ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ В СИСТЕМАХ КЛІЄНТ-СЕРВЕР ЧЕРЕЗ ВИКОРИСТАННЯ МЕРЕЖ CDN

Problem statement: Most of the information retrieval systems used only client-server architectures. The client-server model though powerful, had some limitations. In mobile computing environment which has both wired network and wireless networks with limited communication capabilities, the performance of the system was very low. Approach: Mobile agents are considered a suitable technology to develop applications such as information retrieval system for mobile computing environment. Mobile agents are autonomous and dynamic entities that can migrate between various nodes in the network. They offer many advantages over traditional design methodologies like: reduction in network load, overcoming network latency and disconnected operations. Since the mobile agents do not need continuous communication with the mobile host, they are not affected by the sudden disconnection of wireless network and the situation of turning mobile host off for power saving. In order to get the complete benefit of mobile agent system, the system must be fault tolerant. In the context of mobile agents, fault-tolerance prevents a partial or complete loss of the agent. Results: Our system in mobile computing environment ensured that the agent arrived at its destination with result and performance of the system improved by the way of reduction in the response time. And also, the system allowed sending more requests by the way of creating many mobile agents without affecting the performance. Conclusion: Our research compared the performance of client-server architecture and fault tolerant mobile agent information retrieval system and proved that our system solved the limitations faced by the client server architecture. The system can also be extended to adhoc networks.

5G networks have been experiencing challenges in handling the heterogeneity and influx of user requests brought upon by the constant emergence of various services. As such, network slicing is considered one of the critical technologies for improving the performance of 5G networks. This technology has shown great potential for enhancing network scalability and dynamic service provisioning through the effective allocation of network resources. This paper presents a Deep Reinforcement Learning-based network slicing scheme to improve resource allocation in 5G networks. First, a Contextual Bandit model for the network slicing process is created, and then a Deep Reinforcement Learning-based network slicing agent (NSA) is developed. The agent’s goal is to maximize every action’s reward by considering the current network state and resource utilization. Additionally, we utilize network theory concepts and methods for node selection, ranking, and mapping. Extensive simulation has been performed to show that the proposed scheme can achieve higher action rewards, resource efficiency, and network throughput compared to other algorithms.

Many factors can prevent a Gigabit Ethernet network interface card (NIC) from achieving line rate in a modern web server. In fact, the various commercially available NICs have different performance characteristics that lead to throughput differences for actual web servers. For example, Figure 1 shows the performance achieved by the thttpd web server for client traces extracted from the Rice University computer science department (CS), a NASA web site (NASA), and the 1998 soccer World Cup tournament (World Cup). The latter two traces are available from the Internet Traffic Archive (http://ita.ee.lbl.gov/). The server system tested includes an AMD Athlon 2600+ XP processor running the FreeBSD 4.7 operating system, 2 GB of DDR SDRAM, a 64-bit/66 MHz PCI bus, and a single 40 GB IDE disk (none of the workloads are disk intensive). The tested systems differ only in their NIC, with the Intel Pro-1000/MT Server and Desktop, Alteon AceNIC with parallelized firmware [2], Netgear GA622T, 3Com 3C996B, and Alteon AceNIC with released firmware arranged from left to right. There are substantial performance differences across the NICs in the web environment, as the fastest NIC consistently achieves 40–60% more throughput than the slowest. A web server interacts with the network in two primary ways: receiving client HTTP requests and sending HTTP responses. Requests are typically quite small, on the order of 200 bytes of ASCII text, while responses vary from empty files to several hundred megabytes. Since web clients and servers communicate using TCP, the server must acknowledge requests, leading to minimum-sized (64-byte) Ethernet frames. Response data must be segmented and encapsulated in Ethernet frames, which allow up to 1460 bytes of TCP content in a maximum-sized (1518-byte) frame. Then, those segments are sent out according to TCP flow control policies based on the receipt of acknowledgments. A high-performance server NIC must thus support data volumes dominated by sends of large

Cloud service capabilities can always be clearly indicated by Quality of Service (QoS) properties. The quality and effectiveness of cloud services can be measured at both client and server sides. The parameters such as response time, throughput and failure rate influence the QoS properties at the client side. These parameters may differ with the utilization of the application for the same service. The proposed Bayesian Personalized Ranking-based Rank Prediction Scheme (BPR-RPS) provides rank prediction of QoS properties on the client side, since client-side properties vary with the usage of the application. The proposed rank prediction framework provides ranking to the services when particular cloud services are requested by the client. Personalization of cloud providers and customers is made possible by concentrating on the item recommendation imposed on the cloud by creating user-specific ranking for a set of items. The past response of the user with respect to the item aids in decision-making. The proposed ranking framework includes the following modules: Computation of similarity index between the active and ad hoc users and finding of identical users based on similarity index values.

An integrated approach with feedback control for robust Web QoS design

The amount of dynamic Web contents and secured e‐commerce transactions has been dramatically increasing on the Internet, where proxy servers between clients and Web servers are commonly used for the purpose of sharing commonly accessed data and reducing Internet traffic. A significant and unnecessary Web access delay is caused by the overhead in proxy servers to process two types of accesses, namely dynamic Web contents and secured transactions, not only increasing response time, but also raising some security concerns. Conducting experiments on Squid proxy 2.3STABLE4, we have quantified the unnecessary processing overhead to show its significant impact on increased client access response times. We have also analyzed the technical difficulties in eliminating or reducing the processing overhead and the security loopholes based on the existing proxy structure. In order to address these performance and security concerns, we propose a simple but effective technique from the client side that adds a detector interfacing with a browser. With this detector, a standard browser, such as the Netscape/Mozilla, will have simple detective and scheduling functions, called a detective browser. Upon an Internet request from a user, the detective browser can immediately determine whether the requested content is dynamic or secured. If so, the browser will bypass the proxy and forward the request directly to the Web server; otherwise, the request will be processed through the proxy. We implemented a detective browser prototype in Mozilla version 0.9.7, and tested its functionality and effectiveness. Since we have simply moved the necessary detective functions from a proxy server to a browser, the detective browser introduces little overhead to Internet accessing, and our software can be patched to existing browsers easily. Copyright © 2004 John Wiley & Sons, Ltd.

In modern computing environments such as cloud, edge, and fog computing, as well as IoT networks and real-time systems, meeting workload deadlines is critical to ensure reliability, quality of service, and user satisfaction. The traditional scheduling algorithms often fail to adequately address the constraints associated with the workloads, particularly deadlines, the dynamic nature of workloads and resources, and the inherent resource limitations. Deadlines are the most important constraints, especially for time-sensitive applications. Achieving deadline compliance requires optimal resource provisioning, scheduling, resource allocation, resource scaling, workload migration, etc. This paper proposes a novel deadline-aware cloudlet scheduling algorithm, the Two-Stage Deadline-Aware Cloudlet Scheduling Algorithm (TSDACS), designed to minimize deadline misses through efficient resource provisioning and scaling strategies. In stage one, the algorithm provisions virtual machines with suitable configurations and quantities based on the requirements of the cloudlets to ensure they can be processed within their deadlines. Cloudlets are then scheduled onto the virtual machines in a way that minimizes deadline violations. In stage two, if the initially provisioned virtual machines fail to meet the deadlines, horizontal scaling is applied, up to a limited threshold, to enhance the performance and the deadline compliance. Experimental results demonstrate that the proposed TSDACS algorithm outperforms existing approaches, such as CPDALB, DBS, and RDLBS2, in terms of deadline miss ratio, makespan, response time, and cost efficiency, while maintaining competitive VM utilization and effective load balancing.

While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attributes. In most cases this discovery ability should be restricted to a number of clients based on their attributes, otherwise, any client in the system can discover any registered resource. In a binary discovery policy, any client with the shared secret key can discover and decrypt the address data of a registered resource regardless of the attributes of the client. In this paper we propose Attred, a decentralized resource discovery model using the Region-based Distributed Hash Table (RDHT) that allows secure and location-aware discovery of the resources in IoT network. Using Attribute Based Encryption (ABE) and based on predefined discovery policies by the resources, Attred allows clients only by their inherent attributes, to discover the resources in the network. Attred distributes the workload of key generations and resource registration and reduces the risk of central authority management. In addition, some of the heavy computations in our proposed model can be securely distributed using secret sharing that allows a more efficient resource registration, without affecting the required security properties. The performance analysis results showed that the distributed computation can significantly reduce the computation cost while maintaining the functionality. The performance and security analysis results also showed that our model can efficiently provide the required security properties of discovery correctness, soundness, resource privacy and client privacy.

In modern cloud computing environments, customers increasingly depend on on-demand resource provisioning to handle dynamic workloads. However, fluctuations in job arrival rates can result in prolonged queue times, which negatively affect overall system performance. Although existing scheduling algorithms provide efficient job management, they often fail to account for the combined impact of queue delays and the need for flexible resource provisioning-particularly in business-critical applications. In order to tackle these issues, the paper proposes a new Optimized Job Scheduling and Resource Scaling (OJSRS) algorithm designed to improve job execution efficiency and support elastic resource management in cloud environments. The OJSRS algorithm integrates two key components: Tree-based Job Scheduling (TJS) and Automated Resource Scaling and Scheduling (ARSS). The TJS component constructs a hierarchical structure that concurrently maps incoming jobs to the most suitable Virtual Machines (VMs), thereby minimizing queue delays. Meanwhile, ARSS adjusts resource allocation dynamically, increasing or decreasing capacity according to workload requirements and cloud service provider policies, enabling responsive and adaptive provisioning. Experimental results show that the OJSRS algorithm increases resource utilization by approximately 5-10% and accelerates job completion through proactive resource scaling. This approach provides a significant performance advantage for cloud-native business applications that require both efficiency and scalability.

Nowadays, a number of web services available on the internet have been increasing rapidly. Similarly functional services are offered by different service providers. Quality of Service (QoS) has becomes an important factor to determine the most suitable service provider. Many existing QoS models use uncertain QoS attributes which vary over time and depend on service environment. The service chosen by those models may not provide a QoS as expected. In this paper, we present a QoS model using the uncertain attributes: response time, availability and reliability. In order to accurately selecting a suitable service from these uncertain attributes, our model uses a Non-parametric Test technique which tests historical QoS information that measured from client side.

Online experimentation allows students from anywhere to operate remote instruments at any time. The current techniques constrain users to bind to products from one company and install client side software. We use Web services and Service Oriented Architecture to improve the interoperability and usability of the remote instruments. Under a service oriented architecture for online experiment system, a generic methodology to wrap commercial instruments using IVI and VISA standard as Web services is developed. We enhance the instrument Web services into stateful services so that they can manage user booking and persist experiment results. We also benchmark the performance of this system when SOAP is used as the wire format for communication and propose solutions to optimize performance. In order to avoid any installation at the client side, the authors develop Web 2.0 based techniques to display the virtual instrument panel and real time signals with just a standard Web browser. The technique developed in this article can be widely used for different real laboratories, such as microelectronics, chemical engineering, polymer crystallization, structural engineering, and signal processing.

The development of information technology has brought changes in industrial technology. Traditional industry is declining and intelligent industry is rising. Compared with the traditional industry, intelligent industry has a stronger demand for computing resources and is more sensitive to delay. To meet these requirements, this paper designs a computing platform integrating edge and cloud resources. The platform is mainly composed of edge computing nodes, cloud system, and AI(Artificial Intelligence) component. Edge computing nodes provide network services and computing offload services, which can quickly respond to industrial tasks. Cloud system provides computing resources and storage services for high resource consumption tasks in industry. AI components make full use of idle resources in the edge network to form clusters to accelerate AI inference. Facing different industrial scenarios, the platform can flexibly allocate computing resources in the network and provide computing offload service for tasks in the industry. Finally, we designed three experimental scenarios to verify the effectiveness of our platform. In the three experimental scenarios, compared with the traditional task execution method, our platform has brought obvious productivity improvement and accelerated the completion speed of the task.

Database systems allow quick creation of performance problems. The goal of database systems is to allow the computer-illiterate to write complex and complete applications. It is the job of the syst...

The run-time binding of web services has been recently put forward in order to support rapid and dynamic web service compositions. With the growing number of alternative web services that provide the same functionality but differ in quality parameters, the service composition becomes a decision problem on which component services should be selected such that user's end-to-end QoS requirements (e.g. availability, response time) and preferences (e.g. price) are satisfied. Although very efficient, local selection strategy fails short in handling global QoS requirements. Solutions based on global optimization, on the other hand, can handle global constraints, but their poor performance renders them inappropriate for applications with dynamic and real-time requirements. In this paper we address this problem and propose a solution that combines global optimization with local selection techniques to benefit from the advantages of both worlds. The proposed solution consists of two steps: first, we use mixed integer programming (MIP) to find the optimal decomposition of global QoS constraints into local constraints. Second, we use distributed local selection to find the best web services that satisfy these local constraints. The results of experimental evaluation indicate that our approach significantly outperforms existing solutions in terms of computation time while achieving close-to-optimal results.

This paper presents an analysis of web service performance using queueing theory models. We explore the significance of aligning key web service attributes with queueing theory parameters to enable comprehensive performance evaluation. Through both theoretical modeling and analysis of empirical data, we examine the impact of various factors on system performance, such as response time, throughput, and resource utilization. The results emphasize the importance of understanding web service characteristics for performance optimization, highlighting the need to balance arrival and service rates for maximum efficiency. While this study offers valuable insights into web service performance assessment, future research should focus on exploring additional factors and applying advanced queueing theory techniques to enhance optimization in distributed systems.