Serverless Computing has garnered significant interest for executing High-Performance Computing (HPC) applications in recent years, attracting attention for its elastic scalability, reduced entry barriers, and pay-per-use pricing model. Specifically, highly parallel HPC apps can be divided and offloaded to multiple Serverless Functions (SFs) that execute their respective tasks concurrently and, finally, their results are stored/aggregated. While state-of-the-art userside serverless frameworks have attempted to fine-tune task division amongst the SFs to optimize for performance and/or cost, they have either used static task division parameters or have only focused on minimizing the number of SFs through task packing. However, these methods treat the HPC code as a black-box and usually require significant manual intervention to find the optimal task division. Since a significant portion of the HPC applications have a loop structure, in this work, we try to answer the following two questions: (i) Can modifying the loop structure in the HPC code, originally optimized for monolithic (non-serverless) frameworks, enhance performance and reduce costs in a serverless architecture?, and (ii) Can we develop a framework that allows for an efficient transition of monolithic code to serverless, with minimum user input?

Among the variety of applications (apps) being deployed on serverless platforms, apps such as Machine Learning (ML) inference serving can achieve better performance from leveraging accelerators like GPUs. Yet, major serverless providers, despite having GPU-equipped servers, do not offer GPU support for their serverless functions. Given that serverless functions are deployed on various generations of CPUs already, extending this to various (typically more expensive) GPU generations can offer providers a greater range of hardware to serve incoming requests according to the functions and request traffic. Here, providers are faced with the challenge of selecting hardware to reach a well-proportioned trade-off point between cost and performance. While recent works have attempted to address this, they often fail to do so as they overlook optimization opportunities arising from intelligently leveraging existing GPU sharing mechanisms. To address this point, we devise a heterogeneous serverless framework, PALDIA, which uses a prudent Hardware selection policy to acquire capable, cost-effective hardware and perform intelligent request scheduling on it to yield high performance and cost savings. Specifically, our scheduling algorithm employs hybrid spatio-temporal GPU sharing that intelligently trades off job queueing delays and interference to allow the chosen cost-effective hardware to also be highly performant. We extensively evaluate PALDIA using 16 ML inference workloads with real-world traces on a 6 node heterogeneous cluster. Our results show that PALDIA significantly outperforms state-of-the-art works in terms of Service Level Objective (SLO) compliance (up to 13.3% more) and tail latency (up to ∼50% less), with cost savings up to 86%.

With the increased usage of public clouds for hosting applications, it becomes essential to choose the appropriate services from the public cloud offerings in order to achieve satisfactory performance while minimizing deployment expenses. Prior research has demonstrated that combining different services can be more cost-effective than solutions based on a single service type. However, automating the combination of resources for applications composed of large graphs of loosely-connected microservices has not yet been thoroughly explored, especially in the context of microservice-based cloud applications. Motivated by this, targeting microservice-based applications, we propose MicroBlend, an automated framework that mixes Infrastructure-as-a-Service (IaaS) and Function-as-a-Service (FaaS) cloud services in a way that is both cost-effective and performance-efficient. MicroBlend focuses on: (i) providing an automated approach for blending resources that takes microservice dependencies into account, (ii) generating FaaS-ready code using a compiler-based approach, and (iii) suggesting an optimization plan for combining microservices with user annotation. We implement MicroBlend on Amazon Web Services (AWS) and evaluate its performance using real-world traces from three different applications. Our findings demonstrate that by employing automated microservice-to-cloud service assignment, MicroBlend can significantly reduce Service Level Objective (SLO) violations by 9%, compared to traditional VM-based resource procurement schemes. Additionally, MicroBlend can decrease costs by 11%.

With the rapid growth of users adopting public clouds to run their applications, the types of resources procured from the different public cloud resource offerings are critical in simultaneously achieving satisfactory performance and reducing deployment costs. Typically, no one resource type can meet all application requirements, and thus combining different resource offerings is known to considerably reduce the performance-cost problem. However, it is non-trivial to use blended resources, due to the manual overhead of designing and implementing such blended approaches. Specifically, it necessitates rewriting the application code to suit a given resource and scaling it on demand. In order to overcome this manual hurdle, we take the first step by proposing Splice, an automated framework for cost-and performance-aware blending of IaaS and FaaS services. The three major goals of Splice are: (1) while cost-saving opportunities exist from blending resources, we aim to largely automate the blending process for public cloud services through a compiler-driven approach; (2) more specifically, we focus on automated blending of VMs and serverless functions; and (3) for serverless applications which contain multiple chained functions, we unearth the potential choices in determining a portion of the services to be blended cost-efficiently. We implement Splice on Amazon Web Services (AWS) using an Abstract Syntax Tree (AST), and extensively evaluate its effectiveness using several ap-plications with real-world traces. Our experiments demonstrate that, through automated blending, Splice is able to reduce SLO violations by 31 % compared to VM - based resource procurement schemes, while simultaneously minimizing costs by up to 32 %.

Sign language is the primary medium of communication for the majority of the world's population suffering from disabling hearing loss that creates a barrier between the hearing and the hearing-impaired people. In this paper, sign language translation is undertaken for German Sign Language (GSL) characters from a single image by leveraging the technique of 3D object detection. We make use of a three-network architecture that performs segmentation, keypoint localization, and elevation from a two-dimensional plane to the three-dimensional space, from a single RGB image containing the signed gesture. Thirty gestures have been used and the best results were obtained using a combination of pose representation coordinates, joint angles, and pool layer features of AlexNet for classification. The system gives a character error rate of 0.29, a reduction of error rate by 12.12% when compared to the state-of-the-art approach.