CloudScope: diagnosing and managing performance interference in multi-tenant clouds

© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%

Proceedings of the NSSDC Conference on Mass Storage Systems and Technologies for Space and Earth Science Applications

The proceedings of the National Space Science Data Center Conference on Mass Storage Systems and Technologies for Space and Earth Science Applications held July 23 through 25, 1991 at the NASA/Goddard Space Flight Center are presented. The program includes a keynote address, invited technical papers, and selected technical presentations to provide a broad forum for the discussion of a number of important issues in the field of mass storage systems. Topics include magnetic disk and tape technologies, optical disk and tape, software storage and file management systems, and experiences with the use of a large, distributed storage system. The technical presentations describe integrated mass storage systems that are expected to be available commercially. Also included is a series of presentations from Federal Government organizations and research institutions covering their mass storage requirements for the 1990's

Modeling and scheduling heterogeneous multi-core architectures

Om de prestatie van toekomstige processors en processorarchitecturen te evalueren wordt vaak gebruik gemaakt van een simulator die het gedrag en de prestatie van de processor modelleert. De prestatie bepalen van de uitvoering van een computerprogramma op een gegeven processorarchitectuur m.b.v. een simulator duurt echter vele grootteordes langer dan de werkelijke uitvoeringstijd. Dit beperkt in belangrijke mate de hoeveelheid experimenten die gedaan kunnen worden. In dit doctoraatswerk werd het Multi-Program Performance Model (MPPM) ontwikkeld, een innovatief alternatief voor traditionele simulatie, dat het mogelijk maakt om tot 100.000x sneller een processorconfiguratie te evalueren. MPPM laat ons toe om nooit geziene exploraties te doen. Gebruik makend van dit raamwerk hebben we aangetoond dat de taakplanning cruciaal is om heterogene meerkernige processors optimaal te benutten. Vervolgens werd een nieuwe manier voorgesteld om op een schaalbare manier de taakplanning uit te voeren, namelijk Performance Impact Estimation (PIE). Tijdens de uitvoering van een draad op een gegeven processorkern schatten we de prestatie op een ander type kern op basis van eenvoudig op te meten prestatiemetrieken. Zo beschikken we op elk moment over alle nodige informatie om een efficiënte taakplanning te doen. Dit laat ons bovendien toe te optimaliseren voor verschillende criteria zoals uitvoeringstijd, doorvoersnelheid of fairness

Cloud Services Brokerage: A Survey and Research Roadmap

A Cloud Services Brokerage (CSB) acts as an intermediary between cloud service providers (e.g., Amazon and Google) and cloud service end users, providing a number of value adding services. CSBs as a research topic are in there infancy. The goal of this paper is to provide a concise survey of existing CSB technologies in a variety of areas and highlight a roadmap, which details five future opportunities for research.Comment: Paper published in the 8th IEEE International Conference on Cloud Computing (CLOUD 2015

Survey and Analysis of Production Distributed Computing Infrastructures

This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures

Research in software allocation for advanced manned mission communications and tracking systems

An assessment of the planned processing hardware and software/firmware for the Communications and Tracking System of the Space Station Freedom (SSF) was performed. The intent of the assessment was to determine the optimum distribution of software/firmware in the processing hardware for maximum throughput with minimum required memory. As a product of the assessment process an assessment methodology was to be developed that could be used for similar assessments of future manned spacecraft system designs. The assessment process was hampered by changing requirements for the Space Station. As a result, the initial objective of determining the optimum software/firmware allocation was not fulfilled, but several useful conclusions and recommendations resulted from the assessment. It was concluded that the assessment process would not be completely successful for a system with changing requirements. It was also concluded that memory requirements and hardware requirements were being modified to fit as a consequence of the change process, and although throughput could not be quantitized, potential problem areas could be identified. Finally, inherent flexibility of the system design was essential for the success of a system design with changing requirements. Recommendations resulting from the assessment included development of common software for some embedded controller functions, reduction of embedded processor requirements by hardwiring some Orbital Replacement Units (ORUs) to make better use of processor capabilities, and improvement in communications between software development personnel to enhance the integration process. Lastly, a critical observation was made regarding the software integration tasks did not appear to be addressed in the design process to the degree necessary for successful satisfaction of the system requirements

Predictive Analytics in Cloud Computing: An ARIMA Model Study on Performance Metrics

Predictive analytics is a key aspect of cloud computing as it helps organizations to anticipate future events and take proactive measures to prevent issues before they occur. In this research, the goal was to perform an ARIMA (AutoRegressive Integrated Moving Average) model to predict cloud performance using various performance metrics. The study utilized ten different performance metrics, such as Response Time, Resource Utilization, Availability, Error Rate, Memory Usage, CPU Utilization, Disk I/O, Network Bandwidth and others to model cloud performance. The aim was to investigate the potential of ARIMA models to predict cloud performance by analyzing the impact of these different performance metrics on the model's accuracy. The study also used four performance criteria, namely LogL (Log Likelihood), AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and HQ (Hannan-Quinn Criterion) to evaluate the performance of the ARIMA models. The results of the study showed that the ARIMA model (2,0) and (0,2) had the lowest AIC and BIC values among all the models considered. This indicated that these models were the most suitable for predicting cloud performance, as they had the lowest information loss compared to the other models. The results of the study provided evidence that ARIMA models can effectively predict cloud performance. This research highlights the importance of predictive analytics in cloud computing and the potential for ARIMA models to predict cloud performance. The findings have implications for organizations that rely on cloud computing. However, more research is needed in this area, as the study was limited to only ten performance metrics, and more extensive research is needed to validate the findings and to determine the best approach to predict cloud performance