System Management with IBM Mobile Systems Remote: A Question of Power and Scale

Rellermeyer, Jan S.; Osiecki, T.; Holloway, Ernest A.; Bohrer, Patrick; Kistler, Michael

doi:10.1109/mdm.2012.53

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…With more heterogeneous machines and novel technology on the horizon, this divide is only going to become wider. In some of our prior work, we have attempted to address some of the friction between existing software systems, platforms, and new technology in terms of building distributed systems for the Internet of Things [66], increasing scalability and performance [67], [68], and dealing better with multi-tenancy [41]. However, we believe that at this point it is time to rethink the entire stack and co-design new interfaces between hardware, operating system, and data processing systems.…”

Section: Discussionmentioning

confidence: 99%

The Coming Age of Pervasive Data Processing

Rellermeyer

Khorasani

Graur

et al. 2019

2019 18th International Symposium on Parallel and Distributed Computing (ISPDC)

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Emerging Big Data analytics and machine learning applications require a significant amount of computational power. While there exists a plethora of large-scale data processing frameworks which thrive in handling the various complexities of data-intensive workloads, the ever-increasing demand of applications have made us reconsider the traditional ways of scaling (e.g., scale-out) and seek new opportunities for improving the performance. In order to prepare for an era where data collection and processing occur on a wide range of devices, from powerful HPC machines to small embedded devices, it is crucial to investigate and eliminate the potential sources of inefficiency in the current state of the art platforms. In this paper, we address the current and upcoming challenges of pervasive data processing and present directions for designing the next generation of largescale data processing systems.

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Section: Discussionmentioning

confidence: 99%

The Coming Age of Pervasive Data Processing

Rellermeyer

Khorasani

Graur

et al. 2019

2019 18th International Symposium on Parallel and Distributed Computing (ISPDC)

Self Cite

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“…Modern datacenters typically monitor six to twelve metrics per datacenter node, and typically have ten to thirty system administrators, at large academic institutions (lower end of the range) and commercial organizations with data centers for internal operations (as opposed to having it as a line of business). Further, it is common for system administrators to create multiple subscriptions for a metric as they might have overlapping interests [12,1]. For instance, one system administrator may be interested in the behavior of the hypervisors on a rack of machines, together with the temperature profile of the rack.…”

Section: Problem Backgroundmentioning

confidence: 99%

MAVIS: Managing Datacenters using Smartphones

Shankar,

Kobin,

Bagchi

et al. 2018

Preprint

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Distributed monitoring plays a crucial role in managing the activities of cloud-based datacenters. System administrators have long relied on monitoring systems such as Nagios and Ganglia to obtain status alerts on their desktop-class machines. However, the popularity of mobile devices is pushing the community to develop datacenter monitoring solutions for smartphone-class devices. Here we lay out desirable characteristics of such smartphone-based monitoring and identify quantitatively the shortcomings from directly applying existing solutions to this domain. Then we introduce a possible design that addresses some of these shortcomings and provide results from an early prototype, called MAVIS, using one month of monitoring data from approximately 3,000 machines hosted by Purdue's central IT organization.

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