A dynamically self-configurable service process engine

Abstract

The performance of a process engine is one of the key factors that contribute to the successful deployment of systems, based on a service-oriented architecture. A novel service process engine that can be self-configured dynamically is introduced in the paper. It is based on the Jini platform, and leverages of Jini services to provide key functionalities. It automatically maintains the global performance by performing load balancing and configuring the system structure dynamically. A heuristic algorithm is applied to indicate every client's request with a workload tag after a service process model is designed. Based on workload tags of client requests and the status of available services in the engine, a controller allocates the requests to appropriate services and dynamically reconfigures the engine based on fuzzy control algorithms. Algorithms and the architecture for the engine are discussed in detail; in addition, performance experiments are performed to show the effectiveness and feasibility of the proposed approach.

A DMC Based Dynamically Self-Configuration Service Process Engine

“…The process engine in [6] implemented a load balancing mechanism based on fuzzy theory. And its self-configuration is based on fuzzy control algorithm.…”

“…And its self-configuration is based on fuzzy control algorithm. A shortcoming of [6] is its configuration decision is always lagged behind the real-time performance trend, that is, only when the overall performance is "uncomfortable", will the re-configuration controller act to add or move service nodes. In our improved system, with a well designed sample collector and an in-time updated historical database, we can make the DMC mechanism well trained, and the configuration controller is able to act before a coming performance fluctuation.…”

“…In this case, in [6], we have already implemented a Self-Configurable Service Process Engine (S2PE) based on fuzzy control algorithm [5] , this S2PE implements a fuzzy theory based load balancing and a fuzzy control based self-configuration. However, P2PE in [6] failed to take advantage of an import fact: for a service process engine used in practice, its everyday requests number is, to some extent, a relatively similar sequence.…”

“…However, P2PE in [6] failed to take advantage of an import fact: for a service process engine used in practice, its everyday requests number is, to some extent, a relatively similar sequence. For example, the trading number sequence of a trading system in a day follows some kind of "model line"：busy around opening time and closing time, while relatively not so busy in other time.…”

“…It is built on the Jini platform [7] and employs Jini services to provide functions for a service process engine. The process engine will estimate each client request workload by the algorithm introduced in [6], based on the estimated workload tag for each request and the runtime overall performance, a configuration controller based on dynamic matrix control implement the dynamic configuration of the process engine in order to maintain an optimal and stable overall performance.…”