IBM Intelligent Bricks project—Petabytes and beyond

Wilcke, W. W.; Garner, Robert B.; Fleiner, Claudio; Freitas, R. F.; Golding, Richard; Glider, Joseph; Kenchammana-Hosekote, Deepak R.; Hafner, James Lee; Mohiuddin, K. M.; Rao, K. Koteswara; Becker‐Szendy, R.; Wong, Theodore M.; Zaki, Omer A.; Hernandez, Maria Cristina C.; Fernandez, Ken; Huels, Harald; Lenk, Hans; Smolin, K.; Ries, Markus; Goettert, C.; Picunko, T.; Rubin, B.J.; Kahn, Hyungsik; Loo, Tessa Zhi Wei.

doi:10.1147/rd.502.0181

Cited by 27 publications

(18 citation statements)

References 14 publications

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“…The nodes communicate by coming near each other until the faces touch. Prototypes of capacitive coupling in the Intelligent Bricks project [9] at IBM have demonstrated bandwidth of 10 Gb/s bi-directional over a 4 cm × 4 cm face.…”

Section: Overviewmentioning

confidence: 99%

Go, server, go!

Barber

Lohman

Mueller

et al. 2013

Proceedings of the 4th Annual Symposium on Cloud Computing

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In data centers today, servers are stationary and data flows on a hierarchical network of switches and routers. But such static server arrangements require very scalable networks, and many applications are bottlenecked by network bandwidth. In addition, server density is kept low to enable maintenance and upgrades, as well as to increase air flow. In this paper, we propose a design in which servers move physically, and communicate via point-topoint connections (instead of switches). We argue that this allows data transfer bandwidth to scale linearly with the number of servers, and that moving servers is not as expensive as it sounds, at least in terms of power consumption. Moreover, while servers move around, they regularly reach the perimeters of the system, which helps with heat dissipation and with servicing of failed nodes. This design also helps in traditional switch-based networks, to improve density and maintainability.

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Section: Overviewmentioning

confidence: 99%

Go, server, go!

Barber

Lohman

Mueller

et al. 2013

Proceedings of the 4th Annual Symposium on Cloud Computing

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“…The storage systems group at IBM Almaden Research Center has several research projects in progress to address the complexities of autonomic storage, such as CIB (Collective Intelligent Bricks) [14], Aloms (an autonomic computing system that simplifies storage management for databases), SRM (an autonomic storage resource manager) and Clockwork (Constructing Logical Objective Control Keys through Work-Oriented Relationship Knobs) [15] etc. The objective of IBM's autonomic computing is to deliver significantly the lower total cost of ownership, cost of application development and time to solution delivery.…”

Section: Related Work and Data Storage Metricsmentioning

confidence: 99%

High TPO/TCO for data storage: policy, algorithm and early practice

Zeng

Feng

Jiang

2007

Front. Comput. Sc. China

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Today, the data storage industry faces a rapidly growing volume of data. Adding more primary disk capacity to manage data growth is a costly and non-sustainable strategy. Before investing in new capacity, data managers should rationalize their existing storage infrastructure to maximize the use of existing capacity. There is a growing need for achieving a high ratio of Total Performance of Ownership (TPO) to Total Cost of Ownership (TCO), or TPO/TCO.The storage infrastructure can be made more efficient by assessing data usages, eliminating unnecessary data copies, moving less critical data to less expensive disk devices and repurposing allocated but unused capacity. Optimizing existing storage assets can reduce storage costs by delaying or eliminating the need for new primary capacity to manage information growth. In this paper, we apply the notion of information lifecycle management (ILM) to achieve the above improved efficiencies and optimizations. By balancing data value and storage requirements, we aim to reduce the storage system's dependence on expensive high-performance disk devices and lower its cost per online gigabyte, thus resulting in a higher TPO/TCO.

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“…One solution employed in architectures to solve this problem is to replace single server with multiple servers [6,25,11]. Recently, several research prototypes that offer multi-server storage architectures have been built, e.g., IBM's Intelligent bricks [9], CMU's Ursa Minor [7] and HP Labs' FAB [10]. Such architectures have high availability, scalability and are distributed in nature.…”

Section: Introductionmentioning

confidence: 99%

Dynamic storage cache allocation in multi-server architectures

Prabhakar

Srikantaiah

Patrick

et al. 2009

Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis

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We introduce a dynamic and efficient shared cache management scheme, called Maxperf, that manages the aggregate cache space in multi-server storage architectures such that the service level objectives (SLOs) of concurrently executing applications are satisfied and any spare cache capacity is proportionately allocated according to the marginal gains of the applications to maximize performance. We use a combination of Neville's algorithm and linear-programming-model to discover the required storage cache partition size, on each server, for every application accessing that server. Experimental results show that our algorithm enforces partitions to provide stronger isolation to applications, meets application level SLOs even in the presence of dynamically changing storage cache requirements, and improves I/O latency of individual applications as well as the overall I/O latency significantly compared to two alternate storage cache management schemes, and a state-of-the-art single server storage cache management scheme extended to multi-server architecture.

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IBM Intelligent Bricks project—Petabytes and beyond

Cited by 27 publications

References 14 publications

Go, server, go!

Go, server, go!

High TPO/TCO for data storage: policy, algorithm and early practice

Dynamic storage cache allocation in multi-server architectures

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