Priority-Aware Virtual Machine Selection Algorithm in Dynamic Consolidation

Abstract

In the past few years, many researchers attempted to tackle the problem of decreasing energy consumption in cloud data centers. One of the widely adopted techniques for this purpose is dynamic Virtual Machine (VM) consolidation. Consolidation moves VMs between hosts to decrease energy consumption. However, it has a negative impact on performance leading to Service Level Agreement (SLA) violations. Accordingly, selecting which VM to migrate from one host to another is a challenging task since it can affect performance. Researchers came up with several solutions and policies for efficient VM selection. In this paper, we exploit the fact that many tasks and users may tolerate some performance degradation which means, the tasks running on the VMs can be of different priorities. Accordingly, we propose augmenting consolidation with the priority concept, where low priority tasks are always selected first for migration. Towards this goal, we modified the popular Minimum Migration Time VM selection algorithm using the priority concept. The efficiency of the proposed algorithm is confirmed through extensive simulations using CloudSim toolkit and a real workload. The results show that priority awareness has a positive impact on decreasing energy consumption as well as maximizing SLA obligation.

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

“…Migration time is estimated by dividing the amount of RAM currently used by the virtual machine by available network bandwidth. Paper 38 has changed the method of selecting a virtual machine based on the minimum migration time using the concept of priority. This method divides users' tasks into two priority classes based on their importance and treats the tasks of each class in its own way.…”

QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

“…To realize the load balancing of backbone network, many efficient VM scheduling schemes such as [8][9][10][11][12][13][14] have been proposed. [8] solved VM scheduling for load balancing via using a weighted Round Robin algorithm in Eucalyptus platform, and its advantage was that it could allocate all incoming requests to the available VMs in Round Robin fashion based on the weights without considering the current load on each VM.…”

“…The virtualization, that refers to the logical creation of an Internet resource that does not physically exist, is still a common resource balancing technology in heterogeneous data centers. Many virtualization based studies, such as [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21], had developed various of schedule solutions with different goals to dynamically adjust the workload of data center infrastructures, reduce the cost of network and improve QoS of cloud. For example, [3] explored the task scheduling algorithm using a hybrid approach, which combined the genetic algorithms and the bacterial foraging algorithms, to minimize the makespan and second and reduce the energy consumption in the cloud computing.…”

Migrating Virtual Machine in Heterogen and Homogen Cloud Data Center with Efficient Electricity Consumption Using Fuzzy C-meams

“…To enhance the energy efficiency in the data center, the Dynamic VM integration is considered as one of effective management solution [5]. Dynamic VM integration has four tasks of decision making [6,7]: (1) determine when the hosts are redundant (Host Overload Detection), based on this condition, direct migration is involve to move the VM(s) from host when its overloaded; (2) decide when the hosts are underloaded, at this position, the host is available to switch into sleep mode, when this host's VM(s) is migrated to another host; (3) Select the VM(s) which will migrated from the overfilled host (VM Selection); and (4) select the host location for the assignment of migrating VM(s) (VM Placement).…”

“…Moreover, lots of previous research in this area that discussed dynamic VM consolidation energy efficiency have been done such as in [5][6][7][8][9][10]. Where some research only focus on decreasing energy consumption in VM migration [11], or allocation [12].…”