A multiobjective migration algorithm as a resource consolidation strategy in cloud computing

Abstract

To flexibly meet users’ demands in cloud computing, it is essential for providers to establish the efficient virtual mapping in datacenters. Accordingly, virtualization has become a key aspect of cloud computing. It is possible to consolidate resources based on the single objective of reducing energy consumption. However, it is challenging for the provider to consolidate resources efficiently based on a multiobjective optimization strategy. In this paper, we present a novel migration algorithm to consolidate resources adaptively using a two-level scheduling algorithm. First, we propose the grey relational analysis (GRA) and technique for order preference by similarity to the ideal solution (TOPSIS) policy to simultaneously determine the hotspots by the main selected factors, including the CPU and the memory. Second, a two-level hybrid heuristic algorithm is designed to consolidate resources in order to reduce costs and energy consumption, mainly depending on the PSO and ACO algorithms. The improved PSO can determine the migrating VMs quickly, and the proposed ACO can locate the positions. Extensive experiments demonstrate that the two-level scheduling algorithm performs the consolidation strategy efficiently during the dynamic allocation process.

A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization

“…The fitness value for each particle is calculated according to the objective function, and the individual optimal solution and the global optimal solution are updated at each iteration. The particles continuously update their speed and position according to the individual optimal solution and the global optimal solution, and the updating method is as follows [45]:…”

Positions and covering: A two-stage methodology to obtain optimal solutions for the 2d-bin packing problem

“…Another application is cloud computing, which is considered one of the most promising technologies to meet customer demand flexibly. In [9], the authors mention that researchers have focused mainly on energy consumption and that the virtual machine placement problem is usually solved by using binpacking algorithms. Recent applications in the delineation of rectangular management zones in agricultural fields have led to some of the preliminary ideas of the methodology presented here [10][11][12].…”

“…As in the non-rotation case, P&C only seeks for feasibility with objective function (9). The restrictions (10) ensure that each point of each bin is covered by no more than one item, whether rotated or not.…”

“…Since the P&C is an exact method, it finds optimal solutions for instances where the SCH, GLS, TS3, and BBMV could not (beng 1-8 and/or beng [9][10]. Therefore, we have demonstrated the performance of the P&C method.…”

Container Migration Mechanism for Load Balancing in Edge Network Under Power Internet of Things

“…In [20], a cost model of migration time was constructed and two dynamic migration strategies for different application scenarios were proposed, namely the load balancing and fault tolerance. To flexibly meet users' demands in cloud computing, reference [21] proposed a grey relational analysis (GRA) and technique for order preference and a two-level hybrid heuristic algorithm was designed to consolidate resources in order to reduce costs and energy consumption.…”