Two-stage service system load forecast and balancing method integrating service forecast and real-time load

Abstract

The invention discloses a two-stage service system load forecast and balancing method integrating service forecast and real-time load, aiming to solve the limitation that the software service balance and service migration rely solely on the system load index. The traditional load balance depends on the short-term even or real-time system load index, which is substantially an internal reason for the representation of the software service load and the overloaded load; and an external factor is due to the increase of the traffic loaded by the software service, while the external factor affects the load of the service system by the internal reason; meanwhile, since the information reflected by the real-time load index cannot be ahead of time, and the frequent index changes easily brings about the problems of short balancing processing time and shaking service migration and the like. The method provided by the invention proceeds from the service forecast, and can be used for obtaining a possible variation trend of the service system load according to the rise and fall of the service bearing capacity, making an accurate forecast based on a probabilistic algorithm, combining with the real-time load index of the system and comprehensively making a balancing decision for optimized deployment and dynamic migration of the software service.

Description

technical field [0001] The invention relates to a software service system load forecasting method, in particular to a two-stage load forecasting and balancing method that integrates business forecasting and real-time load. Background technique [0002] Service computing has brought about major changes in software architecture. Typical representatives of service-oriented architecture (SOA) and software-as-a-service (SaaS) have fundamentally changed the existence, release, operation, and maintenance of software systems. Promote the development of software architecture and its theoretical technology. SaaS application systems generally adopt a multi-tenant model. While meeting the performance indicators of each tenant, such as continuous service, service throughput, and adapting to fluctuations in service capabilities, as a service provider, through the optimization of the service system deployment, improving its overall resource utilization and reducing costs. This requires t...

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Problems solved by technology

[0011] In order to solve the problem of service system load forecasting and balancing, the present invention proposes a two-stage load forecasting and balancing method that integrates business forecasting and real-time load for the limitation of relying solely on real-time system load indicators in software service balancing and service migration

Traditional load balancing relies on short-term or even real-time system load indicators. The essence is the appearance of software service load and the internal cause of load overload. The external cause is due to the increase in business volume carried by software services. External factors affect the load of the service system through internal factors; At the same time, since the information reflected by real-time load indicators cannot be ahead of time, and the indicators change frequently, it is easy to cause problems such as short balance processing time and service migration jitter.

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