Now showing 1 - 7 of 7
  • Publication
    TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems
    Nowadays, clustered environments are commonly used in high-performance computing and enterprise-level applications to achieve faster response time and higher throughput than single machine environments. Nevertheless, how to effectively manage the workloads in these clusters has become a new challenge. As a load balancer is typically used to distribute the workload among the cluster's nodes, multiple research efforts have concentrated on enhancing the capabilities of load balancers. Our previous work presented a novel adaptive load balancing strategy (TRINI) that improves the performance of a clustered Java system by avoiding the performance impacts of major garbage collection, which is an important cause of performance degradation in Java. The aim of this paper is to strengthen the validation of TRINI by extending its experimental evaluation in terms of generality, scalability and reliability. Our results have shown that TRINI can achieve significant performance improvements, as well as a consistent behaviour, when it is applied to a set of commonly used load balancing algorithms, demonstrating its generality. TRINI also proved to be scalable across different cluster sizes, as its performance improvements did not noticeably degrade when increasing the cluster size. Finally, TRINI exhibited reliable behaviour over extended time periods, introducing only a small overhead to the cluster in such conditions. These results offer practitioners a valuable reference regarding the benefits that a load balancing strategy, based on garbage collection, can bring to a clustered Java system.
      456Scopus© Citations 11
  • Publication
    Towards an emulated IoT test environment for anomaly detection using NEMU
    The advent of the Internet of Things (IoT) has led to a major change in the way we interact with increasingly ubiquitous connected devices such as smart objects and cyber-physical systems. It has also led to an exponential increase in the number of such Internet-connected devices over the last few years. Conducting extensive functional and performance testing is critical to assess the robustness and efficiency of IoT systems in order to validate them before their deployment in real life. However, creating an IoT test environment is a difficult and expensive task, usually requiring a significant amount of physical hardware and human effort to build it. This paper proposes a method to emulate an IoT environment using the Network Emulator for Mobile Universes (NEMU), itself built on the popular QEMU system emulator, in order to construct a testbed of inter-connected, emulated Raspberry Pi devices. Additionally, we experimentally demonstrate how our method can be successfully applied to IoT by showing how such an emulated environment can be used to detect anomalies in an IoT system.
      700Scopus© Citations 19
  • Publication
    Automated WAIT for Cloud-Based Application Testing
    Cloud computing is causing a paradigm shift in the provision and use of software. This has changed the way of obtaining, managing and delivering computing services and solutions. Similarly, it has brought new challenges to software testing. A particular area of concern is the performance of cloud-based applications. This is because the increased complexity of the applications has exposed new areas of potential failure points, complicating all performance-related activities. This situation makes the performance testing of cloud environments very challenging. Similarly, the identification of performance issues and the diagnosis of their root causes are time-consuming and complex, usually require multiple tools and heavily rely on expertise. To simplify these tasks, hence increasing the productivity and reducing the dependency on human experts, this paper presents a lightweight approach to automate the usage of expert tools in the performance testing of cloud-based applications. In this paper, we use a tool named Whole-system Analysis of Idle Time to demonstrate how our research work solves this problem. The validation involved two experiments, which assessed the overhead of the approach and the time savings that it can bring to the analysis of performance issues. The results proved the benefits of the approach by achieving a significant decrease in the time invested in performance analysis while introducing a low overhead in the tested system.
      474Scopus© Citations 11
  • Publication
    Load balancing of Java applications by forecasting garbage collections
    Modern computer applications, especially at enterprise-level, are commonly deployed with a big number of clustered instances to achieve a higher system performance, in which case single machine based solutions are less cost-effective. However, how to effectively manage these clustered applications has become a new challenge. A common approach is to deploy a front-end load balancer to optimise the workload distribution between each clustered application. Since then, many research efforts have been carried out to study effective load balancing algorithms which can control the workload based on various resource usages such as CPU and memory. The aim of this paper is to propose a new load balancing approach to improve the overall distributed system performance by avoiding potential performance impacts caused by Major Java Garbage Collection. The experimental results have shown that the proposed load balancing algorithm can achieve a significant higher throughput and lower response time compared to the round-robin approach. In addition, the proposed solution only has a small overhead introduced to the distributed system, where unused resources are available to enable other load balancing algorithms together to achieve a better system performance.
      424Scopus© Citations 7
  • Publication
    Adaptive GC-aware load balancing strategy for high-assurance Java distributed systems
    High-Assurance applications usually require achieving fast response time and high throughput on a constant basis. To fulfil these stringent quality of service requirements, these applications are commonly deployed in clustered instances. However, how to effectively manage these clusters has become a new challenge. A common approach is to deploy a front-end load balancer to optimise the workload distribution among the clustered applications. Thus, researchers have been studying how to improve the effectiveness of a load balancer. Our previous work presented a novel load balancing strategy which improves the performance of a distributed Java system by avoiding the performance impacts of Major Garbage Collection, which is a common cause of performance degradation in Java applications. However, as that strategy used a static configuration, it could only improve the performance of a system if the strategy was configured with domain expert knowledge. This paper extends our previous work by presenting an adaptive GC-aware load balancing strategy which self-configures according to the GC characteristics of the application. Our results have shown that this adaptive strategy can achieve higher throughput and lower response time, compared to the round-robin load balancing, while also avoiding the burden of manual tuning.
      445Scopus© Citations 9
  • Publication
    PHOEBE: an automation framework for the effective usage of diagnosis tools in the performance testing of clustered systems
    The identification of performance issues and the diagnosis of their root causes are time-consuming and complex tasks, especially in clustered environments. To simplify these tasks, researchers have been developing tools with built-in expertise for practitioners. However, various limitations exist in these tools that prevent their efficient usage in the performance testing of clusters (e.g. the need of manually analysing huge volumes of distributed results). In a previous work, we introduced a policy-based adaptive framework (PHOEBE) that automates the usage of diagnosis tools in the performance testing of clustered systems, in order to improve a tester's productivity, by decreasing the effort and expertise needed to effectively use such tools. This paper extends that work by broadening the set of policies available in PHOEBE, as well as by performing a comprehensive assessment of PHOEBE in terms of its benefits, costs and generality (with respect to the used diagnosis tool). The performed evaluation involved a set of experiments in assessing the different trade-offs commonly experienced by a tester when using a performance diagnosis tool, as well as the time savings that PHOEBE can bring to the performance testing and analysis processes. Our results have shown that PHOEBE can drastically reduce the effort required by a tester to do performance testing and analysis in a cluster. PHOEBE also exhibited consistent behaviour (i.e. similar time-savings and resource utilisations), when applied to a set of commonly used diagnosis tools, demonstrating its generality. Finally, PHOEBE proved to be capable of simplifying the configuration of a diagnosis tool. This was achieved by addressing the identified trade-offs without the need for manual intervention from the tester.
      801Scopus© Citations 9
  • Publication
    Multi-Layer-Mesh: A Novel Topology and SDN-based Path Switching for Big Data Cluster Networks
    Big Data technologies and tools have being used for the past decade to solve several scientific and industry problems, with Hadoop/YARN becoming the ”de facto” standard for these applications, although other technologies run on top of it. As any other distributed application, those big data technologies rely heavily on the network infrastructure to read and move data from hundreds or thousands of cluster nodes. Although these technologies are based on reliable and efficient distributed algorithms, there are scenarios and conditions that can generate bottlenecks and inefficiencies, i.e., when a high number of concurrent users creates data access contention. In this paper, we propose a novel network topology called MultiLayer-Mesh and a path switching algorithm based on SDN, that can increase the performance of a big data cluster while reducing the amount of utilized resources (network equipment), in turn reducing the energy and cooling consumption. A thorough simulation-based evaluation of our algorithms shows an average improvement in performance of 31.77% and an average decrease in resource utilization of 36.03% compared to a traditional SpineLeaf topology, in the selected test scenarios.
    Scopus© Citations 5  542