Table 3 Studies about AI empowered resource management

Resource prediction

[37]

It captures online features while running the job (e.g., job, data, and cluster characters) and tunes the parameters based on estimated resource consumption (e.g., time, CPU, memory) in job-level tuning.

[38]

It proposes AutoClustC, which estimates the costs of resource provisioning and database repartitioning and chooses the lower cost approach to tune the system in order to re-guarantee the performance SLA when a performance violation occurs.

[39]

It proposes a rapid KPI trend prediction framework TPC (Trend Prediction based on Clustering) to guide the operation and maintenance team to adjust cloud resources reasonably and timely.

Resource scheduling

[40]

It proposes SmartSLA， a cost-aware resource management system， to intelligently manage the resources in a shared cloud database system.

[41]

It advocates the cooperation between VM host- and guest-layer schedulers for optimizing resource management and application performance.

[42]

It presents a Cloud VM scheduling algorithm that considers already running VM resource usage over time by analyzing past VM utilization levels to schedule VMs by optimizing performance.

[43]

It designs iBTune to automatically orchestrate the buffer pool tuning for the entire database instances.

Resource control

[44]

It focuses on controlling CPU (central processing unit) usage and memory consumption of a virtual database machine in a data center under a time-varying heavy workload.

[45]

It proposes a Greedy Particle Swarm Optimization (GPSO) search algorithm in the Virtual Design Advisor (VDA) to estimate the cost of database workloads running in virtual machines with varying resource allocation accurately and quickly.

[46]

It designs ResTune to automatically optimize resource utilization without violating SLA constraints on the throughput and latency requirements.

Resource scaling

[47]

It presents CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant cloud computing infrastructures.

[48]

It proposes a model for resource allocation of a data center that includes clusters of hosts. When the utilization of active hosts reaches a predefined threshold value, a new host is added to prevent response time violation, and when host utilization is reduced to a certain threshold, one of the hosts can be deactivated.

[49]

It proposes a Hybrid Auto-Scaler (HAS) to adjust the required resources automatically to the application in demand. HAS deploys the anticipated resources by computing the required capacity. Further, it scales out the resources in accordance as the provisioned resources are insufficient to deal with the current needs.