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2019 | Book

Introduction Read chapter Read first chapter

Spectrum-Aware Mobile Computing

Convergence of Cloud Computing and Cognitive Networking

Authors: Dr. Seyed Eman Mahmoodi, Prof. Koduvayur Subbalakshmi, Dr. R. N. Uma

Publisher: Springer International Publishing

Book Series : Signals and Communication Technology

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book presents solutions to the problems arising in two trends in mobile computing and their intersection: increased mobile traffic driven mainly by sophisticated smart phone applications; and the issue of user demand for lighter phones, which cause more battery power constrained handhelds to offload computations to resource intensive clouds (the second trend exacerbating the bandwidth crunch often experienced over wireless networks). The authors posit a new solution called spectrum aware cognitive mobile computing, which uses dynamic spectrum access and management concepts from wireless networking to offer overall optimized computation offloading and scheduling solutions that achieve optimal trade-offs between the mobile device and wireless resources. They show how in order to allow these competing goals to meet in the middle, and to meet the promise of 5G mobile computing, it is essential to consider mobile offloading holistically, from end to end and use the power of multi-radio access technologies that have been recently developed. Technologies covered in this book have applications to mobile computing, edge computing, fog computing, vehicular communications, mobile healthcare, mobile application developments such as augmented reality, and virtual reality.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
Recent advances in smartphone technologies and wireless communications have created a strong uptick in the use of smart applications over web-enabled, resource-constrained end devices. In order to support such computation and data intensive applications over wireless devices, two problems must be addressed: (1) the resource constraints on the device (e.g., battery power, memory, etc.) and (2) network constraints (e.g., capacity/spectrum and latency limitations). Multiple radio spectrum access technologies (multi-RAT) is becoming one of the ways to address some of the network related problems (e.g., capacity). The heterogeneous network (HetNets) paradigm, enabling multi-RATs is expected to become a mainstay of future wireless networks. Simultaneous access to multiple RATs or spectrum bands can be implemented at the transport layer, network layer, or PHY/MAC layer of wireless devices. The growth of mobile virtual network operators (MVNO) will also facilitate such multi-RAT opportunistic spectrum access. Google’s recent deal with Sprint and T-Mobile is an example in this direction.
At the device level, cloud offloading has emerged as an indispensable part of the solution to the device level constraints. However, offloading computations to a remote cloud can also place an additional burden on the already overburdened wireless backbone. The term “cloud offloading” can mean data flow offloading or offloading computationally intense tasks to the cloud along with the data resulting from the computations. Here we mean the latter. In this chapter, we discuss the factors affecting computation offloading and discuss the organization of the book.
Seyed Eman Mahmoodi, Koduvayur Subbalakshmi, R. N. Uma
Chapter 2. Classification of Mobile Cloud Offloading
Abstract
Mobile computing has been studied in the literature for a while now. This chapter discusses the key research areas in this vast field. A classification of existing schemes as well as the most recent developments in this field is discussed in this chapter. Although a lot of work has been done in this area, there is still potential for very new directions that this field could take. These are elaborated in this chapter as well.
Seyed Eman Mahmoodi, Koduvayur Subbalakshmi, R. N. Uma
Chapter 3. Joint Scheduling and Cloud Offloading Using Single Radio
Abstract
As discussed in the previous chapter, one of the ways to succinctly describe the structure of a mobile application is through the use of component dependency graphs. This chapter discusses computational offloading in the situations where the solutions are free to consider the arbitrary dependency graphs as is, without adhering to any pre-determined scheduling order that the compiler may introduce. Joint scheduling–offloading schemes that optimally maximize a net utility function for single radio enabled mobile devices are discussed in this chapter. The net utility function trades-off the energy saved at the resource-constrained device with the time and energy costs involved in offloading while meeting the precedence constraints and execution deadline of the application. Optimizing the scheduling of the individual components along with cloud offloading decisions, taking into account the wireless network parameters, allows for an overall better solution compared to optimizing only the offloading decisions using a pre-determined compiler-generated schedule order of execution for the individual components. Besides, using the general dependency graphs (without imposing a sequential ordering for processing) and an optimal joint scheduling–offloading scheme can potentially allow for parallel scheduling of components in the mobile and cloud at the same time, thus reducing time to completion for the application.
Seyed Eman Mahmoodi, Koduvayur Subbalakshmi, R. N. Uma
Chapter 4. Cognitive Cloud Offloading Using Multiple Radios
Abstract
Given recent advances in technologies that enable bandwidth aggregation in wireless devices and the development of the HetNet, it is possible to simultaneously RAT interfaces in a wireless device. This chapter discusses optimal computational offloading that uses all available and viable RAT interfaces of a mobile device to achieve the best possible resource trade-offs when computationally offloading. The concept of cognitive cloud offloading is introduced. The solution discussed in this chapter optimally decides which components of an application to offload and which to execute locally, while simultaneously optimizing the percentage of data (associated with this offloading) to be sent via each viable radio interface. This chapter also discusses other solutions that fall under the general umbrella of radio-aware computation offloading.
This chapter also discusses a comprehensive model for the energy consumed by the mobile device, including energy expended in communicating relevant data between the cloud and the device. Note that unlike in the previous chapter, the solutions studied in this chapter assume a compiler pre-determined scheduling order for the components of the application.
Seyed Eman Mahmoodi, Koduvayur Subbalakshmi, R. N. Uma
Chapter 5. Optimal Cognitive Scheduling and Cloud Offloading Using Multi-Radios
Abstract
In this chapter, we move towards the generalization of the problems considered in Chaps. 3 and 4. This extension is achieved in three ways: (1) by allowing for a natural scheduling order and more general dependencies between the components of the application, (2) using all viable RAT interfaces for cloud offloading, and (3) taking a time-adaptive approach that is cognizant of and responsive to the changes in the wireless network conditions over time. We coin the term cognitive scheduling and cloud offloading (CSCO) for this class of approaches. A mathematical model for the cost function is developed and methods to solve this optimization problem are discussed.
Seyed Eman Mahmoodi, Koduvayur Subbalakshmi, R. N. Uma
Chapter 6. Time-Adaptive and Cognitive Cloud Offloading Using Multiple Radios
Abstract
While the problem setup in the previous chapter is the most general, the solution presented in the previous chapter could be computationally expensive. This chapter introduces more practical heuristic time-adaptive schemes to schedule the components for offloading, while simultaneously optimizing the percentages of data to be sent by the mobile and the cloud via each wireless interface. A comprehensive model for the utility function is described that trades-off resources saved by remote execution (such as energy, memory, and CPU consumption by the mobile device) with the cost of communication required for offloading (such as energy consumed by offloading and the data queue length at the multiple radio interfaces). Two different ways to implement the solution are discussed.
The offloading strategies for transmission at the mobile and cloud end use past wireless interface data, queue status, and the current data flow to update the current queue status.
Seyed Eman Mahmoodi, Koduvayur Subbalakshmi, R. N. Uma
Chapter 7. Evaluation of Cloud Offloading and Scheduling Mechanisms in Different Scenarios
Abstract
This chapter presents a detailed discussion of the various experimental setups used to test all the solutions discussed in the previous chapters. The experimental results are then discussed and the performance of all schemes described in this book is compared with one another as well as with approaches including (1) local execution (no offloading); (2) complete offloading (all components remotely executed); (3) the non-time adaptive dynamic offloading algorithm proposed in the literature extended to applications with sequential dependency graphs; and (4) the approach where offloading takes place only via the best link at each instant of time. It is seen that performance-wise the best overall optimal solution is achieved with cognitive scheduling and cloud offloading.
Seyed Eman Mahmoodi, Koduvayur Subbalakshmi, R. N. Uma
Chapter 8. The Future: Spectrum Aware Cloud Offloading
Abstract
Preliminary work discussed the impact that cognitive scheduling and cloud offloading could have in resource optimization in mobile cloud computing for today’s 5G application. This chapter discusses the future of this field of study and describes some directions in which the research in this area could evolve.
Seyed Eman Mahmoodi, Koduvayur Subbalakshmi, R. N. Uma
Backmatter
Metadata
Title
Spectrum-Aware Mobile Computing
Authors
Dr. Seyed Eman Mahmoodi
Prof. Koduvayur Subbalakshmi
Dr. R. N. Uma
Copyright Year
2019
Electronic ISBN
978-3-030-02411-6
Print ISBN
978-3-030-02410-9
DOI
https://doi.org/10.1007/978-3-030-02411-6