Network Element Scheduling for Achieving Energy-Aware Data Center Networks

Abstract

The goal of data center network is to interconnect a massive number of servers so as to provide reliable and scalable computing and storage infrastructure for cloud-based Internet services and data-intensive scientific applications. Recent studies reveal that the network elements consume 10∼20% of the overall power in a data center, which has introduced a challenge to reducing network energy cost without adversely affecting network performance. Considering unique features of traffic patterns and network topologies in data centers, this paper proposes a novel Network Element Scheduling Scheme (NESS) to reduce data center energy consumption from the networking perspective. The core idea is to turn on only a minimal subset of network elements to satisfy routing requirements, and put to sleep or shut down the rest unneeded ones for energy saving. In NESS, the logical network architecture formed by the active elements not only achieves the basic purpose for server interconnections in data centers, but also can support multi-path routing between pairs of hot servers for load balancing. Simulation experiments are performed in representative data center network topologies, and the results demonstrate the effectiveness of NESS in energy conserving on network elements in data centers.

Energy-Efficient Networking Solutions in Cloud-Based Environments

“…Considering that different areas of administration and ownership exist in multidomain transfer networks, these solution types cannot be deployed in a centralized consistent way. We have identified two primary studies Decision framework Tarutani et al 2012;Heller et al 2010;Thanh et al 2012;Cordeschi et al 2013;Dong et al 2013;Mahadevan et al 2009;Leivadeas et al 2013;Saha et al 2012;Sun et al 2012;Shirayanagi et al 2013;Nguyen Huu et al 2013;Fang et al 2012;Mahadevan et al 2011;McGeer et al 2010;Huang et al 2011;Kuribayashi 2012;Mann et al 2011;Fang et al 2013] Decision framework [Peoples et al 2012;Kantarci et al 2013] 3 9…”

“…Using more than one evaluation method could help derived conclusions be more concrete. For instance, Jin [ Shang et al 2012;Si et al 2012;Tarutani et al 2012;Peoples et al 2012;Aksanli et al 2012;Peoples et al 2011;Shang et al 2010;Kitayama et al 2013;Cordeschi et al 2013;Dong et al 2013;Mahadevan et al 2009;Leivadeas et al 2013;Saha et al 2012;Sun et al 2012;He et al 2012;Shirayanagi et al 2013;Gyarmati and Trinh 2010;Szymanski 2013;Fang et al 2012;Mahadevan et al 2011;McGeer et al 2010;Huang et al 2011;Kachris and Tomkos 2011;…”

“…In a different manner, in NESS [Fang et al 2012] we see that network devices are put into sleeping mode according to prelearned traffic patterns, which shows that "Sleeping mode/Switching off " and "Traffic patterns" strategies are realized. -VM assignment/migration: Using this approach, decision frameworks try to place/reallocate the VMs to aggregate them into fewer numbers of physical machines.…”

“…Moreover, the best energy-efficient route set is always changing because of real-time changes in the incoming load traffic. Some primary studies, such as the one proposed by Fang et al 2012], make use of traffic patterns, which include information regarding the traffic flows in the data center to make energy efficiency-related decisions.…”

“…Shang et al 2012;Tarutani et al 2012;Cerutti et al 2013;Heller et al 2010; Thanh et al 2012;Shang et al 2010;Kitayama et al 2013;Dong et al 2013;Ji et al 2012;Shirayanagi et al 2013;Gyarmati and Trinh 2010;McGeer et al 2010;Huang et al 2011; Tomkos 2013, 2011;Carrega et al 2012; Mann et al Shang et al 2012;Si et al 2012; Thanh et al 2012;Shang et al 2010;Kitayama et al 2013;Cordeschi et al 2013;Dong et al 2013;Leivadeas et al 2013;Glesk et al 2013;Sun et al 2012;He et al 2012;Fang et al 2012;Mann et al 2011] Device[Si et al 2012;Tarutani et al 2012;Cerutti et al 2013;Kitayama et al 2013;Ji et al 2012;Saha et al 2012;Glesk et al 2013; Tomkos 2013, 2011;Carrega et al 2012] 10 Scale: Inter-Data Center (11.3%) 5Decision framework[Aksanli et al 2012;Buysse et al 2011;Peoples et al 2011; Mouftah 2012Aksanli et al 2012;Buysse et al 2011;Peoples et al 2011;Szymanski 2013; Scale: Mixed-(Inter-& Intra-) Data Center (6.8%) 8…”