Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Cost Effectiveness <s> Cloud computing allows the users to efficiently and dynamically provision computing resources to meet their IT needs. Most cloud providers offer two types of payment plans to the user, i.e., reservation and on-demand. The reservation plan is typically cheaper than the on-demand plan but reservation plan has to be provisioned in advance. Reserving the resources would be straightforward if the actual computing demand (e.g., job processing) is known in advance. However, in reality, the actual computing demand can be observed only at the point of actual usage. Therefore, it is difficult to reserve the correct amount of resources during the reservation to meet the computing demands of the users. In this paper, we propose an evolutionary optimal virtual machine placement (EOVMP) algorithm with a demand forecaster. First, a demand forecaster predicts the computing demand. Then, EOVMP uses this predicted demand to allocate the virtual machines using reservation and on-demand plans for job processing. The performance of the proposed schemes is evaluated by simulations and numerical studies. The evaluation result shows that the EOVMP algorithm can provide the solution close to the optimal solution of stochastic integer programming (SIP) and the prediction of the demand forecaster is of reasonable accuracy. <s> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Cost Effectiveness <s> This paper proposes an architecture for optimized resource allocation in Infrastructure-as-a-Service (IaaS)-based cloud systems. Current IaaS systems are usually unaware of the hosted application's requirements and therefore allocate resources independently of its needs, which can significantly impact performance for distributed data-intensive applications. To address this resource allocation problem, we propose an architecture that adopts a what if methodology to guide allocation decisions taken by the IaaS. The architecture uses a prediction engine with a lightweight simulator to estimate the performance of a given resource allocation and a genetic algorithm to find an optimized solution in the large search space. We have built a prototype for Topology-Aware Resource Allocation (TARA) and evaluated it on a 80 server cluster with two representative MapReduce-based benchmarks. Our results show that TARA reduces the job completion time of these applications by up to 59% when compared to application-independent allocation policies. <s> BIB002 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Cost Effectiveness <s> This paper proposes an optimized resource allocation mechanism in Infrastructure-as-a-Service (IaaS)- based cloud systems. Performance of distributed data-intensive applications are impacted significantly as current IaaS systems are usually unaware of the hosted application's requirements and hence allocating resources independent of its needs. To address this resource allocation problem and to optimise the allocation, we enhance an architecture that adopts a "what if" methodology to guide allocation decisions taken by the IaaS. The architecture uses a prediction engine with a lightweight simulator to estimate the performance of a given resource allocation and an evolutionary algorithm that includes an evolution strategies algorithm and a genetic algorithm, to find an optimized solution in the large search space. <s> BIB003
There also exist works showing that the availabilities and prices of different physical resources would be different. Therefore, it is worthwhile optimally to schedule VM resources on physical resources to reduce the total cost. For example, BIB001 proposed an evolutionary optimal VM placement algorithm with a demand forecaster to predict the computational demands of the cloud users. They used a hybridized algorithm consisting of a GA, ACO, and PSO to schedule VMs to physical resources efficiently. Their goal was to guarantee the lowest cost, given the user demand, provider's resource availability, and resource price, with optimized VM placement based on the user's usage history. In addition to direct cost savings, some researchers argue that minimizing the application makespan can lead to indirect cost savings. For example, BIB002 adopted a GA approach to scheduling the VM resources using topological information, as the number of PMs was much larger than that of VMs. Therefore, the chromosome was a binary string with the length being the same as the number of the PMs and the binary value of each bit represented whether the PM was selected to host a VM or not. Moreover, a prediction engine was used to utilize the topological information and estimate the performance of a candidate scheduling solution. Their objective was to reduce the application makespan, which would indirectly save the cost. BIB003 also carried out work for a similar scheduling problem, and enhanced the GA with an evolution strategy approach. However, the experimental results and comparisons are not detailed.