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Predicting resource requirements for cloud services is critical for dimensioning, anomaly detection and service assurance. We demonstrate a system for real-time estimation of the needed amount of infrastructure resources, such as CPU and memory, for a given service. Statistical learning methods on server statistics and load parameters of the service are used for learning a resource prediction model...
We would like to present the idea of our Continuous Defect Prediction (CDP) research and a related dataset that we created and share. Our dataset is currently a set of more than 11 million data rows, representing files involved in Continuous Integration (CI) builds, that synthesize the results of CI builds with data we mine from software repositories. Our dataset embraces 1265 software projects, 30,022...
Background Software systems are relying more and more on multi-core hardware requiring a parallel approach to address the problems and improve performances. Unfortunately, parallel development is error prone and many developers are not very experienced with this paradigm also because identifying, reproducing, and fixing bugs is often difficult. Objective The main goal of this paper is the identification...
In this paper, we present a combined experimental and analytical investigation of the impact of security compliance on a three-tier web application hosted on a virtualized platform. We used two-group experimental design for our experiments, and analyzed the impact of security using the ANCOVA model. The results of experiments suggest that security measures have significant impact on system performance...
Service assurance for the telecom cloud is a challenging task and is continuously being addressed by academics and industry. One promising approach is to utilize machine learning to predict service quality in order to take early mitigation actions. In previous work we have shown how to predict service-level metrics, such as frame rate for a video application on the client side, from operational data...
Performance monitoring of datacenters provides vital information for dynamic resource provisioning, anomaly detection, capacity planning, and metering decisions. Online monitoring, however, incurs a variety of costs: the very act of monitoring a system interferes with its performance, consuming network bandwidth and disk space. With the goal of reducing these costs, we develop and validate a strategy...
A polynomial fitting model for predicting the RTP packet rate of Video-on-Demand received by a client is presented. This approach is underpinned by a parametric statistical model for the client-server system. This model, namely the PQ-model, improves the robustness of the predictor in the presence of a time-varying load on the server. The advantage of our approach is that if we model the load on the...
Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses a regression method that accurately predicts, in real-time, client-side service metrics for a video...
An algorithm for predicting the quality of video received by a client from a shared server is presented. A statistical model for this client-server system, in the presence of other clients, is proposed. Our contribution is that we explicitly account for the interfering clients, namely the load. Once the load on the system is understood, accurate client-server predictions are possible with an accuracy...
While real-time service assurance is critical for emerging telecom cloud services, understanding and predicting performance metrics for such services is hard. In this paper, we pursue an approach based upon statistical learning whereby the behavior of the target system is learned from observations. We use methods that learn from device statistics and predict metrics for services running on these devices...
Data protection using backup is one of the most critical IT management operations to ensure business continuity, which is also constantly evolving due to the emerging challenges in the “Big Data Era.” In this paper, we introduce our ongoing research effort in designing intelligent enterprise backup management solutions by obtaining actionable insights from voluminous backup job metadata across data...
Design and development of multi-player network games is a complex and involved process. Apart from standard software development complexity, the game designer has to carefully think about issues like playability, user experience and scalability aspects of the game during the design phase itself. Most of the existing methodologies to determine playability of a multiplayer game with a targeted user...
Monitoring and predicting resource consumption is a fundamental need when running a virtualized system. Predicting resources is necessary because cloud infrastructures use virtual resources on demand. Current monitoring tools are insufficient to predict resource usage of virtualized systems so, without proper monitoring, virtualized systems can suffer down time, which can directly affect cloud infrastructure...
Data center workload modeling has become a necessity in recent years due to the emergence of large-scale applications and cloud data-stores, whose implementation remains largely unknown. Detailed knowledge of target workloads is critical in order to correctly provision performance, power and cost-optimized systems. In this work we aggregate previous work on data center workload modeling and perform...
In distributed virtual environments (DVEs), maintaining a consistent view of the virtual world among all users is a primary task. Due to the resource limitations such as network capacity and computational power, the consistency of the virtual world cannot be guaranteed sometimes. In this paper, we try to address this problem in client-server-based DVEs by considering server side network capacity limitation...
In this paper we present a system for online power prediction in vir-tualized environments. It is based on Gaussian mixture models that use architectural metrics of the physical and virtual machines (VM) collected dynamically by our system to predict both the physical machine and per VM level power consumption. A real implementation of our system shows that it can achieve average prediction error...
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