TIME AWARE VIRTUAL MACHINE PLACEMENT AND ROUTING FOR POWER EFFICIENCY IN DATA CENTERS

Ph.DDOCTOR OF PHILOSOPH

Congestion control, energy efficiency and virtual machine placement for data centers

Data centers, facilities with communications network equipment and servers for data processing and/or storage, are prevalent and essential to provide a myriad of services and applications for various private, non-profit, and government systems, and they also form the foundation of cloud computing, which is transforming the technological landscape of the Internet. With rapid deployment of modern high-speed low-latency large-scale data centers, many issues have emerged in data centers, such as data center architecture design, congestion control, energy efficiency, virtual machine placement, and load balancing. The objective of this thesis is multi-fold. First, an enhanced Quantized Congestion Notification (QCN) congestion notification algorithm, called fair QCN (FQCN), is proposed to improve rate allocation fairness of multiple flows sharing one bottleneck link in data center networks. Detailed analysis on FQCN and simulation results is provided to validate the fair share rate allocation while maintaining the queue length stability. Furthermore, the effects of congestion notification algorithms, including QCN, AF-QCN and FQCN, are investigated with respect to TCP throughput collapse. The results show that FQCN can significantly enhance TCP throughput performance, and achieve better TCP throughput than QCN and AF-QCN in a TCP Incast setting. Second, a unified congestion detection, notification and control system for data center networks is designed to efficiently resolve network congestion in a uniform solution and to ensure convergence to statistical fairness with “no state” switches simultaneously. The architecture of the proposed system is described in detail and the FQCN algorithm is implemented in the proposed framework. The simulation results of the FQCN algorithm implemented in the proposed framework validate the robustness and efficiency of the proposed congestion control system. Third, a two-level power optimization model, namely, Hierarchical EneRgy Optimization (HERO), is established to reduce the power consumption of data center networks by switching off network switches and links while still guaranteeing full connectivity and maximizing link utilization. The power-saving performance of the proposed HERO model is evaluated by simulations with different traffic patterns. The simulation results have shown that HERO can reduce the power consumption of data center networks effectively with reduced complexity. Last, several heterogeneity aware dominant resource assistant heuristic algorithms, namely, dominant residual resource aware first-fit decreasing (DRR-FFD), individual DRR-FFD (iDRR-FFD) and dominant residual resource based bin fill (DRR-BinFill), are proposed for virtual machine (VM) consolidation. The proposed heuristic algorithms exploit the heterogeneity of the VMs’ requirements for different resources by capturing the differences among VMs’ demands, and the heterogeneity of the physical machines’ resource capacities by capturing the differences among physical machines’ resources. The performance of the proposed heuristic algorithms is evaluated with different classes of synthetic workloads under different VM requirement heterogeneity conditions, and the simulation results demonstrate that the proposed heuristics achieve quite similar consolidation performance as dimension-aware heuristics with almost the same computational cost as those of the single dimensional heuristics

A fast robust optimization-based heuristic for the deployment of green virtual network functions

Network Function Virtualization (NFV) has attracted a lot of attention in the telecommunication field because it allows to virtualize core-business network functions on top of a NFV Infrastructure. Typically, virtual network functions (VNFs) can be represented as chains of Virtual Machines (VMs) or containers that exchange network traffic which are deployed inside datacenters on commodity hardware. In order to achieve cost efficiency, network operators aim at minimizing the power consumption of their NFV infrastructure. This can be achieved by using the minimum set of physical servers and networking equipment that are able to provide the quality of service required by the virtual functions in terms of computing, memory, disk and network related parameters. However, it is very difficult to predict precisely the resource demands required by the VNFs to execute their tasks. In this work, we apply the theory of robust optimization to deal with such parameter uncertainty. We model the problem of robust VNF placement and network embedding under resource demand uncertainty and network latency constraints using robust mixed integer optimization techniques. For online optimization, we develop fast solution heuristics. By using the virtualized Evolved Packet Core as use case, we perform a comprehensive evaluation in terms of performance, solution time and complexity and show that our heuristic can calculate robust solutions for large instances under one second.Peer ReviewedPostprint (author's final draft

Scalable Traffic-Aware Virtual Machine Management for Cloud Data Centers

Virtual Machine (VM) management is a powerful mechanism for providing elastic services over Cloud Data Centers (DC)s. At the same time, the resulting network congestion has been repeatedly reported as the main bottleneck in DCs, even when the overall resource utilization of the infrastructure remains low. However, most current VM management strategies are traffic-agnostic, while the few that are traffic-aware only concern a static initial allocation, ignore bandwidth oversubscription, or do not scale. In this paper we present S-CORE, a scalable VM migration algorithm to dynamically reallocate VMs to servers while minimizing the overall communication footprint of active traffic flows. We formulate the aggregate VM communication as an optimization problem and we then define a novel distributed migration scheme that iteratively adapts to dynamic traffic changes. Through extensive simulation and implementation results, we show that S-CORE achieves significant (up to 87%) communication cost reduction while incurring minimal overhead and downtime. Index Terms—Virtual Machine, Migration, Consolidation, Communication Cost, Scalable, Traffic-Aware, Data Center Networ

Scalable traffic-aware virtual machine management for cloud data centers

Virtual Machine (VM) management is a powerful mechanism for providing elastic services over Cloud Data Centers (DC)s. At the same time, the resulting network congestion has been repeatedly reported as the main bottleneck in DCs, even when the overall resource utilization of the infrastructure remains low. However, most current VM management strategies are traffic-agnostic, while the few that are traffic-aware only concern a static initial allocation, ignore bandwidth oversubscription, or do not scale. In this paper we present S-CORE, a scalable VM migration algorithm to dynamically reallocate VMs to servers while minimizing the overall communication footprint of active traffic flows. We formulate the aggregate VM communication as an optimization problem and we then define a novel distributed migration scheme that iteratively adapts to dynamic traffic changes. Through extensive simulation and implementation results, we show that S-CORE achieves significant (up to 87%) communication cost reduction while incurring minimal overhead and downtime

Network and Server Resource Management Strategies for Data Centre Infrastructures: A Survey

The advent of virtualisation and the increasing demand for outsourced, elastic compute charged on a pay-as-you-use basis has stimulated the development of large-scale Cloud Data Centres (DCs) housing tens of thousands of computer clusters. Of the signi�cant capital outlay required for building and operating such infrastructures, server and network equipment account for 45% and 15% of the total cost, respectively, making resource utilisation e�ciency paramount in order to increase the operators' Return-on-Investment (RoI). In this paper, we present an extensive survey on the management of server and network resources over virtualised Cloud DC infrastructures, highlighting key concepts and results, and critically discussing their limitations and implications for future research opportunities. We highlight the need for and bene �ts of adaptive resource provisioning that alleviates reliance on static utilisation prediction models and exploits direct measurement of resource utilisation on servers and network nodes. Coupling such distributed measurement with logically-centralised Software De�ned Networking (SDN) principles, we subsequently discuss the challenges and opportunities for converged resource management over converged ICT environments, through unifying control loops to globally orchestrate adaptive and load-sensitive resource provisioning

Exploring Wireless Data Center Networks: Can They Reduce Energy Consumption While Providing Secure Connections?

Data centers have become the digital backbone of the modern world. To support the growing demands on bandwidth, Data Centers consume an increasing amount of power. A significant portion of that power is consumed by information technology (IT) equipment, including servers and networking components. Additionally, the complex cabling in traditional data centers poses design and maintenance challenges and increases the energy cost of the cooling infrastructure by obstructing the flow of chilled air. Hence, to reduce the power consumption of the data centers, we proposed a wireless server-to-server data center network architecture using millimeter-wave links to eliminate the need for power-hungry switching fabric of traditional fat-tree-based data center networks. The server-to-server wireless data center network (S2S-WiDCN) architecture requires Line-of-Sight (LoS) between servers to establish direct communication links. However, in the presence of interference from internal or external sources, or an obstruction, such as an IT technician, the LoS may be blocked. To address this issue, we also propose a novel obstruction-aware adaptive routing algorithm for S2S-WiDCN. S2S-WiDCN can reduce the power consumption of the data center network portion while not affecting the power consumption of the servers in the data center, which contributes significantly towards the total power consumption of the data center. Moreover, servers in data centers are almost always underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. To address the high power consumption of the servers, we proposed a network-aware bandwidth-constrained server consolidation algorithm called Network-Aware Server Consolidation (NASCon) for wireless data centers that can reduce the power consumption up to 37% while improving the network performance. However, due to the arrival of new tasks and the completion of existing tasks, the consolidated utilization profile of servers change, which may have an adverse effect on overall power consumption over time. To overcome this, NASCon algorithm needs to be executed periodically. We have proposed a mathematical model to estimate the optimal inter-consolidation time, which can be used by the data center resource management unit for scheduling NASCon consolidation operation in real-time and leverage the benefits of server consolidation. However, in any data center environment ensuring security is one of the highest design priorities. Hence, for S2S-WiDCN to become a practical and viable solution for data center network design, the security of the network has to be ensured. S2S-WiDCN data center can be vulnerable to a variety of different attacks as it uses wireless links over an unguided channel for communication. As being a wireless system, the network has to be secured against common threats associated with any wireless networks such as eavesdropping attack, denial of services attack, and jamming attack. In parallel, other security threats such as the attack on the control plane, side-channel attack through traffic analysis are also possible. We have done an extensive study to elaborate the scope of these attacks as well as explore probable solutions against these issues. We also proposed viable solutions for the attack against eavesdropping, denial of services, jamming, and control-plane attack. To address the traffic analysis attack, we proposed a simulated annealing-based random routing mechanism which can be adopted instead of default routing in the wireless data center

Green Resource Management in Distributed Cloud Infrastructures

Computing has evolved over time according to different paradigms, along with an increasing need for computational power. Modern computing paradigms basically share the same underlying concept of Utility Computing, that is a service provisioning model through which a shared pool of computing resources is used by a customer when needed. The objective of Utility Computing is to maximize the resource utilization and bring down the relative costs. Nearly a decade ago, the concept of Cloud Computing emerged as a virtualization technique where services were executed remotely in a ubiquitous way, providing scalable and virtualized resources. The spread of Cloud Computing has been also encouraged by the success of the virtualization, which is one of the most promising and efficient techniques to consolidate system's utilization on one side, and to lower power, electricity charges and space costs in data centers on the other. In the last few years, there has been a remarkable growth in the number of data centers, which represent one of the leading sources of increased business data traffic on the Internet. An effect of the growing scale and the wide use of data centers is the dramatic increase of power consumption, with significant consequences both in terms of environmental and operational costs. In addition to power consumption, also carbon footprint of the Cloud infrastructures is becoming a serious concern, since a lot of power is generated from non-renewable sources. Hence, energy awareness has become one of the major design constraints for Cloud infrastructures. In order to face these challenges, a new generation of energy-efficient and eco-sustainable network infrastructures is needed. In this thesis, a novel energy-aware resource orchestration framework for distributed Cloud infrastructures is discussed. The aim is to explain how both network and IT resources can be managed while, at the same time, the overall power consumption and carbon footprint are being minimized. To this end, an energy-aware routing algorithm and an extension of the OSPF-TE protocol to distribute energy-related information have been implemented

Distributed virtual machine migration for cloud data centre environments

Virtualisation of computing resources has been an increasingly common practice in recent years, especially in data centre environments. This has helped in the rise of cloud computing, where data centre operators can over-subscribe their physical servers through the use of virtual machines in order to maximise the return on investment for their infrastructure. Similarly, the network topologies in cloud data centres are also heavily over-subscribed, with the links in the core layers of the network being the most over-subscribed and congested of all, yet also being the most expensive to upgrade. Therefore operators must find alternative, less costly ways to recover their initial investment in the networking infrastructure. The unconstrained placement of virtual machines in a data centre, and changes in data centre traffic over time, can cause the expensive core links of the network to become heavily congested. In this thesis, S-CORE, a distributed, network-load aware virtual machine migration scheme is presented that is capable of reducing the overall communication cost of a data centre network. An implementation of S-CORE on the Xen hypervisor is presented and discussed, along with simulations and a testbed evaluation. The results of the evaluation show that S-CORE is capable of operating on a network with traffic comparable to reported data centre traffic characteristics, with minimal impact on the virtual machines for which it monitors network traffic and makes migration decisions on behalf of. The simulation results also show that S-CORE is capable of efficiently and quickly reducing communication across the links at the core layers of the network