Cloud Offerings Research Articles

On the Analysis of Inter-Relationship between Auto-Scaling Policy and QoS of FaaS Workloads

A recent development in cloud computing has introduced serverless technology, enabling the convenient and flexible management of cloud-native applications. Typically, the Function-as-a-Service (FaaS) solutions rely on serverless backend solutions, such as Kubernetes (K8s) and Knative, to leverage the advantages of resource management for underlying containerized contexts, including auto-scaling and pod scheduling. To take the advantages, recent cloud service providers also deploy self-hosted serverless services by facilitating their on-premise hosted FaaS platforms rather than relying on commercial public cloud offerings. However, the lack of standardized guidelines on K8s abstraction to fairly schedule and allocate resources on auto-scaling configuration options for such on-premise hosting environment in serverless computing poses challenges in meeting the service level objectives (SLOs) of diverse workloads. This study fills this gap by exploring the relationship between auto-scaling behavior and the performance of FaaS workloads depending on scaling-related configurations in K8s. Based on comprehensive measurement studies, we derived the logic as to which workload should be applied and with what type of scaling configurations, such as base metric, threshold to maximize the difference in latency SLO, and number of responses. Additionally, we propose a methodology to assess the scaling efficiency of the related K8s configurations regarding the quality of service (QoS) of FaaS workloads.

Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence

Critical scholars contend that ‘There is no AI without Big Tech’. This study delves into the substantial role played by major technology conglomerates, including Amazon, Microsoft, and Google (Alphabet), in the ‘industrialisation of artificial intelligence’. This concept encapsulates the shift of AI technologies from the research and development stage to practical, real-world applications across diverse industry sectors, resulting in new dependencies and associated investments. We employ the term ‘Big AI’ to encapsulate the structural convergence of AI and Big Tech, characterised by the profound interdependence of AI with the infrastructure, resources, and investments of these major technology companies. Using a ‘technographic’ approach, our study scrutinises the infrastructural support and investments of Big Tech in the AI sector, focussing on corporate partnerships, acquisitions, and financial investments. Additionally, we conduct a detailed examination of the complete spectrum of cloud platform products and services offered by Amazon, Microsoft, and Google. We demonstrate that AI is not merely an abstract idea but an actual technology stack encompassing infrastructure, models, applications, and an ecosystem of applications and companies relying on this stack. Significantly, these tech giants have seamlessly integrated all three components of the stack into their cloud offerings. Furthermore, they have developed industry-focussed solutions and marketplaces aimed at attracting third-party developers and businesses, fostering the growth of a broader AI ecosystem. This analysis underscores the intricate interdependence between AI and cloud infrastructure, emphasising the industry-specific aspects of cloud AI.

How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study

This paper aims to answer the question: Can deep learning models be cost-efficiently trained on a global market of spot VMs spanning different data centers and cloud providers? To provide guidance, we extensively evaluate the cost and throughput implications of training in different zones, continents, and clouds for representative CV, NLP and ASR models. To expand the current training options further, we compare the scalability potential for hybrid-cloud scenarios by adding cloud resources to on-premise hardware to improve training throughput. Finally, we show how leveraging spot instance pricing enables a new cost-efficient way to train models with multiple cheap VMs, trumping both more centralized and powerful hardware and even on-demand cloud offerings at competitive prices.

Towards hybrid-cloud infrastructure composition for SAP systems landscapes in smart manufacturing

To achieve the objective of smart manufacturing, the adoption of cloud computing is inevitable. However, the implications of fully or partially moving the manufacturing-related workloads to the cloud is a strategic decision that is rarely investigated thoroughly enough in the literature, especially for standard off-the-shelf enterprise IT applications, such as various SAP products. Specifically, in the related research, it is kept on a high level of conceptual representation without analysis of the specific cloud products required for such infrastructure. In this work, we discuss cloud setup for smart manufacturing while taking into account four main pillars of cloud infrastructure required for standard IT solutions, namely: compute, storage, networking, and backups. We also consider different degrees of high availability requirements since these fundamentally impact how cloud architecture should be designed. After discussing these elements, we demonstrate how these elements are mapped to the real-world cloud offerings on the example use case of Microsoft Azure. We close the paper with a discussion on further consideration of operational costs while pointing out the complexity of the pricing model.

Design and Development of Cloud Based Encrusted Security as a Service

The Cloud computing is the grouping of several variety of computers whichever is related via a intermediate referred to as internet respectively. The Cloud computing usage that net toward propel, obtain and shop these statistics. By way of increasingly extra quit customers, Substructure requests and stage servers pass hooked on this cloud, the cloud offerings have been growing a slice. By this identical period, this require of defense upon safety additionally improved. apiece of this organized utility at this cloud has to have superior security planned pro the situation which will achieve PAIC, that will result in an advanced price. That body of secaas will decrease that price and these with this aid of production this cloud duller and green toward apply. Atop this article we arrived out by some body paintings pro investigating and executing secaas consuming layered method. in which with that, this consumer can choose levels of security conferring toward their software requests. every level has extraordinary algorithms and format in order to triumph over attacks and vulnerabilities, there with the aid of we can obtain PAIC. To advocate exclusive algorithms which are conspicuously utilized inside outdated cloud computing atmosphere. Keyword : Cloud computing, Honesty, IaaS, layered refuge, PaaS, Reviewing, SaaS and secrecy.

Transferable Knowledge for Low-Cost Decision Making in Cloud Environments

Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL) approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures. In this article, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of 60 percent reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenarios.

A Novel Discrete Grey Wolf Optimizer for Scientific Workflow Scheduling in Heterogeneous Cloud Computing Platforms

There are several scientific workflow applications which need vast amount of processing so the cloud offerings give the sense of economic. Workflow scheduling has drastic impact on gaining desired quality of service (QoS). The main objective of workflow scheduling is to minimize the makespan. The workflow scheduling is formulated to a discrete optimization problem which is NP-Hard. This paper presents a novel discrete grey wolf optimizer (D-GWO) for scientific workflow scheduling problems in heterogeneous cloud computing platforms in the aim of minimizing makespan. Although the original GWO had great achievements in continuous optimization problems, it seems clear gap in utilizing GWO for combinatorial discrete optimization problems. It revolves around the fact that the continuous changes in search space during the course of discrete optimization lead inefficient or meaningless solutions. To this end, the proposed algorithm is customized in such a way to optimize discrete workflow scheduling problem by leveraging some new binary operators and Walking Around approaches to balance between exploration and exploitation in discrete search space. Scientific unstructured workflows were investigated in different circumstances to prove effectiveness of proposed novel meta-heuristic algorithm. The simulation results witnessed the superiority of proposed D-GWO against other state-of-the-arts in terms of scheduling metrics.

Self-healing trans-cloud applications

Trans-cloud applications consist of multiple interacting components deployed across different cloud providers and at different service layers (IaaS and PaaS). In such complex deployment scenarios, fault handling and recovery need to deal with heterogeneous cloud offerings and to take into account inter-component dependencies. We propose a methodology for self-healing trans-cloud applications from failures occurring in application components or in the cloud services hosting them, both during deployment and while they are being operated. The proposed methodology enables reducing the time application components rely on faulted services, hence residing in “unstable” states where they can suddenly fail in cascade or exhibit erroneous behaviour. We also present an open-source prototype illustrating the feasibility of our proposal, which we have exploited to carry out an extensive evaluation based on controlled experiments and monkey testing.

Blockchain Based Public Cloud Security for E-Voting System on IoT Environment

Digital Technology is inevitable for better life of the people. Voting by people plays the vital role in democracy as the leaders will be elected by it. IoT helps many edge devices to interact with a Cloud for storage and computation of the Voting results. The major concern for the Voter is whether the vote casted by him is tamperproof or not. Though Cloud offerings are started a decade ago still information security in the cloud is always a concern. This paper focuses on creating a tamperproof framework to ensure the data Integrity. It proposes a novel approach where the user can cast the vote from any of the electronic devices with a unique ID allotted to him, a two-step authentication can be applied like OTP to ensure the security. The votes from these edge devices will be sent to the untrusted third party public cloud for computation, the votes will be saved in the encrypted format. The computation will be done on encrypted data. The Block chain based workflow ensures that the votes which are shared in the encrypted format will not be tampered to ensure trust and Integrity to the proposed model. The model also can be extended to many public shared e-models like E-Auction and healthcare systems.

Selecting a Secure Cloud Provider—An Empirical Study and Multi Criteria Approach

Security has become one of the primary factors that cloud customers consider when they select a cloud provider for migrating their data and applications into the Cloud. To this end, the Cloud Security Alliance (CSA) has provided the Consensus Assessment Questionnaire (CAIQ), which consists of a set of questions that providers should answer to document which security controls their cloud offerings support. In this paper, we adopted an empirical approach to investigate whether the CAIQ facilitates the comparison and ranking of the security offered by competitive cloud providers. We conducted an empirical study to investigate if comparing and ranking the security posture of a cloud provider based on CAIQ’s answers is feasible in practice. Since the study revealed that manually comparing and ranking cloud providers based on the CAIQ is too time-consuming, we designed an approach that semi-automates the selection of cloud providers based on CAIQ. The approach uses the providers’ answers to the CAIQ to assign a value to the different security capabilities of cloud providers. Tenants have to prioritize their security requirements. With that input, our approach uses an Analytical Hierarchy Process (AHP) to rank the providers’ security based on their capabilities and the tenants’ requirements. Our implementation shows that this approach is computationally feasible and once the providers’ answers to the CAIQ are assessed, they can be used for multiple CSP selections. To the best of our knowledge this is the first approach for cloud provider selection that provides a way to assess the security posture of a cloud provider in practice.

Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics

In a new effort to make our research transparent and reproducible by others, we developed a workflow to run and share computational studies on the public cloud Microsoft Azure. It uses Docker containers to create an image of the application software stack. We also adopt several tools that facilitate creating and managing virtual machines on compute nodes and submitting jobs to these nodes. The configuration files for these tools are part of an expanded "reproducibility package" that includes workflow definitions for cloud computing, in addition to input files and instructions. This facilitates re-creating the cloud environment to re-run the computations under the same conditions. Although cloud providers have improved their offerings, many researchers using high-performance computing (HPC) are still skeptical about cloud computing. Thus, we ran benchmarks for tightly coupled applications to confirm that the latest HPC nodes of Microsoft Azure are indeed a viable alternative to traditional on-site HPC clusters. We also show that cloud offerings are now adequate to complete computational fluid dynamics studies with in-house research software that uses parallel computing with GPUs. Finally, we share with the community what we have learned from nearly two years of using Azure cloud to enhance transparency and reproducibility in our computational simulations.

Security and Privacy in Multi-Cloud Environments

In the modern-day panorama of computing, the adoption of multi-cloud environments has emerge as increasingly more commonplace, providing businesses stronger flexibility, scalability, and resource optimization. However, this proliferation introduces complex demanding situations related to safety and privateness. This research makes a speciality of examining the intricacies of protection and privacy concerns within multi-cloud setups, mainly addressing demanding situations and featuring solutions inside the geographical regions of facts encryption, identification control, and compliance. A important mission in multi-cloud environments is the implementation of powerful statistics encryption strategies throughout numerous systems. Protecting data confidentiality and integrity as it traverses distinctive cloud offerings is of paramount importance. This have a look at seriously evaluates present encryption methodologies, assesses their suitability in multi-cloud scenarios, and indicates progressive tactics to reinforce records safety. Identity control emerges as another important factor of the multi-cloud safety paradigm. The secure orchestration of user identities throughout disparate cloud services needs cautious exam. This studies explores the complexities surrounding identification federation, authentication mechanisms, and get entry to controls inside multi-cloud environments. By figuring out vulnerabilities and proposing strong identity management solutions, the study contributes to the improvement of secure multi-cloud infrastructures. Additionally, making sure compliance with regulatory frameworks and industry standards poses a powerful venture in multi-cloud protection. The examine scrutinizes the complex panorama of compliance necessities, encompassing diverse geographical and enterprise-specific policies. By analyzing the gaps and intricacies in present compliance frameworks, the research proposes a comprehensive method to make sure adherence to guidelines whilst operating in a multi-cloud environment. In conclusion, this studies addresses the challenges and solutions inherent in ensuring strong protection and privacy measures within multi-cloud environments. By specializing in facts encryption, identity management, and compliance, the observe provides valuable insights for companies navigating the complex terrain of multi-cloud computing.

Energy-Efficient Cloud Computing

The summary on Energy-Efficient Cloud Computing delves into the vital exam of the environmental implications associated with cloud computing and proposes techniques to enhance the power efficiency of cloud records facilities. In this complete exploration, the focal point is on mitigating the carbon footprint of cloud technology, emphasizing the utilization of environmentally sustainable practices and revolutionary technology. The environmental effect of cloud computing has turn out to be a urgent subject as facts facilities contribute considerably to power intake and greenhouse gasoline emissions. This research scrutinizes the present day state of energy consumption in cloud information facilities and evaluates its environmental outcomes. The goal is to identify possible answers that not most effective mitigate the ecological footprint however also align with the developing call for for scalable and green cloud offerings. A pivotal issue of the proposed techniques includes the implementation of green records centers. These records facilities integrate eco-friendly technology, which include renewable strength sources and superior cooling structures, to decrease energy intake and decrease environmental effect. By exploring the feasibility and scalability of inexperienced records centers, the research ambitions to provide a sustainable version for future cloud infrastructure improvement. Optimization algorithms become a key era to enhance electricity performance in cloud computing. The look at investigates the application of algorithms designed to optimize resource usage, workload distribution, and normal device overall performance. Through the implementation of these algorithms, the research seeks to enhance the operational performance of cloud statistics centers, lowering power waste and promoting a extra sustainable computing infrastructure. Energy-conscious resource allocation is another essential detail addressed inside the abstract. The studies explores mechanisms to dynamically allocate assets primarily based on real-time power consumption information. By incorporating strength-cognizance into resource management, the goal is to strike a stability among overall performance requirements and electricity performance, ensuring most effective usage of assets whilst minimizing the environmental effect In conclusion, this abstract units the degree for a comprehensive exploration of Energy-Efficient Cloud Computing, losing mild at the environmental challenges posed with the aid of cloud statistics facilities. Through the investigation of green records centers, optimization algorithms, and power-aware resource allocation, the studies aspires to provide practical solutions that harmonize cloud computing with ecological sustainability, fostering a greater electricity-green and environmentally responsible cloud infrastructure.

Everything You Should Know About Intel SGX Performance on Virtualized Systems

Intel SGX has attracted much attention from academia and is already powering commercial applications. Cloud providers have also started implementing SGX in their cloud offerings. Research efforts on Intel SGX so far have mainly focused on its security and programmability aspects. However, no work has studied in detail the performance degradation caused by SGX in virtualized systems. Such settings are particularly important, considering that virtualization is the de facto building block of cloud infrastructure, yet often comes with a performance impact. This paper presents the first detailed performance analysis of Intel SGX in a virtualized system compareed against bare-metal. Based on our findings, we identify several optimization strategies that would result in performance improvements of Intel SGX on such systems.

WormBase: a modern Model Organism Information Resource.

WormBase (https://wormbase.org/) is a mature Model Organism Information Resource supporting researchers using the nematode Caenorhabditis elegans as a model system for studies across a broad range of basic biological processes. Toward this mission, WormBase efforts are arranged in three primary facets: curation, user interface and architecture. In this update, we describe progress in each of these three areas. In particular, we discuss the status of literature curation and recently added data, detail new features of the web interface and options for users wishing to conduct data mining workflows, and discuss our efforts to build a robust and scalable architecture by leveraging commercial cloud offerings. We conclude with a description of WormBase's role as a founding member of the nascent Alliance of Genome Resources.

Everything You Should Know About Intel SGX Performance on Virtualized Systems

Intel SGX has attracted much attention from academia and is already powering commercial applications. Cloud providers have also started implementing SGX in their cloud offerings. Research efforts on Intel SGX so far have mainly concentrated on its security and programmability. However, no work has studied in detail the performance degradation caused by SGX in virtualized systems. Such settings are particularly important, considering that virtualization is the de facto building block of cloud infrastructure, yet often comes with a performance impact. This paper presents for the first time a detailed performance analysis of Intel SGX in a virtualized system in comparison with a bare-metal system. Based on our findings, we identify several optimization strategies that would improve the performance of Intel SGX on such systems.

Challenges of Cloud Solutions Adoption in Large Corporations

The concept of cloud-provided IT solutions has changed from being an emerging technology to a reality that most large corporations have adopted or are planing to adopt. Accordingly, many companies have already moved some of their applications or a portion of their infrastructure to the cloud. This adoption varies between the different categories of cloud offerings: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). This paper will discuss the challenges of cloud adoption from the three perspectives of successful organizations: people, process, and technology.

Privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems

With the rapid growth of public cloud offerings, how to design effective prediction models that provide appropriate recommendations for potential users has become more and more important. In dynamic cloud environment, both of user behaviors and service performance are sensitive to contextual information, such as geographic location information. In addition, the increasing number of attacks and security threats also brought the problem that how to protect critical information assets such as sensitive data, cloud resources and communication in a more effective and secure manner. In view of these challenges, we propose a privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems. Specifically, our method is designed as a three-phase process: Firstly, two privacy-preserving mechanisms, i.e., a randomized data obfuscation technique and a region aggregation strategy are presented to protect the private information of users and deal with the data sparsity problem. Then a location-aware latent factor model based on tensor factorization is applied to explore the spatial similarity relationships between services. Finally, predictions are made based on both global and spatial nearest neighbors. Experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method achieves decent prediction accuracy on the premise of privacy preservation.

Performance evaluation of heterogeneous cloud functions

SummaryCloud Functions, often called Function‐as‐a‐Service (FaaS), pioneered by AWS Lambda, are an increasingly popular method of running distributed applications. As in other cloud offerings, cloud functions are heterogeneous due to variations in underlying hardware, runtime systems, as well as resource management and billing models. In this paper, we focus on performance evaluation of cloud functions, taking into account heterogeneity aspects. We developed a cloud function benchmarking framework, consisting of one suite based on Serverless Framework and one based on HyperFlow. We deployed the CPU‐intensive benchmarks: Mersenne Twister and Linpack. We measured the data transfer times between cloud functions and storage, and we measured the lifetime of the runtime environment. We evaluated all the major cloud function providers: AWS Lambda, Azure Functions, Google Cloud Functions, and IBM Cloud Functions. We made our results available online and continuously updated. We report on the results of the performance evaluation, and we discuss the discovered insights into resource allocation policies.

Optimizing cloud solutioning design

The economics of the cloud model has been encouraging IT enterprises to migrate from on-premise environments to public, private, or hybrid cloud solutions. To perform such a migration, a cloud offering needs to be chosen and a cloud solution needs to be built. In industrial settings, cloud designers may spend days or even weeks to come up with an acceptable cloud solution at a low cost/price. Like any manual process, it is obvious that such a cloud solution design process is error prone, time consuming, and does not guarantee an optimal output, e.g. a solution with a minimum cost/price. Different from existing works that solve the problem from the user’s angle, we solve it from the cloud provider’s prospective, who aims at offering customized cloud solutions for different user requirements at low costs. Such difference requires a unique way of problem modeling. Through analyzing real business data, we abstract the problem into a general attribute–value combinations and formulate a powerful integer programming optimization model to solve it. The general form of the optimization model allows various definitions of customer requirements as well as cloud offerings. Our novel optimization approach for cloud solution design satisfies client requirements, cloud offering constraints, and produces a solution at a minimum cost in a short time, if one exists. We evaluated our solution on realistic data against two baseline approaches. The numerical results show both the effectiveness and efficiency of our approach as well as its practical potential.