Automatic performance optimisation of component-based enterprise systems via redundancy

Component technologies, such as J2EE and .NET have been extensively adopted for building complex enterprise applications. These technologies help address complex functionality and flexibility problems and reduce development and maintenance costs. Nonetheless, current component technologies provide little support for predicting and controlling the emerging performance of software systems that are assembled from distinct components. Static component testing and tuning procedures provide insufficient performance guarantees for components deployed and run in diverse assemblies, under unpredictable workloads and on different platforms. Often, there is no single component implementation or deployment configuration that can yield optimal performance in all possible conditions under which a component may run. Manually optimising and adapting complex applications to changes in their running environment is a costly and error-prone management task. The thesis presents a solution for automatically optimising the performance of component-based enterprise systems. The proposed approach is based on the alternate usage of multiple component variants with equivalent functional characteristics, each one optimized for a different execution environment. A management framework automatically administers the available redundant variants and adapts the system to external changes. The framework uses runtime monitoring data to detect performance anomalies and significant variations in the application's execution environment. It automatically adapts the application so as to use the optimal component configuration under the current running conditions. An automatic clustering mechanism analyses monitoring data and infers information on the components' performance characteristics. System administrators use decision policies to state high-level performance goals and configure system management processes. A framework prototype has been implemented and tested for automatically managing a J2EE application. Obtained results prove the framework's capability to successfully manage a software system without human intervention. The management overhead induced during normal system execution and through management operations indicate the framework's feasibility

QoS control of E-business systems through performance modelling and estimation

E-business systems provide the infrastructure whereby parties interact electronically via business transactions. At peak loads, these systems are susceptible to large volumes of transactions and concurrent users and yet they are expected to maintain adequate performance levels. Over provisioning is an expensive solution. A good alternative is the adaptation of the system, managing and controlling its resources. We address these concerns by presenting a model that allows fast evaluation of performance metrics in terms of measurable or controllable parameters. The model can be used in order to (a) predict the performance of a system under given or assumed loading conditions and (b) to choose the optimal configuration set-up for certain controllable parameters with respect to specified performance measures. Firstly, we analyze the characteristics of E-business systems. This analysis leads to the analytical model, which is sufficiently general to capture the behaviour of a large class of commonly encountered architectures. We propose an approximate solution which is numerically efficient and fast. By mean of simulation, we prove that its accuracy is acceptable over a wide range of system configurations and different load levels. We further evaluate the approximate solution by comparing it to a real-life E-business system. A J2EE application of non-trivial size and complexity is deployed on a 2-tier system composed of the JBoss application server and a database server. We implement an infrastructure fully integrated on the application server, capable of monitoring the E-business system and controlling its configuration parameters. Finally, we use this infrastructure to quantify both the static parameters of the model and the observed performance. The latter are then compared with the metrics predicted by the model, showing that the approximate solution is almost exact in predicting performance and that it assesses the optimal system configuration very accurately.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Implementation and evaluation of a container-based software architecture

Recent advances in fields such as Cloud Computing, Web Systems, Internet of Things and Distributed NoSQL DBMS are enabling the development of innovative enterprise information systems that significantly increase the productivity of end users and developers. The aim of this thesis is to explore the new opportunities that these new technologies are bringing to the enterprise world. The new opportunities are explored by investigating the scenario of a medium-sized worldwide-trading company, Fiorital S.p.A. The thesis presents the design of a software architecture for the future information system of the company. The architecture is based on the usage of the Container technology and of the Microservice architectural style. Containers have empowered the usage of Microservices architectures by being lightweight, providing fast start-up times, and having low overhead. Candidate technologies for the implementation of the proposed software architecture are singled out, and the selection rationale is presented. This thesis provides an evaluation of both the candidate architecture and the technologies through the implementation of a prototype and the application of synthetic workloads that mimic stressful use scenarios. The results show that, in spite of the relative immaturity of some of the candidate technologies, the information system's candidate architecture is appropriate and that a company like Fiorital would considerably benefit from it

Security assessment of open source third-parties applications

Free and Open Source Software (FOSS) components are ubiquitous in both proprietary and open source applications. In this dissertation we discuss challenges that large software vendors face when they must integrate and maintain FOSS components into their software supply chain. Each time a vulnerability is disclosed in a FOSS component, a software vendor must decide whether to update the component, patch the application itself, or just do nothing as the vulnerability is not applicable to the deployed version that may be old enough to be not vulnerable. This is particularly challenging for enterprise software vendors that consume thousands of FOSS components, and offer more than a decade of support and security fixes for applications that include these components. First, we design a framework for performing security vulnerability experimentations. In particular, for testing known exploits for publicly disclosed vulnerabilities against different versions and software configurations. Second, we provide an automatic screening test for quickly identifying the versions of FOSS components likely affected by newly disclosed vulnerabilities: a novel method that scans across the entire repository of a FOSS component in a matter of minutes. We show that our screening test scales to large open source projects. Finally, for facilitating the global security maintenance of a large portfolio of FOSS components, we discuss various characteristics of FOSS components and their potential impact on the security maintenance effort, and empirically identify the key drivers

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This thesis presents an approach for the design time analysis of energy efficiency for static and self-adaptive software systems. The quality characteristics of a software system, such as performance and operating costs, strongly depend upon its architecture. Software architecture is a high-level view on software artifacts that reflects essential quality characteristics of a system under design. Design decisions made on an architectural level have a decisive impact on the quality of a system. Revising architectural design decisions late into development requires significant effort. Architectural analyses allow software architects to reason about the impact of design decisions on quality, based on an architectural description of the system. An essential quality goal is the reduction of cost while maintaining other quality goals. Power consumption accounts for a significant part of the Total Cost of Ownership (TCO) of data centers. In 2010, data centers contributed 1.3% of the world-wide power consumption. However, reasoning on the energy efficiency of software systems is excluded from the systematic analysis of software architectures at design time. Energy efficiency can only be evaluated once the system is deployed and operational. One approach to reduce power consumption or cost is the introduction of self-adaptivity to a software system. Self-adaptive software systems execute adaptations to provision costly resources dependent on user load. The execution of reconfigurations can increase energy efficiency and reduce cost. If performed improperly, however, the additional resources required to execute a reconfiguration may exceed their positive effect. Existing architecture-level energy analysis approaches offer limited accuracy or only consider a limited set of system features, e.g., the used communication style. Predictive approaches from the embedded systems and Cloud Computing domain operate on an abstraction that is not suited for architectural analysis. The execution of adaptations can consume additional resources. The additional consumption can reduce performance and energy efficiency. Design time quality analyses for self-adaptive software systems ignore this transient effect of adaptations. This thesis makes the following contributions to enable the systematic consideration of energy efficiency in the architectural design of self-adaptive software systems: First, it presents a modeling language that captures power consumption characteristics on an architectural abstraction level. Second, it introduces an energy efficiency analysis approach that uses instances of our power consumption modeling language in combination with existing performance analyses for architecture models. The developed analysis supports reasoning on energy efficiency for static and self-adaptive software systems. Third, to ease the specification of power consumption characteristics, we provide a method for extracting power models for server environments. The method encompasses an automated profiling of servers based on a set of restrictions defined by the user. A model training framework extracts a set of power models specified in our modeling language from the resulting profile. The method ranks the trained power models based on their predicted accuracy. Lastly, this thesis introduces a systematic modeling and analysis approach for considering transient effects in design time quality analyses. The approach explicitly models inter-dependencies between reconfigurations, performance and power consumption. We provide a formalization of the execution semantics of the model. Additionally, we discuss how our approach can be integrated with existing quality analyses of self-adaptive software systems. We validated the accuracy, applicability, and appropriateness of our approach in a variety of case studies. The first two case studies investigated the accuracy and appropriateness of our modeling and analysis approach. The first study evaluated the impact of design decisions on the energy efficiency of a media hosting application. The energy consumption predictions achieved an absolute error lower than 5.5% across different user loads. Our approach predicted the relative impact of the design decision on energy efficiency with an error of less than 18.94%. The second case study used two variants of the Spring-based community case study system PetClinic. The case study complements the accuracy and appropriateness evaluation of our modeling and analysis approach. We were able to predict the energy consumption of both variants with an absolute error of no more than 2.38%. In contrast to the first case study, we derived all models automatically, using our power model extraction framework, as well as an extraction framework for performance models. The third case study applied our model-based prediction to evaluate the effect of different self-adaptation algorithms on energy efficiency. It involved scientific workloads executed in a virtualized environment. Our approach predicted the energy consumption with an error below 7.1%, even though we used coarse grained measurement data of low accuracy to train the input models. The fourth case study evaluated the appropriateness and accuracy of the automated model extraction method using a set of Big Data and enterprise workloads. Our method produced power models with prediction errors below 5.9%. A secondary study evaluated the accuracy of extracted power models for different Virtual Machine (VM) migration scenarios. The results of the fifth case study showed that our approach for modeling transient effects improved the prediction accuracy for a horizontally scaling application. Leveraging the improved accuracy, we were able to identify design deficiencies of the application that otherwise would have remained unnoticed

A framework for adaptive monitoring and performance management of component-based enterprise applications

Most large-scale enterprise applications are currently built using component-based middleware platforms such as J2EE or .NET. Developers leverage enterprise services provided by such platforms to speed up development and increase the robustness of their applications. In addition, using a component-oriented development model brings benefits such as increased reusability and flexibility in integrating with third-party systems. In order to provide the required services, the application servers implementing the corresponding middleware specifications employ a complex run-time infrastructure that integrates with developer-written business logic. The resulting complexity of the execution environment in such systems makes it difficult for architects and developers to understand completely the implications of alternative design options over the resulting performance of the running system. They often make incorrect assumptions about the behaviour of the middleware, which may lead to design decisions that cause severe performance problems after the system has been deployed. This situation is aggravated by the fact that although application servers vary greatly in performance and capabilities, many advertise a similar set of features, making it difficult to choose the one that is the most appropriate for their task. The thesis presents a methodology and tool for approaching performance management in enterprise component-based systems. By leveraging the component platform infrastructure, the described solution can nonintrusively instrument running applications and extract performance statistics. The use of component meta-data for target analysis, together with standards-based implementation strategies, ensures the complete portability of the instrumentation solution across different application servers. Based on this instrumentation infrastructure, a complete performance management framework including modelling and performance prediction is proposed. Most instrumentation solutions exhibit static behaviour by targeting a specified set of components. For long running applications, a constant overhead profile is undesirable and typically, such a solution would only be used for the duration of a performance audit, sacrificing the benefits of constantly observing a production system in favour of a reduced performance impact. This is addressed in this thesis by proposing an adaptive approach to monitoring which uses execution models to target profiling operations dynamically on components that exhibit performance degradation; this ensures a negligible overhead when the target application performs as expected and a minimum impact when certain components under-perform. Experimental results obtained with the prototype tool demonstrate the feasibility of the approach in terms of induced overhead. The portable and extensible architecture yields a versatile and adaptive basic instrumentation facility for a variety of potential applications that need a flexible solution for monitoring long running enterprise applications

Data-Driven Intelligent Scheduling For Long Running Workloads In Large-Scale Datacenters

Cloud computing is becoming a fundamental facility of society today. Large-scale public or private cloud datacenters spreading millions of servers, as a warehouse-scale computer, are supporting most business of Fortune-500 companies and serving billions of users around the world. Unfortunately, modern industry-wide average datacenter utilization is as low as 6% to 12%. Low utilization not only negatively impacts operational and capital components of cost efficiency, but also becomes the scaling bottleneck due to the limits of electricity delivered by nearby utility. It is critical and challenge to improve multi-resource efficiency for global datacenters. Additionally, with the great commercial success of diverse big data analytics services, enterprise datacenters are evolving to host heterogeneous computation workloads including online web services, batch processing, machine learning, streaming computing, interactive query and graph computation on shared clusters. Most of them are long-running workloads that leverage long-lived containers to execute tasks. We concluded datacenter resource scheduling works over last 15 years. Most previous works are designed to maximize the cluster efficiency for short-lived tasks in batch processing system like Hadoop. They are not suitable for modern long-running workloads of Microservices, Spark, Flink, Pregel, Storm or Tensorflow like systems. It is urgent to develop new effective scheduling and resource allocation approaches to improve efficiency in large-scale enterprise datacenters. In the dissertation, we are the first of works to define and identify the problems, challenges and scenarios of scheduling and resource management for diverse long-running workloads in modern datacenter. They rely on predictive scheduling techniques to perform reservation, auto-scaling, migration or rescheduling. It forces us to pursue and explore more intelligent scheduling techniques by adequate predictive knowledges. We innovatively specify what is intelligent scheduling, what abilities are necessary towards intelligent scheduling, how to leverage intelligent scheduling to transfer NP-hard online scheduling problems to resolvable offline scheduling issues. We designed and implemented an intelligent cloud datacenter scheduler, which automatically performs resource-to-performance modeling, predictive optimal reservation estimation, QoS (interference)-aware predictive scheduling to maximize resource efficiency of multi-dimensions (CPU, Memory, Network, Disk I/O), and strictly guarantee service level agreements (SLA) for long-running workloads. Finally, we introduced a large-scale co-location techniques of executing long-running and other workloads on the shared global datacenter infrastructure of Alibaba Group. It effectively improves cluster utilization from 10% to averagely 50%. It is far more complicated beyond scheduling that involves technique evolutions of IDC, network, physical datacenter topology, storage, server hardwares, operating systems and containerization. We demonstrate its effectiveness by analysis of newest Alibaba public cluster trace in 2017. We are the first of works to reveal the global view of scenarios, challenges and status in Alibaba large-scale global datacenters by data demonstration, including big promotion events like Double 11 . Data-driven intelligent scheduling methodologies and effective infrastructure co-location techniques are critical and necessary to pursue maximized multi-resource efficiency in modern large-scale datacenter, especially for long-running workloads