Expedited Data Transfers for Serverless Clouds

Serverless computing has emerged as a popular cloud deployment paradigm. In serverless, the developers implement their application as a set of chained functions that form a workflow in which functions invoke each other. The cloud providers are responsible for automatically scaling the number of instances for each function on demand and forwarding the requests in a workflow to the appropriate function instance. Problematically, today's serverless clouds lack efficient support for cross-function data transfers in a workflow, preventing the efficient execution of data-intensive serverless applications. In production clouds, functions transmit intermediate, i.e., ephemeral, data to other functions either as part of invocation HTTP requests (i.e., inline) or via third-party services, such as AWS S3 storage or AWS ElastiCache in-memory cache. The former approach is restricted to small transfer sizes, while the latter supports arbitrary transfers but suffers from performance and cost overheads. This work introduces Expedited Data Transfers (XDT), an API-preserving high-performance data communication method for serverless that enables direct function-to-function transfers. With XDT, a trusted component of the sender function buffers the payload in its memory and sends a secure reference to the receiver, which is picked by the load balancer and autoscaler based on the current load. Using the reference, the receiver instance pulls the transmitted data directly from the sender's memory. XDT is natively compatible with existing autoscaling infrastructure, preserves function invocation semantics, is secure, and avoids the cost and performance overheads of using an intermediate service for data transfers. We prototype our system in vHive/Knative deployed on a cluster of AWS EC2 nodes, showing that XDT improves latency, bandwidth, and cost over AWS S3 and ElasticCache.Comment: latest versio

DFlow: Efficient Dataflow-based Invocation Workflow Execution for Function-as-a-Service

The Serverless Computing is becoming increasingly popular due to its ease of use and fine-grained billing. These features make it appealing for stateful application or serverless workflow. However, current serverless workflow systems utilize a controlflow-based invocation pattern to invoke functions. In this execution pattern, the function invocation depends on the state of the function. A function can only begin executing once all its precursor functions have completed. As a result, this pattern may potentially lead to longer end-to-end execution time. We design and implement the DFlow, a novel dataflow-based serverless workflow system that achieves high performance for serverless workflow. DFlow introduces a distributed scheduler (DScheduler) by using the dataflow-based invocation pattern to invoke functions. In this pattern, the function invocation depends on the data dependency between functions. The function can start to execute even its precursor functions are still running. DFlow further features a distributed store (DStore) that utilizes effective fine-grained optimization techniques to eliminate function interaction, thereby enabling efficient data exchange. With the support of DScheduler and DStore, DFlow can achieving an average improvement of 60% over CFlow, 40% over FaaSFlow, 25% over FaasFlowRedis, and 40% over KNIX on 99%-ile latency respectively. Further, it can improve network bandwidth utilization by 2x-4x over CFlow and 1.5x-3x over FaaSFlow, FaaSFlowRedis and KNIX, respectively. DFlow effectively reduces the cold startup latency, achieving an average improvement of 5.6x over CFlow and 1.1x over FaaSFlowComment: 22 pages, 13 figure

GeoGauss: Strongly Consistent and Light-Coordinated OLTP for Geo-Replicated SQL Database

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark

Cost and Latency Optimized Edge Computing Platform

Latency-critical applications, e.g., automated and assisted driving services, can now be deployed in fog or edge computing environments, offloading energy-consuming tasks from end devices. Besides the proximity, though, the edge computing platform must provide the necessary operation techniques in order to avoid added delays by all means. In this paper, we propose an integrated edge platform that comprises orchestration methods with such objectives, in terms of handling the deployment of both functions and data. We show how the integration of the function orchestration solution with the adaptive data placement of a distributed key–value store can lead to decreased end-to-end latency even when the mobility of end devices creates a dynamic set of requirements. Along with the necessary monitoring features, the proposed edge platform is capable of serving the nomad users of novel applications with low latency requirements. We showcase this capability in several scenarios, in which we articulate the end-to-end latency performance of our platform by comparing delay measurements with the benchmark of a Redis-based setup lacking the adaptive nature of data orchestration. Our results prove that the stringent delay requisites necessitate the close integration that we present in this paper: functions and data must be orchestrated in sync in order to fully exploit the potential that the proximity of edge resources enables

FSRM-STS: Cross-dataset pedestrian retrieval based on a four-stage retrieval model with Selection–Translation–Selection

Pedestrian retrieval is widely used in intelligent video surveillance and is closely related to people’s lives. Although pedestrian retrieval from a single dataset has improved in recent years, obstacles such as a lack of sample data, domain gaps within and between datasets (arising from factors such as variation in lighting conditions, resolution, season and background etc.), reduce the generalizability of existing models. Factors such as these can act as barriers to the practical use of this technology. Cross-dataset learning is a way to obtain high-quality images from source datasets and can assist the learning of target datasets, thus helping to address the above problem. Existing studies of cross-dataset learning directly apply translated images from source datasets to target datasets, and seldom consider systematic strategies for further improving the quality of the translated images. There is therefore room for improvement in cross-dataset learning. This paper proposes a four-stage retrieval model based on Selection–Translation–Selection (FSRM-STS), to help address this problem. In the first stage of the model, images in pedestrian retrieval datasets are semantically segmented to provide information for image-translation. In the second stage, STS is proposed, based on four strategies to obtain high quality translation results from all source datasets and to generate auxiliary datasets. In the third stage, a pedestrian feature extraction model is proposed, based on both the auxiliary and target datasets. This converts each image in target datasets into an n-dimensional vector. In the final stage, the extracted image vectors are used for cross-dataset pedestrian retrieval. As the translation quality is improved, FSRM-STS achieves promising results for the cross-dataset pedestrian retrieval. Experimental results on four benchmark datasets Market-1501, DukeMTMC-reID, CUHK03 and VIPeR show the effectiveness of the proposed model. Finally, the use of parallel computing for accelerating the training speed and for realizing online applications is also discussed. A primary demo based on cloud computing is designed to verify the engineering solution in the future