Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

يعد جدول الفديو الاكثر انتشارا اليوم. اضافة الى ذلك، وبسبب انتشار الوباء عالميا، كثير من الناس التزموا المنزل واعتمدوا على الخدمات الجدولية للاخبار والتعليم والتسلية. على اية حال، مستعمل تجربة (QoE (غير مقتنع باختيار محتوى الفديو بينما يتدفق في الاجهزة الذكية. ينزعج المستعملون بمسح نوعية الفيديو الغير متوقعة التي تحدث في اجهزتهم الذكية. في هذا البحث، نقترح مخطط لاختيار الفديو الهيكلي الذي يهدف الى زيادة قناعة مستعمل (QoE ). تم استعمال نظام الحلول الحسابية لاختيار محتوى الفديو لانشاء خريطة لاختيار الفديوالتي ترضي مستعمل نوعية الجدول الاكثراعتبارا.  تصنف اختيار محتوى الفديو الى مجاميع صفات الفديو. سينخفض مستوى جدول ( VCS) بالتدريج ليعتبر اقل اختيار الفديو الذي لا يقبلها المستعمل اعتمادا على نوعية الفديو. لتقييم مستوى القناعة ، استعملنا درجة الرأي الوضيع ( MOS) لقياس تكيف قبول المستعمل اتجاه نوعية جدول الفديو.  أظهرت النتائج الاخيرة بأن نظام الحلول الحسابية المقترح توضح بأن المستعمل يقتنع باختيار الفديو بواسطة تغيير صفات الفديو. Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education,  and entertainment. However,   when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes

Self-Aware resource management in embedded systems

Resource management for modern embedded systems is challenging in the presence of dynamic workloads, limited energy and power budgets, and application and user requirements. These diverse and dynamic requirements often result in conflicting objectives that need to be handled by intelligent and self-aware resource management. State-of-the-art resource management approaches leverage offline and online machine learning techniques for handling such complexity. However, these approaches focus on fixed objectives, limiting their adaptability to dynamically evolving requirements at run-time. In this dissertation, we first propose resource management approaches with fixed objectives for handling concurrent dynamic workload scenarios, mixed-sensitivity workloads, and user requirements and battery constraints. Then, we propose comprehensive self-aware resource management for handling multiple dynamic objectives at run-time. The proposed resource management approaches in this dissertation use machine learning techniques for offline modeling and online controlling. In each resource management approach, we consider a dynamic set of requirements that had not been considered in the state-of-the-art approaches and improve the selfawareness of resource management by learning applications characteristics, users’ habits, and battery patterns. We characterize the applications by offline data collection for handling the conflicting requirements of multiple concurrent applications. Further, we consider user’s activities and battery patterns for user and battery-aware resource management. Finally, we propose a comprehensive resource management approach which considers dynamic variation in embedded systems and formulate a goal for resource management based on that. The approaches presented in this dissertation focus on dynamic variation in the embedded systems and responding to the variation efficiently. The approaches consider minimizing energy consumption, satisfying performance requirements of the applications, respecting power constraints, satisfying user requirements, and maximizing battery cycle life. Each resource management approach is evaluated and compared against the relevant state-of-the-art resource management frameworks

Modeling Mobile Code Acceleration in the Cloud

Peer reviewe

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices

In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications. To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd

A selective approach for energy-aware video content adaptation decision-taking engine in android based smartphone

Rapid advancement of technology and their increasing affordability have transformed mobile devices from a means of communication to tools for socialization, entertainment, work and learning. However, advancement of battery technology and capacity is slow compared to energy need. Viewing content with high quality of experience will consume high power. In limited available energy, normal content adaptation system will decrease the content quality, hence reducing quality of experience. However, there is a need for optimizing content quality of experience (QoE) in a limited available energy. With modification and improvement, content adaptation may solve this issue. The key objective of this research is to propose a framework for energy-aware video content adaptation system to enable video delivery over the Internet. To optimise the QoE while viewing streaming video on a limited available smartphone energy, an algorithm for energy-aware video content adaptation decision-taking engine named EnVADE is proposed. The EnVADE algorithm uses selective mechanism. Selective mechanism means the video segmented into scenes and adaptation process is done based on the selected scenes. Thus, QoE can be improved. To evaluate EnVADE algorithm in term of energy efficiency, an experimental evaluation has been done. Subjective evaluation by selected respondents are also has been made using Absolute Category Rating method as recommended by ITU to evaluate EnVADE algorithm in term of QoE. In both evaluation, comparison with other methods has been made. The results show that the proposed solution is able to increase the viewing time of about 14% compared to MPEG-DASH which is an official international standard and widely used streaming method. In term of QoE subjective test, EnVADE algorithm score surpasses the score of other video streaming method. Therefore, EnVADE framework and algorithm has proven its capability as an alternative technique to stream video content with higher QoE and lower energy consumption

A quality of experience approach in smartphone video selection framework for energy efficiency

Online video streaming is getting more common in the smartphone device nowadays. Since the Corona Virus (COVID-19) pandemic hit all human across the globe in 2020, the usage of online streaming among smartphone user are getting more vital. Nevertheless, video streaming can cause the smartphone energy to drain quickly without user to realize it. Also, saving energy alone is not the most significant issues especially if with the lack of attention on the user Quality of Experience (QoE). A smartphones energy management is crucial to overcome both of these issues. Thus, a QoE Mobile Video Selection (QMVS) framework is proposed. The QMVS framework will govern the tradeoff between energy efficiency and user QoE in the smartphone device. In QMVS, video streaming will be using Dynamic Video Attribute Pre-Scheduling (DVAP) algorithm to determine the energy efficiency in smartphone devices. This process manages the video attribute such as brightness, resolution, and frame rate by turning to Video Content Selection (VCS). DVAP is handling a set of rule in the Rule Post-Pruning (RPP) method to remove an unused node in list tree of VCS. Next, QoE subjective method is used to obtain the Mean Opinion Score (MOS) of users from a survey experiment on QoE. After both experiment results (MOS and energy) are established, the linear regression technique is used to find the relationship between energy consumption and user QoE (MOS). The last process is to analyze the relationship of VCS results by comparing the DVAP to other recent video streaming applications available. Summary of experimental results demonstrate the significant reduction of 10% to 20% energy consumption along with considerable acceptance of user QoE. The VCS outcomes are essential to help users and developer deciding which suitable video streaming format that can satisfy energy consumption and user QoE

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

Teenustele orienteeritud ja tõendite-teadlik mobiilne pilvearvutus

Arvutiteaduses on kaks kõige suuremat jõudu: mobiili- ja pilvearvutus. Kui pilvetehnoloogia pakub kasutajale keerukate ülesannete lahendamiseks salvestus- ning arvutusplatvormi, siis nutitelefon võimaldab lihtsamate ülesannete lahendamist mistahes asukohas ja mistahes ajal. Täpsemalt on mobiilseadmetel võimalik pilve võimalusi ära kasutades energiat säästa ning jagu saada kasvavast jõudluse ja ruumi vajadusest. Sellest tulenevalt on käesoleva töö peamiseks küsimuseks kuidas tuua pilveinfrastruktuur mobiilikasutajale lähemale? Antud töös uurisime kuidas mobiiltelefoni pilveteenust saab mobiilirakendustesse integreerida. Saime teada, et töö delegeerimine pilve eeldab mitmete pilve aspektide kaalumist ja integreerimist, nagu näiteks ressursimahukas töötlemine, asünkroonne suhtlus kliendiga, programmaatiline ressursside varustamine (Web APIs) ja pilvedevaheline kommunikatsioon. Nende puuduste ületamiseks lõime Mobiilse pilve vahevara Mobile Cloud Middleware (Mobile Cloud Middleware - MCM) raamistiku, mis kasutab deklaratiivset teenuste komponeerimist, et delegeerida töid mobiililt mitmetele pilvedele kasutades minimaalset andmeedastust. Teisest küljest on näidatud, et koodi teisaldamine on peamisi strateegiaid seadme energiatarbimise vähendamiseks ning jõudluse suurendamiseks. Sellegipoolest on koodi teisaldamisel miinuseid, mis takistavad selle laialdast kasutuselevõttu. Selles töös uurime lisaks, mis takistab koodi mahalaadimise kasutuselevõttu ja pakume lahendusena välja raamistiku EMCO, mis kogub seadmetelt infot koodi jooksutamise kohta erinevates kontekstides. Neid andmeid analüüsides teeb EMCO kindlaks, mis on sobivad tingimused koodi maha laadimiseks. Võrreldes kogutud andmeid, suudab EMCO järeldada, millal tuleks mahalaadimine teostada. EMCO modelleerib kogutud andmeid jaotuse määra järgi lokaalsete- ning pilvejuhtude korral. Neid jaotusi võrreldes tuletab EMCO täpsed atribuudid, mille korral mobiilirakendus peaks koodi maha laadima. Võrreldes EMCO-t teiste nüüdisaegsete mahalaadimisraamistikega, tõuseb EMCO efektiivsuse poolest esile. Lõpuks uurisime kuidas arvutuste maha laadimist ära kasutada, et täiustada kasutaja kogemust pideval mobiilirakenduse kasutamisel. Meie peamiseks motivatsiooniks, et sellist adaptiivset tööde täitmise kiirendamist pakkuda, on tagada kasutuskvaliteet (QoE), mis muutub vastavalt kasutajale, aidates seeläbi suurendada mobiilirakenduse eluiga.Mobile and cloud computing are two of the biggest forces in computer science. While the cloud provides to the user the ubiquitous computational and storage platform to process any complex tasks, the smartphone grants to the user the mobility features to process simple tasks, anytime and anywhere. Smartphones, driven by their need for processing power, storage space and energy saving are looking towards remote cloud infrastructure in order to solve these problems. As a result, the main research question of this work is how to bring the cloud infrastructure closer to the mobile user? In this thesis, we investigated how mobile cloud services can be integrated within the mobile apps. We found out that outsourcing a task to cloud requires to integrate and consider multiple aspects of the clouds, such as resource-intensive processing, asynchronous communication with the client, programmatically provisioning of resources (Web APIs) and cloud intercommunication. Hence, we proposed a Mobile Cloud Middleware (MCM) framework that uses declarative service composition to outsource tasks from the mobile to multiple clouds with minimal data transfer. On the other hand, it has been demonstrated that computational offloading is a key strategy to extend the battery life of the device and improves the performance of the mobile apps. We also investigated the issues that prevent the adoption of computational offloading, and proposed a framework, namely Evidence-aware Mobile Computational Offloading (EMCO), which uses a community of devices to capture all the possible context of code execution as evidence. By analyzing the evidence, EMCO aims to determine the suitable conditions to offload. EMCO models the evidence in terms of distributions rates for both local and remote cases. By comparing those distributions, EMCO infers the right properties to offload. EMCO shows to be more effective in comparison with other computational offloading frameworks explored in the state of the art. Finally, we investigated how computational offloading can be utilized to enhance the perception that the user has towards an app. Our main motivation behind accelerating the perception at multiple response time levels is to provide adaptive quality-of-experience (QoE), which can be used as mean of engagement strategy that increases the lifetime of a mobile app