Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets

The expansion of Internet of Things (IoT) services and the huge amount of data generated by different sensors signify the importance of cloud computing services such as Storage as a Service more than ever. IoT traffic imposes such extra constraints on the cloud storage service as sensor data preprocessing capability and load-balancing between data centers and servers in each data center. Furthermore, service allocation should be allegiant to the quality of service (QoS). In the current work, an algorithm is proposed that addresses the QoS in storage service allocation. The proposed hybrid multi-objective water cycle and grey wolf optimizer (MWG) considers different QoS objectives (e.g., energy, processing time, transmission time, and load balancing) in both the fog and cloud Layers, which were not addressed altogether. The MATLAB script is used to simulate and implement our algorithms, and services of different servers, e.g., Amazon, Dropbox, Google Drive, etc., are considered. The MWG has 7%, 13%, and 25% improvement, respectively, in comparison with multi-objective water cycle algorithm (MOWCA), k-means based GA (KGA), and non-dominated sorting genetic algorithm (NSGAII) in metric of spacing. Moreover, the MWG has 4%, 4.7%, and 7.3% optimization in metric of quality in comparison to MOWCA, KGA, and NSGAII, respectively. The new hybrid algorithm, MWG, not only yielded to the consideration of three objectives in service selection but also improved the performance compared to the works that considered one or two objective(s). The overall optimization shows that the MWG algorithm has 7.8%, 17%, and 21.6% better performance than MOWCA, KGA, and NSGAII in the obtained best result by considering different objectives, respectively.

Analyzing the Performance of the S3 Object Storage API for HPC Workloads

The line between HPC and Cloud is getting blurry: Performance is still the main driver in HPC, while cloud storage systems are assumed to offer low latency, high throughput, high availability, and scalability. The Simple Storage Service S3 has emerged as the de facto storage API for object storage in the Cloud. This paper seeks to check if the S3 API is already a viable alternative for HPC access patterns in terms of performance or if further performance advancements are necessary. For this purpose: (a) We extend two common HPC I/O benchmarks—the IO500 and MD-Workbench—to quantify the performance of the S3 API. We perform the analysis on the Mistral supercomputer by launching the enhanced benchmarks against different S3 implementations: on-premises (Swift, MinIO) and in the Cloud (Google, IBM…). We find that these implementations do not yet meet the demanding performance and scalability expectations of HPC workloads. (b) We aim to identify the cause for the performance loss by systematically replacing parts of a popular S3 client library with lightweight replacements of lower stack components. The created S3Embedded library is highly scalable and leverages the shared cluster file systems of HPC infrastructure to accommodate arbitrary S3 client applications. Another introduced library, S3remote, uses TCP/IP for communication instead of HTTP; it provides a single local S3 gateway on each node. By broadening the scope of the IO500, this research enables the community to track the performance growth of S3 and encourage sharing best practices for performance optimization. The analysis also proves that there can be a performance convergence—at the storage level—between Cloud and HPC over time by using a high-performance S3 library like S3Embedded.

Efficient Ciphertext-Policy Attribute-Based Encryption Constructions with Outsourced Encryption and Decryption

The invention of the Ciphertext-Policy Attribute-Based Encryption scheme opened a new perspective for realizing attribute-based access control systems without being forced to trust the storage service provider, which is the case in traditional systems where data are sent to the storage service provider in clear and the storage service provider is the party that controls the access to these data. In the Ciphertext-Policy Attribute-Based Encryption model, the data owner encrypts data using an attribute-based access structure before sending them to the storage service, and only users with authorized sets of attributes can successfully decrypt the generated ciphertext. However, Ciphertext-Policy Attribute-Based Encryption schemes employ expensive operations (i.e., bilinear pairings and modular exponentiations) and generate long ciphertexts and secret keys, which makes them hard to implement in real-life applications especially for resource-constrained devices. In this paper, we propose two Ciphertext-Policy Attribute-Based Encryption Key Encapsulation Mechanisms that can be provided as services in the cloud, minimizing the user’s encryption and decryption costs without exposing any sensitive information to the public cloud provider. In the first scheme, the ABE Service Provider is considered fully untrusted. On the other hand, the second scheme requires the ABE Service Provider to be semi-trusted (Honest-but-Curious) and does not collude with illegitimate users. Both schemes are proved to be selectively CPA-secure in the random oracle. The theoretical and experimental performance results show that both our first and second schemes are more efficient than the reviewed outsourced CP-ABE schemes in terms of user-side computation, communication, and storage costs.