Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Multi-objective resource allocation for LTE/LTE-A femtocell/HeNB networks using ant colony optimization

Existing femtocell resource allocation schemes for Long Term Evolution or LTE-Advanced femtocell networks do not jointly achieve efficient resource utilization, fairness guarantee, interference mitigation and reduced complexity in a satisfactory manner. In this paper, a multi-objective resource allocation scheme is proposed to achieve these desired features simultaneously. We first formulate three objective functions to respectively maximize resource utilization efficiency, guarantee a high degree of fairness and minimize interference. A weighted sum approach is then used to combine these objective functions to form a single multi-objective optimization problem. An ant colony optimization algorithm is employed to find the Pareto-optimal solution to this problem. Simulation results demonstrate that the proposed scheme performs jointly well in all aspects, namely resource utilization, fairness and interference mitigation. Additionally, it maintains satisfactory performance in the handover process and has a reasonably low complexity compared to the existing schemes

Resource Allocation in 4G and 5G Networks: A Review

The advent of 4G and 5G broadband wireless networks brings several challenges with respect to resource allocation in the networks. In an interconnected network of wireless devices, users, and devices, all compete for scarce resources which further emphasizes the fair and efficient allocation of those resources for the proper functioning of the networks. The purpose of this study is to discover the different factors that are involved in resource allocation in 4G and 5G networks. The methodology used was an empirical study using qualitative techniques by performing literature reviews on the state of art in 4G and 5G networks, analyze their respective architectures and resource allocation mechanisms, discover parameters, criteria and provide recommendations. It was observed that resource allocation is primarily done with radio resource in 4G and 5G networks, owing to their wireless nature, and resource allocation is measured in terms of delay, fairness, packet loss ratio, spectral efficiency, and throughput. Minimal consideration is given to other resources along the end-to-end 4G and 5G network architectures. This paper defines more types of resources, such as electrical energy, processor cycles and memory space, along end-to-end architectures, whose allocation processes need to be emphasized owing to the inclusion of software defined networking and network function virtualization in 5G network architectures. Thus, more criteria, such as electrical energy usage, processor cycle, and memory to evaluate resource allocation have been proposed.  Finally, ten recommendations have been made to enhance resource allocation along the whole 5G network architecture

QoS and energy efficient resource allocation in downlink OFDMA systems

In this paper we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of downlink Orthogonal Frequency Division Multiple Access (OFDMA) systems. The proposed algorithm performs resource allocation using information on the downlink packet delay, the average delay and data rate of past allocations, as well as the downlink users' buffer status in order to minimize packet segmentation. Based on simulation results, the proposed algorithm achieves significant performance improvement in terms of packet timeout rate, goodput, fairness, and average delay. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit

Millimeter Wave Cellular Networks: A MAC Layer Perspective

The millimeter wave (mmWave) frequency band is seen as a key enabler of multi-gigabit wireless access in future cellular networks. In order to overcome the propagation challenges, mmWave systems use a large number of antenna elements both at the base station and at the user equipment, which lead to high directivity gains, fully-directional communications, and possible noise-limited operations. The fundamental differences between mmWave networks and traditional ones challenge the classical design constraints, objectives, and available degrees of freedom. This paper addresses the implications that highly directional communication has on the design of an efficient medium access control (MAC) layer. The paper discusses key MAC layer issues, such as synchronization, random access, handover, channelization, interference management, scheduling, and association. The paper provides an integrated view on MAC layer issues for cellular networks, identifies new challenges and tradeoffs, and provides novel insights and solution approaches.Comment: 21 pages, 9 figures, 2 tables, to appear in IEEE Transactions on Communication

Joint buffer management and scheduling for input queued switches

Input queued (IQ) switches are highly scalable and they have been the focus of many studies from academia and industry. Many scheduling algorithms have been proposed for IQ switches. However, they do not consider the buffer space requirement inside an IQ switch that may render the scheduling algorithms inefficient in practical applications. In this dissertation, the Queue Length Proportional (QLP) algorithm is proposed for IQ switches. QLP considers both the buffer management and the scheduling mechanism to obtain the optimal allocation region for both bandwidth and buffer space according to real traffic load. In addition, this dissertation introduces the Queue Proportional Fairness (QPF) criterion, which employs the cell loss ratio as the fairness metric. The research in this dissertation will show that the utilization of network resources will be improved significantly with QPF. Furthermore, to support diverse Quality of Service (QoS) requirements of heterogeneous and bursty traffic, the Weighted Minmax algorithm (WMinmax) is proposed to efficiently and dynamically allocate network resources. Lastly, to support traffic with multiple priorities and also to handle the decouple problem in practice, this dissertation introduces the multiple dimension scheduling algorithm which aims to find the optimal scheduling region in the multiple Euclidean space

Load balancing using cell range expansion in LTE advanced heterogeneous networks

The use of heterogeneous networks is on the increase, fueled by consumer demand for more data. The main objective of heterogeneous networks is to increase capacity. They offer solutions for efficient use of spectrum, load balancing and improvement of cell edge coverage amongst others. However, these solutions have inherent challenges such as inter-cell interference and poor mobility management. In heterogeneous networks there is transmit power disparity between macro cell and pico cell tiers, which causes load imbalance between the tiers. Due to the conventional user-cell association strategy, whereby users associate to a base station with the strongest received signal strength, few users associate to small cells compared to macro cells. To counter the effects of transmit power disparity, cell range expansion is used instead of the conventional strategy. The focus of our work is on load balancing using cell range expansion (CRE) and network utility optimization techniques to ensure fair sharing of load in a macro and pico cell LTE Advanced heterogeneous network. The aim is to investigate how to use an adaptive cell range expansion bias to optimize Pico cell coverage for load balancing. Reviewed literature points out several approaches to solve the load balancing problem in heterogeneous networks, which include, cell range expansion and utility function optimization. Then, we use cell range expansion, and logarithmic utility functions to design a load balancing algorithm. In the algorithm, user and base station associations are optimized by adapting CRE bias to pico base station load status. A price update mechanism based on a suboptimal solution of a network utility optimization problem is used to adapt the CRE bias. The price is derived from the load status of each pico base station. The performance of the algorithm was evaluated by means of an LTE MATLAB toolbox. Simulations were conducted according to 3GPP and ITU guidelines for modelling heterogeneous networks and propagation environment respectively. Compared to a static CRE configuration, the algorithm achieved more fairness in load distribution. Further, it achieved a better trade-off between cell edge and cell centre user throughputs. [Please note: this thesis file has been deferred until December 2016