Shadow-Routing Based Dynamic Algorithms for Virtual Machine Placement in a Network Cloud

Abstract

Abstract-We consider a shadow routing based approach to the problem of real-time adaptive placement of virtual machines (VM) in large data centers (DC) within a network cloud. Such placement in particular has to respect vector packing constraints on the allocation of VMs to host physical machines (PM) within a DC, because each PM can potentially serve multiple VMs simultaneously. Shadow routing is attractive in that it allows a large variety of system objectives and/or constraints to be treated within a common framework (as long as the underlying optimization problem is convex). Perhaps even more attractive feature is that the corresponding algorithm is very simple to implement, it runs continuously, and adapts automatically to changes in the VM demand rates, changes in system parameters, etc., without the need to re-solve the underlying optimization problem "from scratch". In this paper we focus on the minmax-DC-load problem. Namely, we propose a combined VM-to-DC routing and VM-to-PM assignment algorithm, referred to as Shadow scheme, which minimizes the maximum of appropriately defined DC utilizations. We prove that the Shadow scheme is asymptotically optimal (as one of its parameters goes to 0). Simulation confirms good performance and high adaptivity of the algorithm. Favorable performance is also demonstrated in comparison with a baseline algorithm based on VMware implementation [7], [8]. We also propose a simplified -"more distributed" -version of the Shadow scheme, which performs almost as well in simulations.

Optimal Virtual Machines Placement in Cloud Computing Environment

“…To be specific, they introduce a united VM-to DC steering and VM-to-PM task calculation, alluded to as obscurity conspire, which reduces the greatest of fittingly characterized DC usage. They demonstrate that the obscurity conspire is asymptotically ideal (as one of its parameters goes to 0) [13]. To begin with plans and executes a cloud information center arrange explore framework, web cloudsim, which can back the join test confirmation of genuine environment and reenactment environment.…”

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

“…In network sharing [3], [17], bin packing [23] approaches are used allocate computing resources to the workloads, and then providers attempt to share the network resources among the different workloads according to their demand, or payment. For explicit allocation, several approaches have been proposed to perform joint reservation of both the computing and networking resources during allocation to guarantee predictability.…”

An energy‐aware virtual machine scheduling method for service QoS enhancement in clouds over big data

“…. , s M }, running in clouds and each server is associated with R basic resource units, that is, independent VMs with identical configurations [8,13]. Each VM is equipped with various kinds of resources, including CPU, memory, bandwidth, etc.…”

“…VM Live migration provides a mechanism to move the VM from a server to another, and it avoids interrupting the running application deployed on the VM. Moreover, the technique of VM migration makes it possible to schedule VMs among servers during service execution, equipping VM‐based clouds and data centers with a powerful feature . However, it is still a challenge to efficiently schedule VMs for service execution in an energy‐aware manner.…”