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.

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This paper presents a new cloudlet mesh architecture for security enforcement to establish trusted mobile cloud computing. The cloudlet mesh is WiFi-or mobile-connected to the Internet. This security framework establishes a cybertrust shield to fight against intrusions to distance clouds, prevent spam/virus/worm attacks on mobile cloud resources, and stop unauthorized access of shared datasets in offloading the cloud. We have specified a sequence of authentication, authorization, and encryption protocols for securing communications among mobile devices, cloudlet servers, and distance clouds. Some analytical and experimental results prove the effectiveness of this new security infrastructure to safeguard mobile cloud services.

An elastic cloud provisions machine instances upon user demand. Auto-scaling, scale-out, scale-up, or any mixture techniques are used to reconfigure the user cluster as workload changes. We evaluate three scaling strategies to upgrade the performance, efficiency and productivity of elastic clouds like EC2, Rackspace, etc. We developed new performance models and run the HiBench benchmark to test Hadoop performance on various EC2 configurations.The strengths and shortcomings of three scaling strategies are revealed in our HiBench experiments: (1). Scale-out overhead is shown lower than that experienced in scale-up or mixed scaling clouds. Scale-out to a larger cluster of small nodes demonstrated high scalability. (2). Scaling up and mixed scaling have high performance in using smaller clusters with a few powerful machine instances. (3). With a mixed scaling mode, the cloud productivity is shown upgradable with higher flexibility in applications with performance/cost tradeoffs.

In this paper, we investigate the optimal location of electric vehicle (EV) charging stations. As one of the crucial infrastructures of EV, electric charging stations must be widely deployed to meet the growing needs of EV. In this study, we propose a locating method of charging station when considering economics, capacity, coverage and convenience. In order to solve the locating problem, an optimization model for charging stations location is established first, which minimizes the investment cost and transportation cost, meanwhile, the constraints of capacity, coverage and convenience should be satisfied simultaneously. Then, an improved genetic algorithm (GA) is proposed to solve the optimization problem. The simulation results indicate that the proposed locating method is effective and practical.

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This paper discusses about the challenges, advantages and shortcomings of existing solutions in data security and privacy in public cloud computing. As in cloud computing, oceans of data will be stored. Data stored in public cloud would face both outside attacks and inside attacks since public cloud provider themselves are untrusted. Conventional encryption could be used for storage, however most data in cloud needs further computation. Decryption before computation will cause large overheads for data operation and lots of inconvenience. Thus, efficient methods to protect data security as well as privacy for large amount of data in cloud are necessary.In the paper, different mechanisms to protect data security and privacy in public cloud are discussed. A data security and privacy enabled multi-cloud architecture is proposed.

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