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Distributed data Managmant for Grid Computing - Stefano M.

Stefano M. Distributed data Managmant for Grid Computing - John Wiley & Sons, 2005. - 308 p.
ISBN 0-471-68719-7
Download (direct link): distributeddatamanagement2005.pdf
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Einstein’s theory of relativity predicts that with mass warping of the fabric of space and time, the mass of a single data body will have no effect on the fabric of the data grid. In fact, it is quite the opposite. The data grid is a tangible physical entity consisting of many machines of finite compute power and data storage capacity. The maximum speed at which matter can travel in the data grid is not a constant, unlike the speed of light in our physical universe. The speed with which data move in the data grid is determined by the bandwidth, throughput, and latency parameters of the physical network connecting the nodes of the data grid.
Where the fabric of space and time is assumed to be homogeneous, the fabric of the data grid is not. The nodes of the data grid can vary in power and capacity, and the networks connecting the nodes can vary from infiniband, to 100baseT, to something less than is typically found on a WAN. Therefore the fabric of the data grid is not homogeneous, and it is not a constant. Over time the fabric of the data grid will change as old machines are cycled out by new ones, as networks are upgraded, and as hardware outages restrict the data grid itself.
The relation of the data grid fabric to the data bodies contained within it is closer to the “unified view” in physics, which states that mass does not warp space and time; rather, it is the warping of space and time that defines mass. Compute tasks are sent to the machines best capable of performing the task (grid computing), and part of that determination should be data locality. If the data are not already local on that machine, the data grid must move them there; the exact phenomenon must be minimized. Therefore, the capacity of each individual grid node and the characteristics of the network connecting the nodes will determine
• Where on the data grid the data must be and the quantity of data that can be held at a node. These conditions will determine the shape and density of the single data body, which has a direct impact on the single data body’s center of gravity.
• The shape of the coefficient of friction surface of the data grid, which will influence the force of friction between two single data bodies and in turn the equilibrium distance between the two single data bodies.
Conclusions
There exists a family of expressions that represent the physical fabric of the data grid, the macro properties that describe single data bodies, and the forces that they exert on each other, resulting in a steady-state data distribution pattern so that systemwide data movement is minimized.
These expressions include, most importantly, the mass of a single data body. Mass is used in the expressions for the force of attraction between two single data bodies and the force of friction that counterbalances the force of attraction to achieve
266
NATURAL ATTRACTION FORCES OF DATA BODIES
the equilibrium distance between the two single data bodies. The expression for the mass of a single data body should describe external and measurable properties of the single data body that describe the composition of the body.
Unique to any two pairs of single data bodes is the coefficient of attraction. This is the measure of the interdependencies between two single data bodies in the larger system. For example, in a risk management system, risk exposure is dependent on the holding’s portfolio and market data. Thus, there will be a coefficient of attraction between the risk exposure single data body and the market data single data body and the portfolio single data body.
The expressions of the forces for data distribution are as follows:
• The force of attraction (F) is equal to the mass of the single data body (m) times the coefficient of attraction between two single data bodies (a):
F ma
• The force of friction (Ff) is the product of the mass of a single data body (m) times the coefficient of friction (m), which describes the physical properties of the fabric of the data grid:
Ff = mm
The physical properties of the data grid have a direct effect on the size, shape, density, and therefore the center of gravity of a single data body. They are collectively expressed in a coefficient, called the coefficient of friction of the fabric of the data grid. It is more realistic that the coefficient of friction is not a single number, but rather a mathematical function that will exhibit different results in different areas of the data grid.
m
( NetworkBandwidth,
Network, Latency,
ComputeNodeStorageCapacity,
ComputeNodeServiceExecutionCapability,
V
/
Thus, the force of attraction between two single data bodies, countered by the force of friction between the same two single data bodies, will result in an equilibrium distance that represents the steady state of the two single data bodies where the data movement within the data grid space is at a minimum.
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