Search

Improvements in data storage may be constrained by the lower performance of hard disk drives (HDD) and the higher cost per gigabyte of solid-state drives (SSD). To mitigate these issues, hybrid storage architectures have been conceived. Some works evaluate the performance of storage architectures, but energy consumption is usually neglected and not simultaneously evaluated with performance. This paper presents an approach based on generalized stochastic Petri nets (GSPN) for performance and energy consumption evaluation of individual and hybrid storage systems. The proposed models can represent distinct workloads and also estimate throughput, response time and energy consumption of storage systems. Experiments based on industry-standard benchmarks are adopted to demonstrate the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Cloud computing is a model in which resources such as storage, applications, and networking infrastructures can be offered as services over the internet. Cloud applications are becoming even bigger and more complex with a high availability requirement. This paper presents a modeling strategy based on a hierarchical and heterogeneous modeling for cloud infrastructure planning. This modeling strategy allows the selection of cloud infrastructures according to dependability and cost requirements. Additionally, a stochastic model generator for cloud infrastructure planning provides automatic generation of dependability and cost models for representing cloud infrastructures. A case study based on Moodle hosted on a Eucalyptus platform is adopted to demonstrate the feasibility of the proposed solution (modeling strategy and tooling). [ABSTRACT FROM AUTHOR]

Summary: Because of the dependence on Internet‐based services, many efforts have been conceived to mitigate the impact of disasters on service provision. In this context, cloud computing has become an interesting alternative for implementing disaster tolerant services due to its resource on‐demand and pay‐as‐you‐go models. This paper proposes a sensitivity analysis approach to assess the parameters that most impact the availability of cloud data centers, taking into account disaster occurrence, hardware and software failures, and disaster recovery mechanisms for cloud systems. The analysis adopts continuous‐time Markov chains, and the results indicate that disaster issues should not be neglected. Hardware failure rate and time for migration of virtual machines (VMs) are the critical factors pointed out for the system modeled in our analysis. Moreover, the location where data centers are placed has a significant impact on system availability, due to time for migrating VMs from a backup server. This paper proposes a sensitivity analysis approach to assess the parameters that most impact the availability of cloud data centers, taking into account disaster occurrence, hardware and software failures, and disaster recovery mechanisms for cloud systems. The analysis adopts continuous‐time Markov chains, and the results indicate that disaster issues should not be neglected. Hardware failure rate and time for migration of virtual machines are the critical factors pointed out for the system modeled in our analysis. [ABSTRACT FROM AUTHOR]