Improving availability and performance with application-specific data replication

Native Web communication protocols are the pivotal components of Web services, applications and systems. In particular, HTTP is a de facto protocol standard used in almost all Web services and systems. Consequently, it is one of the crucial protocols responsible for the performance of Web services and systems. HTTP/1.1 has been successfully deployed in Web services and systems for the last two decades. However, one of the most significant issues with HTTP/1.1 is the Round Trip Time and Web latency. To resolve this issue, two successor protocols SPDY and HTTP/2 have been developed recently, with some studies suggesting that SPDY improved the performance of Web services and systems, whilst some did not find significant improvements in the performance. HTTP/2 is a relatively new protocol and has yet to be tested with any rigour. Therefore, it is important to investigate the effects of these two enhanced protocols SPDY and HTTP/2 on the performance of Web services and systems. This paper conducts a number of practical investigations to evaluate the performance of Web services and systems with and without the support of SPDY and HTTP/2 protocols at the client and server. This study investigates the impact of SPDY and HTTP/2 on the overall performance of Web services and systems from the end-user's perspective.

This article presents an enhanced duplex context-wise sanitisation generator and dynamic parser on the hierarchical distributed structure of cloud data centres and edge (fog) servers for obstructing the execution of XSS worms that was recently found on HTML5 and Twitter-based web applications. The online HTTP response generated by such edge servers undergoes through the phase of dynamic runtime parsing. This phase finds out the nested context of variables of script code that cannot be statically determined during the determination of nested context of such variables in a static manner. Finally, sanitised version of templates of HTML5 web pages are generated as an HTTP response and redirected to the network of smart devices. Cloud data centres and edge servers of fog nodes are utilised for integrating the infrastructure settings of our prototype framework that was developed in Java developed framework. Numerous tested open source platforms of OSN were utilised for assessing the performance of runtime nested context determination and sanitisation of suspicious JavaScript strings. Performance evaluation outcomes revealed that the proposed work experienced better response time at online phase and tolerable performance overhead caused due to the runtime nested context-wise parsing and sanitisation of XSS worms.

Edge computing is a powerful tool to face the challenging performance requirements of modern Internet applications. By replicating applications' data and logic across a large number of geographically distributed servers, edge computing platforms allow to achieve significant enhancements of the proximity between clients and contents, and of the system scalability. These platforms reveal highly effective when handling requests entailing read-only access to the application data, as these requests can be autonomously served by some edge server typically located closer to the client than the origin site. However, in contexts where end users can trigger transactional manipulations of the application state (e.g., e-Commerce, auctions or financial applications), the corresponding update requests typically need to be redirected to the origin transactional data sources, thus, nullifying any performance benefit arising from data replication and client proximity. To cope with this issue, in this paper, we present a parallel invocation protocol, which exploits the path-diversity along the end-to-end interaction toward the origin sites by concurrently routing transactional requests toward multiple-edge servers. Request processing is finally carried out by a single-edge server, adaptively selected as the most responsive one depending on current system conditions. The proposed edge server selection scheme does not require coordination among (geographically distributed) edge server instances, thus, being very light and scalable. The benefits from our protocol in terms of both reduced and more predictable end-to-end latency are quantified via an extended simulation study.

Most of the IoT‐based smart systems require low latency and crisp response time for their applications. Achieving the demand of this high Quality of Service (QoS) becomes quite challenging when computationally intensive tasks are offloaded to the cloud for execution. Edge computing therein plays an important role by introducing low network latency, quick response, and high bandwidth. However, offloading computations at a large scale overwhelms the edge server with many requests and the scalability issue originates. To address the above issues, an efficient resource management technique is required to maintain the workload over the edge and ensure the reduction of response time for IoT applications. Therefore, in this paper, we introduce a metaheuristic and nature‐inspired Artificial Bee Colony (ABC) optimization technique that effectively manages the workload over the edge server under the strict constraints of low network latency and quick response time. The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Round‐Robin (RR) Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. Furthermore, the proposed technique scales the edge server to meet the demand of high QoS for IoT applications.

i»? A. A. Helal, A. A. Heddaya, and B. B. Bhargrava Kluwer Academic Publishers, Boston, 1996, 176 pp. ISBN 0-7923-9800-9, $118.50 Computing in the 1990s has reached the state of distributed computing. A basis of this form of computing is a distributed computing system, which is built on the following three components: (a) personal computers, (b) local and fast wide area networks, and (c) system and application software. By amalgamating computers and networks into one single computing system and providing an appropriate system software, a distributed computing system has created the possibility of sharing information and peripheral resources. Furthermore, these systems improved performance of a computing system and individual users through parallel execution of programs, load balancing and sharing, and replication of programs and data. Distributed computing systems are also characterized by enhanced availability, and increased reliability. Replication is the key to providing high availability, fault tolerance, and enhanced performance in a distributed computing system. As companies move toward systems that are more open and distributed, replication is becoming increasingly important in the ability to provide data and services that are current, correct and available, which is a key factor in maintaining a competitive advantage over rivals. However, replication has also generated some serious challenges. For example, when a replica is updated, how do we propagate such an update to other replicas? If multiple replicas are updated simultaneously and some of the updates conflict one another, how do we resolve the conflict? How do we deal with the situation where a replica is down and then subsequently recovered? How do we deal with network partitions? Not surprisingly, considerable research efforts have been directed towards the solution of these challenges. However, most of the research results are scattered among many journals, conference proceedings, theses, and technical reports. The book by Helal, Heddaya and Bhargava has gathered the best material of these research results and formed a coherent collection that includes definitions, theoretical background, algorithms, annotated bibliographies of commercial and experimental prototype systems, and more than 200 references. This book classifies the entities that can be replicated on a distributed computing system into the following categories: (a) data, (b) process, (c) objects, and (d) messages. After the introduction on the goals and main approaches of replication techniques, the book uses individual chapters to describe major methods for each of the above replication categories. The main focus of this part, however, is on the replication of data. The book also contains two chapters on the replication issues in heterogeneous, mobile, and large-scale systems, and on the future of replication techniques. Apart from the normal chapters, the book contains a rich set of appendices that are very useful for further studies of replication techniques. These appendices include brief descriptions of two dozen commercial and experimental systems that use replication techniques, annotated bibliographies of selected literature on various topics of replication, and an introduction on serializability theory. Each annotated bibliography contains an overview of the specific topic and is written by an invited expert in that field. The combination of the normal chapters and appendices cover the entire spectrum of replication, from definitions, concepts, theories, algorithms, techniques, to systems. The book serves as an excellent introduction and roadmap to the issues of replication. Although it does not provide a detailed description of every replication techniques and systems, the book covers most of the fundamental work that allows the reader to understand the roots of these techniques and systems, and to find the ways to search for the details. This book is a valuable reference book for anyone studying replication techniques or implementing replication systems, specifically, it can be very useful to post-graduate students, practitioners, and beginning researchers of this exciting field. Wanlei Zhou, School of Computing and Mathematics, Deakin University

Performance is one of the most important business requirements for web based applications. The suitability of better platform for any web based application depends on various factors attributable to processor architecture, memory, local/ global network, input/output characteristics etc. Performance testing is an important part of any distributed or web application testing plan. Inclusion of performance estimates into planning and development cycles ensures that the application delivered to the user satisfies sustained performance even at high load, availability and scalability requirements. In this paper we have thrown light on performance comparison of web applications on single and multi core servers. The tests have been carried out on single processor single core server and also on dual processor quad core Xeon based server. The performance comparison was carried out based on response time, throughput (No. of hits v/s response time) and connection pooling v/s no connection pooling. It was found that performance of Java applications running on the multi-core servers is far better than those running on single core servers. Web applications on quad-core server give better performance, high availability and scalability. Apache JMeter was used to load test the functional behaviour and measure performance based on throughput (No of hits v/s response time) for dynamic querying of data using Java servlets on Tomcat web server. Based on the analysis of performance of web server under varying load of no. of users and threads, it was found that applications on quad core server give more throughput and less response time as compared to single core server. Connection pooling increases the performance of web applications by reusing active database connections instead of creating a new connection with every request. Performance was compared by running the tests using Java servlets for connection pooling and also by acquiring connections directly from the JDBC driver without connection pooling. Based on the test results it was found that in case of quad core server, throughput i.e the number of requests served per second is much higher and reuse of connections from pool of connections showed significant improvement in application performance. This paper describes the results of tests carried out for comparing performance (based on response time) between web applications running on single core server and on quad core server. Tests have also been carried out to analyze advantage of connection pooling over no connection pooling. The test results have been shown graphically.

Ajax-based web applications are designed to mimic more traditional desktop applications and require quick response times from the underlying Web services. However, since availability and performance of Web services cannot be guaranteed, response time and overall performance of Ajax-based applications can vary. In this paper we describe a framework for developing autonomic self-healing Web service-based applications that rely on the notion of differentiated services (i.e., services that provide common behavior with variable quality of service) in order to maintain required performance characteristics. We present the expected impact of the framework through the use of a theoretical QN model, demonstrate the framework with an example, and provide an evaluation of the technique.

On the performance of web services, google cloud messaging and firebase cloud messaging

Collaborative Edge Computing (CEC) is a new edge computing paradigm that enables neighboring edge servers to share computational resources with each other. Although CEC can enhance the utilization of computational resources, it still suffers from resource waste. The primary reason is that end-users from the same area are likely to offload similar tasks to edge servers, thereby leading to duplicate computations. To improve system efficiency, the computation results of previously executed tasks can be cached and then reused by subsequent tasks. However, most existing computation reuse algorithms only consider one edge server, which significantly limits the effectiveness of computation reuse. To address this issue, this paper applies computation reuse in CEC networks to exploit the collaboration among edge servers. We formulate an optimization problem that aims to minimize the overall task response time and decompose it into a caching subproblem and a scheduling subproblem. By analyzing the properties of optimal solutions, we show that the optimal caching decisions can be efficiently searched using the bisection method. For the scheduling subproblem, we utilize projected gradient descent and backtracking to find a local minimum. Numerical results show that our algorithm significantly reduces the response time in various situations.

The essential services such as banking, transportation, medicine, education and defense are being progressively replaced by cheaper and more efficient Internet-based web applications. Therefore, the availability of web services is very critical for the socio-economic growth of the society. Distributed denial-of-service (DDoS) attacks pose an immense threat to the availability of these services. The services are severely degraded and hence lot of business loses are incurred due to these attacks. To objectively evaluate DDoS attack's impact, its severity and the effectiveness of a potential defense, we need precise, quantitative and comprehensive DDoS impact metrics that are applicable to web services. In this paper an attempt has been made to analyze impact of DDoS attacks on the web services. The experiments are conducted in NS-2. The attack traffic is mixed with legitimate traffic at different strengths and impact of DDoS attacks are measured in terms of throughput, response time, ratio of average serve to request rate, percentage link utilization, and normal packet survival ratio (NPSR). electronic document is a live template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

Replication is a widely used method for achieving high availability in database systems. The data replication approach can be used for solutions such as sharing data among remote offices, sharing data among dispersed users, make server data more accessible, distribute solution updates, back up data, provide internet or intranet replication. The main aim of this research is to provide a better technique for solving the drawbacks that currently exist in the literary works of data replica environment. Here, we intend to propose data replication system based on data mining techniques. That is in proposed method FDDRA (data mining based dynamic replication algorithm), we will use a data mining with combination of replication algorithm and data grid policy for data replication in the data mining environment through monitoring all data process. The data replication will be done by identifying the frequently used data patterns in the large database of a node. This will be done by Apriorialgorithm. The system availability and replica will be measured and identifying the location in which the replicated data will be stored. The result shows the efficiency of our algorithm in terms latency time and shortest path of nodes.

The development of new mobile services can benefit from adopting XML Web services technologies, because this can dramatically reduce the time to market for many types of mobile services. However, it is often believed that XML Web services will become too large a bottleneck in service performance, rendering the service unusable, as well as it can compromise service usage due to lack of security features. This paper provides a thorough study of these topics, i.e., a study of the typical performance of mobile services built on top of XML Web services, how security features can be introduced for these services and how these security features influence on the performance of services. The results of this paper should apply to any mobile services implementation based on XML Web services technologies.

We present GlobeDB, a system for hosting Web applications that performs autonomic replication of application data. GlobeDB offers data-intensive Web applications the benefits of low access latencies and reduced update traffic. The major distinction in our system compared to existing edge computing infrastructures is that the process of distribution and replication of application data is handled by the system automatically with very little manual administration. We show that significant performance gains can be obtained this way. Performance evaluations with the TPC-W benchmark over an emulated wide-area network show that GlobeDB reduces latencies by a factor of 4 compared to non-replicated systems and reduces update traffic by a factor of 6 compared to fully replicated systems.

We identify parameters impacting Web services dependability, describe the methods of dependability enhancement by redundancy in space and redundancy in time, and perform a series of experiments to evaluate the availability of Web services. To increase the availability of Web services, we employ several replication schemes and compare them with a single service. The Web services are coordinated by a replication manager. It provides a round robin algorithm for scheduling the workload of the Web services and keeps updating the availability of each Web service. The replication algorithm and the detailed system configuration are described. Experiments are performed to evaluate the resulting service availability. Modeling on the Web services with Petri-net is constructed and verified through experiments with different applications. With the parameters obtained from the experiments, the proposed model can be engaged to demonstrate the characteristics of the Web service.

Mobile applications can leverage high-quality deep learning models such as convolutional neural networks and deep neural networks to provide high-performance cognitive services. Prior work on deep learning models-based mobile applications in a cloud-edge computing environment focuses on performing lightweight data pre-processing tasks on edge servers for cloud-hosted cognitive servers. These approaches have two major limitations. First, it is uneasy for the mobile applications to assure satisfactory user experience in terms of network communication delay, because the intermediary edge servers are used only to pre-process data (e.g., images and videos) and the cloud servers are used to complete the tasks. Second, these approaches assume the pre-trained deep learning models deployed on cloud servers are static, and will not attempt to automatically upgrade in a context-aware manner. In this article, we propose a cloud-edge collaboration framework that facilitates delivering cognitive services with long-lasting, fast response, and high accuracy properties. We fist deploy a shallow model (i.e., EdgeCNN) on the edge server and a deep model (i.e., CloudCNN) on the cloud server. EdgeCNN can provide durable and rapid response cognitive services, because edge servers not only provide computing resources for mobile applications, but also close to users. Then, we enable CloudCNN to assist in training EdgeCNN to improve the performance of the latter. Thus, EdgeCNN also provides high-accuracy cognitive services. Furthermore, because users may continue to upload data to edge servers in real-world scenarios, we propose to use the ongoing assistance of CloudCNN to further improve the accuracy of the shallow model. Experimental results show that EdgeCNN can reduce the average response time of cognitive services by up to 55.08 percent and improve accuracy by up to 26.70 percent.