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| Subproblems | Solutions | Literatures | Advantages |
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| Server management | A policy of dynamic allocation of powering servers switches | [7, 10] | Maximizes benefits, improves QoS, and minimizes power consumption |
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| Workloads consolidation | A technique for consolidating workloads | [14] | Achieves energy savings by using fewest servers while reducing SLA violations |
| VM management | An architecture that can administrate itself and a mixed queuing model | [15] | Makes more revenues and meets different requirements of customers and decides the number of VMs for each layer of a virtual application |
| A system to integrate and dynamically redistribute VMs | [17] | Achieves the goal of saving energy through VM integration and provides a high QoS level at the same time |
| A QoS-aware VM scheduling strategy named QVMS | [23] | Effectively manages resources in the network physical system to reduce the energy consumption and improves QoS |
| A VM integration method with several targets | [24] | Saves energy and reduces SLA violations by applying different strategies to different load states of the host |
| Self-management | A technology named STAR | [16] | Reduces SLA violations and improves payment efficiency of cloud services |
| A dynamic resource management system | [18] | Self-manages the resources of cloud infrastructures to provide appropriate QoS and fits the changing workloads dynamically |
| Resource management | A model that can realize the independent allocation of resources | [19] | Obtains the optimal resource configuration, meets the QoS requirements, and provides economical cloud resources |
| A dynamic resource allocation strategy | [20, 21] | Reduces SLA and maximizes revenues and resource utilization on the cloud |
| A mixed queue model | [22] | Reasonably configures the resources in the cloud data center, improves the system performance, reduces the additional cost of using resources, meets the required QoS, and provides virtual resources to each layer of virtual application services |
| Service management | A unified semantic model that can describe cloud service | [12] | Improves the ability of model on service ranking and enriches the language expression |
| A recommendation service strategy | [25] | Emphasizes the influence of time factors on QoS and improves the traditional location-sensitive hash technology to protect users’ privacy |
| A cloud service selection method using fuzzy measure and Choquet integral and a framework based on priority | [9] | Selects service when historical information is insufficient to determine the criteria relationships and weights |
| Three service selection methods that support QoS and can combine multitenant service-based systems | [11] | Achieves three degrees of multitenant maturity, which is more efficient than the traditional single-user approach |
| A fault-tolerant strategy based on multitenant service criticality | [13] | Guarantees the quality of the multitenant-based service system |
| A service-based system supporting keyword search | [8] | Effectively helps system engineers who are not familiar with service-oriented architecture technology to build service-oriented systems |
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