Serverless Environments Research Articles

ServlessSimPro: A comprehensive serverless simulation platform

Serverless computing represents an emerging paradigm within cloud computing, characterized by the fundamental concept of enabling developers to run applications without the need for concerns related to the management of underlying servers. Although there are several mature serverless computing platforms currently exist, there is limited availability of open-source simulation platforms that can accurately simulate the characteristics of serverless environments and provide a free and convenient tool for researchers to conduct investigations. Furthermore, existing simulation platforms do not provide comprehensive interfaces for scheduling strategies and do not provide the diverse monitoring metrics required by researchers. In response to this gap, we have developed the ServlessSimPro simulation platform. This platform offers the most comprehensive set of scheduling algorithms, diverse evaluation metrics, and extensive interfaces and parameters among all existing serverless simulators. The experimental results demonstrate that the scheduling algorithm of the simulator can effectively reduce latency, enhance resource utilization, and decrease energy consumption.

Optimizing Serverless Function Orchestration for Complex Workflows in Cloud Platforms

Purpose: This paper aims to investigate ways of increasing the efficiency of various workloads of serverless function orchestration in cloud systems while decreasing latency and optimizing resources. Materials and Methods: The report suggests adaptive learning systems and a feedback loop framework for managing the orchestrating serverless environment. Findings: It has also been affirmed that case studies help validate the framework rather than presenting the practical implications of the proposed techniques. Implications to Theory, Practice and Policy: The paper recommends focusing on managing performance, dependencies, and expenses in serverless function orchestration by employing adaptive learning systems and feedback loop frameworks.

Word frequency statistics based on MapReduce on serverless platforms

This paper investigates the application of serverless computing in conjunction with the MapReduce framework, particularly in machine learning (ML) tasks. The MapReduce programming model has been widely used to process large-scale datasets by simplifying parallel and distributed data processing. This study explores how the combination of these two technologies can provide more efficient and cost-effective ML solutions. Through a detailed analysis of serverless environments and the MapReduce framework, this paper shows how the combination can advance the fields of cloud computing and machine learning. The experimental part includes the implementation of Map-Reduce model on a serverless platform, exploring the impact of different parameter settings on performance and improving efficiency by optimizing the data processing flow. In addition, the paper analyzes the use of memory and CPU resources and derives the relationship between dataset size, memory consumption and processor configuration and execution time. Through these experiments and analyses, this paper provides an empirical basis and theoretical support for the optimization of cloud computing frameworks.

StarShip: Mitigating I/O Bottlenecks in Serverless Computing for Scientific Workflows

This work highlights the significance of I/O bottlenecks that data-intensive HPC workflows face in serverless environments - an issue that has been largely overlooked by prior works. We propose StarShip, a framework that leverages different storage options and multi-tier functions to reduce I/O overhead by co-optimizing for service time and service cost. StarShip leverages the Levenberg-Marquardt optimization to find an effective solution in a large, complex search space. It outperforms existing methods with a 45% improvement in service time and a 37.6% reduction in service cost.

Data pipeline approaches in serverless computing: a taxonomy, review, and research trends

Serverless computing has gained significant popularity due to its scalability, cost-effectiveness, and ease of deployment. With the exponential growth of data, organizations face the challenge of efficiently processing and analyzing vast amounts of data in a serverless environment. Data pipelines play a crucial role in managing and transforming data within serverless architectures. This paper provides a taxonomy of data pipeline approaches in serverless computing. Classification is based on architectural features, data processing techniques, and workflow orchestration mechanisms, these approaches are categorized into three primary methods: heuristic-based approach, Machine learning-based approach, and framework-based approach. Furthermore, a systematic review of existing data pipeline frameworks and tools is provided, encompassing their strengths, limitations, and real-world use cases. The advantages and disadvantages of each approach, also the challenges and performance metrics that influence their effectuality have been examined. Every data pipeline approach has certain advantages and disadvantages, whether it is framework-based, heuristic-based, or machine learning-based. Each approach is suitable for specific use cases. Hence, it is crucial assess the trade-offs between complexity, performance, cost, and scalability, while selecting a data pipeline approach. In the end, the paper highlights a number of open issues and future investigations directions for data pipeline in the serverless computing, which involve scalability, fault tolerance, data real time processing, data workflow orchestration, function state management with performance and cost in the serverless computing environments.

Building Communication Efficient Asynchronous Peer-to-Peer Federated LLMs with Blockchain

Large language models (LLM) have gathered attention with the advent of ChatGPT. However, developing personalized LLM models faces challenges in real-world applications due to data scarcity and privacy concerns. Federated learning addresses these issues, providing collaborative training while preserving the client’s data. Although it has made significant progress, federated learning still faces ongoing challenges, such as communication efficiency, heterogeneous data, and privacy-preserving methods. This paper presents a novel, fully decentralized federated learning framework for LLMs to address these challenges. We utilize different blockchain-federated LLM (BC-FL) algorithms, effectively balancing the trade-off between latency and accuracy in a decentralized-federated learning environment. Additionally, we address the challenge of communication overhead in peer-to-peer networks by optimizing the path for weight transfer and mitigating node anomalies. We conducted experiments to evaluate memory usage and latency in server and serverless environments. Our results demonstrate a decrease in latency by 5X and a 13% increase in accuracy for serverless cases. Comparisons between synchronous and asynchronous scenarios revealed a 76% reduction in information passing time for the latter. The PageRank method is most efficient in eliminating anomalous nodes for better performance of the global federated LLM model. The code is available on GitHub (https://github.com/Sreebhargavibalijaa/Federated_finetuning_LLM-s_p2p_environment)

Smart Healthcare System in Server-Less Environment: Concepts, Architecture, Challenges, Future Directions

Smart Healthcare System in Server-Less Environment: Concepts, Architecture, Challenges, Future Directions

The Promise of Serverless Computing within Peer-to-Peer Architectures for Distributed ML Training

My thesis focuses on the integration of serverless computing with Peer to Peer (P2P) architectures in distributed Machine Learning (ML). This research aims to harness the decentralized, resilient nature of P2P systems, combined with the scalability and automation of serverless platforms. We explore using databases not just for communication but also for in-database model updates and gradient averaging, addressing the challenges of statelessness in serverless environments.

Enabling massively parallel, ad hoc exploration of the design space for simulation models within a serverless environment

This paper presents a massively parallel, cloud-computing framework for the ad hoc evaluation of discrete-event simulation (DES) models to enable broad exploration of the design space for model parameters. Parallel evaluation is enabled through use of a serverless computing environment allowing thousands of simultaneous experiments, on demand, without the need to explicitly provision or manages hardware. A standard Simulation Evaluation application programming interface (API) was designed for evaluating simulation functions that enables language independence between client application and simulation model, encouraging reuse of simulation models for multiple purposes (what-if analysis, ranking and selection, sensitivity analysis, or optimization). Extensions to the Java Simulation Library (JSL)27 enable rapid deployment of models built with the JSL as parameterized serverless functions implementing the Simulation Evaluation API. New Java packages facilitate the calling of any serverless functions that implement the Simulation Evaluation API.

StarShip: Mitigating I/O Bottlenecks in Serverless Computing for Scientific Workflows

This work highlights the significance of I/O bottlenecks that data-intensive HPC workflows face in serverless environments - an issue that has been largely overlooked by prior works. To address this challenge, we propose a novel framework, StarShip, which effectively addresses I/O bottlenecks for HPC workflows executing in serverless environments by leveraging different storage options and multi-tier functions, co-optimizing for service time and service cost. StarShip exploits the Levenberg-Marquardt optimization method to find an effective solution in a large, complex search space. StarShip achieves significantly better performance and cost compared to competing techniques, improving service time by 45% and service cost by 37.6% on average over state-of-the-art solutions.

FASE: fast deployment for dependent applications in serverless environments

FASE: fast deployment for dependent applications in serverless environments

Execution of Serverless Functions Lambda in AWS Serverless Environment

Serverless computing, epitomized by AWS Lambda, has revolutionized application development and deployment by abstracting away server management and offering auto-scalability. AWS Lambda's 15-minute maximum timeout for function execution, however, presents a unique challenge for users aiming to process longer tasks within the serverless framework. In this paper, we dive deep into the execution dynamics of AWS Lambda within the AWS serverless environment, with a particular focus on extended execution times. As serverless computing gains traction, understanding the nuances and limitations of AWS Lambda's default settings becomes paramount.
 This paper investigates different request handling mechanisms within AWS Lambda and presents empirical data to demystify the underlying processes. By addressing the challenges posed by long-running serverless functions, we aim to provide practical insights and potential solutions for developers and architects seeking to optimize their serverless applications. In the quest for efficient triggering mechanisms for extended serverless functions, this paper offers valuable guidance, empowering users to leverage AWS Lambda effectively while circumventing the default timeout constraints.

A Novel Approach for Triggering the Serverless Function in Serverless Environment

Serverless computing has gained significant popularity in recent years due to its scalability, cost efficiency, and simplified development process. In a serverless environment, functions are the basic units of computation that are executed on-demand, without the need for provisioning and managing servers. However, efficiently triggering serverless functions remains a challenge, as traditional methodologies often suffer from latency, Time limit and scalability issues and the efficient execution and management of serverless functions heavily rely on effective triggering mechanisms. This research paper explores various design considerations and proposes a novel approach for designing efficient triggering mechanisms in serverless environments. By leveraging our proposed methodology, developers can efficiently trigger serverless functions in a variety of scenarios, including event-driven architectures, data processing pipelines, and web application backend.

A Study of Triggering Serverless Functions in Serverless Environment

Serverless computing has gained significant popularity due to its ability to provide scalable and cost-efficient cloud services. However, the adoption of serverless functions comes with its own set of challenges. One critical aspect is the triggering mechanism that initiates the execution of serverless functions in a serverless environment. This research paper explores the various triggering-related issues that can arise when deploying serverless functions and proposes potential solutions to mitigate these problems. The paper also highlights the importance of trigger design and its impact on the overall performance and reliability of serverless applications.

Disaggregated GPU Acceleration for Serverless Applications

Serverless platforms have been attracting applications from traditional platforms because infrastructure management responsibilities are shifted from users to providers. Many applications well-suited to serverless environments could leverage GPU acceleration to enhance their performance. Unfortunately, current serverless platforms do not expose GPUs to serverless applications.

Shipping code towards data in an inter-region serverless environment to leverage latency

Shipping code towards data in an inter-region serverless environment to leverage latency

Securing Serverless Computing: Challenges, Solutions, and Opportunities

Serverless computing is a new cloud service model that reduces both cloud providers' and consumers' costs through agile development, operation, and charging mechanisms. It has been widely applied since its emergence. Nevertheless, some characteristics of serverless computing, such as fragmented application boundaries, have raised new security challenges. Considerable literature has been committed to addressing these challenges. Commercial and open-source serverless platforms implement many security measures to enhance serverless environments. This paper presents the first survey of serverless security that considers both the literature and industrial security measures. We summarize the primary security challenges, analyze corresponding solutions from the literature and industry, and identify potential research opportunities. Then, we conduct a gap analysis of the academic and industrial solutions, as well as commercial and open-source serverless platforms' security capabilities. Finally, we present a complete picture of current serverless security research.

Penetration Testing Methodologies for Serverless Cloud Architectures

As organizations increasingly adopt serverless cloud architectures to enhance scalability and reduce operational costs, the security landscape has evolved, introducing new challenges and vulnerabilities. Serverless computing, characterized by its abstraction of infrastructure management and dynamic resource allocation, presents unique security concerns that traditional penetration testing methodologies may not adequately address. This research paper explores penetration testing methodologies specifically tailored for serverless cloud environments, aiming to identify effective strategies for evaluating and mitigating security risks in these modern architectures. The paper begins by defining serverless computing and its key characteristics, such as event-driven execution, automatic scaling, and micro-billing models. Unlike traditional server-based environments, serverless architectures often rely on Functions-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) components, which can obscure the underlying infrastructure and introduce complex attack vectors. Consequently, traditional penetration testing approaches, designed for monolithic or microservices-based systems, may fall short in identifying and exploiting vulnerabilities specific to serverless environments.

FuncPipe: A Pipelined Serverless Framework for Fast and Cost-Efficient Training of Deep Learning Models

Training deep learning (DL) models in the cloud has become a norm. With the emergence of serverless computing and its benefits of true pay-as-you-go pricing and scalability, systems researchers have recently started to provide support for serverless-based training. However, the ability to train DL models on serverless platforms is hindered by the resource limitations of today's serverless infrastructure and DL models' explosive requirement for memory and bandwidth. This paper describes FuncPipe, a novel pipelined training framework specifically designed for serverless platforms that enable fast and low-cost training of DL models. FuncPipe is designed with the key insight that model partitioning can be leveraged to bridge both memory and bandwidth gaps between the capacity of serverless functions and the requirement of DL training. Conceptually simple, we have to answer several design questions, including how to partition the model, configure each serverless function, and exploit each function's uplink/downlink bandwidth. In particular, we tailor a micro-batch scheduling policy for the serverless environment, which serves as the basis for the subsequent optimization. Our Mixed-Integer Quadratic Programming formulation automatically and simultaneously configures serverless resources and partitions models to fit within the resource constraints. Lastly, we improve the bandwidth efficiency of storage-based synchronization with a novel pipelined scatter-reduce algorithm. We implement FuncPipe on two popular cloud serverless platforms and show that it achieves 7%-77% cost savings and 1.3X-2.2X speedup compared to state-of-the-art serverless-based frameworks.

Code Layering for the Detection of Network Covert Channels in Agentless Systems

The growing interest in agentless and serverless environments for the implementation of virtual/container network functions makes monitoring and inspection of network services challenging tasks. A major requirement concerns the agility of deploying security agents at runtime, especially to effectively address emerging and advanced attack patterns. This work investigates a framework leveraging the extended Berkeley Packet Filter to create ad-hoc security layers in virtualized architectures without the need of embedding additional agents. To prove the effectiveness of the approach, we focus on the detection of network covert channels, i.e., hidden/parasitic network conversations difficult to spot with legacy mechanisms. Experimental results demonstrate that different types of covert channels can be revealed with a good accuracy while using limited resources compared to existing cybersecurity tools (i.e., Zeek and libpcap).