4,906 research outputs found

    Cloud Computing Systems Exploration over Workload Prediction Factor in Distributed Applications

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    This paper highlights the different techniques of workload prediction in cloud computing. Cloud computing resources have a special kind of arrangement in which resources are made available on demand to the customers. Today, most of the organizations are using cloud computing that results in reduction of the operational cost. Cloud computing also reduces the overhead of any organization due to implementation of many hardware and software platforms. These services are being provided by cloud provider on the basis of pay per use. There are lots of cloud service providers in the modern era. In this competitive era, every cloud provider works to provide better services to the customer. To fulfill the customer?s requirements, dynamic provisioning can serve the purpose in cloud system where resources can be released and allocated on later stage as per needs. That?s why resource scaling becomes a great challenge for the cloud providers. There are many approaches to scale the number of instances of any resource. Two main approaches namely: proactive and reactive are used in cloud systems. Reactive approach reacts at later stage while proactive approach predicts resources in advance. Cloud provider needs to predict the number of resources in advance that an application is intended to use. Historical data and patterns can be used for the workload prediction. The benefit of the proactive approach lies in advance number of instances of a resource available for the future use. This results in improved performance for the cloud systems

    Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures

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    Model-based techniques are a powerful approach to engineering autonomic and self-adaptive systems. This thesis presents a model-based approach for proactive and autonomic performance-aware resource management in dynamic IT infrastructures. Core of the approach is an architecture-level modeling language to describe performance and resource management related aspects in such environments. With this approach, it is possible to autonomically find suitable system configurations at the model level

    Predictive dynamic resource allocation for web hosting environments

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    E-Business applications are subject to significant variations in workload and this can cause exceptionally long response times for users, the timing out of client requests and/or the dropping of connections. One solution is to host these applications in virtualised server pools, and to dynamically reassign compute servers between pools to meet the demands on the hosted applications. Switching servers between pools is not without cost, and this must therefore be weighed against possible system gain. This work is concerned with dynamic resource allocation for multi-tiered, clusterbased web hosting environments. Dynamic resource allocation is reactive, that is, when overloading occurs in one resource pool, servers are moved from another (quieter) pool to meet this demand. Switching servers comes with some overhead, so it is important to weigh up the costs of the switch against possible system gains. In this thesis we combine the reactive behaviour of two server switching policies – the Proportional Switching Policy (PSP) and the Bottleneck Aware Switching Policy (BSP) – with the proactive properties of several workload forecasting models. We evaluate the behaviour of the two switching policies and compare them against static resource allocation under a range of reallocation intervals (the time it takes to switch a server from one resource pool to another) and observe that larger reallocation intervals have a negative impact on revenue. We also construct model- and simulation-based environments in which the combination of workload prediction and dynamic server switching can be explored. Several different (but common) predictors – Last Observation (LO), Simple Average (SA), Sample Moving Average (SMA) and Exponential Moving Average (EMA), Low Pass Filter (LPF), and an AutoRegressive Integrated Moving Average (ARIMA) – have been applied alongside the switching policies. As each of the forecasting schemes has its own bias, we also develop a number of meta-forecasting algorithms – the Active Window Model (AWM), the Voting Model (VM), the Selective Model (SM), the Dynamic Active Window Model (DAWM), and a method based on Workload Pattern Analysis (WPA). The schemes are tested with real-world workload traces from several sources to ensure consistent and improved results. We also investigate the effectiveness of these schemes on workloads containing extreme events (e.g. flash crowds). The results show that workload forecasting can be very effective when applied alongside dynamic resource allocation strategies

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

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    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

    A Reliable and Cost-Efficient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances

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    Cloud providers sell their idle capacity on markets through an auction-like mechanism to increase their return on investment. The instances sold in this way are called spot instances. In spite that spot instances are usually 90% cheaper than on-demand instances, they can be terminated by provider when their bidding prices are lower than market prices. Thus, they are largely used to provision fault-tolerant applications only. In this paper, we explore how to utilize spot instances to provision web applications, which are usually considered availability-critical. The idea is to take advantage of differences in price among various types of spot instances to reach both high availability and significant cost saving. We first propose a fault-tolerant model for web applications provisioned by spot instances. Based on that, we devise novel auto-scaling polices for hourly billed cloud markets. We implemented the proposed model and policies both on a simulation testbed for repeatable validation and Amazon EC2. The experiments on the simulation testbed and the real platform against the benchmarks show that the proposed approach can greatly reduce resource cost and still achieve satisfactory Quality of Service (QoS) in terms of response time and availability

    Predictive auto-scaling with OpenStack Monasca

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    Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome when scaling-up a cluster involves non-negligible times to bootstrap new instances, as it happens frequently in production cloud services. To deal with this problem, we propose an architecture for auto-scaling cloud services based on the status in which the system is expected to evolve in the near future. Our approach leverages on time-series forecasting techniques, like those based on machine learning and artificial neural networks, to predict the future dynamics of key metrics, e.g., resource consumption metrics, and apply a threshold-based scaling policy on them. The result is a predictive automation policy that is able, for instance, to automatically anticipate peaks in the load of a cloud application and trigger ahead of time appropriate scaling actions to accommodate the expected increase in traffic. We prototyped our approach as an open-source OpenStack component, which relies on, and extends, the monitoring capabilities offered by Monasca, resulting in the addition of predictive metrics that can be leveraged by orchestration components like Heat or Senlin. We show experimental results using a recurrent neural network and a multi-layer perceptron as predictor, which are compared with a simple linear regression and a traditional non-predictive auto-scaling policy. However, the proposed framework allows for the easy customization of the prediction policy as needed

    AUTONOMIC MANAGEMENT OF SOFTWARE AS A SERVICE SYSTEMS WITH MULTIPLE QUALITY OF SERVICE CLASSES

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    In recent years the emergence of Software as a Service (SaaS) provision and cloud computing in general had a tremendous impact on corporate information technology. While the implementation and successful operation of powerful information systems continues to be a cornerstone of success in modern enterprises, the ability to acquire IT infrastructure, software, or platforms on a pay-as-you-go basis has opened a new avenue for optimizing operational costs and processes. In this context we target elastic SaaS systems with on-demand cloud resource provisioning and implement an autonomic management artifact. Our framework forecasts future user behavior based on historic data, analyzes the impact of different workload levels on system performance based on a non-linear performance model, analyzes the economic impact of different provisioning strategies, derives an optimal operation strategy, and automatically assigns requests from users belonging to different Quality of Service (QoS) classes to the appropriate server instances. More generally, our artifact optimizes IT system operation based on a holistic evaluation of key aspects of service operation (e.g., system usage patterns, system performance, Service Level Agreements). The evaluation of our prototype, based on a real production system workload trace, indicates a cost-of-operation reduction by up to 60 percent without compromising QoS requirements

    RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

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    Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin
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