Joint Host-Network Optimization for Energy-Efficient Data Center Networking

Abstract

Abstract-Data centers consume significant amounts of energy. As severs become more energy efficient with various energy saving techniques, the data center network (DCN) has been accounting for 20% or more of the energy consumed by the entire data center. While DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns. The objective of this work is to improve the energy efficiency of DCNs during off-peak traffic time by powering off idle devices. Although there exist a number of energy optimization solutions for DCNs, they consider only either the hosts or network, but not both. In this paper, we propose a joint optimization scheme that simultaneously optimizes virtual machine (VM) placement and network flow routing to maximize energy savings, and we also build an OpenFlow based prototype to experimentally demonstrate the effectiveness of our design. First, we formulate the joint optimization problem as an integer linear program, but it is not a practical solution due to high complexity. To practically and effectively combine host and network based optimization, we present a unified representation method that converts the VM placement problem to a routing problem. In addition, to accelerate processing the large number of servers and an even larger number of VMs, we describe a parallelization approach that divides the DCN into clusters for parallel processing. Further, to quickly find efficient paths for flows, we propose a fast topology oriented multipath routing algorithm that uses depth-first search to quickly traverse between hierarchical switch layers and uses the best-fit criterion to maximize flow consolidation. Finally, we have conducted extensive simulations and experiments to compare our design with existing ones. The simulation and experiment results fully demonstrate that our design outperforms existing host-or network-only optimization solutions, and well approximates the ideal linear program.

Energy efficient temporal load aware resource allocation in cloud computing datacenters

“…The work assumes that servers are homogeneous and doesnot consider resource constraints of the servers. The joint server and network power consumption optimization has also been studied in [10]. They proposed a unified model that combines server and network optimization by converting the VM assignment to a routing problem.…”

“…Second term represents the static part of communication power consumption and finally the last term corresponds to the power consumption of the servers. Constraint group (10) ensures that VM requirements of each job are satisfied and group (11) guarantees that resource demands of jobs scheduled on a server do not exceed that server's resource capacities. The constraints (12) and (13) ensure that bandwidth demands do not violate the capacities of TORs to CS and CS to CS links respectively.…”

Green Approach for Joint Management of Geo-Distributed Data Centers and Interconnection Networks

“…In [16], the authors proposed an optimization approach to jointly minimize the energy consumption in data center hosts and network. The basic idea is to consider both VM placement and traffic routing for energy saving.…”

“…Even if there are only a few existing works in literature that have investigated the impact of joint optimization solutions for energy saving in Cloud systems [15][16][17], the effectiveness of such solutions from energy cost point of view, and their contribution to reducing environmental impact through the use of green energy remain open issues that have motivated our work (see Section 2 for more details).…”

Power-efficient resource-guaranteed VM placement and routing for time-aware data center applications

“…In addition to increased power bills, carbon emissions and power supply limitations significantly affect their ability to support more tenants and hence, maximize their economic benefits [1][2][3][4][5][6]. On the other hand, unpredictable tenancy costs and performance concerns have deterred many potential tenants from adopting cloud services [7][8][9][10][11].…”