Towards Ubiquitous and Adaptive Web-Based Multimedia Communications via the Cloud

This chapter at first surveys the Web technologies that can enable ubiquitous and pervasive multimedia communications over the Web and then reviews the challenges that are raised by their combination. In this context, the relevant HTML5 APIs and technologies provided for service adaptation are introduced and the MPEG-DASH, X3Dom, and WebRTC frameworks are discussed. What is envisaged for the future of mobile multimedia is that with the integration of these technologies one can shape a diversity of future pervasive and personalized cloud-based Web applications, where the client-server operations are obsolete. In particular, it is believed that in the future Web cloud-based Web applications will be able to communicate, stream, and transfer adaptive events and content to their clients, creating a fully collaborative and pervasive Web 3D environment.

Context-Awareness in Opportunistic Mobile Cloud Computing

Smartphones have shaped the mobile computing community. Unfortunately, their power consumption overreaches the limits of current battery technology. Most solutions for energy efficiency turn towards offloading code from the mobile device into the cloud. Although mobile cloud computing inherits all the Cloud Computing advantages, it does not treat user mobility, the lack of connectivity, or the high cost of mobile network traffic. In this chapter, the authors introduce mobile-to-mobile contextual offloading, a novel collaboration concept for handheld devices that takes advantage of an adaptive contextual search algorithm for scheduling mobile code execution over smartphone communities, based on predicting the availability and mobility of nearby devices. They present the HYCCUPS framework, which implements the contextual offloading model in an on-the-fly opportunistic hybrid computing cloud. The authors emulate HYCCUPS based on real user traces and prove that it maximizes power saving, minimizes overall execution time, and preserves user experience.

Mobile Cloud Computing

Mobile Cloud Computing (MCC) has become an important research area due to rapid growth of mobile applications and emergence of cloud computing. MCC refers to integration of cloud computing into a mobile environment. Cloud providers (e.g. Google, Amazon, and Salesforce) support mobile users by providing the required infrastructure (e.g. servers, networks, and storage), platforms, and software. Mobile devices are rapidly becoming a fundamental part of human lives and these enable users to access various mobile applications through remote servers using wireless networks. Traditional mobile device-based computing, data storage, and large-scale information processing is transferred to “cloud,” and therefore, requirement of mobile devices with high computing capability and resources are reduced. This chapter provides a survey of MCC including its definition, architecture, and applications. The authors discuss the issues in MCC, existing solutions, and approaches. They also touch upon the computation offloading mechanism for MCC.

H.265 Video Streaming in Mobile Cloud Networks

Mobile video applications have started to dominate the global mobile data traffic in recent years, and both opportunities and challenges have arisen when the emerging mobile cloud paradigm is introduced to support the resource-demanding video processing and networking services. This chapter offers in-depth discussions for content- and context-aware, adaptive, robust, secure, and real-time video applications in mobile cloud networks. The chapter describes and analyses the essential building blocks including the state-of-the-art technologies and standards on video encoding, adaptive streaming, mobile cloud computing, and resource management, and the associated security issues. The focus is context-aware adaptive streaming based on the latest video coding standard H.265 in the context of Internet-centric mobile cloud networking. Built upon selected building blocks underpinned by promising approaches and emerging standards, an integrated architecture is proposed towards achieving next-generation smart video streaming for mobile cloud users, with future research directions in this field identified.

Traffic Analyses and Measurements

Resource management schemes in current data centers, including cloud environments, are not well equipped to handle the dynamic variation in traffic caused by the large diversity of traffic sources, source mobility patterns, and underlying network characteristics. Part of the problem is lacking knowledge on the traffic source behaviour and its proper representation for development and operation. Inaccurate, static traffic models lead to incorrect estimation of traffic characteristics, making resource allocation, migration, and release schemes inefficient, and limit scalability. The end result is unsatisfied customers (due to service degradation) and operators (due to costly inefficient infrastructure use). The authors argue that developing appropriate methods and tools for traffic predictability requires carefully conducted and analysed traffic experiments. This chapter presents their measurements and statistical analyses on various traffic sources for two network settings, namely local Area Network (LAN) and 3G mobile network. LAN traffic is organised in DiffServ categories supported by MPLS to ensure Quality of Service (QoS) provisioning. 3G measurements are taken from a live network upon entering the IP domain. Passive monitoring was used to collect the measurements in order to be non-obtrusive for the networks. The analyses indicate that the gamma distribution has general applicability to represent various traffic sources by proper setting of the parameters. The findings allow the construction of traffic models and simulation tools to be used in the development and evaluation of flexible resource management schemes that meet the real-time needs of the users.

Virtualization Evolution

This chapter discusses the evolution of the cloud computing paradigm and its applicability in various sections of the computing and networking/telecommunications industry, such as the cloud networking, the cloud offloading, and the network function virtualization. The new heterogeneous virtualized ecosystem that is formulated creates new needs and challenges for management and administration at the network part. For this purpose, the approach of Software-Defined Networking is discussed and its future perspectives are further analyzed.

A Social-Oriented Mobile Cloud Scheme for Optimal Energy Conservation

This chapter elaborates on energy usage optimization issues by exploiting a resource offloading process based on a social-oriented mobile cloud scheme. The adoption of the proposed scheme enables for increasing the reliability in services provision to the mobile users by guaranteeing sufficient resources for the mobile application execution. More specifically, this chapter describes the process to improve the energy consumption of the mobile devices through the exploitation of a social-oriented model and a cooperative partial process offloading scheme. This research approach exploits social centrality, as the connectivity model for the resource offloading, among the interconnected mobile devices to increase the energy usage efficiency, the mobile nodes availability, as well as the process of execution reliability. The proposed scheme is thoroughly evaluated to define the validity and the efficiency for the energy conservation increase of future mobile computing devices.

Mobile Cloud Resource Management

Providing mobile cloud services requires seamless integration between various platforms to offer mobile users optimum performance. To achieve this, many fundamental problems such as bandwidth availability and reliability, resource scarceness, and finite energy must be addressed before rolling out such services. This chapter aims to explore technological challenges for mobile cloud computing in the area of resource management focusing on both parts of the infrastructure: mobile devices and cloud networks. Starting with introducing mobile cloud computing, it then stresses the importance of resource management in the operation of mobile cloud services presenting various types of resources available for cloud computing. Furthermore, it examines the various types of resource management techniques available for mobile clouds. Finally, future directions in the field of resource management for mobile cloud computing environment are presented.

Adaptation of Cloud Resources and Media Streaming in Mobile Cloud Networks for Media Delivery

Multimedia content delivery is one of the use cases of Mobile Cloud Networks. Cloud Networks are then called Media Clouds. Since mobile devices are becoming increasingly important receptors of Multimedia content, Mobile Cloud Computing is undertaking an important role for delivering Multimedia content from the Cloud through the Internet towards the Mobile users. On the other hand, high requirements of Multimedia content streaming establish the necessity of crossp-layer mechanisms for avoiding or decreasing the effects of, for example, mobile network congestion or cloud congestion. This chapter introduces an exemplary solution, at the application layer, which takes into account the state of the network for efficient Media streaming in Mobile Cloud networks (Media Mobile Cloud). Concretely, the presented solution proposes a novel adaptation algorithm that adapts not only Media bitrate in the case when there is a congestion in Mobile last mille, but also adapts Media content source when the Cloud suffers from congestion.

Anomaly Detection in Cloud Environments

Cloud environments compose unique operational characteristics and intrinsic capabilities such as service transparency and elasticity. By virtue of their exclusive properties as being outcomes of their virtualized nature, these environments are prone to a number of security threats either from malicious or legitimate intent. By virtue of the minimal proactive properties attained by off-the-shelf signature-based commercial detection solutions employed in various infrastructures, cloud-specific Intrusion Detection System (IDS) Anomaly Detection (AD)-based methodologies have been proposed in order to enable accurate identification, detection, and clustering of anomalous events that could manifest. Therefore, in this chapter the authors firstly aim to provide an overview in the state of the art related with cloud-based AD mechanisms and pinpoint their basic functionalities. They subsequently provide an insight and report some results derived by a particular methodology that jointly considers cloud-specific properties and relies on the Empirical Mode Decomposition (EMD) algorithm.