Performance-oriented Cloud Provisioning: Taxonomy and Survey

Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table

Optimizing Cloud-Service Performance: Efficient Resource Provisioning Via Optimal Workload Allocation

Cloud computing is being widely accepted and utilized in the business world. From the perspective of businesses utilizing the cloud, it is critical to meet their customers\u27 requirements by achieving service-level-objectives. Hence, the ability to accurately characterize and optimize cloud-service performance is of great importance. In this dissertation, a stochastic multi-tenant framework is proposed to model the service of customer requests in a cloud infrastructure composed of heterogeneous virtual machines (VMs). The proposed framework addresses the critical concepts and characteristics in the cloud, including virtualization, multi-tenancy, heterogeneity of VMs, VM isolation for the purpose of security and/or performance guarantee and the stochastic response time of a customer request. Two cloud-service performance metrics are mathematically characterized, namely the percentile of the stochastic response time and the mean of the stochastic response time of a customer request. Based upon the proposed multi-tenant framework, a workload-allocation algorithm, termed max-min-cloud algorithm, is then devised to optimize the performance of the cloud service. A rigorous optimality proof of the max-min-cloud algorithm is given when the stochastic response time of a customer request assumed exponentially distributed. Furthermore, extensive Monte-Carlo simulations are conducted to validate the optimality of the max-min-cloud algorithm by comparing with other two workload-allocation algorithms under various scenarios. Next, the resource provisioning problem in the cloud is studied in light of the max-min-cloud algorithm. In particular, an efficient resource-provisioning strategy, termed the MPC strategy, is proposed for serving dynamically arriving customer requests. The efficacy of the MPC strategy is verified through two practical cases when the arrival of the customer requests is predictable and unpredictable, respectively. As an extension of the max-min-cloud algorithm, we further devise the max-load-first algorithm to deal with the VM placement problem in the cloud. MC simulation results show that the max-load-first VM-placement algorithm outperforms the other two heuristic algorithms in terms of reducing the mean of stochastic completion time of a group of arbitrary customers\u27 requests. Simulation results also provide insight on how the initial loads of servers affect the performance of the cloud system. In summary, the findings in this dissertation work can be of great benefit to both service providers (namely business owners) and cloud providers. For business owners, the max-min-cloud workload-allocation algorithm and the MPC resource-provisioning strategy together can be used help them build a better understanding of how much virtual resources in the cloud they may need to meet customers\u27 expectations subject to cost constraints. For cloud providers, the max-load-first VM-placement algorithm can be used to optimize the computational performance of the service by appropriately utilizing the physical machines and efficiently placing the VMs in their cloud infrastructures

Utility-based Allocation of Resources to Virtual Machines in Cloud Computing

In recent years, cloud computing has gained a wide spread use as a new computing model that offers elastic resources on demand, in a pay-as-you-go fashion. One important goal of a cloud provider is dynamic allocation of Virtual Machines (VMs) according to workload changes in order to keep application performance to Service Level Agreement (SLA) levels, while reducing resource costs. The problem is to find an adequate trade-off between the two conflicting objectives of application performance and resource costs. In this dissertation, resource allocation solutions for this trade-off are proposed by expressing application performance and resource costs in a utility function. The proposed solutions allocate VM resources at the global data center level and at the local physical machine level by optimizing the utility function. The utility function, given as the difference between performance and costs, represents the profit of the cloud provider and offers the possibility to capture in a flexible and natural way the performance-cost trade-off. For global level resource allocation, a two-tier resource management solution is developed. In the first tier, local node controllers are located that dynamically allocate resource shares to VMs, so to maximize a local node utility function. In the second tier, there is a global controller that makes VM live migration decisions in order to maximize a global utility function. Experimental results show that optimizing the global utility function by changing the number of physical nodes according to workload maintains the performance at acceptable levels while reducing costs. To allocate multiple resources at the local physical machine level, a solution based on feed-back control theory and utility function optimization is proposed. This dynamically allocates shares to multiple resources of VMs such as CPU, memory, disk and network I/O bandwidth. In addressing the complex non-linearities that exist in shared virtualized infrastructures between VM performance and resource allocations, a solution is proposed that allocates VM resources to optimize a utility function based on application performance and power modelling. An Artificial Neural Network (ANN) is used to build an on- line model of the relationships between VM resource allocations and application performance, and another one between VM resource allocations and physical machine power. To cope with large utility optimization times in the case of an increased number of VMs, a distributed resource manager is proposed. It consists of several ANNs, each responsible for modelling and resource allocation of one VM, while exchanging information with other ANNs for coordinating resource allocations. Experiments, in simulated and realistic environments, show that the distributed ANN resource manager achieves better performance-power trade-offs than a centralized version and a distributed non-coordinated resource manager. To deal with the difficulty of building an accurate online application model and long model adaptation time, a solution that offers model-free resource management based on fuzzy control is proposed. It optimizes a utility function based on a hill-climbing search heuristic implemented as fuzzy rules. To cope with long utility optimization time in the case of an increased number of VMs, a multi-agent fuzzy controller is developed where each agent, in parallel with others, optimizes its own local utility function. The fuzzy control approach eliminates the need to build a model beforehand and provides a robust solution even for noisy measurements. Experimental results show that the multi-agent fuzzy controller performs better in terms of utility value than a centralized fuzzy control version and a state-of-the-art adaptive optimal control approach, especially for an increased number of VMs. Finally, to address some of the problems of reactive VM resource allocation approaches, a proactive resource allocation solution is proposed. This approach decides on VM resource allocations based on resource demand prediction, using a machine learning technique called Support Vector Machine (SVM). To deal with interdependencies between VMs of the same multi-tier application, cross- correlation demand prediction of multiple resource usage time series of all VMs of the multi-tier application is applied. As experiments show, this results in improved prediction accuracy and application performance

Truthful Mechanisms For Resource Allocation And Pricing In Clouds

A major challenging problem for cloud providers is designing efficient mechanisms for Virtual Machine (VM) provisioning and allocation. Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. Recently, cloud providers have introduced auction-based models for VM provisioning and allocation which allow users to submit bids for their requested VMs. We formulate the dynamic VM provisioning and allocation problem for the auction-based model as an integer program considering multiple types of resources. We then design truthful greedy and optimal mechanisms for the problem such that the cloud provider provisions VMs based on the requests of the winning users and determines their payments. We show that the proposed mechanisms are truthful, that is, the users do not have incentives to manipulate the system by lying about their requested bundles of VM instances and their valuations. We perform extensive experiments using real workload traces in order to investigate the performance of the proposed mechanisms. Our proposed mechanisms achieve promising results in terms of revenue for the cloud provider