Abstract-Cloud providers often choose to operate datacenters over a large geographic span, in order that users may be served by resources in their proximity. Due to time and spatial diversities in utility prices and operational costs, different datacenters typically have disparate charges for the same services. Cloud users are free to choose the datacenters to run their jobs, based on a joint consideration of monetary charges and quality of service. A fundamental problem with significant economic implications is how the cloud should price its datacenter resources at different locations, such that its overall profit is maximized. The challenge escalates when dynamic resource pricing is allowed and long-term profit maximization is pursued. We design an efficient online algorithm for dynamic pricing of VM resources across datacenters in a geo-distributed cloud, together with job scheduling and server provisioning in each datacenter, to maximize the profit of the cloud provider over a long run. Theoretical analysis shows that our algorithm can schedule jobs within their respective deadlines, while achieving a time-average overall profit closely approaching the offline maximum, which is computed by assuming that perfect information on future job arrivals are freely available. Empirical studies further verify the efficacy of our online profit maximizing algorithm.

No abstract

No abstract

Emerging computing paradigm edge computing expects to store and process data at the network edge with reduced latency and improved network bandwidth. To the best of our knowledge, key performance issues such as coding performance of erasure-coded storage systems haven't been investigated for edge computing. In this paper, we present an erasure-coded storage system for edge computing. Unlike the data center and cloud storage systems, it employs edge devices to perform encoding and decoding operations, which can be a performance bottleneck of the whole storage system due to limited computing power. Hence, we present a comprehensive study of the performance of erasure coding to see if it can match the network performance of 5G and Wi-Fi 6 at the network edge. We use the popular edge device Jetson Nano and two state-of-the-art coding libraries: Jerasure and G-CRS. Our evaluation results reveal unsatisfied performance for Jerasure and high variance for G-CRS. To obtain better and stable performance, we accelerate erasure code with OpenMP on a multi-core CPU. Our work demonstrates our acceleration can bring stable performance and match the network bandwidth of 5G and Wi-Fi 6 for some commonly used cases. Besides, our work offers a better understanding of erasure-coded storage systems for edge computing and can be served as a reference to any further optimization for such kind of systems at the network edge.INDEX TERMS Erasure-coded storage system, edge computing, erasure coding, jetson nano.