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?

As demand for cloud computing in bioinformatics increases, various studies have explored options for running large-scale workloads with reduced costs, often leveraging spot instances in multi-region deployments. For example, spot instances offer lower prices but come with the risk of interruption, contrasting with regular (on-demand) instances. However, transitioning to regions with high interruption rates can undermine the benefits of spot instances, adversely affecting performance and cost efficiency. Additionally, regular instances sometimes outperform spot instances based on their specifications. Existing IaaS frameworks focus primarily on cost savings without adequately addressing performance stability in high-interruption regions. To address these challenges, we introduce SpotVerse, a framework designed to optimize cloud resource allocation for bioinformatics workloads, including those within Galaxy - an open-source, web-based platform widely used for managing bioinformatics workflows. SpotVerse efficiently manages long workloads at reduced costs while navigating the complexities of high-interruption regions and strategically selecting between on-demand and spot instances. Our experiments compare SpotVerse with traditional single-region deployments, on-demand instances, and other existing frameworks to evaluate its performance and cost efficiency. Through advanced algorithms for resilient workflows and heuristic resource management, SpotVerse minimizes disruption risks and showcases potential cost savings of up to 52% over traditional single-region deployments.

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 %.

Many data mining workloads are being analyzed in large-scale distributed cloud computing environments which provide nearly infinite resources with diverse hardware configurations. To maintain cost-efficiency in such environments, understanding the characteristics and estimating the overheads of a distributed matrix multiplication task that is a core computation kernel in many machine learning algorithms are essential. This article aims to propose a Matrix Multiplication Performance Estimator on Cloud (MPEC) algorithm. The proposed algorithm predicts the latency incurred when executing distributed matrix multiplication tasks of various input sizes and shapes with diverse instance types and a different number of worker nodes on cloud computing environments. To achieve this goal, we first analyze the characteristics of distributed matrix multiplication tasks. With characteristics generated from qualitative analysis, we propose to apply an ensemble of non-linear regression algorithm to predict the execution time of arbitrary matrix multiplication tasks. Thorough experimental results reveal that the proposed algorithm demonstrates higher accuracy than a state-of-the-art machine learning task performance estimation engine, Ernest, by decreasing the Mean Absolute Percentage Error (MAPE) in half.

Matrix multiplication is an important kernel task in many machine learning algorithms. As the size of input datasets increases, multiple workloads are analyzed in large-scale distributed cloud computing environments. Therefore, understanding the characteristics of a distributed matrix multiplication task is essential for running machine learning jobs in the cloud. Herein, we propose Matrix multiplication Performance Estimator for Cloud computing, a method to predict the latency of matrix multiplication of various sizes and shapes in a distributed cloud computing environment. We first characterize the overhead of a distributed matrix multiplication task and propose features to model the latency of a task with different input types. Using the proposed features, a latency prediction model is developed by applying a data mining algorithm and a parameter optimization step iteratively. In experiments with 236 distinct types of matrix multiplications on diverse cloud instances running Apache Spark, we confirm that the proposed method can model the latency of various types of matrix multiplication tasks effectively and capture the non-linear interactions among the proposed features. A comparison with the state-of-the-art cloud computing performance predictor, Ernest, reveals that the proposed method provides 63% lower Root Mean Square Error (RMSE) for a distributed matrix multiplication latency prediction task and confirms the uniqueness of the distributed matrix multiplication workload.

Cloud computing resources that are equipped with GPU devices are widely used for applications that require extensive parallelism, such as deep learning. When the demand of cloud computing instance is low, the surplus of resources is provided at a lower price in the form of spot instance by AWS EC2. This paper proposes DeepSpotCloud that utilizes GPU-equipped spot instances to run deep learning tasks in a cost efficient and fault-tolerant way. Thorough analysis about spot instance price history logs reveals that GPU spot instances show more dynamic price change pattern than other general types of cloud computing resources. To deal with the price dynamicity of the GPU spot instance, DeepSpotCloud utilizes instances in different regions across continents as a single resource pool. This paper also proposes a task migration heuristic by utilizing a checkpointing mechanism of existing deep learning analysis platform to conduct fast task migration when a running spot instance is interrupted. Extensive experiments using real AWS services prove that the proposed task migration method is effective even in a WAN environment with limited network bandwidth. Comprehensive simulations by replaying AWS EC2 price history logs reveal that DeepSpotCloud can achieve 13% more cost gain than a state-of-the-art interrupt-driven scheduling policy. The prototype of DeepSpotCloud is implemented using various cloud computing services provided by AWS to serve real deep learning tasks.