Let's stay together: Towards traffic aware virtual machine placement in data centers

Abstract

Abstract-As tenants take networked virtual machines (VMs) as their requirements, effective placement of VMs is needed to reduce the network cost in cloud data centers. The cost is one of the major concerns for the cloud providers. In addition to the cost caused by network traffics (N-cost), the cost caused by the utilization of physical machines (PM-cost) is also non-negligible. In this paper, we focus on the optimized placement of VMs to minimize the cost, the combination of N-cost and PM-cost. We define N-cost by various functions, according to different communication models. We formulate the placement problem, and prove it to be NP-hard. We investigate the problem from two aspects. Firstly, we put a special emphasis on minimizing the Ncost with fixed PM-cost. For the case that tenants request the same amount of VMs, we present optimal algorithms under various definitions of N-cost. For the case that tenants require different numbers of VMs, we propose an approximation algorithm. Also, a greedy algorithm is implemented as the baseline to evaluate the performance. Secondly, we study the general case of the VM placement problem, in which both N-cost and PM-cost are taken into account. We present an effective binary-searchbased algorithm to determine how many PMs should be used, which makes a tradeoff between PM-cost and N-cost. For all of the algorithms, we conduct theoretical analysis and extensive simulations to evaluate their performance and efficiency.

Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

“…However, this approach is too simple, which only considers CPU and does not consider other resources. Li et al [9] have proposed heuristic binary search algorithm, which places virtual machines in a few number of servers as far as possible to reduce energy consumption of the whole data center, and virtual machines of the same tenant are placed in the same server that can reduce the network consumption. The practicality of this algorithm is not good because only one resource type is considered.…”

“…if (server has enough resources to hold vm) then (4) if ( V .isEmpty()) then (5) server.setDisplacement(1); (6) end if (7) server.add(vm); (8) break; (9) end if (10) end for (11) end for (12) for server in ServerList (13) server.setVelocity(0); (14) end for (15) return ServerList;…”

Multi-Capacity Bin Packing with Dependent Items and its Application to the Packing of Brokered Workloads in Virtualized Environments

“…This approach is fairly recent in the cloud computing domain, and algorithms vary according to their placement strategies. In [25], performance predictability is improved by greedily placing communicating tasks in VMs with high bandwidth among them, while in [26], the application components are divided into smaller tasks, and packed inside the virtual machines to maximize virtual machine utilization, while minimizing the cost of tasks communicating across these virtual machines.…”

NACER: A Network-Aware Cost-Efficient Resource Allocation Method for Processing-Intensive Tasks in Distributed Clouds

“…This is because recent studies [16] [9] show that assuming that the cloud provider knows VM traffic is a very strong assumption and typically not realistic. NACER1 aims at placing VMs of a task as near as possible to each other and this practical rule generally leads to an improvement in communication cost.…”