Managing Dynamic Enterprise and Urgent Workloads on Clouds Using Layered Queuing and Historical Performance Models

The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic-urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: i.) comparatively evaluate the layered queuing and historical techniques; ii.) evaluate the effectiveness of the management algorithm in different operating scenarios; and iii.) provide guidance on using prediction-based workload and resource management

Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data

Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application

Performance modelling of a multiple threshold RED mechanism for bursty and correlated Internet traffic with MMPP arrival process

Access to the large web content hosted all over the world by users of the Internet engage many hosts, routers/switches and faster links. They challenge the internet backbone to operate at its capacity to assure e±cient content access. This may result in congestion and raises concerns over various Quality of Service (QoS) issues like high delays, high packet loss and low throughput of the system for various Internet applications. Thus, there is a need to develop effective congestion control mechanisms in order to meet various Quality of Service (QoS) related performance parameters. In this paper, our emphasis is on the Active Queue Management (AQM) mechanisms, particularly Random Early Detection (RED). We propose a threshold based novel analytical model based on standard RED mechanism. Various numerical examples are presented for Internet traffic scenarios containing both the burstiness and correlation properties of the network traffic

Probabilistic Performance Testing of Web Applications

IT süsteemid muutuvad oma elutsükli vältel järjest keerulisemaks. Veebirakendusi kasutatakse eriti laialt erinevatel eesmärkidel, sest võrgupõhine juurdepääs informatsioonile on väga mugav. Kuid võrgupõhise juurdepääsu juures tekivad mõned probleemid, mida tuleks silmas pidada. Kasutajad eeldavad prognoositavat jõudlust (nt nõuetekohane reaktsiooniaeg), seega teenusepakkujad peavad teadma, kuidas nende süsteem töötab erinevate koormuste all. Selles teesis loome tõhususe analüütilise mudeli ja töötame välja programmi, mis selle lahendab. Antud programm lubab analüüsida veebirakenduste jõudlust ja vastata järgmistele küsimustele: 1)missugune on keskmine süsteemi reaktsiooniaeg? 2)missugune on süsteemi kasutamine üldiselt? Parameetrid programmi jaoks nagu keskmine teenindusaeg, uute taotluste keskmine saabumisaeg, keskmine mõtlemisaeg, on saadud testsüsteemi reaalse koormuse logidest. Jõudluse mudel on välja töötatud Queuing Networksi abil, mis lubab analüüsida süsteemi matemaatiliste valemite abil.Web systems are used widely for reaching different purposes, as remote access to information is very convenient. However, the remote access brings many aspects which should be handled. Users expect predictable performance levels (e.g., acceptable response time), therefore, service providers should know how their system performs under different loading conditions. In this thesis I design an analytical performance model and develop a tool which can solve that model. The tool allows analyzing the performance of web applications and answer the following questions: 1)What is the average response time of the system? 2)What is the utilization of the system as a whole? The input parameters, such as the average service time of transactions, average arrival rate of requests, and the average think time, are estimated from a real workload (of a system under test). The performance model is developed by means of Queuing Networks, a framework which enables the analysis of a system in terms of mathematical formula

ASIdE: Using Autocorrelation-Based Size Estimation for Scheduling Bursty Workloads.

Temporal dependence in workloads creates peak congestion that can make service unavailable and reduce system performance. To improve system performability under conditions of temporal dependence, a server should quickly process bursts of requests that may need large service demands. In this paper, we propose and evaluateASIdE, an Autocorrelation-based SIze Estimation, that selectively delays requests which contribute to the workload temporal dependence. ASIdE implicitly approximates the shortest job first (SJF) scheduling policy but without any prior knowledge of job service times. Extensive experiments show that (1) ASIdE achieves good service time estimates from the temporal dependence structure of the workload to implicitly approximate the behavior of SJF; and (2) ASIdE successfully counteracts peak congestion in the workload and improves system performability under a wide variety of settings. Specifically, we show that system capacity under ASIdE is largely increased compared to the first-come first-served (FCFS) scheduling policy and is highly-competitive with SJF. © 2012 IEEE

Slicing with guaranteed quality of service in wifi networks

Network slicing has recently been proposed as one of the main enablers for 5G networks. The slicing concept consists of the partition of a physical network into several self-contained logical networks (slices) that can be tailored to offer different functional or performance requirements. In the context of 5G networks, we argue that existing ubiquitous WiFi technology can be exploited to cope with new requirements. Therefore, in this paper, we propose a novel mechanism to implement network slicing in WiFi Access Points. We formulate the resource allocation problem to the different slices as a stochastic optimization problem, where each slice can have bit rate, delay, and capacity requirements. We devise a solution to the problem above using the Lyapunov drift optimization theory, and we develop a novel queuing and scheduling algorithm. We have used MATLAB and Simulink to build a prototype of the proposed solution, whose performance has been evaluated in a typical slicing scenario.This work has been supported in part by the European Commission and the Spanish Government (Fondo Europeo de Desarrollo Regional, FEDER) by means of the EU H2020 NECOS (777067) and ADVICE (TEC2015-71329) projects, respectivel

Auto-scaling techniques for cloud-based Complex Event Processing

One key topic in cloud computing is elasticity, which is the ability of the cloud environment to timely adapt the resource assignment along with the workload demand. According to cloud on-demand model, the infrastructure should be able to scale up and down to unpredictable workloads, in order to achieve both a guaranteed service level and cost efficiency. This work addresses the cloud elasticity problem, with particular reference to the Complex Event Processing (CEP) systems. CEP systems are designed to process large volumes of event-driven data streams and continuously provide results with a low latency and in real-time. CEP systems need to adapt to changing query and events loads. Because of the high computational requirements and varying loads, CEP are distributed system and running on cloud infrastructures. In this work we review the cloud computing auto-scaling solutions, and study their suit- ability in the CEP model. We implement some solutions in a CEP prototype and evaluate the experimental results