A Novel Family Genetic Approach for Virtual Machine Allocation

Abstract

The concept of virtualization forms the heart of systems like the Cloud and Grid. Efficiency of systems that employ virtualization greatly depends on the efficiency of the technique used to allocate the virtual machines to suitable hosts. The literature contains many evolutionary approaches to solve the virtual machine allocation problem, a broad category of which employ Genetic Algorithm. This paper proposes a novel technique to allocate virtual machines using the Family Gene approach. Experimental analysis proves that the proposed approach reduces energy consumption and the rate of migrations, and hence offers much scope for future research.

Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green Computing

“…2) Rosenbrock function (20) 3) Cube function (21) 4) Chunk function (22) 5) Rastrigin function (23) Each of the algorithm were examined in various conditions, i.e., adjusting the value of recursive iteration (100, 200, 1000), keeping the iteration size fixed 40, turning the population size (20, 50, 60), and keeping the number of iteration constant 1000, and using a population size n = 25 and p = 0.8 for FPA, crossover probability 0.95 [35,45], and learning parameters 2 for ACS [33]. The result is analyzed based on their performance regarding maximum, average, and standard deviation.…”

“…Joseph, Chandrasekaran [22], Wu, Tang [23] and Wang, Wang [24] proposed VM placement using Genetic Algorithm (GA) to improve the convergence speed of the GA to produce global optimal solution by the cloud datacenter resource allocation strategy. Furthermore, Particle Swarm Optimization (PSO) algorithm has been explore by various researchers e.g., [25,26].…”

An Approach for Energy Efficient Dynamic Virtual Machine Consolidation in Cloud Environment

“…In [17], the authors divided up the entire population to a number of families and performed genetic operations on these families in parallel in order to generate an optimal mapping between the set of hosts and VMs. Thus, they presented Family GA, which is a model of Parallel GA.…”

Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

“…Joseph et al [19] introduced a Parallel GA model known as Family GA (FGA) with the aim to generate an optimized mapping between the set of hosts and VMs. This model divides the entire population into a number of families on which it performs genetic operations to overcome the GA limitations.…”

“…In most research studies conducted in this area [6,8,[17][18][19][20][21], the necessary decisions are made based on current utilization of hosts and VMs are immediately migrated from hosts which researchers currently identify them as overloaded [8,15]. The proposed algorithm in the present work attempts to predict CPU utilization using Time-Series Method and SES technique and identifies a VM as overloaded or underloaded based on current and predicted utilization values and their comparison with dynamic upper and lower thresholds.…”