# Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression
## 2 Related Work  
At present, research on streaming big data has continuously expanded and deepened on the international plane, and research on real-time prediction of stream data load and elastic resource allocation has attracted people's attention. For instance, ([PERSON] at al [6]; [PERSON] et al. [7]; [PERSON] et al. [8]) proposed some resource distribution strategies related to \"delay detection\", but they were not considering any other metric. ([PERSON] et al. 2013) [9] showed that trigger elastic adjustments based on CPU utilization, memory, or network resource setting threshold rules. Unlike the timing of the \"delay detection\" strategy, the \"pre-adjustment\" strategy is based on load forecasting to adjust resources ahead of time and can improve the efficiency of resource distribution. [PERSON] et al. [10] proposed a cloud resource allocation framework, where the global layer uses deep reinforcement learning to deal with complex control problems, and local layer uses LSTM to predict workloads. This achieved the best tradeoff between delay, power consumption and energy consumption in a cloud server cluster. [PERSON] et al. [11] proposed a dynamic resource scheduling system, which includes the he performance model of queuing theory, and can dynamically configure and schedule the real-time response of cloud resources to achieve the best resource consumption. [PERSON] et al. [12] used time series analysis to predict the input data of the operation and calculates the parallelism of operation based on queuing theory. [PERSON] et al. [13] considered the volatility of resource performance and used a heuristic resource adjustment method to maintain the throughput of the application at the minimum resource cost. [PERSON] et al. [14] proposed a method to predict the stream data load and performance, which adaptively plans resource distribution to limit throughput while satisfying resource adjustment costs. [PERSON] et al. [15] attempted to anticipate the micro-architecture-level interference by using an offline profiling phase. [PERSON] et al. [16] proposed an effective metric to predict the performance of applications running in a NUMA system. Such a metric can be leveraged to design a resource allocation that is aware of contention among shared resources. However, obtaining such an interference signature through profiling might not be feasible in every practical case, as the interference attributes of applications could change over the run-time. [PERSON] et al. [17] integrated fault-tolerance and dynamic resource managements to avoid waste from excessive processing delay. [PERSON] et al. [18] introduced Chrono Stream, which provides vertical and horizontal elasticity. [PERSON] [19] proposed a multi-input, multi-output (MIMO) resource controller that automatically adapts to dynamic changes in a shared infrastructure. Such models try to estimate the complex relationship between the application performance and the resource allocation and adjust the embedded model by measuring the clients' response time. [PERSON] et al. [20] presented an approach based on monetary cost model for cloud resource provisioning for Spark with sample running, which inspires us to leverage the historical information of previous batches in batched streaming system to make resource allocation decision in our design. [PERSON] et al. [21] dynamically increased or decreased the computing capability of each node to enhance locality-aware task scheduling with dynamic virtual CPU number configurations. Recently, some new algorithms for solving different problems of various fields have appeared in the artificial intelligence and reputable Operations Research field. [PERSON] et al. [22]suggested a Pareto-based algorithm, which is a Multi-objective Hybrid Particle Swarm Optimization metaheuristic for the design of an effective decarbonized supply chain in mining. A novel heuristic algorithm, which accounts for the truck service priority and the truck service order restrictions, was proposed for initializing the chromosomes and population [23].