A comparison of resource allocation process in grid and cloud technologies

Grid Computing and Cloud Computing are two different technologies that have emerged to validate the long-held dream of computing as utilities which led to an important revolution in IT industry. These technologies came with several challenges in terms of middleware, programming model, resources management and business models. These challenges are seriously considered by Distributed System research. Resources allocation is a key challenge in both technologies as it causes the possible resource wastage and service degradation. This paper is addressing a comprehensive study of the resources allocation processes in both technologies. It provides the researchers with an in-depth understanding of all resources allocation related aspects and associative challenges, including: load balancing, performance, energy consumption, scheduling algorithms, resources consolidation and migration. The comparison also contributes an informal definition of the Cloud resource allocation process. Resources in the Cloud are being shared by all users in a time and space sharing manner, in contrast to dedicated resources that governed by a queuing system in Grid resource management. Cloud Resource allocation suffers from extra challenges abbreviated by achieving good load balancing and making right consolidation decision

Multi-capacity combinatorial ordering GA in application to cloud resources allocation and efficient virtual machines consolidation

This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4%, 34%, and 39%, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given

Complexity of combinatorial ordering genetic algorithms COFFGA and CONFGA

This paper analyses the complexity of two Algorithms called COFFGA (Combinatorial Ordering First Fit Genetic Algorithm) and CONFGA (Combinatorial Ordering Next Fit Genetic Algorithm). It also identifies the parameters that affect the performance of these algorithms. The complexity of the GA depends on the problem being solved by this GA, as well as the operators of the GA itself. The complexity of COFFGA and CONFGA are analysed individually. Even of these algorithms are slightly different, they may have extremely different complexities depending on the differences in their fitness function or termination condition. To provide a provable bound on a problem, there must be a bound on the evaluation function as well as a manner by which the underlying problem is tied to the representation. Given that there is no standard complexity of the GA, and the complexity of any GA depends on the problem that being solved by this GA and its operators, then CONFGA and COFFGA are analysed with different complexities; although they built upon the same algorithm and they are used to solve the same problem (Cloud resource allocation problem), but they are different in their operators their fitness function and termination condition