• We present 26 design principles, which are applicable to Internet of Things (IoT)-enabled mobile augmented reality (MAR) applications, summarized from 23 design principles and 195 usability heuristics identified from systematic literature review. • We demonstrate how design principles can be applied to user interface (UI) and user experience design of IoT-enabled MAR applications by presenting UI mockups. • We propose five new design principles derived from the analysis of IoT-enabled MAR applications designed for healthcare and energy management scenarios. Technological developments, such as mobile augmented reality (MAR) and Internet of Things (IoT) devices, have expanded available data and interaction modalities for mobile applications. This development enables intuitive data presentation and provides real-time insights into the user’s context. Due to the proliferation of available IoT data sources, user interfaces (UIs) have become complex and diversified, while mobile devices have limited screen spaces. This state increases the necessity of design principles that help to secure sufficient user experience (UX). We found that studies of design principles for IoT-enabled MAR applications are limited. Therefore, we conducted a systematic literature review to identify existing design principles applicable to IoT-enabled MAR applications. From the state-of-the-art research, we compiled and categorized 26 existing design principles into seven categories. We analyzed the UIs of three IoT-enabled MAR applications with the identified design principles and user feedback gathered from each application’s evaluation to understand what design principles can be considered in designing these applications. Among the 26 principles, we find eight principles that are commonly identified as possible improvements for the applications based on their purposes. We demonstrate the practical use of the identified principles by redesigning the UIs, and we propose five new design principles derived from the application analysis. As a result, we summarized a total of 31 design principles, including the five new ones. We expect that our findings will give insight into the UX/UI design of IoT-enabled MAR applications for researchers, educators, and practitioners interested in UX/UI development.

The next generation of immersive applications, such as eXtended reality (XR), will likely be cloud-based and streamed over mobile networks using myriad technologies such as WiFi and 6th-generation mobile networks. Mobile networks promise ubiquitous connectivity but are prone to stochastic network conditions that may be detrimental to end users' quality of experience (QoE). The impact of network conditions on QoE has been studied extensively by industry and academia regarding various multimedia services such as audio, video, and gaming. However, the impact of network conditions on users' QoE for XR-based social applications has yet to be thoroughly investigated. This paper presents novel results assessing the impact of network conditions$(\boldsymbol{N}=\mathbf{20})$involving factors such as round trip time (RTT), jitter (RJ), and packet losses (PL) on users' QoE via realistic subjective tests$(\boldsymbol{N}=\mathbf{28})$regarding social XR application. Our results show that social XR applications require stringent QoS conditions. In particular, our results show that increasing RTT values do not significantly affect users' QoE up to 77ms. Combined PL and RTT cases cause significant QoE degradation from 77ms onward with greater than 2% PL. Most importantly, results show that a very small jitter value with one standard deviation beyond 52 milliseconds can lead to significant QoE degradation. Further, jitter values beyond three standard deviations for 27ms RTT and beyond should be avoided.

Cloud-based social eXtended Reality (XR) services are the cornerstone for realizing the promises of the Metaverse. These services hosted either on datacenters or edge, will demand stringent mobile network quality of service (QoS) to operate effectively and provide an acceptable user quality. It becomes fundamental to study how mobile networks QoS factors round-trip time (RTT), packet loss (PL), and jitter affect these services by measuring their effect on users' perceived quality of experience (QoE). Subjective QoE assessment involves carefully controlled laboratory environments to generate the desired conditions between a large set of users. The requirements for a cloud-based social XR service lab-setup are complex: Identify a reliable streaming service, a customizable VR application, emulate network conditions, define activities or tasks for users to perform, collect their data, label it; all while mitigating possible human mistakes. To address these requirements, we present an effective technical setup that can consistently repeat the same conditions between users and that can be easily replicated to other labs conducting cloud-based social XR research.

In the future, extended reality (XR) applications will be hosted on cloud and edge infrastructures and streamed over heterogeneous access networks such as Wi-Fi and 6G. These infrastructures promise ubiquity but are prone to stochastic conditions, such as network congestion and wireless signal fading and attenuation, that can be detrimental to the quality of experience (QoE) regarding XR applications. This paper presents extensive and novel results assessing the impact of network conditions (N = 20) on users’ QoE via realistic subjective tests (N = 28) involving factors such as round-trip time (RTT), jitter (RJ), and packet loss (PL) in a social XR application context. Our results indicate that social XR applications require stringent quality of service to support users’ QoE. We demonstrate that RTT values up to 77 ms do not significantly impact users’ QoE. Furthermore, combined (PL and RTT) values lead to significant QoE degradation when RTT values exceed 77 ms and PL exceeds 2%. We also demonstrate that even minimal jitter values, with 1 standard deviation beyond 52 ms RTT values, can lead to significant QoE degradations. Furthermore, jitter values exceeding 3 standard deviations for 27ms RTT value and beyond should be avoided. Finally, based on network traffic data between Sweden and various AWS data centers in Europe, we show that social XR applications can be hosted at a few data center locations with minimal QoE impact for wired network connections. However, due to high jitter values, both 4G and 5G networks are not conducive to users’ QoE.

Cloud gaming (CG) services are a key enabler of more accessible and affordable gaming experiences for users worldwide. However, to deliver these services with sufficient Quality of Experience (QoE) over heterogeneous access networks, stakeholders, including network and cloud service providers, and game developers, must first understand their specific performance requirements. For that, this study investigates QoE of PC-based CG under controlled and repeatable network conditions, emulating a range of impairments including round-trip time (RTT), packet loss (PL), random jitter (RJ), bursty jitter (BJ), and bitrate. Two representative games, Terraria and Counter-Strike 2, were tested across 15 network conditions, streamed using the Moonlight/Sunshine platform. Thirty participants rated their QoE, with orchestration and data collection managed by the AL-TRUIST tool. Results show that low bitrate (0.5Mb/s), high RTT (402ms), bursty jitter (702ms), and high RJ (std=3) significantly degrade QoE, especially in QoS-sensitive games. Additionally, we evaluate mobile-CG trained QoE models on PC-CG data and observe significant prediction errors, highlighting the importance of accounting for differences in streaming platform and context when estimating QoE. These findings provide valuable insights for stakeholders optimizing PC-CG over HANs.

Subjective Quality of Experience (QoE) studies often require setting up complex lab environments to study users' perceptions of the application or service under controlled test conditions. These lab environments must control applications and devices to generate the required test conditions accurately, reliably, repeatedly, and error-free under study. Further, the data collection should be performed on many devices, such as clients and servers, often in real-time, and correctly labelled according to each test condition. To circumvent the complex task of configuring the lab environment and the laborious and error-prone work of data collection, we demonstrate ALTRUIST, a multi-platform tool to conduct subjective tests efficiently. In particular, we present the use of ALTRUIST in two lab setups involving immersive applications such as mobile cloud gaming and virtual reality gaming.

Mobile cloud gaming (MCG) lets users play cloud games (CG) on mobile devices anywhere via mobile networks. However, the stochastic nature of network quality of service (QoS) can result in varying user quality of experience (QoE). Understanding, modeling, and predicting the impact of mobile networks' QoS on users' QoE is crucial. This helps stakeholders optimize networks, and game developers efficiently create cloud-hosted games provisioned over mobile networks. This paper investigates the impact of QoS on users' QoE and proposes, develops and validates novel models for predicting QoE for MCG in mobile networks using realistic subjective tests. In particular, we propose and develop three QoE models using multiple, polynomial, and non-linear regression. Our results validate that multiple regression (with R2=0.79, RMSE=0.45) can model complex relationships between QoS factors that impact QoE. Multiple polynomial regression achieved the overall fit with (R2=0.94, RMSE=0.24). Lastly, the non-linear model achieved a good RMSE of 0.24. To select the best model out of the three, we applied the F-test and determined that polynomial regression had the best statistical fit.

Virtual reality cloud-based gaming (VRCG) services are becoming widely available on virtual reality (VR) devices delivered over computer networks. VRCG brings users worldwide an extensive catalog of games to play anywhere and anytime. Delivering these gaming services in existing broadband mobile networks is challenging due to their stochastic nature and the user’s perceived Quality of Experience (QoE)’ sensitivity towards them. More research is needed regarding developing effective methods to measure the impact of network QoS factors on users’ QoE in the VRCG context. Therefore, this paper proposes, develops, and validates three novel regression models trained on a real dataset collected via subjective tests (N=30); the dataset contains subjective users’ QoE ratings regarding VR shooter games affected by network conditions (N=28), such as round-trip time (RTT), random jitter (RJ), and packet loss (PL). Our findings reveal that due to the nonlinear relationship of (RTT and RJ) tested together, nonlinear (mean absolute error (MAE)=0.14) and polynomial (MAE=0.15) regression models have the best performance; yet, simple linear regression model (MAE=0.19) is also suitable to predict QoE for VRCG. Further, we found that feature importance depends on the model’s choice (either RTT or RJ). Finally, our models’ prediction of QoE for real-world traffic measurements suggests that mobile network traffic (4G, 5G non-standalone, 5G standalone) provides a 2.5 ≤MOSQoE≤ 3.0 experience for VRCG, while 4.2 ≤MOSQoE≤ 4.4 for wired connections, suggesting the need for improvements in the current commercial 5G network deployments to deliver VRCG.

Quality of experience (QoE) is an essential metric for stakeholders to understand how customers perceive the quality of their products or services. Gaming-as-a-Service (GaaS) is a challenging model to deliver efficiently to customers worldwide since it involves the joint force of cloud service providers, network operators, and game developers. The recent move of the cloud gaming (CG) industry to virtual reality (VR) platforms brings the benefits of the cloud to the most immersive quality of service (QoS) and QoE-sensitive VR content. Virtual reality cloud-based gaming (VRCG) necessitates understanding of stochastic broad-band network connections on users' QoE so that stakeholders can deliver quality content by optimizing their services to underlying QoS conditions. Very few studies exist in the literature that study the impact of network QoS on users' QoE for VRCG. This paper presents subjective tests (N=30) and investigates the effect of network-emulated QoS metrics (N=28) on the commercial Nvidia CloudXR service and their impact on the users' perceived QoE while playing Serious Sam VR shooter game. Our findings reveal that QoE was most affected by round trip time (RTT)$\geq 75$ms or packet loss (PL)$> 6\%$. Random jitter (RJ) caused QoE degradation for values more significant than one standard deviation, while the combined RTT and PL degraded QoE the most for RTT$\geq 25$ms and PL$\geq 4\%$, Finally, based on actual network traffic data between Sweden and various data centers in Europe, we suggest VRCG can be hosted anywhere in these data centers with minimal impact on QoE for wired connections. However, for 4G and 5G networks, high jitter values could pose a challenge to VRCG services.

Quality of Experience (QoE) subjective assessment often demands setting up expensive lab experiments that involve controlling several software programs and services. In addition, these experiments may pose significant challenges regarding man-agement of testbed software components as they may have to be synchronized for efficient data collection, leading to human errors or loss of time. Further, maintaining error-free repeatability between subsequent subjective tests and comprehensive data collection is essential. Therefore, this paper proposes, develops and validates ALTRUIST, a multi-platform tool that assists the experimenter in conducting subjective tests by controlling external applications, facilitates data collection and automates test execution for conducting repeatable subjective tests in broad application areas.

The emergence of novel cellular network technologies, within 5G, are envisioned as key enablers of a new set of use-cases, including industrial automation, intelligent transportation, and tactile internet. The critical nature of the traffic requirements ranges from ultra-reliable communications, massive connectivity, and enhanced mobile broadband. Thus, the growing research on cellular network monitoring and prediction aims for ensuring a satisfied user-base and fulfillment of service level agreements. The scope of this study is to develop an approach for predicting the cellular link throughput of end-users, with a goal to benchmark the performance of network slices. First, we report and analyze a measurement study involving real-life cases, such as driving in urban, sub-urban, and rural areas, as well as tests in large crowded areas. Second, we develop machine learning models using lower-layer metrics, describing the radio environment, to predict the available throughput. The models are initially validated on the LTE network and then applied to a non-standalone 5G network. Finally, we suggest scaling the proposed model into the future standalone 5G network. We have achieved 93% and 84% R^2 accuracy, with 0.06 and 0.17 mean squared error, in predicting the end-user's throughput in LTE and non-standalone 5G network, respectively.

The rise of mobile cloud gaming (MCG) has necessitated understanding its impact on mobile network design and deployment for end users' QoE maximization. MCG is a dynamic service that requires stringent quality from network operators. Therefore, this paper investigates the subjective QoE of MCG over mobile networks played on smartphones. We conducted subjective tests (N=31); our results indicate that MCG is affected differently by QoS attributes such as packet loss (PL), round trip time (RTT) and jitter compared to cloud games and online mobile games. We identify that RTT values above 100 milliseconds significantly impact users' QoE, measured via the mean opinion score (MOS). Further, lower RTT values with high PL; and higher RTT values with low PL cause a strong negative effect on MOS. Lastly, bursty jitter seems to affect the MOS, while random jitter does not significantly impact MOS.

Big data processing systems, such as Hadoop and Spark, usually work in large-scale, highly-concurrent, and multi-tenant environments that can easily cause hardware and software malfunctions or failures, thereby leading to performance degradation. Several systems and methods exist to detect big data processing systems’ performance degradation, perform root-cause analysis, and even overcome the issues causing such degradation. However, these solutions focus on specific problems such as stragglers and inefficient resource utilization. There is a lack of a generic and extensible framework to support the real-time diagnosis of big data systems. In this article, we propose, develop and validate AutoDiagn. This generic and flexible framework provides holistic monitoring of a big data system while detecting performance degradation and enabling root-cause analysis. We present an implementation and evaluation of AutoDiagn that interacts with a Hadoop cluster deployed on a public cloud and tested with real-world benchmark applications. Experimental results show that AutoDiagn can offer a high accuracy root-cause analysis framework, at the same time as offering a small resource footprint, high throughput, and low latency.

With the rapid growth of the Internet of Things (IoT) applications in smart regions/cities, for example, smart healthcare, smart homes/offices, there is an increase in security threats and risks. The IoT devices solve real-world problems by providing real-time connections, data and information. Besides this, the attackers can tamper with sensors, add or remove them physically or remotely. In this study, we address the IoT security sensor tampering issue in an office environment. We collect data from real-life settings and apply machine learning to detect sensor tampering using two methods. First, a real-time view of the traffic patterns is considered to train our isolation forest-based unsupervised machine learning method for anomaly detection. Second, based on traffic patterns, labels are created, and the decision tree supervised method is used, within our novel Anomaly Detection using Machine Learning (AD-ML) system. The accuracy of the two proposed models is presented. We found 84% with silhouette metric accuracy of isolation forest. Moreover, the result based on 10 cross-validations for decision trees on the supervised machine learning model returned the highest classification accuracy of 91.62% with the lowest false positive rate.

Connected and automated vehicles (CAVs) are envisioned to revolutionize the transportation industry, enabling autonomous processes and real-time exchange of information among vehicles and infrastructure. To safely navigate the roadways, CAVs rely on sensor readings and data from the surrounding vehicles. Hence, a fault or anomaly arising from the hardware, software, or the network can lead into devastating consequences regarding safety. This study investigates potential performance degradation caused by anomalies, by analyzing real-life vehicles’ sensory and network-related data. The aim is to utilize unsupervised learning for anomaly detection, with a goal to describe the cause and effect of the detected anomalies from a performance perspective. The results show around 93% F1-score when detecting anomalies imposed by the cellular network and the vehicle’s sensors. Moreover, with approximately 90% F1-score we can detect anomalous predictions from a deployed network-related ML model predicting cellular throughput and describe the root-causes behind the detected anomalies.

Today's research on Quality of Experience (QoE) mainly addresses multimedia services. With the introduction of the Internet of Things (IoT), there is a need for new ways of evaluating the QoE. Emerging IoT services, such as autonomous vehicles (AVs), are more complex and involve additional quality requirements, such as those related to machine-to-machine communication that enables self-driving. In fully autonomous cases, it is the intelligent machines operating the vehicles. Thus, it is not clear how intelligent machines will impact end-user QoE, but also how end users can alter and affect a self-driving vehicle. This article argues for a paradigm shift in the QoE area to cover the relationship between humans and intelligent machines. We introduce the term Quality of IoT-experience (QoIoT) within the context of AV, where the quality evaluation, besides end users, considers quantifying the perspectives of intelligent machines with objective metrics. Hence, we propose a novel architecture that considers Quality of Data (QoD), Quality of Network (QoN), and Quality of Context (QoC) to determine the overall QoIoT in the context of AVs. Finally, we present a case study to illustrate the use of QoIoT.

Microservices have emerged as a new approach for developing and deploying cloud applications that require higher levels of agility, scale, and reliability. To this end, a microservice-based cloud application architecture advocates decomposition of monolithic application components into independent software components called "microservices". As the independent microservices can be developed, deployed, and updated independently of each other, it leads to complex run-time performance monitoring and management challenges. To solve this problem, we propose a generic monitoring framework, Multi-microservices Multi-virtualization Multi-cloud (M3) that monitors the performance of microservices deployed across heterogeneous virtualization platforms in a multi-cloud environment. We validated the efficacy and efficiency of M3 using a Book-Shop application executing across AWS and Azure.

The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users’ Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R2value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R2value) regarding the same parameters using regression.

Modern big data processing systems are becoming very complex in terms of large-scale, high-concurrency and multiple talents. Thus, many failures and performance reductions only happen at run-time and are very difficult to capture. Moreover, some issues may only be triggered when some components are executed. To analyze the root cause of these types of issues, we have to capture the dependencies of each component in real-time. In this paper, we propose SmartMonit, a real-time big data monitoring system, which collects infrastructure information such as the process status of each task. At the same time, we develop a real-time stream processing framework to analyze the coordination among the tasks and the infrastructures. This coordination information is essential for troubleshooting the reasons for failures and performance reduction, especially the ones propagated from other causes.

Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS-Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

Special issue on Big Data and Cloud of Things (CoT)Cloud computing and Internet of Things (IoT) are two technologies that are already becoming part of our daily lives and are attracting significant interest from both industry and academia.The Cloud of Things (CoT) is a vision inspired from the IoT paradigm where everyday devices, namely, 'smart objects', are fully connected to the internet and are integrated with the cloud.It is expected the IoT will grow to 35 billion units by 2020, making it one of the main sources of 'Big Data' with characteristics such as volume, heterogeneity, complexity, velocity, and value.In recent years, IoT has given rise to a number of new CoT paradigms (but not limited to) including: Sensing-as-a-Service, Sensing-and Actuation-as-a-Service, Video-Surveillance-as-a-Service, Big Data Analytics-asa-Service, Data-as-a-Service, Sensor-as-a-Service, and Sensor-Event-as-a-Service. Cloud computing is a more mature technology compared to IoT.It can offer virtually unrestricted capabilities (e.g., storage and computation) to support IoT services and application that can exploit the data produced from IoT devices.The cloud essentially acts as a transparent layer between the IoT and applications providing flexibility, scalability, and hiding the complexities between the two layers (IoT and applications).However, the integration of cloud and IoT into Cloud of Things is not straightforward and imposes several challenges.These challenges include IoT device and service discovery, IoT device integration, big data management and analytics, cloud monitoring and orchestration for distributed IoT applications, mobility issues in cloud access, privacy and security, and SLA management for both cloud and IoT.Specific attention must be paid to address a range of issues from IoT data collection, storage, processing, analytics on demand to automatic provision and management of cloud resources to support the growing population of things.Hence, this special issue solicits paper related to topics including CoT architectures and models for smart provision of CoT applications, data management challenges facing CoT applications, software and tools to monitor, manage, deploy and deliver CoT applications, quality of service and related SLA management and policies for CoT applications, and security and privacy challenges facing CoT applications.The call for special issues received a number of submissions.After a two-phase peer review process, we have accepted 10 high-quality papers related to the aforementioned areas of interest.The first paper titled Using adaptive resource allocation to implement an elastic MapReduce framework by Jiaqi Zhao, Changlong Xue, Xinlin Tao, Shugong Zhang, and Jie Tao addresses the runtime resource demand challenge faced by application running on MapReduce frameworks.The proposed approach is capable of making the map reduce application, aware of overloading or under-loading situations with the resources allocated.They have extended the existing Hadoop MapReduce resource manager to implement the proposed strategy and validated the concept on an high-performance computing cluster with standard benchmark applications.Experimental results show a significant performance gain, for example, an up to 45% improvement in execution time for running multiple applications.The second paper titled A traffic hotline discovery method over cloud of things using big taxi GPS data by Xiaolong Xu, Wanchun Dou, Xuyun Zhang, Chunhua Hu, and Jinjun Chen addresses the challenge of discovering traffic hotline in CoT environments.Traffic hotlines are identified as the traffic lines with intensive traffic flows among traffic spots.They propose a hotline discovery method over CoT by establishing a hotline discovery principle.They have implemented their approach on SAP HANA cloud and tested it using big taxi global positioning system data under two application scenarios.

The stochastic nature of the cloud systems makes cloud quality of service (QoS) performance diagnosis and prediction a challenging task. A plethora of factors including virtual machine types, data centre regions, CPU types, time-of-the-day, and day-of-the-week contribute to the variability of the cloud QoS. The state-of-the-art methods for cloud performance diagnosis do not capture and model complex and uncertain inter-dependencies between these factors for efficient cloud QoS diagnosis and prediction. This paper presents ALPINE, a proof-of-concept system based on Bayesian networks. Using a real-life dataset, we demonstrate that ALPINE can be utilised for efficient cloud QoS diagnosis and prediction under stochastic cloud conditions.

The ageing population worldwide is constantly rising, both in urban and regional areas. There is a need for IoT-based remote health monitoring systems that take care of the health of elderly people without compromising their convenience and preference of staying at home. However, such systems may generate large amounts of data. The key research challenge addressed in this paper is to efficiently transmit healthcare data within the limit of the existing network infrastructure, especially in remote areas. In this paper, we identified the key network requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, we studied the network communication protocols such as CoAP, MQTT and HTTP to understand the needs of such a system, in particular the bandwidth requirements and the volume of generated data. Subsequently, we have proposed IReHMo - an IoT-based remote health monitoring architecture that efficiently delivers healthcare data to the servers. The CoAP-based IReHMo implementation helps to reduce up to 90% volume of generated data for a single sensor event and up to 56% required bandwidth for a healthcare scenario. Finally, we conducted a scalability analysis to determine the feasibility of deploying IReHMo in large numbers in regions of north Sweden.

Mobile devices have become an integral part of our daily lives. Applications running on these devices may avail storage and compute resources from the cloud(s). Further, a mobile device may also connect to heterogeneous access networks (HANs) such as WiFi and LTE to provide ubiquitous network connectivity to mobile applications. These devices have limited resources (compute, storage and battery) that may lead to service disruptions. In this context, mobile cloud computing enables offloading of computing and storage to the cloud. However, applications running on mobile devices using clouds and HANs are prone to unpredictable cloud workloads, network congestion and handoffs. To run these applications efficiently the mobile device requires the best possible cloud and network resources while roaming in HANs. This paper proposes, develops and validates a novel system called M2C2which supports mechanisms for: i.) multihoming, ii.) cloud and network probing, and iii.) cloud and network selection. We built a prototype system and performed extensive experimentation to validate our proposed M2C2. Our results analysis shows that the proposed system supports mobility efficiently in mobile cloud computing.

Quality of Experience (QoE) as an aggregate of Quality of Service (QoS) and human user-related metrics will be the key success factor for current and future mobile computing systems. QoE measurement and prediction are complex tasks as they may involve a large parameter space such as location, delay, jitter, packet loss, and user satisfaction just to name a few. These tasks necessitate the development of practical context-aware QoE models that efficiently determine relationships between user context and QoE parameters. In this paper, we propose, develop, and validate a novel decision-theoretic approach called CaQoEM for QoE modelling, measurement, and prediction. We address the challenge of QoE measurement and prediction where each QoE parameter can be measured on a different scale and may involve different units of measurement. CaQoEM is context-aware and uses Bayesian networks and utility theory to measure and predict users' QoE under uncertainty. We validate CaQoEM using extensive experimentation, user studies and simulations. The results soundly demonstrate that CaQoEM correctly measures range-defined QoE using a bipolar scale. For QoE prediction, an overall accuracy of 98.93% was achieved using 10-fold cross validation in multiple diverse network conditions such as vertical handoffs, wireless signal fading and wireless network congestion.

The service delivery model of cloud computing acts as a key enabler for big data analytics applications enhancing productivity, efficiency and reducing costs. The ever increasing flood of data generated from smart phones and sensors such as RFID readers, traffic cams etc require innovative provisioning and QoS monitoring approaches to continuously support big data analytics. To provide essential information for effective and efficient bid data analytics application QoS monitoring, in this paper we propose and develop CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework: (a) performs multi-cloud monitoring, and (b) addresses the issue of cross-layer monitoring of applications. We implement and demonstrate CLAMS functions on real-world multi-cloud platforms such as Amazon and Azure.

Cloud monitoring involves dynamically tracking the Quality of Service (QoS) parameters related to virtualized services (e.g., CPU, storage, network, appliances, etc.), the physical resources they share, and the applications running on them or data hosted on them. Monitoring techniques and services can help a cloud provider or application developer in regards to: (i) keeping the cloud services and hosted applications operating at peak efficiency; (ii) detecting variations in service and application performance; (iii) accounting the SLA violations of certain QoS parameters; and (iv) tracking the leave and join operations of cloud services due to failures and other dynamic configuration changes. In this paper, we describe the PhD research motivation, question, and approach and methodology related to developing novel cloud monitoring techniques and services enabling automated application QoS management under uncertainties.

Measuring and predicting users quality of experience (QoE) in dynamic network conditions is a challenging task. This paper presents results related to a decision-theoretic methodology incorporating Bayesian networks (BNs) and utility theory for quality of experience (QoE) measurement and prediction in mobile computing scenarios. In particular, we show how both generative and discriminative BNs can be used to measure and predict users QoE accurately for voice applications under several wireless network conditions such as wireless signal fading, vertical handoffs, wireless network congestion and normal hotspot traffic. Through extensive simulation studies and results analysis, we show that our proposed methodology can achieve an average accuracy of 98.70% using three different types of Bayesian network.

This paper presents a pioneering context-aware approach for quality of experience (QoE) measurement and prediction. The proposed approach incorporates an intuitive context-aware framework and decision theory. It is capable of incorporating several QoE related classes and context information to correctly measure and predict the overall QoE on a single scale. Our approach can be used in measuring and predicting QoE in both lab and living-lab settings based on user, device and network related context parameters. The predicted QoE can be beneficial for network operators to minimize network churn and can help application developers to build smart user-centric applications. We perform extensive experimentation and the results validate our approach.

This paper presents a blueprint for proactive context-aware mobility support architecture for heterogeneous access networks called PRONET. In particular, we leverage upon the principles of cognitive networking to support proactive context-awareness for user-centric application adaptation via quality-of-experience (QoE) provisioning. Our proposed architecture is built upon port-based multi-homed mobile IPv6 (PM-MIPv6) solution to support several applications via path diversity. In this paper our contributions are two-fold. Firstly, we identify and present gaps in our research domain related to mobility, QoE, cognitive networks and cross-layer design. We then present our architecture for providing seamless mobility in heterogeneous access networks. Currently, we are in the process of collecting results via our test bed and prototype implementation for 802.11g and HSDPA wireless networks.