Towards Autonomic Service Provisioning Systems

This paper discusses our experience in building SPIRE, an autonomic system for service provision. The architecture consists of a set of hosted Web Services subject to QoS constraints, and a certain number of servers used to run session-based traffic. Customers pay for having their jobs run, but require in turn certain quality guarantees: there are different SLAs specifying charges for running jobs and penalties for failing to meet promised performance metrics. The system is driven by an utility function, aiming at optimizing the average earned revenue per unit time. Demand and performance statistics are collected, while traffic parameters are estimated in order to make dynamic decisions concerning server allocation and admission control. Different utility functions are introduced and a number of experiments aiming at testing their performance are discussed. Results show that revenues can be dramatically improved by imposing suitable conditions for accepting incoming traffic; the proposed system performs well under different traffic settings, and it successfully adapts to changes in the operating environment.Comment: 11 pages, 9 Figures, http://www.wipo.int/pctdb/en/wo.jsp?WO=201002636

Quantifying the Benefits of Resource Multiplexing in On-Demand Data Centers

On-demand data centers host multiple applications on server farms by dynamically provisioning resources in response to workload variations. The efficiency of such dynamic provisioning on the required server farm capacity is dependent on several factors — the granularity and frequency of reallocation, the number of applications being hosted, the amount of resource overprovisioning and the accuracy of workload prediction. In this paper, we quantify the effect of these factors on the multiplexing benefits achievable in an on-demand data center. Using traces of real e-commerce workloads, we demonstrate that the ability to allocate fractional server resources at fine time-scales of tens of seconds to a few minutes can increase the multiplexing benefits by 162-188% over coarsegrained reallocation. Our results also show that these benefits increase in the presence of large number of hosted applications as a result of high level of multiplexing. In addition, we demonstrate that such fine-grained multiplexing is achievable even in the presence of real-world (inaccurate) workload predictors and allows overprovisioning slack of nearly 35-70% over coarse-grained multiplexing

Auto-Scaling Network Resources using Machine Learning to Improve QoS and Reduce Cost

Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Hence, auto-scaling (of resources without human intervention) has been receiving attention. Prior studies on auto-scaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto-scaling, our study explores how the properties (e.g., start-up time) of underlying virtualization technology impacts Quality of Service (QoS) and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier using real data collected from a private ISP. We report in-depth analysis of the learning process (learning-curve analysis), feature ranking (feature selection, Principal Component Analysis (PCA), etc.), impact of different sets of features, training time, and testing time. Our results show how the proposed methods improve QoS and reduce operational cost for network owners. We also demonstrate a practical use-case example (Software-Defined Wide Area Network (SD-WAN) with VNFs and backbone network) to show that our ML methods save significant cost for network service leasers

Система управління ресурсами в центрах обробки даних оператора мережі мобільного зв'язку

The problem of the mobile data traffic and the number of services growing becomes global, moreover, volume and frequency of control traffic transmitted through the network are increasing. Therefore there is a need for its effective management to ensure the quality of service required by users and optimal use of mobile network resources. In such circumstances the load on the server that is created in the process of the connection establishing and its serving has its considerations. Dynamic resource provisioning is a useful technique for handling the variations seen in communication systems workloads. Virtualization technology allows to implement this approach. An analytic model of a system would be attractive as it would be able to evaluate system characteristics under a wide range of conditions, and to be computed comparatively easily. It also can incorporate numerical optimization techniques for system design. In this paper the problem of provisioning system design for virtualized network functions is solved. This paper presents a novel approach to correctly allocate resources in data centers, such that SLA violations and energy consumption are minimized. Proposed approach first analyzes historical workload traces to identify long-term patterns that establish a "base" workload. Then it employs two techniques to dynamically allocate capacity: predictive and reactive provisioning. The combination of predictive and reactive provisioning achieves a significant improvement in meeting SLAs, conserving energy and reduces provisioning costs. The method for adapting the size of network function's resource allocation control interval is proposed. It provides dynamic configuration of the system, reducing excessive service data transmitted in the network and decreasing the load of network nodes. The workload service system model is built. It outlines a method of workload forecasting taking into account long-term accumulated statistics and recent trends observed in a network. It allows to get a rational level of management costs and final values of service quality. Experiments on the study of the proposed methods in the system of Mathcad are conducted.Рассматривается возможность виртуализации сети мобильной связи. Представлен метод построения системы управления ресурсами для виртуальных сетевых функций в центрах обработки данных оператора мобильной связи. Предложенный метод использует гибкую аналитическую модель для определения оптимального количества ресурсов, которые выделяются функциональным блокам системы, разворачиваемых в центрах, обработки данных и учитывает как ранее полученные статистические данные, так и текущие тенденции.Розглядається можливість віртуалізації мережі мобільного зв'язку. Представлено метод побудови системи управління ресурсами для віртуальних мережевих функцій в центрах обробки даних оператора мережі мобільного зв'язку. Запропонований метод використовує гнучку аналітичну модель для визначення оптимальної кількості ресурсів, які виділяються функціональним блокам системи, що розгортаються в центрах обробки даних, та враховує як попередньо отримані статистичні дані, так і поточні тенденції

Workload-Aware Database Monitoring and Consolidation

In most enterprises, databases are deployed on dedicated database servers. Often, these servers are underutilized much of the time. For example, in traces from almost 200 production servers from different organizations, we see an average CPU utilization of less than 4%. This unused capacity can be potentially harnessed to consolidate multiple databases on fewer machines, reducing hardware and operational costs. Virtual machine (VM) technology is one popular way to approach this problem. However, as we demonstrate in this paper, VMs fail to adequately support database consolidation, because databases place a unique and challenging set of demands on hardware resources, which are not well-suited to the assumptions made by VM-based consolidation. Instead, our system for database consolidation, named Kairos, uses novel techniques to measure the hardware requirements of database workloads, as well as models to predict the combined resource utilization of those workloads. We formalize the consolidation problem as a non-linear optimization program, aiming to minimize the number of servers and balance load, while achieving near-zero performance degradation. We compare Kairos against virtual machines, showing up to a factor of 12× higher throughput on a TPC-C-like benchmark. We also tested the effectiveness of our approach on real-world data collected from production servers at Wikia.com, Wikipedia, Second Life, and MIT CSAIL, showing absolute consolidation ratios ranging between 5.5:1 and 17:1

Profit-oriented resource allocation using online scheduling in flexible heterogeneous networks

In this paper, we discuss a generalized measurement-based adaptive scheduling framework for dynamic resource allocation in flexible heterogeneous networks, in order to ensure efficient service level performance under inherently variable traffic conditions. We formulate our generalized optimization model based on the notion of a “profit center” with an arbitrary number of service classes, nonlinear revenue and cost functions and general performance constraints. Subsequently, and under the assumption of a linear pricing model and average queue delay requirements, we develop a fast, low complexity algorithm for online dynamic resource allocation, and examine its properties. Finally, the proposed scheme is validated through an extensive simulation study.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47990/1/11235_2006_Article_6525.pd

Distributed workload and response time management for web applications

Abstract-Managing workload for large scale web applications is a fundamental task for satisfactory quality of service, low management and operation cost. In this paper, we present SCOPS, a system of distributed workload management to achieve service differentiation and overload protection in such large scale deployment. Our system splits the workload management logic into distributed components on each back-end server and frontend proxy. The control solution is designed to protect the backend server from overloading and to achieve both efficient usage of system resource and service differentiation by employing a unique optimization target. The control components are automatically organized based on the flow of workloads, such that management overhead is minimized. SCOPS is extremely flexible because it requires no source code changes to host OS, application servers, or web applications. Additionally, the distributed design makes it scalable and robust for cloud scale server deployment. Experiments with our implementation confirm SCOPS's performance with dynamic heavy workload, incurring neglectable runtime overhead. More importantly, SCOPS also ensures fault-tolerance and fast convergence to system failures