Abstract-In this paper, we present generic cloud performance models for evaluating Iaas, PaaS, SaaS, and mashup or hybrid clouds. We test clouds with real-life benchmark programs and propose some new performance metrics. Our benchmark experiments are conducted mainly on IaaS cloud platforms over scaleout and scale-up workloads. Cloud benchmarking results are analyzed with the efficiency, elasticity, QoS, productivity, and scalability of cloud performance. Five cloud benchmarks were tested on Amazon IaaS EC2 cloud: namely YCSB, CloudSuite, HiBench, BenchClouds, and TPC-W. To satisfy production services, the choice of scale-up or scale-out solutions should be made primarily by the workload patterns and resources utilization rates required. Scaling-out machine instances have much lower overhead than those experienced in scale-up experiments. However, scaling up is found more cost-effective in sustaining heavier workload. The cloud productivity is greatly attributed to system elasticity, efficiency, QoS and scalability. We find that autoscaling is easy to implement but tends to over provision the resources. Lower resource utilization rate may result from auto-scaling, compared with using scale-out or scale-up strategies. We also demonstrate that the proposed cloud performance models are applicable to evaluate PaaS, SaaS and hybrid clouds as well.
Multi-cloud storage can provide better features such as availability and scalability. Current works use multiple cloud storage providers with erasure coding to achieve certain benefits including fault-tolerance improving or vendor lock-in avoiding. However, these works only use the multi-cloud storage in ad-hoc ways, and none of them considers the optimization issue in general. In fact, the key to optimize the multi-cloud storage is to effectively choose providers and erasure coding parameters. Meanwhile, the data placement should satisfy system or application developers' requirements. As developers often demand various objectives to be optimized simultaneously, such complex requirement optimization cannot be easily fulfilled by ad-hoc ways. This paper presents Triones, a systematic model to formally formulate data placement in multi-cloud storage by using erasure coding. Firstly, Triones addresses the problem of data placement optimization by applying non-linear programming and geometric space abstraction. It could satisfy complex requirements involving multi-objective optimization. Secondly, Triones can effectively balance among different objectives in optimization and is scalable to incorporate new ones. The effectiveness of the model is proved by extensive experiments on multiple cloud storage providers in the real world. For simple requirements, Triones can achieve 50% access latency reduction, compared with the model in µLibCloud. For complex requirements, Triones can improve fault-tolerance level by 2x and reduce access latency and vendor lock-in level by 30%∼70% and 49.85% respectively with about 19.19% more cost, compared with the model only optimizing cost in Scalia.Index Terms-Systematic model, data placement optimization, multi-cloud storage, complex requirements ! 0018-9340 (c)
The increasing popularity of cloud storage services attracts large amounts of companies to store their data in cloud instead of building their own infrastructures. With large amounts of data stored in the cloud, it is expected to provide high availability and fine global access experiences. However, there are still major concerns of the availability of major cloud services, especially in a sparsely connected global network with complicated issues. In this paper, we introduce μLibCloud, a system based on Apache libCloud, aiming to improve the availability and global access experience of clouds, and to tolerate provider failures and outages. μLibCloud works as a library at client side, transparently spreading and collecting data smartly to/from different cloud providers through erasure code. In evaluation, we deployed the system into 7 major cloud providers and run a global benchmarks from 9 locations around the world. The results were compared to the original clouds and a content delivery network. We observed that μLibCloud achieved a higher and more uniformed read availability in most cases, with reasonable estimated extra costs. For example, the read latency of some original providers could be reduced by 50%-70% at different locations.
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