Autoscaling is a hallmark of cloud computing as it allows flexible just-in-time allocation and release of computational resources in response to dynamic and often unpredictable workloads. This is especially important for web applications whose workload is time dependent and prone to flash crowds. Most of them follow the 3-tier architectural pattern, and are divided into presentation, application domain and data layers. In this work we focus on the application layer. Reactive autoscaling policies of the type "Instantiate a new Virtual Machine (VM) when the average server CPU utilisation reaches X " have been used successfully since the dawn of cloud computing. But which VM type is the most suitable for the specific application at the moment remains an open question. In this work, we propose an approach for dynamic VM type selection. It uses a combination of online machine learning techniques, works in real time and adapts to changes in the users' workload patterns, application changes as well as middleware upgrades and reconfigurations. We have developed a prototype, which we tested with the CloudStone benchmark deployed on AWS EC2. Results show that our method quickly adapts to workload changes and reduces the total cost compared to the industry standard approach.
The area of static computing resource management has been well studied in the context of grids, clouds, and even multi-clouds @cite_28 . However, the field of dynamic resource management in response to continuously varying workloads, which is especially important for web facing applications @cite_28 , is still in its infancy. Horizontal autoscaling policies are the predominant approach for dynamic resource management and thus they have gained significant attention in recent years.
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Continuously adjusting the horizontal scaling of applications hosted by data centers appears as a good candidate to automatic control approaches allocating resources in closed-loop given their current workload. Despite several attempts, real applications of these techniques in cloud computing infrastructures face some difficulties. Some of them essentially turn back to the core concepts of automatic control: controllability, inertia of the controlled system, gain and stability. In this paper, considering our recent work to build a management framework dedicated to automatic resource allocation in virtualized applications, we attempt to identify from experiments the sources of instabilities in the controlled systems. As examples, we analyze two types of policies: threshold-based and reinforcement learning techniques to dynamically scale resources. The experiments show that both approaches are tricky and that trying to implement a controller without looking at the way the controlled system reacts to actions, both in time and in amplitude, is doomed to fail. We discuss both lessons learned from the experiments in terms of simple yet key points to build good resource management policies, and longer term issues on which we are currently working to manage contracts and reinforcement learning efficiently in cloud controllers. Improving resource provisioning of heterogeneous cloud infrastructures is an important research challenge. The wide diversity of cloud-based applications and customers with different QoS requirements have recently exhibited the weaknesses of current provisioning systems. Today's cloud infrastructures provide provisioning systems that dynamically adapt the computational power of applications by adding or releasing resources. Unfortunately, these scaling systems are fairly limited:(i) They restrict themselves to a single type of resource, (ii)they are unable to fulfill QoS requirements in face of spiky workload, and (iii) they offer the same QoS level to all their customers, independent of customer preferences such as different levels of service availability and performance. In this paper, we present an autoscaling system that overcomes these limitations by exploiting heterogeneous types of resources, and by defining multiple levels of QoS requirements. The proposed system selects a resource scaling plan according to both workload and customer requirements. Our experiments conducted on both public and private infrastructures show significant reductions in QoS-level violations when faced with highly variable workloads. Infrastructure-as-a-Service (IaaS) cloud computing offers new possibilities to scientific communities. One of the most significant is the ability to elastically provision and relinquish new resources in response to changes in demand. In our work, we develop a model of an “elastic site” that efficiently adapts services provided within a site, such as batch schedulers, storage archives, or Web services to take advantage of elastically provisioned resources. We describe the system architecture along with the issues involved with elastic provisioning, such as security, privacy, and various logistical considerations. To avoid over- or under-provisioning the resources we propose three different policies to efficiently schedule resource deployment based on demand. We have implemented a resource manager, built on the Nimbus toolkit to dynamically and securely extend existing physical clusters into the cloud. Our elastic site manager interfaces directly with local resource managers, such as Torque. We have developed and evaluated policies for resource provisioning on a Nimbus-based cloud at the University of Chicago, another at Indiana University, and Amazon EC2. We demonstrate a dynamic and responsive elastic cluster, capable of responding effectively to a variety of job submission patterns. We also demonstrate that we can process 10 times faster by expanding our cluster up to 150 EC2 nodes. Resource management of virtualized servers in data centers has become a critical task, since it enables cost-effective consolidation of server applications. Resource management is an important and challenging task, especially for multitier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done toward adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8p performance penalty in the case of high-intensity workload changes. In addition, our controllers are enhanced to deal with multitier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such resource-coupling mechanism. Our approaches are evaluated and their performance is illustrated on a 3-tier Rubis benchmark website deployed on a prototype Xen-virtualized cluster. Efficient management of distributed resources, under conditions of unpredictable and varying workload, requires enforcement of dynamic resource management policies. Execution of such policies requires a relatively fine-grain control over the resources allocated to jobs in the system. Although this is a difficult task using conventional job management and program execution models, reconfigurable applications can be used to make it viable. With reconfigurable applications, it is possible to dynamically change, during the course of program execution, the number of concurrently executing tasks of an application as well as the resources allocated. Thus, reconfigurable applications can adapt to internal changes in resource requirements and to external changes affecting available resources. In this paper, we discuss dynamic management of resources on distributed systems with the help of reconfigurable applications. We first characterize reconfigurable parallel applications. We then present a new programming model for reconfigurable applications and the Distributed Resource Management System (DRMS), an integrated environment for the design, development, execution, and resource scheduling of reconfigurable applications. Experiments were conducted to verify the functionality and performance of application reconfiguration under DRMS. A detailed breakdown of the costs in reconfiguration is presented with respect to several different applications. Our results indicate that application reconfiguration is effective under DRMS and can be beneficial in improving individual application performance as well as overall system performance. We observe a significant reduction in average job response time and an improvement in overall system utilization. Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd. Summary form only given. Grids and peer-to-peer (P2P) networks have emerged as popular platforms for the next generation parallel and distributed computing. In these environments, resources are geographically distributed, managed and owned by various organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, and dynamic resource conditions. In these dynamic distributed computing environments, it is hard and challenging to carry out resource management design studies in a repeatable and controlled manner as resources and users are autonomous and distributed across multiple organizations with their own policies. Therefore, simulations have emerged as the most feasible technique for analyzing policies for resource allocation. This paper presents emerging trends in distributed computing and their promises for revolutionizing the computing field, and identifies distinct characteristics and challenges in building them. We motivate opportunities for modeling and simulation communities and present our discrete-event grid simulation toolkit, called GridSim, used by researchers world-wide for investigating the design of utility-oriented computing systems such as data centers and grids. We present various case studies on the use of GridSim in modeling and simulation of business grids, parallel applications scheduling, workflow scheduling, and service pricing and revenue management. The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers’ solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized probabilistic auto-scaling algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our experiments (simulations and real deployments), which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches. We model a web application to support analysis and simulation of cloud environments.We implement the model as an extension of the CloudSim simulator.We found that the user behavior has a strong influence on the resource utilization.We found Generalized Extreme Value and Generalized Lambda distributions instead of Exponential distribution to represent session time. Display Omitted Workload modeling enables performance analysis and simulation of cloud resource management policies, which allows cloud providers to improve their systems' Quality of Service (QoS) and researchers to evaluate new policies without deploying expensive large scale environments. However, workload modeling is challenging in the context of cloud computing due to the virtualization layer overhead, insufficient tracelogs available for analysis, and complex workloads. These factors contribute to a lack of methodologies and models to characterize applications hosted in the cloud. To tackle the above issues, we propose a web application model to capture the behavioral patterns of different user profiles and to support analysis and simulation of resources utilization in cloud environments. A model validation was performed using graphic and statistical hypothesis methods. An implementation of our model is provided as an extension of the CloudSim simulator. Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm . Scalability is critical to the success of many enterprises currently involved in doing business on the web and in providing information that may vary drastically from one time to another. Maintaining sufficient resources just to meet peak requirements can be costly. Cloud computing provides a powerful computing model that allows users to access resources on-demand. In this paper, we will describe a novel architecture for the dynamic scaling of web applications based on thresholds in a virtualized Cloud Computing environment. We will illustrate our scaling approach with a front-end load-balancer for routing and balancing user requests to web applications deployed on web servers installed in virtual machine instances. A dynamic scaling algorithm for automated provisioning of virtual machine resources based on threshold number of active sessions will be introduced. The on-demand capability of the Cloud to rapidly provision and dynamically allocate resources to users will be discussed. Our work has demonstrated the compelling benefits of the Cloud which is capable of handling sudden load surges, delivering IT resources on-demands to users, and maintaining higher resource utilization, thus reducing infrastructure and management costs. Cloud computing is gaining tremendous momentum in both academia and industry. In this context, we define the term "Cloud Workflow" as the specification, execution and provenance tracking of large-scale scientific workflows, as well as the management of data and computing resources to support the execution of large-scale scientific workflows in the Cloud. In this paper, we first analyze the gap between these two complementary technologies, and what it means to bring Clouds and workflows together. Then, we present the key challenges in supporting Cloud workflows, and present our reference framework for scientific workflow management in the Cloud. Last we present our experience in integrating a scientific workflow management system-Swift into the Cloud. We discuss the performance of cluster provisioning within the OpenNebula Cloud platform, the Eucalyptus Cloud platform and Amazon EC2, and we demonstrate the capability and efficiency of the integration using a NASA MODIS image processing workflow and the Montage image mosaic workflow.Note to practitioners. Scientific workflow management plays a very important role for scientific computing and application coordination, while Cloud computing offers scalability and resource on-demand. We devise autonomous methods to integrate scientific workflow management systems with Cloud platforms and also provision resources for large scale workflows, which can facilitate scientists to easily manage their workflows in the Cloud, and take advantage of large scale Cloud resources. There are a few integration options and many challenges in the process, and the experience we gain will help researchers in migrating their workflow management systems and workflow applications into the Cloud. We analyze the major challenges of running scientific workflows on the Cloud.We propose a reference framework to standardize the integration.The implementation experience proves that the framework is feasible and extendible.Cluster-recycling mechanism can improve the resource provisioning efficiency. Abstract The cloud phenomenon is quickly becoming an important service in Internet computing. Infrastructure as a Service (IaaS) in cloud computing is one of the most significant and fastest growing field. In this service model, cloud providers offer resources to users machines that include computers as virtual machines, raw (block) storage, firewalls, load balancers, and network devices. One of the most pressing issues in cloud computing for IaaS is the resource management. Resource management problems include allocation, provisioning, requirement mapping, adaptation, discovery, brokering, estimation, and modeling. Resource management for IaaS in cloud computing offers following benefits: scalability, quality of service, optimal utility, reduced overheads, improved throughput, reduced latency, specialized environment, cost effectiveness and simplified interface. This paper focuses on some of the important resource management techniques such as resource provisioning, resource allocation, resource mapping and resource adaptation. It brings out an exhaustive survey of such techniques for IaaS in cloud computing, and also put forth the open challenges for further research. Contemporary continuous dataflow systems use elastic scaling on distributed cloud resources to handle variable data rates and to meet applications' needs while attempting to maximize resource utilization. However, virtualized clouds present an added challenge due to the variability in resource performance -- over time and space -- thereby impacting the application's QoS. Elastic use of cloud resources and their allocation to continuous dataflow tasks need to adapt to such infrastructure dynamism. In this paper, we develop the concept of "dynamic dataflows" as an extension to continuous dataflows that utilizes alternate tasks and allows additional control over the dataflow's cost and QoS. We formalize an optimization problem to perform both deployment and runtime cloud resource management for such dataflows, and define an objective function that allows trade-off between the application's value against resource cost. We present two novel heuristics, local and global, based on the variable sized bin packing heuristics to solve this NP-hard problem. We evaluate the heuristics against a static allocation policy for a dataflow with different data rate profiles that is simulated using VM performance traces from a private cloud data center. The results show that the heuristics are effective in intelligently utilizing cloud elasticity to mitigate the effect of both input data rate and cloud resource performance variabilities on QoS. Server consolidation based on virtualization is an important technique for improving power efficiency and resource utilization in cloud infrastructures. However, to ensure satisfactory performance on shared resources under changing application workloads, dynamic management of the resource pool via online adaptation is critical. The inherent tradeoffs between power and performance as well as between the cost of an adaptation and its benefits make such management challenging. In this paper, we present Mistral, a holistic controller framework that optimizes power consumption, performance benefits, and the transient costs incurred by various adaptations and the controller itself to maximize overall utility. Mistral can handle multiple distributed applications and large-scale infrastructures through a multi-level adaptation hierarchy and scalable optimization algorithm. We show that our approach outstrips other strategies that address the tradeoff between only two of the objectives (power, performance, and transient costs). The recent surge in the popularity and usage of Cloud Computing services by both the enterprise and individual consumers has necessitated efficient and proactive management of data center resources which host services having varied characteristics. One of the major issues concerning both the cloud service providers and consumers is the automatic scalability of resources (i.e., compute, storage and bandwidth) in response to the highly unpredictable demands. To this end, an opportunity exists to harness the predictive and diagnostic capabilities of machine learning approaches to incorporate dynamic scaling up and scaling down of resources without violating the Service Level Agreements (SLA) and simultaneously ensuring adequate revenue to the providers. This paper proposes, implements and evaluates a Bayesian Networks based predictive modeling framework to provide for an autonomic scaling of utility computing resources in the Cloud Computing scenario. In essence, the BN-based model captures the historical behavior of the system involving various performance metrics (indicators) and predicts the desired unknown metric (e.g. SLA parameter). Initial simulated experiments involving random demand scenarios provide insights into the feasibility and applicability of the proposed approach for improving the management of present data center facilities. Scalability and performance are key factors to the success of many enterprises involved in doing business on the web. Maintaining sufficient web resources just to meet performance during peak demands can be costly. Compute Cloud provides a powerful environment to allow dynamic scaling of web applications without the needs for user intervention. In this paper, we present a case study on the scalability and performance of web applications in a Cloud. We describe a novel dynamic scaling architecture with a front-end load-balancer for routing user requests to web applications deployed on virtual machine instances with the goal of maximizing resource utilization in instances while minimizing total number of instances. A scaling algorithm for automated provisioning of virtual resources based on threshold number of active user sessions will be introduced. The on-demand capability of the Cloud to rapidly provision and dynamically allocate resources to users will be discussed. Our work has demonstrated the compelling benefits of a Cloud which is capable of sustaining performance upon sudden load surges, delivering satisfactory IT resources on-demands to users, and maintaining high resource utilization, thus reducing infrastructure and management costs. Significant achievements have been made for automated allocation of cloud resources. However, the performance of applications may be poor in peak load periods, unless their cloud resources are dynamically adjusted. Moreover, although cloud resources dedicated to different applications are virtually isolated, performance fluctuations do occur because of resource sharing, and software or hardware failures (e.g. unstable virtual machines, power outages, etc.). In this paper, we propose a decentralized economic approach for dynamically adapting the cloud resources of various applications, so as to statistically meet their SLA performance and availability goals in the presence of varying loads or failures. According to our approach, the dynamic economic fitness of a Web service determines whether it is replicated or migrated to another server, or deleted. The economic fitness of a Web service depends on its individual performance constraints, its load, and the utilization of the resources where it resides. Cascading performance objectives are dynamically calculated for individual tasks in the application workflow according to the user requirements. By fully implementing our framework, we experimentally proved that our adaptive approach statistically meets the performance objectives under peak load periods or failures, as opposed to static resource settings. Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands. Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud. In computing clouds, it is desirable to avoid wasting resources as a result of under-utilization and to avoid lengthy response times as a result of over-utilization. In this paper, we propose a new approach for dynamic autonomous resource management in computing clouds. The main contribution of this work is two-fold. First, we adopt a distributed architecture where resource management is decomposed into independent tasks, each of which is performed by Autonomous Node Agents that are tightly coupled with the physical machines in a data center. Second, the Autonomous Node Agents carry out configurations in parallel through Multiple Criteria Decision Analysis using the PROMETHEE method. Simulation results show that the proposed approach is promising in terms of scalability, feasibility and flexibility.