Application acceleration for wireless and mobile data networks

This work studies application acceleration for wireless and mobile data networks. The problem of accelerating application can be addressed along multiple dimensions. The first dimension is advanced network protocol design, i.e., optimizing underlying network protocols, particulary transport layer protocol and link layer protocol. Despite advanced network protocol design, in this work we observe that certain application behaviors can fundamentally limit the performance achievable when operating over wireless and mobile data networks. The performance difference is caused by the complex application behaviors of these non-FTP applications. Explicitly dealing with application behaviors can improve application performance for new environments. Along this overcoming application behavior dimension, we accelerate applications by studying specific types of applications including Client-server, Peer-to-peer and Location-based applications. In exploring along this dimension, we identify a set of application behaviors that significantly affect application performance. To accommodate these application behaviors, we firstly extract general design principles that can apply to any applications whenever possible. These design principles can also be integrated into new application designs. We also consider specific applications by applying these design principles and build prototypes to demonstrate the effectiveness of the solutions. In the context of application acceleration, even though all the challenges belong to the two aforementioned dimensions of advanced network protocol design and overcoming application behavior are addressed, application performance can still be limited by the underlying network capability, particularly physical bandwidth. In this work, we study the possibility of speeding up data delivery by eliminating traffic redundancy present in application traffics. Specifically, we first study the traffic redundancy along multiple dimensions using traces obtained from multiple real wireless network deployments. Based on the insights obtained from the analysis, we propose Wireless Memory (WM), a two-ended AP-client solution to effectively exploit traffic redundancy in wireless and mobile environments. Application acceleration can be achieved along two other dimensions: network provision ing and quality of service (QoS). Network provisioning allocates network resources such as physical bandwidth or wireless spectrum, while QoS provides different priority to different applications, users, or data flows. These two dimensions have their respective limitations in the context of application acceleration. In this work, we focus on the two dimensions of overcoming application behavior and Eliminating traffic redundancy to improve application performance. The contribution of this work is as follows. First, we study the problem of application acceleration for wireless and mobile data networks, and we characterize the dimensions along which to address the problem. Second, we identify that application behaviors can significantly affect application performance, and we propose a set of design principles to deal with the behaviors. We also build prototypes to conduct system research. Third, we consider traffic redundancy elimination and propose a wireless memory approach.Ph.D.Committee Chair: Sivakumar, Raghupathy; Committee Member: Ammar, Mostafa; Committee Member: Fekri, Faramarz; Committee Member: Ji, Chuanyi; Committee Member: Ramachandran, Umakishor

Exploring Computing Continuum in IoT Systems: Sensing, Communicating and Processing at the Network Edge

As Internet of Things (IoT), originally comprising of only a few simple sensing devices, reaches 34 billion units by the end of 2020, they cannot be defined as merely monitoring sensors anymore. IoT capabilities have been improved in recent years as relatively large internal computation and storage capacity are becoming a commodity. In the early days of IoT, processing and storage were typically performed in cloud. New IoT architectures are able to perform complex tasks directly on-device, thus enabling the concept of an extended computational continuum. Real-time critical scenarios e.g. autonomous vehicles sensing, area surveying or disaster rescue and recovery require all the actors involved to be coordinated and collaborate without human interaction to a common goal, sharing data and resources, even in intermittent networks covered areas. This poses new problems in distributed systems, resource management, device orchestration,as well as data processing. This work proposes a new orchestration and communication framework, namely CContinuum, designed to manage resources in heterogeneous IoT architectures across multiple application scenarios. This work focuses on two key sustainability macroscenarios: (a) environmental sensing and awareness, and (b) electric mobility support. In the first case a mechanism to measure air quality over a long period of time for different applications at global scale (3 continents 4 countries) is introduced. The system has been developed in-house from the sensor design to the mist-computing operations performed by the nodes. In the second scenario, a technique to transmit large amounts of fine-time granularity battery data from a moving vehicle to a control center is proposed jointly with the ability of allocating tasks on demand within the computing continuum

Smartphone traffic characteristics and context dependencies

Smartphone traffic contributes a considerable amount to Internet traffic. The increasing popularity of smartphones in recent reports suggests that smartphone traffic has been growing 10 times faster than traffic generated from fixed networks. However, little is known about the characteristics of smartphone traffic. A few recent studies have analyzed smartphone traffic and given some insight into its characteristics. However, many questions remain inadequately answered. This thesis analyzes traffic characteristics and explores some important issues related to smartphone traffic. An application on the Android platform was developed to capture network traffic. A user study was then conducted where 39 participants were given HTC Magic phones with data collection applications installed for 37 days. The collected data was analyzed to understand the workload characteristics of smartphone traffic and study the relationship between participant contexts and smartphone usage. The collected dataset suggests that even in a small group of participants a variety of very different smartphone usage patterns occur. Participants accessed different types of Internet content at different times and under different circumstances. Differences between the usage of Wi-Fi and cellular networks for individual participants are observed. Download-intensive activities occurred more frequently over Wi-Fi networks. Dependencies between smartphone usage and context (where they are, who they are with, at what time, and over which physical interface) are investigated in this work. Strong location dependencies on an aggregate and individual user level are found. Potential relationships between times of the day and access patterns are investigated. A time-of-day dependent access pattern is observed for some participants. Potential relationships between movement and proximity to other users and smartphone usage are also investigated. The collected data suggests that moving participants used map applications more. Participants generated more traffic and primarily downloaded apps when they were alone. The analyses performed in this thesis improve basic understanding and knowledge of smartphone use in different scenarios

Reducing Internet Latency : A Survey of Techniques and their Merit

Bob Briscoe, Anna Brunstrom, Andreas Petlund, David Hayes, David Ros, Ing-Jyh Tsang, Stein Gjessing, Gorry Fairhurst, Carsten Griwodz, Michael WelzlPeer reviewedPreprin

A Task Offloading Framework for Energy Saving on Mobile Devices using Cloud Computing

Over the last decade, mobile devices have become popular among people, and their number is ever growing because of the computing functionality they offer beyond primary voice communication. However, mobile devices are unable to accommodate most of the computing demand as long as they suffer the limited energy supply caused by the capacity of their small battery to store only a relatively small amount of energy. The literature describes several specialist techniques proposed in academia and industry that save the mobile device energy and solve this problem to some extent but not satisfactorily. Task offloading from mobile devices to cloud computing is a promising technique for tackling the problem especially with the emergence of high-speed wireless networks and the ubiquitous resources from the cloud computing. Since task offloading is in its nascent age, it lacks evaluation and development in-depth studies. In this dissertation, we proposed an offloading framework to make task offloading possible to save energy for mobile devices. We achieved a great deal of progress toward developing a realistic offloading framework. First, we examined the feasibility of exploiting the offloading technique to save mobile device energy using the cloud as the place to execute the task instead of executing it on the mobile device. Our evaluation study reveals that the offloading does not always save energy; in cases where the energy for the computation is less than the energy for communication no energy is saved. Therefore, the need for the offloading decision is vital to make the offloading beneficial. Second, we developed mathematical models for the energy consumption of a mobile device and its applications. These models were then used to develop mathematical models that estimate the energy consumption on the networking and the computing activities at the application level. We modelled the energy consumption of the networking activity for the Transmission Control Protocol (TCP) over Wireless Local Area Network (WLAN), the Third Generation (3G), and the Fourth Generation (4G) of mobile telecommunication networks. Furthermore, we modelled the energy consumption of the computing activity for the mobile multi-core Central Processing Unit (CPU) and storage unit. Third, we identified and classified the system parameters affecting the offloading decision and built our offloading framework based on them. In addition, we implemented and validated the proposed framework experimentally using a real mobile device, cloud, and application. The experimental results reveal that task offloading is beneficial for mobile devices given that in some cases it saves more than 70% of the energy required to execute a task. Additionally, our energy models accurately estimate the energy consumption for the networking and computing activities. This accuracy allows the offloading framework to make the correct decision as to whether or not offloading a given task saves energy. Our framework is built to be applicable to modern mobile devices and expandable by considering all system parameters that have impact on the offloading decision. In fact, the experimental validation proves that our framework is practical to real life scenarios. This framework gives researchers in the field useful tools to design energy efficient offloading systems for the coming years when the offloading will be common.4 month