Table 2 Comparative analysis of cloud availability and reliability solutions
From: Reliability and high availability in cloud computing environments: a reference roadmap
| Papers’ reference | Main idea | Advantages | Challenges | Evaluation metrics |
|---|---|---|---|---|
| [46] | Investigating the repair policy and system parameters on cloud availability |
Repair policy effectiveness is evaluated Using differential analysis for analyzing parameter sensitivity |
Types of Failures are not considered Limitation on number of physical machines The lack of different types repair facilities in evaluation |
MTTR Steady state availability MTTM Pool size |
| [47] | Storage services availability evaluation using hierarchical models |
Adoption of availability importance index Critical components availability identification | Study case study and assessments are limited to the Eucalyptus platform |
MTTF MTTR File Size MaxClients InService Throughput |
| [32] | Using VM replicas in cloud datacenter to provide high availability | Resource optimization while assuring availability |
VM and application scheduling is not considered in the proposed method Evaluation on a small cloud infrastructure |
Latency OMG DDS QoS Standard Deviation |
| [48] | Applying non-sequential Monte Carlo Simulation to reliability evaluation |
A new cloud computing test-bed were developed A new algorithm for expansion planning were presented | This approach cannot be used for modeling other reliability features such as live VM migration |
Number of failures Number of VM allocations |
| [49] | Using a combination of a stochastic Petri net model and a proposed cloud scoring system |
Considering both cloud consumers and cloud providers in the proposed method Proposing a cloud scoring system |
The proposed cloud scoring system overhead and cost is not considered The user requirements are limited to only cost and energy in this study |
OPEX option Carbon footprint option Overload factor Deployment Distances Relative average utilization |
| [50] | Comparing two fault tolerance techniques according to the cloud consumers’ and providers’ requirements | Considering both cloud consumers’ and cloud providers’ requirements | Failure prediction mechanism is required |
MTBF Electricity Bill Failure prediction accuracy Energy consumption Task completion rate |
| [51] | Amending the current cloud simulators to support HA features | Considering green computing | Limited availability evaluation metrics |
Request per second Average service time Power consumption |
