Power Management Strategies for Wired Communication Networks by Qun Yu Bachelor of Engineering Beijing Information Science and Technology University 2000 Submitted to the Graduate Faculty of the School of Computing and Information in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2020 UNIVERSITY OF PITTSBURGH SCHOOL OF COMPUTING AND INFORMATION This dissertation was presented by Qun Yu It was defended on December 13th, 2019 and approved by Dr. Taieb Znati, School of Computing and Information, University of Pittsburgh Dr. Martin B.H. Weiss, School of Computing and Information, University of Pittsburgh Dr. Prashant Krishnamurthy, School of Computing and Information, University of Pittsburgh Dr. Daniel Mosse, School of Computing and Information, University of Pittsburgh Dr. Balaji Palanisamy, School of Computing and Information, University of Pittsburgh Dissertation Director: Dr. Taieb Znati, School of Computing and Information, University of Pittsburgh ii Copyright c by Qun Yu 2020 iii Power Management Strategies for Wired Communication Networks Qun Yu, PhD University of Pittsburgh, 2020 With the exponential traffic growth and the rapid expansion of communication infrastruc- tures worldwide, energy expenditure of the Internet has become a major concern in IT-reliant society. This energy problem has motivated the urgent demands of new strategies to reduce the consumption of telecommunication networks, with a particular focus on IP networks. In addition to the development of a new generation of energy-efficient network equipment, a significant body of research has concentrated on incorporating power/energy-awareness into network control and management, which aims at reducing the network power/energy con- sumption by either dynamically scaling speeds of each active network component to make it capable of adapting to its current load or putting to sleep the lightly loaded network elements and reconfiguring the network. However, the fundamental challenge of greening the Internet is to achieve a balance between the power/energy saving and the demands of quality-of-service (QoS) performance, which is an issue that has received less attention but is becoming a major problem in future green network designs. In this dissertation, we study how energy consumption can be reduced through different power/energy- and QoS-aware strategies for wired communication networks. To sufficiently reduce energy consumption while meeting the desire QoS requirements, we introduce several different schemes combing power management techniques with different scheduling strategies, which can be classified into experimental power management (EPM) and algorithmic power management (APM). In these proposed schemes, the power manage- ment techniques that we focus on are speed scaling and sleep mode. When the network processor is active, its speed and supply voltage can be decreased to reduce the energy con- sumption (speed scaling), while when the processor is idle, it can be put in a low power mode to save the energy consumption (sleep mode). The resulting problem is to deter- mine how and when to adjust speeds for the processors, and/or to put a device into sleep mode. In this dissertation, we first discuss three families of dynamic voltage/frequency iv scaling (DVFS) based, QoS-aware EPM schemes, which aim to reduce the energy consump- tion in network equipment by using different packet scheduling strategies, while adhering to QoS requirements of supported applications. Then, we explore the problem of energy minimization under QoS constraints through a mathematical programming model, which is a DVFS-based, delay-aware APM scheme combing the speed scaling technique with the existing rate monotonic scheduling policy. Among these speed scaling based schemes, up to 26:76% dynamic power saving of the total power consumption can be achieved. In addition to speed scaling approaches, we further propose a sleep-based, traffic-aware EPM scheme, which is used to reduce power consumption by greening routing light load and putting the related network equipment into sleep mode according to twelve flow traffic density changes in 24-hour of an arbitrarily selected day. Meanwhile, a speed scaling technique without violat- ing network QoS performance is also considered in this scheme when the traffic is rerouted. Applying this sleep-based strategy can lead to power savings of up to 62:58% of the total power consumption. v Table of Contents Preface ........................................... xiii 1.0 INTRODUCTION .................................1 1.1 Problem Statement . .2 1.2 Research Overview . .4 1.2.1 DVFS-based Power Management and QoS-aware Scheduling Strategies5 1.2.2 DVFS-based Power Management and Delay-aware, Optimal Energy Strategies . .6 1.2.3 Sleep-based Power Management and a Traffic-aware Strategy . .6 1.3 Claims and Contributions . .7 1.4 Structure of this Dissertation . .8 2.0 BACKGROUND ..................................9 2.1 Characteristics of Power Consumption in Wired IP Networks . .9 2.2 Toward Energy- and QoS-aware Network Devices . 11 2.3 Dynamic Power Management Techniques for Wired Network Resources . 12 2.3.1 Power Scaling Techniques . 12 2.3.1.1 Current Approaches and Concepts . 13 2.3.1.2 Dynamic Voltage/Frequency Scaling . 15 2.3.2 Power/Energy Measuring Techniques . 16 2.3.2.1 Power measurement . 16 2.3.2.2 A general power-aware model for router power consumption . 16 2.4 Conclusions . 17 3.0 DVFS-based Power Management and QoS-aware Scheduling Strategies 18 3.1 Introduction . 18 3.2 Related Work . 20 3.3 DVFS-Scheduler Design and Architecture . 21 3.4 Load-aware DVFS-Schedulers . 24 vi 3.4.1 Load-aware Scheduler (LA) . 24 3.4.2 Predicted Load-aware Scheduler (LA)¯ . 26 3.5 QL-aware DVFS-Schedulers . 27 3.5.1 Single-threshold, QL-aware Scheduler (sQLA) . 28 3.5.2 Multi-threshold, QL-aware Scheduler (mQLA) . 29 3.5.3 Single-threshold Average QL-aware Scheduler (sQLA)¯ . 31 3.5.4 Multi-threshold Average QL-aware Scheduler (mQLA)¯ . 32 3.6 Delay-aware DVFS-Scheduler . 32 3.6.1 Delay-aware DVFS-Scheduler Design and Architecture . 33 3.6.2 QL-based Delay-Aware Packet Scheduler (QLDA) . 34 3.7 Evaluation . 38 3.7.1 Packet- and Router-based Energy Consumption Models . 38 3.7.1.1 Packet-based Energy Model . 39 3.7.1.2 Router-based Energy Model . 40 3.7.2 Simulation Setup . 42 3.7.3 Sensitivity to the main parameters of Load-aware Schemes . 47 3.7.3.1 Sensitivity to τ .......................... 47 3.7.3.2 Sensitivity to ca .......................... 47 3.7.4 Sensitivity to the main parameters of QL-aware Schemes . 47 3.7.4.1 Sensitivity to η .......................... 48 3.7.4.2 Sensitivity to τ .......................... 48 3.7.4.3 Sensitivity to cq .......................... 49 3.7.4.4 Sensitivity to ql and qh ...................... 49 3.7.5 Comparative analysis . 50 3.7.5.1 The class of Load-aware schemes . 51 3.7.5.2 The class of QL-aware schemes . 52 3.7.5.3 Cross class comparative analysis . 52 3.7.5.4 Comparison with the related work . 53 3.7.6 Sensitivity to the main parameters of QLDA . 53 3.7.6.1 Sensitivity to η .......................... 54 vii 3.7.6.2 Sensitivity to τ .......................... 55 3.7.6.3 Sensitivity to cq .......................... 56 3.7.6.4 Sensitivity to cd .......................... 57 3.7.6.5 Sensitivity to ql and qh ...................... 57 3.7.7 Comparative analysis . 57 3.8 Conclusions . 59 4.0 DVFS-based Power Management and Delay-aware, Optimal Energy Strategies ...................................... 61 4.1 Introduction . 61 4.2 Related Work . 63 4.3 Periodic task scheduling . 65 4.3.1 Utilization factor . 67 4.3.2 Rate Monotonic scheduling . 67 4.4 Network and Flow Specification . 68 4.5 The General Problem Formulation . 69 4.5.1 Delay-based Packet Scheduling Policy . 71 4.5.2 Per-router Delay Computation . 72 4.5.2.1 Smallest Feasible Delay . 72 4.5.2.2 Largest Feasible Delay . 72 4.5.3 Power Model . 73 4.5.4 Router-based Energy Consumption Model . 73 4.5.5 Path-based Energy Consumption Model . 74 4.5.6 Energy- and Delay-aware Flow Scheduling . 75 4.5.6.1 Opt ED Solution . 77 4.5.6.2 Opt LD Solution . 80 4.5.6.3 Opt EDF S Solution . 83 4.5.7 Delay Assignment Heuristics . 86 4.5.7.1 Processing-capability based heuristic, PCH() . 86 4.5.7.2 Load-balancing based heuristic, LBH() . 87 4.6 Performance Evaluation . 92 viii 4.6.1 Comparison with Two Heuristics . 93 4.6.2 Energy and Power Gain Evaluation of Opt EDF S ........... 95 4.7 Conclusions . 97 5.0 Sleep-based Power Management and a Traffic-aware Strategy ...... 99 5.1 Introduction . 99 5.2 Related Work . 101 5.3 Sleep-based Power Controller . 103 5.3.1 Sleep-based Traffic-aware Power Controller Architecture . 104 5.3.2 A Sleep-based, Traffic-aware Power Management Strategy . 105 5.3.3 Departure Handler Algorithm . 106 5.3.4 Sleep Control Algorithms . 108 5.4 Performance Evaluation . 110 5.4.1 A Traffic-aware Power Management Simulation Framework . 110 5.4.1.1 Initialization Module (IM) . 111 5.4.1.2 Event Processing Module (EPM) . 113 5.4.1.3 Data Collection Module (DCM) . 114 5.4.2 Router-based Power Model and Network-based Energy-efficient Metrics 117 5.4.2.1 Power measurement . 117 5.4.2.2 A general power-aware model for router power consumption . 117 5.4.2.3 Network-based energy-efficient metrics . 118 5.4.3 JPNAP Daily Traffic Study . 120 5.4.4 Simulation-based Performance and Analysis . 122 5.5 Conclusions . 124 6.0 Conclusions and Future Work Directions ................... 127 6.1 Summary . 127 6.2 Conclusions .