Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> SCHEDULING IN THE VIRTUALIZATION LAYER <s> Cloud computing builds upon advances on virtualization and distributed computing to support cost-efficient usage of computing resources, emphasizing on resource scalability and on demand services. Moving away from traditional data-center oriented models, distributed clouds extend over a loosely coupled federated substrate, offering enhanced communication and computational services to target end-users with quality of service (QoS) requirements, as dictated by the future Internet vision. Toward facilitating the efficient realization of such networked computing environments, computing and networking resources need to be jointly treated and optimized. This requires delivery of user-driven sets of virtual resources, dynamically allocated to actual substrate resources within networked clouds, creating the need to revisit resource mapping algorithms and tailor them to a composite virtual resource mapping problem. In this paper, toward providing a unified resource allocation framework for networked clouds, we first formulate the optimal networked cloud mapping problem as a mixed integer programming (MIP) problem, indicating objectives related to cost efficiency of the resource mapping procedure, while abiding by user requests for QoS-aware virtual resources. We subsequently propose a method for the efficient mapping of resource requests onto a shared substrate interconnecting various islands of computing resources, and adopt a heuristic methodology to address the problem. The efficiency of the proposed approach is illustrated in a simulation/emulation environment, that allows for a flexible, structured, and comparative performance evaluation. We conclude by outlining a proof-of-concept realization of our proposed schema, mounted over the European future Internet test-bed FEDERICA, a resource virtualization platform augmented with network and computing facilities. <s> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> SCHEDULING IN THE VIRTUALIZATION LAYER <s> Cloud computing providers provision their resources into different types of virtual machine (VM) instances that are then allocated to the users for specific periods of time. The allocation of VM instances to users is usually determined through fixed-price allocation mechanisms that cannot guarantee an economically efficient allocation and the maximization of cloud provider's revenue. A better alternative would be to use combinatorial auction-based resource allocation mechanisms. This argument is supported by the economic theory; when the auction costs are low, as is the case in the context of cloud computing, auctions are especially efficient over the fixed-price markets because products are matched to customers having the highest valuation. The existing combinatorial auction-based VM allocation mechanisms do not take into account the user's demand when making provisioning decisions, that is, they assume that the VM instances are statically provisioned. We design an auction-based mechanism for dynamic VM provisioning and allocation that takes into account the user demand, when making provisioning decisions. We prove that our mechanism is truthful (i.e., a user maximizes its utility only by bidding its true valuation for the requested bundle of VMs). We evaluate the proposed mechanism by performing extensive simulation experiments using real workload traces. The experiments show that the proposed mechanism yields higher revenue for the cloud provider and improves the utilization of cloud resources. <s> BIB002 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> SCHEDULING IN THE VIRTUALIZATION LAYER <s> The main advantage of cloud computing is that it is capable of handling a huge amount of growing work in a predestined manner for the usage of the business customers. The main enabling technology for cloud computing is virtualization which generalize the physical infrastructure and makes it easy to use and manage.In this paper virtualization is used to allocate resources based on their needs and also supports green computing concept."Skewness" concept is introduced here in which the same is minimized to combine various workloads to improve the utilization of the server. Overload avoidance is maintained in this paper which leads to achieve good performance. <s> BIB003
Scheduling for cloud resource utilization is to allocate resources for user tasks so as to optimize the objective of the cloud user, the cloud provider, or both, which is also often coupled with scheduling for resource virtualization. To facilitate the cloud resources as utility provided over the Internet, the physical resources (e.g., the computational resource, storage resource, and network resource) are better virtualized as uniform resources, that is, the VM resources  . Therefore, how to allocate and migrate VMs on the physical resources becomes a significant research topic BIB001 BIB003 BIB002 . In the literature, some works argue that VM resource scheduling should aim at the load balance of physical resources, while some argue that allocating as many VMs as possible to a minimal number of physical nodes to save energy should be the major aim. There also exist works on taking the resource virtualization cost as the main consideration. Here, we compare in Table V and classify these works on scheduling for cloud resource virtualization 