According to Gartner, the market for cloud computing will expand to 623 billion USD by 2030. With this growth, there is increased demand from cloud providers to make the best use of their resources in terms of performance efficiency. Three fairly common approaches to performance analysis include experiment-based performance analysis, discrete event-simulation-based performance analysis, and stochastic model-based performance analysis. However, the stochastic model-based performance analysis is the preferred method due to the lower cost point, as well as timeliness. Sakr and Gaber developed a simplified three-pool cloud architecture-based stochastic model to address some of the common barriers encountered with scaling.
Stochastic Model & Three-Pool Cloud Architecture
Sakr and Gaber proposed a simplified model from Markov’s stochastic model to support scalability and tractability solution at a lower cost. Their model leverages three-pool cloud architecture that has several interacting sub-models, including resource provisioning decision engine, virtual machine sub-models, and pool sub-models. Sakr and Gaber leveraged three-pool cloud architecture which includes the concepts of the hot pool sub-model, warm pool sub-model, and cold pool sub-model. What pool is used depends on how the response time and power consumption are sorted. Each sub-model can be used to represent a machine in the pool. The hot pool sub-model addresses the group needing maximum power with low response times. The warm pool sub-model addresses machines in sleep model that are waiting for the next run. Finally, the cold pool sub-model has machines that are in the off state and have minimum power needs and longer response times. Figure 1 shows Sakr and Gaber’s model of what happens when there is a service request and how the resource provisioning decision engine tries to leverage the various pool sub-models.
The biggest challenge to the model is the potential service request rejection. When the buffer is at capacity or the resource provisioning decision engine cannot find an available machine due to capacity issues, request rejection is possible. However, there is some understanding related to the service request probability that can be helpful. For example, the longer the mean service time, the increased likelihood of higher service requests. Therefore, if the capacity of the machines is increased, the potential for service rejection can decrease.
Additional challenges for the three-pool cloud architecture model to work effectively is that both service requests and the machines have to have the same type. However, virtual provisioning sub-models can be used with different machines where the machines are grouped into classes, and each class is this represented by a pool so that the individual pool is homogeneous.
Development of Applications
The advantage of the Stochastic Model and pool architecture is that the pools can be strategically used to identify performance bottleneck through what-if analysis and planning capacity. For example, that Symbolic Hierarchical Automated Reliability and Performance Evaluator (SHARPE) is a modeling application that can look at performance, reliability, and availability. This application has been installed at over 450 sites and lets users choose different what-if analysis with alternative algorithms depending on their objectives. Also, three-pool architecture can be useful for data recovery efforts in the sense that mission-critical information can be in the hot pool for faster recovery whereas less critical data can be stored in the other sub-pools.
Cloud services are growing exponentially in demand putting increased pressure on finding both timely and cost-effective solutions to address performance issues. The Stochastic Model is desirable because of its’ low cost and timeliness as compared to other models. The Stochastic Model leverages a three-pool cloud architecture with pool sub-models to process service requests. While there are some challenges related to rejecting service requests and heterogeneous machines, the model can be adapted to handle these challenges. Several applications have demonstrated the promise of the pool architecture in strategically managing performance bottlenecks.
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