Invited Abstract: A Simulation Package for Energy Consumption of Content Delivery Networks (CDNs)

Content Delivery Networks (CDNs) are becoming an integral part of the future generation Internet. Traditionally, these networks have been designed with the goals of traffic offload and the improvement of users' quality of experience (QoE), but the energy consumption is also becoming an indispensable design factor for CDNs to be a sustainable solution. To study and improve the CDN architectures using this new design metric, we are planning to develop a generic and flexible simulation package in OMNet++. This package is aimed to render a holistic view about the CDN energy consumption behaviour by incorporating the state-of-the-art energy consumption models proposed for the individual elements of CDNs (e.g. servers, routers, wired and wireless links, wireless devices, etc.) and for the various Internet contents (web pages, files, streaming video, etc.).Comment: Published in: A. F\"orster, C. Minkenberg, G. R. Herrera, M. Kirsche (Eds.), Proc. of the 2nd OMNeT++ Community Summit, IBM Research - Zurich, Switzerland, September 3-4, 2015, arXiv:1509.03284, 201

Content and Resource Management in Edge Networks

In this thesis, we investigate and develop new methods for efficient and functional use of resources in edge networks. Setting this work aside from previous work, we study User Generated Content (UGC) such as social media information and data generated in the new emerging Internet of Things systems. We present efficient solutions for placing such content and managing which network resources should be used to make the edge networks effective. By effective we for example mean; using little energy, processing data with short delay or carrying out their tasks with little load on the network. In order to achieve this, we have used a range of optimization and control theoretic tools and studied different aspects of content and resource management in operator managed content distribution networks (CDN). The main parts of the contributions of the thesis can be summarized as follows:First, we have studied end-to-end energy usage in video delivery systems. We studied the energy usage of a sample video considering separate delivery components and created a model for overall energy usage when delivering video over the Internet. The study comprises experimental and simulated measurements of encoding with different qualities, transmissions over core and wireless access networks and decoding in user devices. We showed how video popularity affects end-to-end energy usage by codec selection.Second, we proposed optimal and on-line placement algorithms for content placement at the edge. We focused on UGC, considering its distributed bottom-up trajectory pattern. ISP-managed CDNs are considered to be suitable caching hosts of popular UGCs. Furthermore, we proposed on-line learning algorithms to enable decision agents at the edge to predict content popularity from users' social activities. Third, we took the data center viewpoint of a delivery system. We designed scheduling and request assignment algorithms with an energy usage objective. We showed that an energy-efficient dynamic server provisioning (DSP)-based assignment may lead to an unstable system if sufficient care has not be taken. We then investigated ways of keeping the servers stable, energy efficient and performing load balancing to provide better quality of service (QoS) for end users. Fourth, we expanded the idea of edge placement in an IoT service offloading context. We investigated the service placement problem in a distributed 5G F-RAN (fog radio access network) architecture with an existing centralized cloud. We proposed optimal and reinforcement learning based algorithms to perform joint service scheduling and placement in fog-cloud hosts based on a utilization objective. We showed that the learning algorithm converges to an optimal policy when there are uncertainties in positioning and service demand parameters

Energy-Efficient Stable and Balanced Task Scheduling in Data Centers

It is well known that load balancing in data centers can lead to unnecessary energy usage if all servers are kept active. Usingdynamic server provisioning, the number of servers that serve requests can be reduced by turning off idle servers and thereby savingenergy. However, such a scheme, usually increases the risk of instability of server queues. In this work, we analyze the trade-offbetween energy usage and stability of servers in a data center when we balance the load by dispatching arriving jobs. We proposealgorithms to solve a stability and energy objective stochastic optimization problem with a high degree of flexibility to handle the trade-offbetween these two objectives. We consider variable size jobs to apply load balancing on selected active servers and find that the optimalsolution is an NP-hard problem. We therefore develop two computationally efficient greedy and randomized approximation schemes toachieve the trade-off between these objectives. We investigate the performance of our proposed algorithms in minimizing the risk ofqueue length growth as well as the number of active servers needed to serve jobs, and compare it with several metrics in heterogeneousload scenarios

An Online Placement Mechanism for Efficient Delivery of User Generated Content

User Generated Content (UGC) is projected to make up a significant part of the total Internet traffic in the future. As such, it will significantly contribute to the total cost for Internet traffic worldwide. Arguably, UGC is a suitable content type for which the Internet Service Providers (ISPs) can take initiative and enter the content delivery market. However, despite its significance, UGC content management has attracted very little research attention, and the existing works stop short of developing placement and delivery solutions for UGC. Hence, we are motivated to address this content type, and exploit its properties to support ISPs in making optimal placement decisions. Specifically, we leverage the inherent tie between UGC and social networking context, take into consideration the persistence limitation of UGC (in contrast to commercial content), and derive model with the objective to minimize power usage. Also, derived from the problem formulation, we propose an online algorithm which enables each ISP to individually decide which contents should be placed and served locally. We provide simulation results showing that the proposed algorithm performs close to optimal in terms of power used for content delivery

Online Learning and Placement Algorithms for Efficient Delivery of User Generated Contents in Telco-CDNs

User generated content (UGC) makes up a significant portion of Internet traffic. As opposed to other content, UGC has so far been left outside over-the-top providing network operators content distribution networks (telco-CDN) due to the difficulty in determining optimised placement of such content. The side effect of this is that UGC content is not placed close to end users and therefore occupy unnecessary network resources. The difficulty in determining optimal placement of UGC stems from the different geographical and dynamic behaviour of the content generators, and a further complication is that with UGC, it is necessary to place content in real-time which this has an impact on performance optimality. Even though CDNs have been widely studied in the literature, little attention has been given to the challenging case of UGC placement. In this paper, we propose an on-line placement algorithm and compare its performance with the off-line counterpart based on integer programming, both under the assumption that the popularity of content is known to the algorithms. In order to determine the popularity, we present an on-line learning model to predict spatial patterns in content requests. Furthermore, we couple the model with an algorithm for learning the early popularity of content, i.e. shortly after the content becomes known. We show that together, these approaches enable service providers to effectively place UGC and minimise the cost of serving UGC in their networks

Dynamic Server Provisioning with Queue Stabilization in Data Centres

When load balancing data centres with all serversactive to serve jobs, the result can be excessive energy usage. Onthe other hand, using dynamic server provisioning, the numberof servers that serve requests can be reduced by turning off idleservers and thereby save energy. However, such a scheme, usuallyincreases the risk of instability of server queues. In this work,we analyze the trade-off between energy usage and stability ofservers in a data center when we balance the load by dispatchingarriving jobs. We propose an algorithm, SEOL, to solve a stabilityand energy objective stochastic optimization problem with a highdegree of flexibility to handle the trade-off between these twoobjectives. We show the performance of our proposed algorithmin minimizing the risk of queue length growth as well as thenumber of active servers needed to serve jobs, and compare itwith a number of well-established commercial load balancingmechanisms

Competition in higher education – good or bad?

Competition is an integrated part of most civilizations, from sports to education. Often competition arise whether or not it was intended and higher education is no exception from this. Should teachers acknowledge this and try to introduce competition as a part of the course or should they try to prevent competition, even spontaneously arisen competition? In this paper an overview of effects of competition reported in literature is presented together with deeper analyzes of how grading affect competition among students, projectbased competition and competition within a class. As always, no one answer is found, competition can be good or bad, but a general trend seems to be that when competition is combined with cooperation good results have been reported. Furthermore, the competitive part of the course should not be the sole factor for grading but rather seen as a complement to regular teaching

Resource Management for OFDMA based Next Generation 802.11 WLANs

Recently, IEEE 802.11ax Task Group has adapted OFDMA as a new technique for enabling multi-user transmission. It has been also decided that the scheduling duration should be same for all the users in a multi-user OFDMA so that the transmission of the users should end at the same time. In order to realize that condition, the users with insufficient data should transmit null data (i.e. padding) to fill the duration. While this scheme offers strong features such as resilience to Overlapping Basic Service Set (OBSS) interference and ease of synchronization, it also poses major side issues of degraded throughput performance and waste of devices' energy. In this work, for OFDMA based 802.11 WLANs we first propose practical algorithm in which the scheduling duration is fixed and does not change from time to time. In the second algorithm the scheduling duration is dynamically determined in a resource allocation framework by taking into account the padding overhead, airtime fairness and energy consumption of the users. We analytically investigate our resource allocation problems through Lyapunov optimization techniques and show that our algorithms are arbitrarily close to the optimal performance at the price of reduced convergence rate. We also calculate the overhead of our algorithms in a realistic setup and propose solutions for the implementation issues