Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Negotiation <s> Resource management is one of the main issues in Cloud Computing. In order to improve resource utilization of large Data Centers while delivering services with higher QoS to Cloud Clients, a resource allocation strategy based on market (RAS-M) is proposed. Firstly, the architecture and the market model of RAS-M are constructed, in which a QoS-refection utility function is designed according to different resource requirements of the Cloud Client, the equilibrium state of RAS-M is defined and the proof of its optimality is given. Secondly, GA-based price adjusted algorithm is introduced to deal with the problem of achieving the equilibrium state of RAS-M. Finally, RAS-M is implemented upon Xen to reallocate the VM’s weight. Experiments results obtained by setting different parameters show that RAS-M can achieve the equilibrium state approximately, that is, demand and supply is balanced nearly, which validates RAS-M is effective and practicable, and is capable of achieving its goal. <s> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Negotiation <s> Cloud computing is one of the rapidly improving technologies. It provides scalable resources needed for the ap- plications hosted on it. As cloud-based services become more dynamic, resource provisioning becomes more challenging. The QoS constrained resource allocation problem is considered in this paper, in which customers are willing to host their applications on the provider's cloud with a given SLA requirements for performance such as throughput and response time. Since, the data centers hosting the applications consume huge amounts of energy and cause huge operational costs, solutions that reduce energy consumption as well as operational costs are gaining importance. In this work, we propose an energy efficient mechanism that allocates the cloud resources to the applications without violating the given service level agreements(SLA) using Ant colony framework. <s> BIB002 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Negotiation <s> A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time. <s> BIB003 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Negotiation <s> In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer's future demand and providers' resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing environments. <s> BIB004 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Negotiation <s> Task scheduling is an important and challenging issue of Cloud computing. Existing solutions to task scheduling problems are unsuitable for Cloud computing because they only focus on a specific purpose like the minimization of execution time or workload and do not use characteristics of Cloud computing for task scheduling. A task scheduler in Cloud computing has to satisfy cloud users with the agreed QoS and improve profits of cloud providers. In order to solve task scheduling problems in Cloud computing, this paper proposes a task scheduling model based on the genetic algorithm. In the proposed model, the task scheduler calls the GA scheduling function every task scheduling cycle. This function creates a set of task schedules and evaluates the quality of each task schedule with user satisfaction and virtual machine availability. The function iterates genetic operations to make an optimal task schedule. Experimental results show effectiveness and efficiency of the genetic algorithm-based task scheduling model in comparison with existing task scheduling models, which are the round-robin task scheduling model, the load index-based task scheduling model, and the ABC based task scheduling model. <s> BIB005 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Negotiation <s> The necessity of balancing the obtained performance with the energy consumed is an emerging ambition for cloud computing research. Performance in cloud computing is defined through Service Level Agreement contracts between the cloud provider and cloud customer, being a projection of the customer's perspective on the service offered by the cloud provider. Although more and more research efforts go into standardizing Service Level Agreement in cloud systems, the area is still at its early ages. This paper proposes a Service Level Agreement negotiation protocol based on particle swarm optimization techniques, for obtaining a balance between the energy consumed and performance offered in the cloud. The two parties of the defined negotiation protocol are the performance-oriented cloud customer and the energy-oriented cloud provider. The agreement resulted from the negotiation process satisfies the two major negotiation properties we aim for: closeness to Pareto optimality and high social welfare. <s> BIB006 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Negotiation <s> Cloud computing (CC) is a configurable resource model that can be rapidly provisioned and released with minimal management effort or service provider interaction. In CC services, buyers and sellers always have strong (leader) or weak (follower) influence, and leaders have the authority to make decisions. Considering how to only reduce the buyer's cost or increase the seller's profit does not reflect the economic situation in reality. Hence, a novel model based on the Stackelberg leadership is proposed and formulated by bi-level programming (BLP) to analyze such non-cooperative decision relationships in CC. A new method called bi-level simplified swarm optimization (BLSSO) is developed to solve the BLP. This BLSSO uses a dynamic regional search and guarantees global convergence. Computational results show that the proposed BLSSO technique is very competitive and satisfies a number of criteria, including the number of times the best solution is found, the average number of the earliest best solution found, and the total computational time. Thus, both the Stackelberg leadership and the BLSSO can provide decision makers with a different view on CC. <s> BIB007 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Negotiation <s> Abstract Since the appearance of cloud computing, computing capacity has been charged as a service through the network. The optimal scheduling of computing resources (OSCR) over the network is a core part for a cloud service center. With the coming of virtualization, the OSCR problem has become more complex than ever. Previous work, either on model building or scheduling algorithms, can no longer offer us a satisfactory resolution. In this paper, a more comprehensive and accurate model for OSCR is formulated. In this model, the cloud computing environment is considered to be highly heterogeneous with processors of uncertain loading information. Along with makespan, the energy consumption is considered as one of the optimization objectives from both economic and ecological perspectives. To provide more attentive services, the model seeks to find Pareto solutions for this bi-objective optimization problem. On the basis of classic multi-objective genetic algorithm, a case library and Pareto solution based hybrid Genetic Algorithm (CLPS-GA) is proposed to solve the model. The major components of CLPS-GA include a multi-parent crossover operator (MPCO), a two-stage algorithm structure, and a case library. Experimental results have verified the effectiveness of CLPS-GA in terms of convergence, stability, and solution diversity. <s> BIB008
Users go for cloud computing to enjoy cost savings and avoid resource inefficiencies. However, cloud service providers have varying pricing for computing instances based on different times of the day or SLAs. For instance, an on-demand instance would be more expensive than a reserved instance BIB004 . The user would hence predict their workload or demand patterns in advance, so as not to pay more, and submit their respective workload requests to a "cloud broker" that can aggregate these demands and subsequently distribute the workloads for cost optimization. The research challenge is thus an optimization problem that involves multiple objectives with uncertainly in both ends of the supply and demand chain, where parameters such as pricing, performance, load type, and capacity continuously changes. Although work on scheduling resources in the application layer oriented by user QoS objectives or provider efficiency objectives has gained much attention in recent years, there exist reports focusing on how to efficiently schedule the cloud resource so that both the cloud user's and the cloud provider's objectives can be met in tandem. Here, we term this kind of scheduling as scheduling oriented by negotiation. The related works are summarized according to the methods for negotiation in Table IV and are described as follows. For example, in the study by BIB005 , a GA-based approach with the same encoding scheme as Figure 4 was used. However, they defined the fitness function by combining the satisfaction of user QoS and the VM availability. Therefore, the scheduling approach can obtain a solution that had good negotiation between cloud users and providers. In addition,  combined the load balance for cloud provider objective and the makespan for cloud user objective. enhanced the GA by not only using the Min-Min and Max-Min strategies to generate promising solutions to initialization population, but also by defining the objective that combines both the task makespan and resource load balance. ACO has also been used in negotiation-oriented scheduling. BIB002 argued that the throughput and response time were a concern of cloud users, while the power energy consumption and operational cost were a concern of the cloud providers. Therefore, their power-efficient ACO approach was an adaptive mechanism that can dynamically schedule the resource in cloud computing environments to host services on a minimal number of server resources. This way, not only SLA of the cloud users can be satisfied, but also energy savings of the data center can be achieved. When making a negotiation in ACO, another strategy is to guide the search by considering the negotiation between the cloud user and the provider. proposed scheduling the cloud resource oriented by the objectives of minimizing the makespan of the tasks set (for the cloud users) and optimizing the load balance of the entire system (for the cloud provider). Therefore, in their ACO algorithm, the ant chooses the VM for each task step by step similar to the process shown in Figure 6 , but the pheromone for selecting VM is the combination of the VM computing capacity and its current load. Another example of negotiation-oriented scheduling between the cloud user and the cloud provider was reported in the study by BIB006 , where the energy consumed by the cloud provider and the performance obtained by the cloud user were regarded as twofold objectives when scheduling the cloud resource within an SLA negotiation. Therefore, it was proposed using a two-population-based PSO approach to the objectives oriented by the cloud user and by the cloud provider separately. These two populations exchange solutions with each other generation by generation, hence facilitating negotiations between the cloud user and the provider. Some researchers scheduled the resources by introducing an equilibrium theory to the negotiation. In the study by BIB001 , an equilibrium state based on a market mechanism balancing the cloud user demand and cloud provider supply was defined. They proposed the use of a GA-based price adjusting algorithm to solve the cloud resource scheduling problem of approximating the equilibrium state to improve resource utilization and to maximize the benefits of both cloud providers and users. BIB007 also defined a Stackelberg equilibrium from the economic community to formulate the bilevel programming that can be used to solve decentralized decision-making problems arising from cloud providers and cloud users. They proposed a simplified swarm optimization algorithm to find the Stackelberg equilibrium solution. Recently, BIB003 proposed using constraint strategy, while BIB008 proposed using multiobjectives to satisfy the negotiation between cloud users and providers when scheduling the resources. BIB003 argued that in a marketoriented cloud computing environment, the satisfaction of QoS from users, the optimization of profit for the providers, and other factors all should be taken into consideration when scheduling the resources. They designed GA-, ACO-, and PSO-based metaheuristic approaches to scheduling the cloud resources so as to minimize the overall running cost while satisfying all the constraints from the cloud users and providers. BIB008 defined makespan and energy consumption as the multiobjectives of cloud user and provider, and designed a GA with a multiparent crossover operator to search for promising Pareto solutions.