Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Partner Federation <s> In our previous paper (2), we proposed a novel combinatorial auction (CA) based Cloud market model for trading services that allows Cloud providers (CPs) to make groups and submit their bids collaboratively as a single bid for winning the auction. But to find a good combination of CP partners and make groups is a NP-hard problem. Since each provider only has limited information about other providers, past collaborative task information (i.e. no. of times collaboratively win the auctions, failed to make groups, etc.) needs to be analyzed and utilized for partner selection. We call it multi-task and multi-objective optimization problem. To solve this problem, in this paper, we propose a hybrid algorithm that utilizes artificial neural network (ANN) for gathering and analyzing information about previous tasks and then uses multi-objective genetic algorithm (MOGA) to solve the partner selection problem. Simulation results show that our proposed algorithm is effective. <s> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Partner Federation <s> As Internet data centers (IDCs) have been increasing in scale and complexity, they are currently a significant source of energy consumption and CO2 emission. This paper proposes and evaluates a new framework to operate a federation of IDCs in a "green" way. The proposed framework, called Green Monster, dynamically moves services (i.e., workload) across IDCs for increasing renewable energy consumption while maintaining their performance. It makes decisions of service migration and placement with an evolutionary multiobjective optimization algorithm (EMOA) that evolves a set of solution candidates through global and local search processes. The proposed EMOA seeks the Pareto-optimal solutions by balancing the trade-offs among conflicting optimization objectives such as renewable energy consumption, cooling energy consumption and response time performance. <s> BIB002
In a recent cloud computing environment, a partner federation that allows groups of cloud service providers to collaborate with each other and to publish their services as a single service to the cloud users has become appealing. BIB001 modeled a cloud partner federation problem as a multitask and multiobjective optimization problem, and proposed using a multiobjective GA to solve this problem. In a chromosome, a binary code is used to represent whether to select a cloud partner in the federation group or not. The multiobjectives include the total price, reliability of services, and relationship between the providers. In Zhang and Zhang's work , an approach based on ACO and complexity theory was developed to schedule the resources provided by various service providers in the open cloud computing federation. The objective is to best solve the complex and dynamic load balancing problem in the cloud federation, and to maximize the ratio of user satisfaction and facility utilization among the clouds. Besides scheduling cloud resources among the cloud partner federation to improve the service quality and to balance workload, work on saving energy in deployment is also reported. BIB002 made decisions of service migration and placement with an evolutionary multiobjective GA. The chromosome code is of the same length as the number of the services, and each gene stands for the index of the cloud provider in the federation. In order to provide green clouds, a Pareto-optimal solution balancing trade-offs among renewable energy consumption, cooling energy consumption, and response time performance are optimized.