Energy-efficient Transitional Near-* Computing

Studies have shown that communication networks, devices accessing the Internet, and data centers account for 4.6% of the worldwide electricity consumption. Although data centers, core network equipment, and mobile devices are getting more energy-efficient, the amount of data that is being processed, transferred, and stored is vastly increasing. Recent computer paradigms, such as fog and edge computing, try to improve this situation by processing data near the user, the network, the devices, and the data itself. In this thesis, these trends are summarized under the new term near-* or near-everything computing. Furthermore, a novel paradigm designed to increase the energy efficiency of near-* computing is proposed: transitional computing. It transfers multi-mechanism transitions, a recently developed paradigm for a highly adaptable future Internet, from the field of communication systems to computing systems. Moreover, three types of novel transitions are introduced to achieve gains in energy efficiency in near-* environments, spanning from private Infrastructure-as-a-Service (IaaS) clouds, Software-defined Wireless Networks (SDWNs) at the edge of the network, Disruption-Tolerant Information-Centric Networks (DTN-ICNs) involving mobile devices, sensors, edge devices as well as programmable components on a mobile System-on-a-Chip (SoC). Finally, the novel idea of transitional near-* computing for emergency response applications is presented to assist rescuers and affected persons during an emergency event or a disaster, although connections to cloud services and social networks might be disturbed by network outages, and network bandwidth and battery power of mobile devices might be limited

ReactiFi: Reactive Programming of Wi-Fi Firmware on Mobile Devices

Network programmability will be required to handle future increased network traffic and constantly changing application needs. However, there is currently no way of using a high-level, easy to use programming language to program Wi-Fi firmware. This impedes rapid prototyping and deployment of novel network services/applications and hinders continuous performance optimization in Wi-Fi networks, since expert knowledge is required for both the used hardware platforms and the Wi-Fi domain. In this paper, we present ReactiFi, a high-level reactive programming language to program Wi-Fi chips on mobile consumer devices. ReactiFi enables programmers to implement extensions of PHY, MAC, and IP layer mechanisms without requiring expert knowledge of Wi-Fi chips, allowing for novel applications and network protocols. ReactiFi programs are executed directly on the Wi-Fi chip, improving performance and power consumption compared to execution on the main CPU. ReactiFi is conceptually similar to functional reactive languages, but is dedicated to the domain-specific needs of Wi-Fi firmware. First, it handles low-level platform-specific details without interfering with the core functionality of Wi-Fi chips. Second, it supports static reasoning about memory usage of applications, which is important for typically memory-constrained Wi-Fi chips. Third, it limits dynamic changes of dependencies between computations to dynamic branching, in order to enable static reasoning about the order of computations. We evaluate ReactiFi empirically in two real-world case studies. Our results show that throughput, latency, and power consumption are significantly improved when executing applications on the Wi-Fi chip rather than in the operating system kernel or in user space. Moreover, we show that the high-level programming abstractions of ReactiFi have no performance overhead compared to manually written C code

Energy-Efficient Management of Virtual Machines in Eucalyptus

Abstract-In this paper, an approach for improving the energy efficiency of infrastructure-as-a-service clouds is presented. The approach is based on performing live migrations of virtual machines to save energy. In contrast to related work, the energy costs of live migrations including their pre-and post-processing phases are taken into account, and the approach has been implemented in the Eucalyptus open-source cloud computing system by efficiently combining a multi-layered file system and distributed replication block devices. To evaluate the proposed approach, several short-and long-term tests based on virtual machine workloads produced with common operating system benchmarks, web-server emulations as well as different MapReduce applications have been conducted. The results indicate that energy savings of up to 16 percent can be achieved in a productive Eucalyptus environment

Dynamic Role Assignment in Software-Defined Wireless Networks

Software-defined networking paradigms have found their way into wireless edge networks, allowing network slicing, mobility management, and resource allocation. This paper presents dynamic role assignment as a novel approach to software-defined network topology management for wireless edge devices, such as laptops, tablets and smartphones. It combines the centralized control of wireless Network Interface Controller (NIC) modes with Network Function Virtualization (NFV) to integrate network topology transitions as well as network service and application service placement within a single mechanism. Our proposal is evaluated with respect to latency, bandwidth, and power consumption of the edge nodes. The experimental results show significant differences in both bandwidth (up to 18%) and power consumption (up to 15%) for playing different roles, and when using (a) a web proxy and (b) an intrusion prevention system as examples of application services

Opportunistic Named Functions in Disruption-tolerant Emergency Networks

Information-centric disruption-tolerant networks (ICN-DTNs) are useful to re-establish mobile communication in disaster scenarios when telecommunication infrastructures are partially or completely unavailable. In this paper, we present opportunistic named functions, a novel approach to operate ICN-DTNs during emergencies. Affected people and first responders use their mobile devices to specify their interests in particular content and/or application-specific functions that are then executed in the network on the fly, either partially or totally, in an opportunistic manner. Opportunistic named functions rely on user-defined interests and on locally optimal decisions based on battery lifetimes and device capabilities. In the presented emergency scenario, they are used to preprocess, analyze, integrate and transfer information extracted from images produced by smartphone cameras, with the aim of supporting the search for missing persons and the assessment of critical conditions in a disaster area. Experimental results show that opportunistic named functions reduce network congestion and improve battery lifetime in a network of battery-powered sensors, mobile devices, and mobile routers, while delivering crucial information to carry out situation analysis in disasters