Dr Muhammad Younas

Metaverse is a collective term for all economic and social activities in the space where the physical world, digital world and consciousness world are interactively integrated and mutually empowered. The metaverse is the advanced stage of digital civilization and the future formation of human society. The basis for developing metaverse is general digital technologies such as high-performance network, high-performance storage, high-performance computing, high-performance security and artificial intelligence. On the basis of the above, the key to developing the metaverse lies in researching core technologies such as digital life technologies, trusted collaborative network technologies, natural interaction technologies, ubiquitous operating system technologies, technologies and methods for computational experiments, and theories and technologies for crowd intelligence science. We should take typical metaverse application scenarios as entry points, such as the key fields of agriculture, industry, service industry, military, social governance and other economic and social areas, to break through key metaverse technologies and implement pilot demonstration projects of metaverse. Through the demonstration, we can systematically promote the application of metaverse in economy and society from point to line, and continuously iterate and evolve the metaverse technology to advance the metaverse to a higher stage. This paper systematically analyzes the current development status and future directions of metaverse from the concept, key technology and vision of metaverse, paving the way for the subsequent research of metaverse.

Security and scalability are considered as two major challenges in rapid and smooth deployment of blockchains in businesses, enterprises and organizations. Common belief is that the ability to scale up a blockchain lies mainly in improving the underlying technology rather than deploying new hardware. Though recent research works have applied sharding techniques in enhancing scalability of blockchains, they do not cater for addressing the joint issue of data security and scalability in blockchains. This paper proposes an approach that makes a trade-off between security and scalability when designing blockchain based systems. It designs an efficient replication model, which creates dynamic sharding wherein blocks are stored in a varying number of nodes. In order to maintain required level of security, the proposed approach shows that the replication of blockchain over peer-to-peer network is minimized as the blockchain’s length evolves according to a replication factor.

Microservices architecture has increasingly been adopted for building distributed and scalable applications. The premise is that microservices are designed as smaller software components which are easier to be preserved and which enable separation between different components. This paper proposes a new transaction platform for microservices architecture to manage processing of big data stored in a cluster of NoSQL databases. New asynchronous protocols are designed to execute database operations as transactions, and to maintain their correctness and consistency. A prototype system has been developed that simulates London bus service across bus routes. It is evaluated through simulation experiments using big data from ‘Transport for London’ data service in order to analyse effects of transaction processing on response time and throughput in microservices architecture. The transaction platform reliably processes database operations, and enables data availability and consistency in failure-free and failure-prone environments.

The Internet of Things (IoT) generates huge amount of data at an extremely fast pace. Thus, it is important to classify such data objects into different groups or clusters in order to gain some valuable insights from data. This paper aims to develop a dendrograms-based method for 3D visualization of hierarchical clustering for multidimensional data which can be collected from IoT devices and open databases. This method is built on hierarchical clustering algorithm which is simple and efficient. It presents areas of the selected clusters and their objects on a plane, according to the coordinates defined by the open dendrogram. It defines rules for visualization of the dendrogram and allows to find the nature of clusters. The paper also proposes quantitative indicators of localization of objects and evaluation of clusters being formed. The proposed method is evaluated using IoT-based dataset prepared in two different forms. The proposed method significantly improves the quality of visualization and evaluation of cluster analysis results. It is also efficient as the time complexity is significantly less for factorial analysis.

Cloud service selection process is significantly challenging and complicated as there are various QoS factors to consider when selecting cloud services. This paper proposes a new QoS-aware selection model systematically and succinctly representing QoS attributes which cloud consumers can easily use and understand when selecting cloud services. In order to ensure the credibility of the cloud service selection, the proposed model collects QoS data from different sources including cloud providers, users’ reviews and cloud monitoring tools. It implements Multi-Criteria Decision Making models in order to rank services based on various QoS attributes. The proposed model is implemented as a simulation tool which is deployed on Amazon cloud platform. Using the simulation tool, the proposed model is rigorously evaluated through a number of experiments by taking into account data from widely used commercial cloud service providers. The experimental results show that the proposed model ranks and selects cloud services according to the QoS requirements of cloud service consumers. Unlike existing approaches the proposed model takes into account multi-level QoS attributes and data from real cloud providers when ranking cloud services.

The current rise in malicious attacks shows that existing security systems are bypassed by malicious files. Similarity hashing has been adopted for sample triaging in malware analysis and detection. File similarity is used to cluster malware into families such that their common signature can be designed. This paper explores four hash types currently used in malware analysis for portable executable (PE) files. Although each hashing technique produces interesting results, when applied independently, they have high false detection rates. This paper investigates into a central issue of how different hashing techniques can be combined to provide a quantitative malware score and to achieve better detection rates. We design and develop a novel approach for malware scoring based on the hashes results. The proposed approach is evaluated through a number of experiments. Evaluation clearly demonstrates a significant improvement (> 90%) in true detection rates of malware.

Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular check-pointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), classification and regression trees (CART) and linear discriminant analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting failure is 90% accurate and effective compared to other algorithms. This finding implies that our method can effectively predict all possible future system and application failures within the system.

Software as a Service (SaaS) has recently emerged as one the most popular service delivery models in cloud computing. The number of SaaS services and their users is continuously increasing and new SaaS service providers emerge on a regular basis. As users are exposed to a wide range of SaaS services, they may soon become more demanding when receiving/consuming such services. Similar to the web and/or mobile applications, personalization can play a critical role in modern SaaS-based cloud services. This paper introduces a fully designed, cloud-enabled personalization framework to facilitate the collection of preferences and the delivery of corresponding SaaS services. The approach we adapt in the design and development of the proposed framework is to synthesize various models and techniques in a novel way. The objective is to provide an integrated and structured environment wherein SaaS services can be provisioned with enhanced personalization quality and performance.

The paper proposes a novel model to handle high amount of data in cloud environment. It is a model of aggregated data that are confidences of association rules. We first propose a method to discover the association rules locally on each node of a cloud in the presence of missing rules. Afterward, we provide a tensor based model to perform a global correlation between all the local models of each node of the network.

The proposed approach based on tensor decomposition, deals with a multi modal network where missing association rules are detected and their confidences are approximated. The approach is scalable in terms of factorizing multi-way arrays (i.e. tensor) in the presence of missing association rules. It is validated through experimental results which show its significance and viability in terms of detecting missing rules.

As part of a defense-in-depth security solution for domain-controlled enterprise networks, a proposed self-healing system architecture is designed to increase resiliency against botnets with minimal disruption to network services.

During last decade more and more old people live alone and the number of old people that need medical care is increased. The lack of doctors and the increase of the medical cost is becoming a big problem. Therefore, IT-based information systems should be implemented to help solve this problem. In this paper, we present the experimental results and evaluation of the SmartBox stimulation device for medical applications using a P2P system which is based on JXTA-Overlay. The SmartBox is integrated with our P2P system as a useful tool for monitoring and controlling patients activities. We found by experimental results that by SmartBox we can check the patient’s situation. We carried out experiments with a patient in the bed and changed the temperature of the room. From the experimental results, we conclude that the use of SmartBox is an effective way for medical applications.

Rapid advances in software systems, wireless networks,and embedded devices have led to the development of apervasive and mobile cyberspace that provides an infrastructurefor anywhere/anytime service provisioning in different domainssuch as engineering, commerce, education, and entertainment.This style of service provisioning enables users to freely move betweengeographical areas and to continuously access informationand conduct online transactions. However, such a high mobilitymay cause performance and reliability problems during the executionof transactions. For example, the unavailability of sufficientbandwidth can result in failure of transactions when usersmove from one area (cell) to another. We present a context-awaretransaction model that dynamically adapts to the users' needs andexecution environments. Accordingly, we develop a new mobilitymanagement scheme that ensures seamless connectivity andreliable execution of context-aware transactions during mobilityof users. The proposed scheme is designed and developed usinga combination of different queuing models. We conduct variousexperiments in order to show that the proposed scheme optimizesthe mobility management process

Web services (WS) transactions are important in order to reliably compose distributed and autonomous services into composite web services and to ensure that their execution is consistent and correct. But such transactions are generally complex and they require longer processing time, and manipulate critical data. Thus various techniques have been developed in order to perform quality assessment of WS transactions in terms of response time efficiency, failure recovery and throughput. This paper focuses on the testing aspect of WS transactions - another key quality issue that has not been examined in the literature. Accordingly it proposes multi-dimensional criteria for testing the WS transactions. The proposed criteria have the potential to capture the behaviour of WS transactions and to analyse and classify the possible (failure) situations that effect the execution of such transactions. These criteria are used to generate various test cases and to provide (WS transactions) ! tester with flexibility of adjusting the method in terms of test efforts and effectiveness. The proposed criteria have been designed, implemented and evaluated through a case study and a number of experiments have been performed. The evaluation shows that these criteria have the capability to effectively generate test cases for testing WS transactions as well as enable tester to decide on the trade-off between test efforts and the quality.

Over the last decade Peer-to-Peer (P2P) systems have become very popular, and have been used to provide solutions in areas such as distributed computation, voice over IP, and knowledge sharing. P2P architectures will be very important for future distributed systems and applications. In such networks, peers are heterogeneous in providing the services and they do not have the same competence of reliability. Therefore, it is necessary to estimate whether a peer is trustworthy or not for file sharing and other services. In this paper, we propose an integrated fuzzy-based trustworthiness system for P2P communications in JXTA-overlay, which consists of two Fuzzy Logic Controllers (FLC1 and FLC2). For FLC1, we use three input parameters: Number of Jobs (NJ), Number of Connections (NC) and Connection Lifetime (CL) and the output is Actual Behavioral Criterion (ABC). We use ABC and Reputation (R) as input linguistic parameters for FLC2 and the output is Peer Reliability (PR). We evaluate the proposed system by computer simulations. The simulation results has shown that the proposed system has a good behavior.

Web portals work as a point of access to a large volume of information on the Web. This paper focuses on the performance of Web portals in an E-commerce environment which involves the processing of a large number of users' requests. It proposes a class-based priority scheme which classifies users' requests into high and low priorities. In E-commerce, some requests (e.g. buy) are generally considered more important than others (e.g. search or browse). We contend that the requests received from a Web portal should generally get higher priority as such requests are more likely to lead to purchases. We believe that assigning such priorities at multiple service levels can improve the performance of Web portals' requests of higher priority. The proposed scheme is formally specified and implemented, and performance results are obtained and compared to a server that does not prioritise requests. The results show significant performance improvements in the processing of high priority requests.

This paper is an investigation into the performance of E-commerce applications. E-commerce has become one of the most popular applications of the web as a large population of web users is now benefiting from various on-line services including product searches, product purchases and product comparison. E-commerce provides users with 24-7 shopping facilities. However, the consequence of these benefits and facilities is the excessive load on E-commerce web servers and the performance degradation of E-commerce (eCom) requests they process. This paper addresses this issue and proposes a class-based priority scheme which classifies eCom requests into high and low priority requests. In E-commerce, some requests (e.g. payment) are generally considered more important than others (e.g. search or browse). We believe that by assigning class-based priorities at multiple service levels, E-commerce web servers can perform better and can improve the performance of high priority eCom requests. In this paper, we formally specify and implement the proposed scheme and evaluate its performance using multiple servers. Experimental results demonstrate that the proposed scheme significantly improves the performance of high priority eCom requests.

In this paper we reflect on the differences between prototype-based and class-based programming languages and we introduce a new approach, called the Oea framework, that will allow JavaScript developers to write programs using a class-based style, which we assert is easy to use, has a syntax style that resembles that of the Java class-based approach and has high performance and reliability. We present a survey of the most widely used class-based techniques for JavaScript describing their methods and highlighting their shortcomings and compare the new approach with this earlier work. The approach has been developed in the context of a framework for developing Web applications using Scalable Vector Graphics (SVG), JavaScript and the Resource Description Framework (RDF) which involved the construction of substantial libraries and other code using the approach.

Modern cloud, web and other emerging distributed services have complex network requirements that cannot be fulfilled via classical networks. This paper presents a novel architecture of a noble Software-Defined Wide Area Network (SD-WAN) that provides the framework for incorporating AI/ML based components for managing different centralised services of the WAN.To leverage the benefit of this framework, a crucial early stage requirement is to accurately identify the traffic category of a flow based on which follow-up actions such as QoS provisioning, resource orchestration, etc. can be implemented. To address this, the research then presents the model of a supervised ML based traffic prediction module and presents a detailed comparison and performance analysis of a shortlisted set of ML models with a variety of traffic categories. The research also takes into account the serialized processes in the models’ training and learning phases emphasizing on the sensitivity of the feature selection process in the performance of these algorithms.