Function-as-a-service (FaaS) is a popular serverless computing paradigm for event-driven functions that elastically scale on public clouds. FaaS workflows (e.g.,AWS Step FunctionsandAzure Durable Functions), are composed from FaaS functions (e.g., AWS Lambda and Azure Functions) to build practical applications. But, the complex interactions between functions in the workflow and limited visibility into the internals of proprietary FaaS platforms are major impediments to analyzing a FaaS workflow's performance. While several works characterize FaaS platforms to derive such insights, or offer FaaS Workflow benchmarks, there is a lack of a principled of FaaS workflow platforms, which have unique scaling, performance and costing behavior influenced by the platform design, dataflow and workloads. In this article, we perform extensive evaluations of three popular FaaS workflow platforms from AWS and Azure, running 25 micro-benchmark and application workflows over$139k$invocations. Our detailed analysis confirms some conventional wisdom but also uncovers unique insights on the function execution, workflow orchestration, inter-function interactions, cold-start scaling and monetary costs. Our observations help developers better configure and program these platforms, set performance and scalability expectations, and identify research gaps on enhancing the platforms.

Functions-as-a-Service (FaaS) is a widely used serverless computing abstraction that helps developers build applications using event-driven, stateless functions that execute on the cloud. Commercial FaaS platforms such as AWS Lambda and Azure Functions offer elastic auto-scaling and invocation-level billing to ease operations. Applications are often composed as a dataflow of FaaS functions that are orchestrated by FaaS workflow platforms such as AWS Step Functions or Azure Durable Functions. However, the proprietary nature of FaaS platforms on public clouds means that their internals are less understood. While benchmarks to characterize FaaS platforms exist, none are available for a principled evaluation of FaaS workflow platforms. Further, they are less configurable and often limited to simple workloads and a single cloud provider. We address this limitation by proposing XFBench, an end-to-end automated benchmarking framework for FaaS workflows that works across clouds, and an accompanying function, workflow, and workload suite. The user provides a generic definition of the workflow and workload for benchmarking, and XFBench automatically deploys the workflows across multiple cloud platforms, generates client requests, and profiles the execution. We validate XFBench with realistic workflows and workloads on AWS and Azure platforms in different global regions to offer early insights into understanding the inter-function communication, function execution time, and cold start scaling.

The healthcare industry, through digitization, is trying to achieve interoperability, but has not been able to achieve complete Health Information Exchange (HIE). One of the major challenges in achieving this is the inability to accurately match patient data. Mismatching of patient records can lead to improper treatment which can prove to be fatal. Also, the presence of duplicate overheads has caused inaccessibility to crucial information in the time of need. Existing solutions to patient matching are both time-consuming and non-scalable. This paper proposes a framework, namely, Electronic Patient Matching System (EPMS), which attempts to overcome these barriers while achieving a good accuracy in matching patient records. The framework encodes the patient records using variational autoencoder and amalgamates them by performing locality sensitive hashing on an Apache spark cluster. This makes the process faster and highly scalable. Furthermore, a fuzzy matching of the records in each block is performed using Levenshtein distances to identify the duplicate patient records. Experimental investigations were performed on a synthetically generated dataset consisting of 44555 patient records. The proposed framework achieved a matching accuracy of 81.15% on this dataset.

In a cloud storage, while deduplication enables optimal storage space utilization, it also incurs a substantial overhead in maintaining the metadata, namely, the Fingerprint Index and the File Recipe. As this metadata is huge, it has to be stored in the disk which causes considerable read-latency in Deduplicated Cloud Storage (DCS). In order to improve this read-latency, it would be highly beneficial to prefetch the relevant fingerprints in a cache. Many existing research solutions have utilized either the spatial locality or the similarity among the files to prefetch the relevant fingerprints. However, the DCS that has been designed and implemented in this paper is intended to cater to non-backup workloads that do not exhibit significant spatial locality or similarity among the files. Hence, this paper proposes a suitable alternative prefetching approach that mines the pattern of client read accesses to find the most frequently accessed files. The proposed prefetching approach has been implemented and incorporated in the DCS. The experimental investigations indicate that the proposed prefetching approach improves the cache hit rates by 140% and increases the read throughput by 88% when compared with the Extreme Binning approach (Bhagwat, D., Eshghi, K. Long, D.D.E. and Lillibridge, M. (2009) Extreme Binning: Scalable, Parallel Deduplication for Chunk-based File Backup. Proc. MASCOTS'09, London, UK, September 21–23, pp. 1–9. IEEE) while incurring only a marginal computational overhead of 1.7 s.

Web service composition, that recursively constructs a composite web service out of the existing services based on a business workflow has been acknowledged as a promising approach to meet the user demands, whenever a single service alone cannot fulfil the needs. In view of frequent failures in the internet environment where the composed service is executed, reliability of the composed service must be ensured. The reliability is determined by the behavioral or transactional properties of component services. The component services for each activity of the workflow must be selected based on their behavior so that their execution results in a consistent termination. Service selection must happen at run-time in order to consider the services available in a service registry at the time of execution. Towards this need, a dynamic transaction aware web service selection approach is proposed in this paper. Further, whenever user requirements change, a long running transaction must be interrupted and cancelled which is not addressed by any of the existing works. Hence, service cancellability property is proposed in this paper and incorporated in the dynamic selection approach. The overhead of the proposed run-time selection approach is assessed and the impact of increased services on its performance is also measured.

Software size estimation at the early analysis phase of software development lifecycle is crucial for predicting the associated effort and cost. Analysis phase captures the functionality addressed in the software to be developed in object-oriented software development life-cycle. Unified modeling language captures the functionality of the software at the analysis phase based on use case model. This paper proposes a new method named as use case model function point to estimate the size of the object-oriented software at the analysis phase itself. While this approach is based on use case model, it also adapts the function point analysis technique to use case model. The various features such as actors, use cases, relationship, external reference, flows, and messages are extracted from use case model. Eleven rules have been derived as guidelines to identify the use case model components. The function point analysis components are appropriately mapped to use case model components and the complexity based on the weightage is specified to calculate use case model function point. This proposed size estimation approach has been evaluated with the object-oriented software developed in our software engineering laboratory to assess its ability to predict the developmental size. The results are empirically analysed based on statistical correlation for substantiating the proposed estimation method.

Software architecture plays a significant role in determining the quality of a software system. It exposes important system properties for consideration and analysis. Performance related properties are frequently of interest in determining the acceptability of a given software design. This paper focuses mainly on developing an architectural model for applications that use service oriented architecture (SOA). This enables predicting the performance of the application even before it is completely developed. The performance characteristics of the components and connectors are modeled using queuing network model. This approach facilitates the performance prediction of service oriented applications. Further, it also helps in identification of various bottlenecks. A prototype service oriented application has been implemented and the actual performance is measured. This is compared against the predicted performance in order to analyze the accuracy of the prediction.