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

Modeling and Simulation of Multi-tier Enterprise IT System

This paper discusses modelling and simulation of multi-tier enterprise IT system. The layers in multi-tier architecture consist of web layer, application layer and database layer. Entities in the multi-tier system have been abstracted out into 3 categories- consumer, resource and router. Existing modelling and simulation frameworks for multi-tier systems focus on power management or performance of load balancing algorithms. Our framework enables seamless modelling, simulation, and experimentation of a wide range of what-if scenarios in multi-tier systems while encapsulating all the variations that arise due to configuration, composition, design and deployment. As an illustration, we discuss and simulate prediction of bottleneck scenario with results

Mining association rules for admission control and service differentiation in e-commerce applications

Workload demands in e-commerce applications are very dynamic in nature, therefore it is essential for internet service providers to manage server resources effectively to maximise total revenue in server overloading situations. In this paper, a data mining technique is applied to a typical e-commerce application model for identification of composite association rules that capture user navigation patterns. Two algorithms are then developed based on the derived rules for admission control, service differentiation and priority scheduling. Our approach takes the following into consideration: a) only final purchase requests result in company revenue; b) any other request can potentially lead to a final purchase, depending upon the likelihood of the navigation sequence that starts from current request and leads to final purchase; c) service differentiation and priority assignment are based on aggregated confidence and average support of the composite association rules. As identification of composite association rules and computation of confidence and support of the rules can be pre-computed offline, the proposed approach incurs minimum performance overheads. The evaluation results suggest that the proposed approach is effective in terms of request management for revenue maximisation

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

Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for End-to-end Delay Guarantee

Abstract—Autonomic server provisioning for performance as-surance is a critical issue in data centers. It is important but challenging to guarantee an important performance metric, percentile-based end-to-end delay of requests flowing through a virtualized multi-tier server cluster. It is mainly due to dynamically varying workload and the lack of an accurate system performance model. In this paper, we propose a novel autonomic server allocation approach based on a model-independent and self-adaptive neural fuzzy control. There are model-independent fuzzy controllers that utilize heuristic knowledge in the form of rule base for performance assurance. Those controllers are designed manually on trial and error basis, often not effective in the face of highly dynamic workloads. We design the neural fuzzy controller as a hybrid of control theoretical and machine learning techniques. It is capable of self-constructing its structure and adapting its parameters through fast online learning. Unlike other supervised machine learning techniques, it does not require off-line training. We further enhance the neural fuzzy controller to compensate for the effect of server switching delays. Extensive simulations demonstrate the effectiveness of our new approach in achieving the percentile-based end-to-end delay guarantees. Com-pared to a rule-based fuzzy controller enabled server allocation approach, the new approach delivers superior performance in the face of highly dynamic workloads. It is robust to workload variation, change in delay target and server switching delays. I

Provisioning multi-tier cloud applications using statistical bounds on sojourn time

In this paper we present a simple and effective approach for re-source provisioning to achieve a percentile bound on the end to end response time of a multi-tier application. We, at first, model the multi-tier application as an open tandem network of M/G/1-PS queues and develop a method that produces a near optimal appli-cation configuration, i.e, number of servers at each tier, to meet the percentile bound in a homogeneous server environment – using a single type of server. We then extend our solution to a K-server case and our technique demonstrates a good accuracy, independent of the variability of service-times. Our approach demonstrates a provisioning error of no more than 3 % compared to a 140 % worst case provisioning error obtained by techniques based on anM/M/1-FCFS queue model. In addition, we extend our approach to han-dle a heterogenous server environment, i.e., with multiple types of servers. We find that fewer high-capacity servers are preferable for high percentile provisioning. Finally, we extend our approach to account for the rental cost of each server-type and compute a cost efficient application configuration with savings of over 80%. We demonstrate the applicability of our approach in a real world sys-tem by employing it to provision the two tiers of the java implemen-tation of TPC-W – a multi-tier transactional web benchmark that represents an e-commerce web application, i.e. an online book-store

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