Fairness in a data center

Existing data centers utilize several networking technologies in order to handle the performance requirements of different workloads. Maintaining diverse networking technologies increases complexity and is not cost effective. This results in the current trend to converge all traffic into a single networking fabric. Ethernet is both cost-effective and ubiquitous, and as such it has been chosen as the technology of choice for the converged fabric. However, traditional Ethernet does not satisfy the needs of all traffic workloads, for the most part, due to its lossy nature and, therefore, has to be enhanced to allow for full convergence. The resulting technology, Data Center Bridging (DCB), is a new set of standards defined by the IEEE to make Ethernet lossless even in the presence of congestion. As with any new networking technology, it is critical to analyze how the different protocols within DCB interact with each other as well as how each protocol interacts with existing technologies in other layers of the protocol stack. This dissertation presents two novel schemes that address critical issues in DCB networks: fairness with respect to packet lengths and fairness with respect to flow control and bandwidth utilization. The Deficit Round Robin with Adaptive Weight Control (DRR-AWC) algorithm actively monitors the incoming streams and adjusts the scheduling weights of the outbound port. The algorithm was implemented on a real DCB switch and shown to increase fairness for traffic consisting of mixed-length packets. Targeted Priority-based Flow Control (TPFC) provides a hop-by-hop flow control mechanism that restricts the flow of aggressor streams while allowing victim streams to continue unimpeded. Two variants of the targeting mechanism within TPFC are presented and their performance evaluated through simulation

Internet performance modeling: the state of the art at the turn of the century

Seemingly overnight, the Internet has gone from an academic experiment to a worldwide information matrix. Along the way, computer scientists have come to realize that understanding the performance of the Internet is a remarkably challenging and subtle problem. This challenge is all the more important because of the increasingly significant role the Internet has come to play in society. To take stock of the field of Internet performance modeling, the authors organized a workshop at Schloß Dagstuhl. This paper summarizes the results of discussions, both plenary and in small groups, that took place during the four-day workshop. It identifies successes, points to areas where more work is needed, and poses “Grand Challenges” for the performance evaluation community with respect to the Internet

ActMesh- A Cognitive Resource Management paradigm for dynamic mobile Internet Access with Reliability Guarantees

Wireless Mesh Networks (WMNs) are going increasing attention as a flexible low-cost networking architecture to provide media Internet access over metropolitan areas to mobile clients requiring multimedia services. In WMNs, Mesh Routers (MRs) from the mesh backbone and accomplish the twofold task of traffic forwarding, as well as providing multimedia access to mobile Mesh Clients (MCs). Due to the intensive bandwidth-resource requested for supporting QoS-demanding multimedia services, performance of the current WMNs is mainly limited by spectrum-crowding and traffic-congestion, as only scarce spectrum-resources is currently licensed for the MCs' access. In principle, this problem could be mitigated by exploiting in a media-friendly (e.g., content-aware) way the context-aware capabilities offered by the Cognitive Radio (CR) paradigm. As integrated exploitation of both content and context-aware system's capabilities is at the basis of our proposed Active Mesh (ActMesh) networking paradigm. This last aims at defining a network-wide architecture for realizing media-friendly Cognitive Mesh nets (e.g., context aware Cognitive Mesh nets). Hence, main contribution of this work is four fold: 1. After introducing main functional blocks of our ActMesh architecture, suitable self-adaptive Belief Propagation and Soft Data Fusion algorithms are designed to provide context-awareness. This is done under both cooperative and noncooperative sensing frameworks. 2. The resulting network-wide resource management problem is modelled as a constrained stochastic Network Utility Maximization (NUM) problem, with the dual (contrasting) objective to maximize spectrum efficiency at the network level, while accounting for the perceived quality of the delivered media flows at the client level. 3. A fully distributed, scalable and self-adaptive implementation of the resulting Active Resource Manager (ARM) is deployed, that explicitly accounts for the energy limits of the battery powered MCs and the effects induced by both fading and client mobility. Due to informationally decentralized architecture of the ActMesh net, the complexity of (possibly, optimal) centralized solutions for resource management becomes prohibitive when number of MCs accessing ActMesh net grow. Furthermore, centralized resource management solutions could required large amounts of time to collect and process the required network information, which, in turn, induce delay that can be unacceptable for delay sensitive media applications, e.g., multimedia streaming. Hence, it is important to develop network-wide ARM policies that are both distributed and scalable by exploiting the radio MCs capabilities to sense, adapt and coordinate themselves. We validate our analytical models via simulation based numerical tests, that support actual effectiveness of the overall ActMesh paradigm, both in terms of objective and subjective performance metrics. In particular, the basic tradeoff among backbone traffic-vs-access traffic arising in the ActMesh net from the bandwidth-efficient opportunistic resource allocation policy pursued by the deployed ARM is numerically characterized. The standardization framework we inspire to is the emerging IEEE 802.16h one

ActMesh- A Cognitive Resource Management paradigm for dynamic mobile Internet Access with Reliability Guarantees

Wireless Mesh Networks (WMNs) are going increasing attention as a flexible low-cost networking architecture to provide media Internet access over metropolitan areas to mobile clients requiring multimedia services. In WMNs, Mesh Routers (MRs) from the mesh backbone and accomplish the twofold task of traffic forwarding, as well as providing multimedia access to mobile Mesh Clients (MCs). Due to the intensive bandwidth-resource requested for supporting QoS-demanding multimedia services, performance of the current WMNs is mainly limited by spectrum-crowding and traffic-congestion, as only scarce spectrum-resources is currently licensed for the MCs' access. In principle, this problem could be mitigated by exploiting in a media-friendly (e.g., content-aware) way the context-aware capabilities offered by the Cognitive Radio (CR) paradigm. As integrated exploitation of both content and context-aware system's capabilities is at the basis of our proposed Active Mesh (ActMesh) networking paradigm. This last aims at defining a network-wide architecture for realizing media-friendly Cognitive Mesh nets (e.g., context aware Cognitive Mesh nets). Hence, main contribution of this work is four fold: 1. After introducing main functional blocks of our ActMesh architecture, suitable self-adaptive Belief Propagation and Soft Data Fusion algorithms are designed to provide context-awareness. This is done under both cooperative and noncooperative sensing frameworks. 2. The resulting network-wide resource management problem is modelled as a constrained stochastic Network Utility Maximization (NUM) problem, with the dual (contrasting) objective to maximize spectrum efficiency at the network level, while accounting for the perceived quality of the delivered media flows at the client level. 3. A fully distributed, scalable and self-adaptive implementation of the resulting Active Resource Manager (ARM) is deployed, that explicitly accounts for the energy limits of the battery powered MCs and the effects induced by both fading and client mobility. Due to informationally decentralized architecture of the ActMesh net, the complexity of (possibly, optimal) centralized solutions for resource management becomes prohibitive when number of MCs accessing ActMesh net grow. Furthermore, centralized resource management solutions could required large amounts of time to collect and process the required network information, which, in turn, induce delay that can be unacceptable for delay sensitive media applications, e.g., multimedia streaming. Hence, it is important to develop network-wide ARM policies that are both distributed and scalable by exploiting the radio MCs capabilities to sense, adapt and coordinate themselves. We validate our analytical models via simulation based numerical tests, that support actual effectiveness of the overall ActMesh paradigm, both in terms of objective and subjective performance metrics. In particular, the basic tradeoff among backbone traffic-vs-access traffic arising in the ActMesh net from the bandwidth-efficient opportunistic resource allocation policy pursued by the deployed ARM is numerically characterized. The standardization framework we inspire to is the emerging IEEE 802.16h one

A Game-Theoretic Approach to Strategic Resource Allocation Mechanisms in Edge and Fog Computing

With the rapid growth of Internet of Things (IoT), cloud-centric application management raises questions related to quality of service for real-time applications. Fog and edge computing (FEC) provide a complement to the cloud by filling the gap between cloud and IoT. Resource management on multiple resources from distributed and administrative FEC nodes is a key challenge to ensure the quality of end-user’s experience. To improve resource utilisation and system performance, researchers have been proposed many fair allocation mechanisms for resource management. Dominant Resource Fairness (DRF), a resource allocation policy for multiple resource types, meets most of the required fair allocation characteristics. However, DRF is suitable for centralised resource allocation without considering the effects (or feedbacks) of large-scale distributed environments like multi-controller software defined networking (SDN). Nash bargaining from micro-economic theory or competitive equilibrium equal incomes (CEEI) are well suited to solving dynamic optimisation problems proposing to ‘proportionately’ share resources among distributed participants. Although CEEI’s decentralised policy guarantees load balancing for performance isolation, they are not faultproof for computation offloading. The thesis aims to propose a hybrid and fair allocation mechanism for rejuvenation of decentralised SDN controller deployment. We apply multi-agent reinforcement learning (MARL) with robustness against adversarial controllers to enable efficient priority scheduling for FEC. Motivated by software cybernetics and homeostasis, weighted DRF is generalised by applying the principles of feedback (positive or/and negative network effects) in reverse game theory (GT) to design hybrid scheduling schemes for joint multi-resource and multitask offloading/forwarding in FEC environments. In the first piece of study, monotonic scheduling for joint offloading at the federated edge is addressed by proposing truthful mechanism (algorithmic) to neutralise harmful negative and positive distributive bargain externalities respectively. The IP-DRF scheme is a MARL approach applying partition form game (PFG) to guarantee second-best Pareto optimality viii | P a g e (SBPO) in allocation of multi-resources from deterministic policy in both population and resource non-monotonicity settings. In the second study, we propose DFog-DRF scheme to address truthful fog scheduling with bottleneck fairness in fault-probable wireless hierarchical networks by applying constrained coalition formation (CCF) games to implement MARL. The multi-objective optimisation problem for fog throughput maximisation is solved via a constraint dimensionality reduction methodology using fairness constraints for efficient gateway and low-level controller’s placement. For evaluation, we develop an agent-based framework to implement fair allocation policies in distributed data centre environments. In empirical results, the deterministic policy of IP-DRF scheme provides SBPO and reduces the average execution and turnaround time by 19% and 11.52% as compared to the Nash bargaining or CEEI deterministic policy for 57,445 cloudlets in population non-monotonic settings. The processing cost of tasks shows significant improvement (6.89% and 9.03% for fixed and variable pricing) for the resource non-monotonic setting - using 38,000 cloudlets. The DFog-DRF scheme when benchmarked against asset fair (MIP) policy shows superior performance (less than 1% in time complexity) for up to 30 FEC nodes. Furthermore, empirical results using 210 mobiles and 420 applications prove the efficacy of our hybrid scheduling scheme for hierarchical clustering considering latency and network usage for throughput maximisation.Abubakar Tafawa Balewa University, Bauchi (Tetfund, Nigeria

Cross-layer design for OFDMA wireless networks with finite queue length based on game theory

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.In next generation wireless networks such as 4G- LTE and WiMax, the demand for high data rates, the scarcity of wireless resources and the time varying channel conditions has led to the adoption of more sophisticated and robust techniques in PHY such as orthogonal frequency division multiplexing (OFDM) and the corresponding access technique known as orthogonal frequency division multiplexing access (OFDMA). Cross-layer schedulers have been developed in order to describe the procedure of resource allocation in OFDMA wireless networks. The resource allocation in OFDMA wireless networks has received great attention in research, by proposing many different ways for frequency diversity exploitation and system’s optimization. Many cross-layer proposals for dynamic resource allocation have been investigated in literature approaching the optimization problem from different viewpoints i.e. maximizing total data rate, minimizing total transmit power, satisfying minimum users’ requirements or providing fairness amongst users. The design of a cross-layer scheduler for OFDMA wireless networks is the topic of this research. The scheduler utilizes game theory in order to make decisions for subcarrier and power allocation to the users with the main concern being to maintain fairness as well as to maximize overall system’s performance. A very well known theorem in cooperative game theory, the Nash Bargaining Solution (NBS), is employed and solved in a close form way, resulting in a Pareto optimal solution. Two different cases are proposed. The first one is the symmetric NBS (S-NBS) where all users have the same weight and therefore all users have the same opportunity for resources and the second one, is the asymmetric NBS (A-NBS), where users have different weights, hence different priorities where the scheduler favours users with higher priorities at expense of lower priority users. As MAC layer is vital for cross-layer, the scheduler is combined with a queuing model based on Markov chain in order to describe more realistically the incoming procedure from the higher layers

Energy-efficient LTE transmission techniques : introducing Green Radio from resource allocation perspective

Energy consumption has recently become a key issue from both environmental and economic considerations. A typical mobile phone network in the UK may consume approximately 40- 50 MW, contributing a significant proportion of the total energy consumed by the information technology industry. With the worldwide growth in the number of mobile subscribers, the associated carbon emissions and growing energy costs are becoming a significant operational expense, leading to the need for energy reduction. The Mobile VCE Green Radio Project has been launched, which targets to achieve 100x energy reduction of the current wireless networks by 2020. In this thesis, energy-efficient resource allocation strategies have been investigated taking the LTE system as an example. Firstly, theoretical analysis of energy-efficient design in cellular environments is provided according to the Shannon Theory. Based on a two-link scenario the performance of simultaneous transmission and orthogonal transmission for network power minimization under the specified rate constraints is investigated. It is found that simultaneous transmission consumes less power than orthogonal transmission close to the base station, but much more power in the cell-edge area. Also, simulation results suggest that the energy-efficient switching margins between these two schemes are dominated by the sum total of their required data rates. New definitions of power-utility and fairness metrics are further proposed, following by the design of weighted resource allocation approaches based on efficiency-fairness trade-offs. Apart from energy-efficient multiple access between different links, the energy used by individual base stations can also be reduced. For example, deploying sleep modes is an effective approach to reduce radio base station operational energy consumption. By periodically switching off the base station transmission, or using fewer transmit antennas, the energy consumption of base station hardware may decrease. By delivering less control signalling overhead, the radio frequency energy consumption can also be reduced. Simulation results suggest that up to 90% energy reduction can be obtained in low traffic conditions by employing time-domain optimization in each radio frame. The optimum on/off duty cycle is derived, enabling the energy consumption of the base station to scale with traffic loads. In the spatial-domain, an antenna selection criterion is proposed, indicating the most energy-efficient antenna configuration with the knowledge of users’ locations and quality of service requirements. Without time-domain sleep modes, using fewer transmit antennas could outperform full antenna transmission. However, with time-domain sleep modes, using all available antennas is generally the most energy-efficient choice

An Optimization Theoretical Framework for Resource Allocation over Wireless Networks

With the advancement of wireless technologies, wireless networking has become ubiquitous owing to the great demand of pervasive mobile applications. Some fundamental challenges exist for the next generation wireless network design such as time varying nature of wireless channels, co-channel interferences, provisioning of heterogeneous type of services, etc. So how to overcome these difficulties and improve the system performance have become an important research topic. Dynamic resource allocation is a general strategy to control the interferences and enhance the performance of wireless networks. The basic idea behind dynamic resource allocation is to utilize the channel more efficiently by sharing the spectrum and reducing interference through optimizing parameters such as the transmitting power, symbol transmission rate, modulation scheme, coding scheme, bandwidth, etc. Moreover, the network performance can be further improved by introducing diversity, such as multiuser, time, frequency, and space diversity. In addition, cross layer approach for resource allocation can provide advantages such as low overhead, more efficiency, and direct end-to-end QoS provision. The designers for next generation wireless networks face the common problem of how to optimize the system objective under the user Quality of Service (QoS) constraint. There is a need of unified but general optimization framework for resource allocation to allow taking into account a diverse set of objective functions with various QoS requirements, while considering all kinds of diversity and cross layer approach. We propose an optimization theoretical framework for resource allocation and apply these ideas to different network situations such as: 1.Centralized resource allocation with fairness constraint 2.Distributed resource allocation using game theory 3.OFDMA resource allocation 4.Cross layer approach On the whole, we develop a universal view of the whole wireless networks from multiple dimensions: time, frequency, space, user, and layers. We develop some schemes to fully utilize the resources. The success of the proposed research will significantly improve the way how to design and analyze resource allocation over wireless networks. In addition, the cross-layer optimization nature of the problem provides an innovative insight into vertical integration of wireless networks