Adaptive Middleware for Data Replication
J. M. Milan-Franco, R. Jiménez-Peris, M.
Patiño-Martínez, B. Kemme
Dynamically adaptive systems sense their environment and adjust
themselves to accommodate to changes in order to maximize
performance. Depending on the type of change (e.g., modifications
of the load, the type of workload, the available resources, the client
distribution, etc.), different adjustments have to be made.
Coordinating them is already difficult in a centralized system.
Doing so in the currently prevalent component-based distributed systems
is even more challenging. In this paper, we present an adaptive
distributed middleware for data replication that is able to adjust to
changes in the amount of load submitted to the different replicas and
to the type of workload submitted. Its novelty lies in combining
load-balancing techniques with feedback driven adjustments of
multiprogramming levels (number of transactions that are allowed to
execute concurrently). An extensive performance analysis shows that the
proposed adaptive replication solution can provide high throughput,
good scalability, and low response times for changing loads and
workloads with little overhead.
Proc. of the ACM/IFIP/USENIX
Conference on Middleware, Toronto, Canada, October 2004.
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