Efficient Heuristics for Virtual Machine Migration in Data Centers

Live migration of virtual machines is one of the essential virtualization technologies which enables the consolidation and load balancing in cloud data centers without interrupting the services. Main goals for optimizing a single virtual machine live migration is to minimize migration time, transferred data and downtime. Planning multiple live migrations in a data center has an essential impact on feasibility of consolidation and quality of services during migrations, however, optimizing parallel VM migrations has been studies less. Minimizing makespan (total migration time) while reducing energy and service quality degradation caused by using datacenter resources for migrations, are the main objectives of the problem. One of the issues in planning multiple live migrations is to detect and consider migrations order dependency constraints and possible deadlocks caused by lack of enough free resources in servers during the process. In the literature, exact mathematical models are not scalable and heuristics are not optimal and they don't consider the quality of service and energy efficiency of migration process when resources are restricted. In this work we propose a heuristic algorithm for scheduling the migration of virtual machines in a data center in order to minimize makespan (total migration time) and solve the conflicts (deadlocks) caused by limitation of resources with minimum cost and quality degradation

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Balancing the Migration of Virtual Network Functions with Replications in Data Centers

The Network Function Virtualization (NFV) paradigm is enabling flexibility, programmability and implementation of traditional network functions into generic hardware, in form of the so-called Virtual Network Functions (VNFs). Today, cloud service providers use Virtual Machines (VMs) for the instantiation of VNFs in the data center (DC) networks. To instantiate multiple VNFs in a typical scenario of Service Function Chains (SFCs), many important objectives need to be met simultaneously, such as server load balancing, energy efficiency and service execution time. The well-known \emph{VNF placement} problem requires solutions that often consider \emph{migration} of virtual machines (VMs) to meet this objectives. Ongoing efforts, for instance, are making a strong case for migrations to minimize energy consumption, while showing that attention needs to be paid to the Quality of Service (QoS) due to service interruptions caused by migrations. To balance the server allocation strategies and QoS, we propose using \emph{replications} of VNFs to reduce migrations in DC networks. We propose a Linear Programming (LP) model to study a trade-off between replications, which while beneficial to QoS require additional server resources, and migrations, which while beneficial to server load management can adversely impact the QoS. The results show that, for a given objective, the replications can reduce the number of migrations and can also enable a better server and data center network load balancing