ChunkSim - Data Warehousing Design and Advanced Engineering Applications - page 133

For this reason, there has been a lot of emphasis
on automatic workload-based determination of
partitioning indexes, materialized views and cube
configurations in the past.
We concentrate on a different issue of providing
desired load and availability levels with minimum
degree of replication in parallel partitioned archi-
tectures and on system planning, both in the pres-
ence of heterogeneity. Consider a shared-nothing
environment created in an ad-hoc fashion, by put-
ting together a number of PCs. A data warehouse
can be setup at a low cost in such a context, but
then not only partitioning but also heterogeneity
and availability become relevant issues. These
issues are dealt-with efficiently using load- and
availability- balancing, where data is partitioned
into pieces and replicated into processing nodes;
there is a controller node orchestrating balanced
execution, and processing nodes ask the control-
ler node for the next piece of the data to process
whenever they are idle (on-demand processing).
Under this scheme, the best possible performance
and availability balancing is guaranteed if all nodes
have all the data (fully mirrored data), but smaller
degrees of replication (partial replication) also
achieve satisfactory levels of performance and
load balancing without the loading and storage
burdens of full mirroring. The issue then is how to
predict and take decisions concerning the degree of
partial replication that is necessary and how to size
a system, which are objectives of the ChunkSim
simulator that we present in this work. There is
a tradeoff between the costs of maintaining large
amounts of replicas (loading and storage costs)
and the efficiency of the system in dealing with
both heterogeneity/non-dedication of nodes and
availability limitations, and there is a need to size
a system while taking heterogeneity and replica-
tion alternatives into consideration. ChunkSim is
a what-if analysis tool for determining the benefit
of performance-wise placement and replication
degree for heterogeneity and availability balanc-
ing in partitioned, partially replicated on-demand
processing. Our contribution is to propose the

tool and the model underlying it and to use it for
system planning and the analysis of placement
and replication alternatives.
The chapter is organized as follows: in the
Background section we review partitioning,
replication and load-balancing. Our review of
replication will include works on both low-level
replication (Patterson et al. 1998), relation-wise
replication (e.g. chained declustering by Hsiao
et al. 1990) and OLAP-wise replication (e.g.
the works by Akal et al. 2002, Furtado 2004
and Furtado 2005). Then we review basic query
processing in our shared-nothing environment, in
section 3. In section 4 we describe the ChunkSim
Model and parameters, including also a discussion
on Placement and Processing approaches. The
ChunkSim tool and underlying model is discussed
next, and finally we use the tool to analyze the
merits of different placement and replication
configurations.

BACKGROUND

Shared-nothing parallel systems (SN) are systems
where a possibly large number of computers
(nodes) are interconnected such that, other than
the network, no other resources are shared. These
architectures are scalable, in the sense that it is
possible to add a large number of computing
nodes to handle larger data sets efficiently. The
idea is that, if each node is able to process its part
independently of the remaining nodes, the system
will have good scalability properties. On the other
hand, interconnections between processing units
may become a bottleneck if large amounts of
data need to be exchanged between nodes. For
this reason, physical database design and query
processing optimizations are most relevant in
SN systems. In this context, self-tuning database
systems most often rely on what-if analysis to
determine the relevant physical parameters and
organization. For instance, theAutoAdmin project
(Chaudhuri et al. 2007) supported the creation