validation of delegation policies stored on BC. We implemented the proposed framework using Node.js. The results show that the proposed xDBAuth is a lightweight framework with less computational overhead. xDBAuth produces high throughput in an environment having a large number of concurrent requests.

Virtual Replication of IoT Hubs in the Cloud: A Flexible Approach to Smart Object Management

In future years, the Internet of Things is expected to interconnect billions of highly heterogeneous devices, denoted as “smart objects”, enabling the development of innovative distributed applications. Smart objects are constrained sensor/actuator-equipped devices, in terms of computational power and available memory. In order to cope with the diverse physical connectivity technologies of smart objects, the Internet Protocol is foreseen as the common “language” for full interoperability and as a unifying factor for integration with the Internet. Large-scale platforms for interconnected devices are required to effectively manage resources provided by smart objects. In this work, we present a novel architecture for the management of large numbers of resources in a scalable, seamless, and secure way. The proposed architecture is based on a network element, denoted as IoT Hub, placed at the border of the constrained network, which implements the following functions: service discovery; border router; HTTP/Constrained Application Protocol (CoAP) and CoAP/CoAP proxy; cache; and resource directory. In order to protect smart objects (which cannot, because of their constrained nature, serve a large number of concurrent requests) and the IoT Hub (which serves as a gateway to the constrained network), we introduce the concept of virtual IoT Hub replica: a Cloud-based “entity” replicating all the functions of a physical IoT Hub, which external clients will query to access resources. IoT Hub replicas are constantly synchronized with the physical IoT Hub through a low-overhead protocol based on Message Queue Telemetry Transport (MQTT). An experimental evaluation, proving the feasibility and advantages of the proposed architecture, is presented.

QoS Based Cloud Service Selection to Handle Large Volume of Concurrent Requests

Background/Objectives: With the exponential growth of cloud providers and users, the possibility of arrival of large number of concurrent requests within a small interval, may reduce the performance of broker. This paper presents a cloud service selection mechanism with service grouping. The main objective of this paper to show the performance difference between the selection mechanism with grouping and without grouping. Methods/Statistical Analysis: For the concurrent services groups and sub groups are created using functional and QoS parameters. Analytical hierarchy based selection algorithm is proposed. Performance of selection algorithm with grouping is evaluated. Performance of selection algorithm with grouping is compared with algorithm without grouping and the sufficient reduction in execution time is noted. Findings: AHP based selection algorithm with grouping improves the performance by reducing the computation time and reducing the repetitive repository access. Applications/Improvements: The limitation of AHP based algorithm is the rank reversal problem. The improved AHP based algorithm can be used to eliminate the rank reversal problem.

Hybrid P2P schemes for remote terrain interactive visualization systems

Over the last few years, there has been a lot of development of interactive terrain visualization applications using remote databases. One of the main problems that these applications must face is scalability. These applications usually use a client–server model that cannot support a large number of concurrent requests without using a considerable number of servers. In this paper, we present a full comparative study of new hybrid P2P schemes for terrain interactive visualization systems. The performance evaluation results show that the best strategy consists of avoiding the periodical reporting among peer nodes about the current information contained in each node, while using some servers as cache memories specialized by regions of the virtual world. The system throughput achieved with this strategy can be more than three times higher than the hybrid P2P schemes proposed in the literature, significantly improving the scalability of these systems.

Enhancing Scalability in On-Demand Video Streaming Services for P2P Systems

Recently, many video applications like video telephony, video conferencing, Video-on-Demand (VoD), and so forth have produced heterogeneous consumers in the Internet. In such a scenario, media servers play vital role when a large number of concurrent requests are sent by heterogeneous users. Moreover, the server and distributed client systems participating in the Internet communication have to provide suitable resources to heterogeneous users to meet their requirements satisfactorily. The challenges in providing suitable resources are to analyze the user service pattern, bandwidth and buffer availability, nature of applications used, and Quality of Service (QoS) requirements for the heterogeneous users. Therefore, it is necessary to provide suitable techniques to handle these challenges. In this paper, we propose a framework for peer-to-peer- (P2P-) based VoD service in order to provide effective video streaming. It consists of four functional modules, namely, Quality Preserving Multivariate Video Model (QPMVM) for efficient server management, tracker for efficient peer management, heuristic-based content distribution, and light weight incentivized sharing mechanism. The first two of these modules are confined to a single entity of the framework while the other two are distributed across entities. Experimental results show that the proposed framework avoids overloading the server, increases the number of clients served, and does not compromise on QoS, irrespective of the fact that the expected framework is slightly reduced.

A Fast Job Scheduling System for a Wide Range of Bioinformatic Applications

Bioinformatic tools are often used by researchers through interactive Web interfaces, resulting in a strong demand for computational resources. The tools are of different kind and range from simple, quick tasks, to complex analyses requiring minutes to hours of processing time and often longer than that. Batteries of computational nodes, such as those found in parallel clusters, provide a platform of choice for this application, especially when a relatively large number of concurrent requests is expected. Here, we describe a scheduling architecture operating at the application level, able to distribute jobs over a large number of hierarchically organized nodes. While not contrasting and peacefully living together with low-level scheduling software, the system takes advantage of tools, such as SQL servers, commonly used in Web applications, to produce low latency and performance which compares well and often surpasses that of more traditional, dedicated schedulers. The system provides the basic functionality necessary to node selection, task execution and service management and monitoring, and may combine loosely linked computational resources, such as those located in geographically distinct sites.

Merging video streams in a multimedia storage server: complexity and heuristics

Due to recent advances in network, storage and data compression technologies, video-on-demand (VOD) service has become economically feasible. It is a challenging task to design a video storage server that can efficiently service a large number of concurrent requests on demand. One approach to accomplishing this task is to reduce the I/O demand to the VOD server through data- and resource-sharing techniques. One form of data sharing is the stream-merging approach proposed in [5]. In this paper, we formalize a static version of the stream-merging problem, derive an upper bound on the I/O demand of static stream merging, and propose efficient heuristic algorithms for both static and dynamic versions of the stream-merging problem.