A Hybrid Discrete Grey Wolf Optimization Algorithm Imbalance-ness Aware for Solving Two-dimensional Bin-packing Problems

Abstract

In different industries, there are miscellaneous applications that require multi-dimensional resources. These kinds of applications need all of the resource dimensions at the same time. Since the resources are typically scarce/expensive/pollutant, presenting an efficient resource allocation is a very favorable approach to reducing overall cost. On the other hand, the requirement of the applications on different dimensions of the resources is variable, usually, resource allocations have a high rate of wastage owing to the unpleasant resource skew-ness phenomenon. For instance, micro-service allocation in the Internet of Things (IoT) applications and Virtual Machine Placement (VMP) in a cloud context are challenging tasks because they diversely require imbalanced all resource dimensions such as CPU and Memory bandwidths, so inefficient resource allocation raises issues. In a special case, the problem under study associated with the two-dimensional resource allocation of distributed applications is modeled to the two-dimensional bin-packing problems which are categorized as the famous NP-Hard. Several approaches were proposed in the literature, but the majority of them are not aware of skew-ness and dimensional imbalances in the list of requested resources which incurs additional costs. To solve this combinatorial problem, a novel hybrid discrete gray wolf optimization algorithm (HD-GWO) is presented. It utilizes strong global search operators along with several novel walking-around procedures each of which is aware of resource dimensional skew-ness and explores discrete search space with efficient permutations. To verify HD-GWO, it was tested in miscellaneous conditions considering different correlation coefficients (CC) of resource dimensions. Simulation results prove that HD-GWO significantly outperforms other state-of-the-art in terms of relevant evaluation metrics along with a high potential of scalability.