A price-aware congestion control protocol for cloud services

In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.

Streamline Tracing and Applications in Dual Porosity Dual Permeability Models

Streamline-based methods have proven to be effective for various subsurface flow and transport modeling problems. However, the applications are limited in dual-porosity and dual-permeability (DPDK) system due to the difficulty in describing interactions between matrix and fracture during streamline tracing. In this work, we present a robust streamline tracing algorithm for DPDK models and apply the new algorithm to rate allocation optimization in a waterflood reservoir. In the proposed method, streamlines are traced in both fracture and matrix domains. The inter-fluxes between fracture and matrix are described by switching streamlines from one domain to another using a probability computed based on the inter-fluxes. The approach is fundamentally similar to the existing streamline tracing technique and can be utilized in streamline-assisted applications, such as flow diagnostics, history matching, and production optimization. The proposed method is benchmarked with a finite-volume based approach where grid-based time-of-flight was obtained by solving the stationary transport equation. We first validated our method using simple examples. Visual time-of-flight comparisons as well as tracer concentration and allocation factors at wells show good agreement. Next, we applied the proposed method to field scale models to demonstrate the robustness. The results show that our method offers reduced numerical artifacts and better represents reservoir heterogeneity and well connectivity with sub-grid resolutions. The proposed method is then used for rate allocation optimization in DPDK models. A streamline-based gradient free algorithm is used to optimize net present value by adjusting both injection and production well rates under operational constraints. The results show that the optimized schedule offers significant improvement in recovery factor, net present value, and sweep efficiency compared to the base scenario using equal rate injection and production. The optimization algorithm is computationally efficient as it requires only a few forward reservoir simulations.

Automation & Digitalization Implementation – A New Normal in Oil & Gas Industry: A Success Story

Nowadays automation and digitalization in oil & gas industry have become a new normal practice to replace traditional workflows. The implementation of automation and digitalization is driven by the need to automate the repetitive and low-cognitive tasks, so it allows engineers to spend more time on high-cognitive and high-level analytical evaluations or studies, and to finally lead up to smarter decisions. One of the solutions is by developing and implementing "fit for purpose" automation tools which consist of various data analytics inside the tools. Saka Energi Indonesia, as the operator of Pangkah PSC, has developed and implemented automation and digitalization in Ujung Pangkah field. Located in northern side of East Java, the field's reservoir consists of multi-layered carbonate oil and gas zone, which is being produced through horizontal and directional wells. Solutions of automation and digitalization have been developed for the Ujung Pangkah field to minimize loss opportunity, increase oil production and reduce the field decline rate. With some collaboration efforts from Subsurface, Operation and IT Department Team, some automation tools have been developed and implemented in Ujung Pangkah Field, which are as follows: Exception Based Surveillance (EBS) tool: An automation tool to identify real-time well problems & opportunities. Auto Gas Lift Rate Allocation (GALAA) tool: An optimization tool to automate gas lift rate allocation. SAKA Well Opportunity, Register, Define and Select (SWORDS): An automation tool to evaluate well opportunity portfolio. Well Model Update Automation: A tool to update well model automatically for every individual well. By implementing the automation solutions, various repetitive tasks can be completed significantly faster and more efficiently. Saka engineers have more time to perform high-cognitive analytical evaluations on other technical areas. Ujung Pangkah field oil production decline rate has been successfully decreased from 21%/year to 8.6%/year after the automation solutions have been implemented in 2018. Ujung Pangkah success story of automation & digitalization implementation will be used as a reference for managing other Saka assets in different fields. The new automation solutions are a faster and a more efficient way of optimizing existing field production and it will give positive impacts exponentially with increasing well numbers.

A Trellis Based Temporal Rate Allocation and Virtual Reference Frames for High Efficiency Video Coding

The High Efficiency Video Coding (HEVC) standard has now become the most popular video coding solution for video conferencing, broadcasting, and streaming. However, its compression performance is still a critical issue for adopting a large number of emerging video applications with higher spatial and temporal resolutions. To advance the current HEVC performance, we propose an efficient temporal rate allocation solution. The proposed method adaptively allocates the compression bitrate for each coded picture in a group of pictures by using a trellis-based dynamic programming approach. To achieve this task, we trained the trellis-based quantization parameter for each frame in a group of pictures considering the temporal layer position. We further improved coding efficiency by incorporating our proposed framework with other inter prediction methods such as a virtual reference frame. Experiments showed around 2% and 5% bitrate savings with our trellis-based rate allocation method with and without a virtual reference frame compared to the conventional HEVC standard, respectively.

A Price-Aware Congestion Control Protocol for Cloud Services

In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.