2,108 research outputs found
A smart hill-climbing algorithm for application server configuration
The overwhelming success of the Web as a mechanism for facilitating information retrieval and for conducting business transactions has led to an increase in the deployment of complex enterprise applications. These applications typically run on Web Application Servers, which assume the burden of managing many tasks, such as concurrency, memory management, database access, etc., required by these applications. The performance of an Application Server depends heavily on appropriate configuration. Configuration is a difficult and error-prone task due to the large number of configuration parameters and complex interactions between them. We formulate the problem of finding an optimal configuration for a given application as a black-box optimization problem. We propose a Smart Hill-Climbing algorithm using ideas of importance sampling and Latin Hypercube Sampling (LHS). The algorithm is efficient in both searching and random sampling. It consists of estimating a local function, and then, hill-climbing in the steepest descent direction. The algorithm also learns from past searches and restarts in a smart and selective fashion using the idea of importance sampling. We have carried out extensive experiments with an online brokerage application running in a WebSphere environment. Empirical results demonstrate that our algorithm is more efficient than and superior to traditional heuristic methods. Categories and Subject Descriptor
Proactive cloud management for highly heterogeneous multi-cloud infrastructures
Various literature studies demonstrated that the cloud computing paradigm can help to improve availability and performance of applications subject to the problem of software anomalies. Indeed, the cloud resource provisioning model enables users to rapidly access new processing resources, even distributed over different geographical regions, that can be promptly used in the case of, e.g., crashes or hangs of running machines, as well as to balance the load in the case of overloaded machines. Nevertheless, managing a complex geographically-distributed cloud deploy could be a complex and time-consuming task. Autonomic Cloud Manager (ACM) Framework is an autonomic framework for supporting proactive management of applications deployed over multiple cloud regions. It uses machine learning models to predict failures of virtual machines and to proactively redirect the load to healthy machines/cloud regions. In this paper, we study different policies to perform efficient proactive load balancing across cloud regions in order to mitigate the effect of software anomalies. These policies use predictions about the mean time to failure of virtual machines. We consider the case of heterogeneous cloud regions, i.e regions with different amount of resources, and we provide an experimental assessment of these policies in the context of ACM Framework
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
An ever increasing number of configuration parameters are provided to system
users. But many users have used one configuration setting across different
workloads, leaving untapped the performance potential of systems. A good
configuration setting can greatly improve the performance of a deployed system
under certain workloads. But with tens or hundreds of parameters, it becomes a
highly costly task to decide which configuration setting leads to the best
performance. While such task requires the strong expertise in both the system
and the application, users commonly lack such expertise.
To help users tap the performance potential of systems, we present
BestConfig, a system for automatically finding a best configuration setting
within a resource limit for a deployed system under a given application
workload. BestConfig is designed with an extensible architecture to automate
the configuration tuning for general systems. To tune system configurations
within a resource limit, we propose the divide-and-diverge sampling method and
the recursive bound-and-search algorithm. BestConfig can improve the throughput
of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce
the running time of Hive join job by about 50% and that of Spark join job by
about 80%, solely by configuration adjustment
ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees
To support the variety of Big Data use cases, many Big Data related systems
expose a large number of user-specifiable configuration parameters. Highlighted
in our experiments, a MySQL deployment with well-tuned configuration parameters
achieves a peak throughput as 12 times much as one with the default setting.
However, finding the best setting for the tens or hundreds of configuration
parameters is mission impossible for ordinary users. Worse still, many Big Data
applications require the support of multiple systems co-deployed in the same
cluster. As these co-deployed systems can interact to affect the overall
performance, they must be tuned together. Automatic configuration tuning with
scalability guarantees (ACTS) is in need to help system users. Solutions to
ACTS must scale to various systems, workloads, deployments, parameters and
resource limits. Proposing and implementing an ACTS solution, we demonstrate
that ACTS can benefit users not only in improving system performance and
resource utilization, but also in saving costs and enabling fairer
benchmarking
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
Intrusion Detection System using Bayesian Network Modeling
Computer Network Security has become a critical and important issue due to ever increasing cyber-crimes. Cybercrimes are spanning from simple piracy crimes to information theft in international terrorism. Defence security agencies and other militarily related organizations are highly concerned about the confidentiality and access control of the stored data. Therefore, it is really important to investigate on Intrusion Detection System (IDS) to detect and prevent cybercrimes to protect these systems. This research proposes a novel distributed IDS to detect and prevent attacks such as denial service, probes, user to root and remote to user attacks. In this work, we propose an IDS based on Bayesian network classification modelling technique. Bayesian networks are popular for adaptive learning, modelling diversity network traffic data for meaningful classification details. The proposed model has an anomaly based IDS with an adaptive learning process. Therefore, Bayesian networks have been applied to build a robust and accurate IDS. The proposed IDS has been evaluated against the KDD DAPRA dataset which was designed for network IDS evaluation. The research methodology consists of four different Bayesian networks as classification models, where each of these classifier models are interconnected and communicated to predict on incoming network traffic data. Each designed Bayesian network model is capable of detecting a major category of attack such as denial of service (DoS). However, all four Bayesian networks work together to pass the information of the classification model to calibrate the IDS system. The proposed IDS shows the ability of detecting novel attacks by continuing learning with different datasets. The testing dataset constructed by sampling the original KDD dataset to contain balance number of attacks and normal connections. The experiments show that the proposed system is effective in detecting attacks in the test dataset and is highly accurate in detecting all major attacks recorded in DARPA dataset. The proposed IDS consists with a promising approach for anomaly based intrusion detection in distributed systems. Furthermore, the practical implementation of the proposed IDS system can be utilized to train and detect attacks in live network traffi
Resource optimization of edge servers dealing with priority-based workloads by utilizing service level objective-aware virtual rebalancing
IoT enables profitable communication between sensor/actuator devices and the cloud. Slow network causing Edge data to lack Cloud analytics hinders real-time analytics adoption. VRebalance solves priority-based workload performance for stream processing at the Edge. BO is used in VRebalance to prioritize workloads and find optimal resource configurations for efficient resource management. Apache Storm platform was used with RIoTBench IoT benchmark tool for real-time stream processing. Tools were used to evaluate VRebalance. Study shows VRebalance is more effective than traditional methods, meeting SLO targets despite system changes. VRebalance decreased SLO violation rates by almost 30% for static priority-based workloads and 52.2% for dynamic priority-based workloads compared to hill climbing algorithm. Using VRebalance decreased SLO violations by 66.1% compared to Apache Storm\u27s default allocation
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