3,481 research outputs found
On the Energy Efficiency of Virtual Machines’ Live Migration in Future Cloud Mobile Broadband Networks
In this chapter, a live migration of the virtual machine (VM) power consumption (PC) model is introduced. The model proposed an easy and parameterised method to evaluate the power cost of migrating the VMs from one server to another. This work is different from other research works found in the literature. It is not based on software, utilisation ratio or heuristic algorithms. Rather, it is based on converting and generalising the concepts of live migration process and experimental results from other works, which are based on the aforementioned tools. The resulting model eventually converts the power cost of live migration from a function of utilisation ratio to a function of server PC. This means there will be neither a need for additional hardware, a separate software, nor a heuristics-based algorithms to measure the utilisation. The resulting model is simple, on the fly and accurate PC evaluation. Furthermore, the latency cost of live migration process, included the time it take the VM to be completely transferred to the target server, alongside the link distance/delay between the two servers is discussed
Topics in Power Usage in Network Services
The rapid advance of computing technology has created a world powered
by millions of computers. Often these computers are idly consuming energy
unnecessarily in spite of all the efforts of hardware manufacturers. This thesis
examines proposals to determine when to power down computers without
negatively impacting on the service they are used to deliver, compares and
contrasts the efficiency of virtualisation with containerisation, and investigates
the energy efficiency of the popular cryptocurrency Bitcoin.
We begin by examining the current corpus of literature and defining the key
terms we need to proceed.
Then we propose a technique for improving the energy consumption of servers
by moving them into a sleep state and employing a low powered device to act
as a proxy in its place.
After this we move on to investigate the energy efficiency of virtualisation and
compare the energy efficiency of two of the most common means used to do
this.
Moving on from this we look at the cryptocurrency Bitcoin. We consider the
energy consumption of bitcoin mining and if this compared with the value of
bitcoin makes this profitable.
Finally we conclude by summarising the results and findings of this thesis.
This work increases our understanding of some of the challenges of energy
efficient computation as well as proposing novel mechanisms to save energy
Algorithms for advance bandwidth reservation in media production networks
Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
5G Multi-access Edge Computing: Security, Dependability, and Performance
The main innovation of the Fifth Generation (5G) of mobile networks is the
ability to provide novel services with new and stricter requirements. One of
the technologies that enable the new 5G services is the Multi-access Edge
Computing (MEC). MEC is a system composed of multiple devices with computing
and storage capabilities that are deployed at the edge of the network, i.e.,
close to the end users. MEC reduces latency and enables contextual information
and real-time awareness of the local environment. MEC also allows cloud
offloading and the reduction of traffic congestion. Performance is not the only
requirement that the new 5G services have. New mission-critical applications
also require high security and dependability. These three aspects (security,
dependability, and performance) are rarely addressed together. This survey
fills this gap and presents 5G MEC by addressing all these three aspects.
First, we overview the background knowledge on MEC by referring to the current
standardization efforts. Second, we individually present each aspect by
introducing the related taxonomy (important for the not expert on the aspect),
the state of the art, and the challenges on 5G MEC. Finally, we discuss the
challenges of jointly addressing the three aspects.Comment: 33 pages, 11 figures, 15 tables. This paper is under review at IEEE
Communications Surveys & Tutorials. Copyright IEEE 202
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Maximising the Energy Efficiency of Virtualised C-RAN Via Optimising the Number of Virtual Machines
In cloud radio access networks (C-RAN), more
accurate prediction of the number of virtual machines (VMs)
one server can support would improve network capacity and
energy efficiency (EE). In this paper, the problem of allocating
an optimal number of VMs to the cloud server is introduced.
Monte Carlo based evolutionary algorithm (PSO, QPSO or GA)
are used to find the suboptimal number of VMs that optimises
the energy efficiency (EE) of C-RAN. To enable such evaluation, a
power model is proposed to evaluate the power consumption (PC)
of each unit within a virtualised server. This evaluation occurs
under the circumstances of increased number of hosted VMs, and
processed resource blocks (RBs) at each VM. Moreover, power
allocation methods are proposed to transmit the power from base
band unit (BBU) pool to the remote radio heads (RRHs), and
from RRHs to the users (UEs). This allocation is based on the
combination of one or more of RRH distance, RRH channel gain,
UE distance, UE channel gain, and UE path loss. The EE problem
was constrained to crucial quality of service (QoS) indicators,
including minimum UE data rate, number of allocated RBs, and
latency imposed due to virtualisation
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