Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <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 Service Placement <s> Cloud computing is a latest new computing paradigm where applications, data and IT services are provided over the Internet. Cloud computing has become a main medium for Software as a Service (SaaS) providers to host their SaaS as it can provide the scalability a SaaS requires. The challenges in the composite SaaS placement process rely on several factors including the large size of the Cloud network, SaaS competing resource requirements, SaaS interactions between its components and SaaS interactions with its data components. However, existing applications' placement methods in data centres are not concerned with the placement of the component's data. In addition, a Cloud network is much larger than data center networks that have been discussed in existing studies. This paper proposes a penalty-based genetic algorithm (GA) to the composite SaaS placement problem in the Cloud. We believe this is the first attempt to the SaaS placement with its data in Cloud provider's servers. Experimental results demonstrate the feasibility and the scalability of the GA. <s> BIB002 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> Cloud computing has become a main medium for Software as a Service (SaaS) hosting as it can provide the scalability a SaaS requires. One of the challenges in hosting the SaaS is the placement process where the placement has to consider SaaS interactions between its components and SaaS interactions with its data components. A previous research has tackled this problem using a classical genetic algorithm (GA) approach. This paper proposes a cooperative coevolutionary algorithm (CCEA) approach. The CCEA has been implemented and evaluated and the result has shown that the CCEA has produced higher quality solutions compared to the GA. <s> BIB003 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> Large-scale computing platforms that serve thousands or even millions of users through the Internet are on a path to become a pervasive technology available to companies of all sizes. However, existing technologies to enable this kind of scaling are based on a hierarchically managed approach that does not scale equally well. Moreover, existing systems are also not equipped to handle the dynamism that may emerge as a result of severe failures or load surges. In this paper, we conjecture that using self-organizing techniques for system (re)configuration can improve both the scalability properties of such systems as well as their ability to tolerate churn. Specifically, the paper focuses on deployment of virtual machine images onto physical machines that reside in different parts of the network. The objective is to construct balanced and dependable deployment configurations that are resilient. To accomplish this, a method based on a variant of Ant Colony Optimization is used to find efficient deployment mappings for a large number of virtual machine image replicas that are deployed concurrently. The method is completely decentralized; ants communicate indirectly through pheromone tables located in the nodes. An example scenario is presented and simulation results are obtained for the method. <s> BIB004 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> Cloud Computing is developing as a key computing platform for sharing resources. How to effectively route storage resource in cloud is so difficulty. This paper presents Cloud Computing Dynamic Route Scheduling for Optimization of Cloud Database for enhancing the efficiency searching database of cloud computing. <s> BIB005 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> In cloud computing the allocation and scheduling of multiple virtual resources, such as virtual machines (VMs), are still a challenge. The optimization of these processes bring the advantage of improving the energy savings and load balancing in large data centers. Resource allocation and scheduling also impact in federated clouds where resources can be leased from partner domains. This paper proposes a bio-inspired VM allocation method based on Genetic Algorithms to optimize the VM distribution across federated cloud domains. The main contribution of this work is an inter-domain allocation algorithm that takes into account the capacity of the links connecting the domains in order to avoid quality of service degradation for VMs allocated on partner domains. An architecture to simulate federated clouds is also a contribution of this paper. <s> BIB006 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> Cloud storage is a massive and public accessible storage available for use on internet. Since the number of users and data access request will be massive, a good performance improvement algorithm is needed. In this work, a technique of using a smart object placement is presented. The model for cloud storage and object placement are discussed. Then, genetic algorithm is used to optimize the placement function to gain a better average access speed for any storage object. The experimental results show an obvious performance increases due to a better object placement. The result of this work can be applied to enhance performance of both private and public storage cloud. <s> BIB007 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> Abstract For the cloud database route scheduling problem, this paper designed a cloud database route scheduling algorithm according to the dynamic combination of the genetic algorithm and ant colony algorithm. The initial solution got by the Genetic Algorithm was transformed into the pheromone initial value, which was needed by ant colony algorithm, then the optimal solution by the ant colony algorithm was obtained .Genetic control function was set up to control the opportunity of two algorithm's fusion .This paper proposed a reasonable algorithm,which could find the required database rapidly and effectively, reduce the dynamical load of cloud database routing, and improve the efficiency of cloud computing. <s> BIB008 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> Resources from the Internet, How to faster find the resources from cloud database, it is the database resource allocation algorithm cloud combinatorial optimization problem. Try buzzers and genetic algorithm fusion and normal cloud model algorithm, fast convergence to optimal solutions, and it improved phased efficiency of genetic algorithm from a optimal solution to an optimal solution. <s> BIB009 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> Cloud computing has received a lot of attention and adopted by Software as a Service (SAAS) providers. However, there are still many challenges in placing a SAAS across globally distributed datacenters, such as reducing transmission time and achieve load balancing simultaneously. This paper proposes an adaptive simulated annealing genetic algorithm (ASAGA) approach which can change crossover rate and mutation rate adaptively and combines simulated annealing mechanism to address this problem. Experimental results show that compared with simple genetic algorithm, ASAGA is feasible and scalable, and it has shorter execution time and convergence times. <s> BIB010 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> Software as a Service (SaaS) is gaining more and more attention from software users and providers recently. This has raised many new challenges to SaaS providers in providing better SaaSes that suit everyone needs at minimum costs. One of the emerging approaches in tackling this challenge is by delivering the SaaS as a composite SaaS. Delivering it in such an approach has a number of benefits, including flexible offering of the SaaS functions and decreased cost of subscription for users. However, this approach also introduces new problems for SaaS resource management in a Cloud data centre. We present the problem of composite SaaS resource management in Cloud data centre, specifically on its initial placement and resource optimization problems aiming at improving the SaaS performance based on its execution time as well as minimizing the resource usage. Our approach differs from existing literature because it addresses the problems resulting from composite SaaS characteristics, where we focus on the SaaS requirements, constraints and interdependencies. The problems are tackled using evolutionary algorithms. Experimental results demonstrate the efficiency and the scalability of the proposed algorithms. <s> BIB011 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components' placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA. <s> BIB012 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <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> BIB013 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Service Placement <s> Since cloud computing platform can provide infinite storage capacity, computing ability as well as information services, it now has become the popular new application platform for both individuals and enterprises. The storage capacity of a data center is limited. Therefore, how to place data slices in appropriate data center proves to be an important factor influencing the platform ability. The data placement strategy we design in this paper takes the cooperation costs among data slices into account. It lowers the distributed transaction costs as much as possible, especially the cost differences among different distributed transactions. At the same time, this strategy also cares about the global load balance problem in data center. It is developed on the basis of genetic algorithm and ensures that the strategy can quickly converge to efficient data placement solutions. According to the result of the experiment, this strategy can better realize the global load balance and can save about 10% of the distributed cooperation costs when being compared with other strategies. <s> BIB014
For service placement oriented scheduling, BIB002 focused their attention on SaaS placement optimization. Their study argued that as Amazon has its storage server resources in America and Europe, while Nirvanix deploys its storage GA for SaaS placement BIB002 : optimally place the SaaS software/data components to different data centers Enhance GA by parallelism BIB003  , simulated annealing BIB010 , and cooperative coevolutionary strategy BIB011 BIB012 GA for PaaS placement to save energy BIB006 ACO to place PaaS for load balance BIB004 GA for IaaS (storage) placement BIB007 BIB014 Scheduling for partner federation (How to federate cloud providers?) Multiobjective GA, binary code to indicate whether a cloud provider is selected to the federation BIB001 ] ACO distributed schedule resources of various cloud providers in the federation, so as to balance the dynamic load of providers Multiobjective GA selects a partner to carry a service, balance energy consumption and response time BIB013 Scheduling for data routing (How to find cloud resources fast?) ACO is used to find the cloud data and services rapidly and effectively BIB005 Hybrid GA and ACO to design the cloud database route scheduling algorithm BIB008 Hybrid GA and ABC to design the cloud database route scheduling algorithm BIB009 services in America, Germany, and Singapore, a SaaS placement problem arises because the software components and data components of the SaaS should be strategically deployed on the worldwide cloud resources, for example, the computational servers resources and the storage servers resources. In their study, each gene is a triplet <Con, Reg, Ser> to represent a component, where Con, Reg, Ser are the IDs of continent, region, and server, respectively. This is, in fact, similar to the encoding scheme in Figure 4 , but further takes the geographical position of the cloud resources into consideration. As the code may cause infeasible solution, for example, if Reg is larger than Con, a penalty is imposed on the fitness of the infeasible chromosomes in scheduling the SaaS components on different cloud resources. The optimization objective is the estimated total execution time for the SaaS components. The authors later decomposed the scheduling problem into two interacting subproblems and proposed to solve them by using cooperative coevolutionary GAs BIB003 . One subproblem is to schedule the placement of SaaS's software components to computational servers, while the other is to schedule the placement of SaaS data components to storage servers. The chromosome encoding scheme was still the same as that used in BIB002 . In order to accelerate the evolutionary speed,  further enhanced the GA approach with a parallel cooperative coevolutionary strategy, while BIB010 proposed using an adaptive simulated annealing GA to schedule the globally distributed data center resources for the SaaS placement. BIB011 further reported that the dynamic characteristics of the cloud computing environment required dynamic resource scheduling for the SaaS service placement. They proposed a grouping GA approach to cater to the structural group of a composite SaaS and to reconfigure the placement of the SaaS's components. Moreover, the cooperative coevolutionary GA for the SaaS initial placement problem and a repair-based grouping GA for the SaaS resource optimization problem were further developed by BIB012 . In addition to the service placement scheduling in SaaS, work on service placement scheduling in PaaS and IaaS was also reported in the literature. BIB006 proposed using a GA-based bio-inspired approach to scheduling the VM services distribution across federated cloud domains, which can be regarded as PaaS service placement scheduling. The optimization objective is to improve energy savings and load balancing in large cloud data centers. For such PaaS service placement scheduling for deploying the VMs, BIB004 proposed an ACO approach for deploying the VM images to the server clusters that reside in different parts of the network. The objective is to balance the load of private and public clouds. Differently, BIB007 , and BIB014 proposed the GA-based approach to optimally schedule data placements in the cloud storage, which can be regarded as IaaS service placement scheduling. The optimization objective in BIB007 is to improve the average data access time for users and the workload balance of the cloud systems, while the objective in BIB014 is to reduce the distributed cooperation costs.