Multiple Cloud Environments Research Articles

Rational load balancing in collaborated cloud computing environments *

Abstract Optimum efficiency and fault tolerance are the most demanding and challenging issues in the domain of performance and reliability management in cloud computing environments. Optimized resource utilization is a key aspect for yielding efficiency in cloud platforms. Workload balancing through resource sharing is one of the key solutions for attaining performance in cloud environments. In addition, multiple cloud environments join hands to offer performance and fault tolerance through resource sharing. We provide a better and cloud-instances’ priority-based efficient load balancing method for collaborative cloud platforms. The recommended efficient load balancing method shortens the waiting timespan and overcomes the starvation problem of low priority instances in intercloud environments. A functional prototype of the recommended load balancing method was deployed on a physical cloud infrastructure which was setup with the OpenStack cloud software on the Fedora Linux operating system. The pilot project execution findings exhibit a reduction in the timespan borne by instances for executing load balancing. This technique is useful for attaining fault tolerance and efficient resource utilization in intracloud and intercloud environments.

Developing A Novel Load Balancing Approach for Mitigating Resource Contention in Multi-Cloud Environments

In the rapidly evolving landscape of cloud computing, the management of resources and load balancing across multiple cloud environments presents significant challenges. This paper proposes a novel approach to address resource contention issues in multi-cloud environments through the development of an innovative load-balancing mechanism. By leveraging advanced algorithms and dynamic resource allocation strategies, our approach aims to effectively distribute workloads across diverse cloud infrastructures, thereby optimizing resource utilization and mitigating contention. Through extensive experimentation and evaluation, we demonstrate the effectiveness and efficiency of our proposed solution in enhancing performance and resilience in multi-cloud deployments. Our findings underscore the importance of tailored load-balancing mechanisms in enabling scalable and reliable operations in complex cloud environments.

Neural network inspired efficient scalable task scheduling for cloud infrastructure

The rapid development of Cloud Computing in the 21st Century is landmark occasion, not only in the field of technology, but also in the field of engineering and services. The development in cloud architecture and services has enabled fast and easy transfer of data from one unit of a network to other. Cloud services support the latest transport services like smart cars, smart aviation services and many others. In the current trend, smart transport services depend on the performance of cloud Infrastructure and its services. Smart cloud services derive real time computing and allows it to make smart decision. For further improvement in cloud services, cloud resource optimization is a vital cog that defines the performance of cloud. Cloud services have certainly aimed to make the optimum use of all available resources to the become as cost efficient and time efficient as possible. One of the issues that still occur in multiple Cloud Environments is a failure in task execution. While there exist multiple methods to tackle this problem in task scheduling, in the recent times, the use of smart scheduling techniques has come to prominence. In this work, we aim to use the Harmony Search Algorithm and neural networks to create a fault aware system for optimal usage of cloud resources. Cloud environments are in general expected to be free of any errors or faults but with time and experience, we know that no system can be faultless. With our approach, we are looking to create the best possible time-efficient system for faulty environments, Where the result shows that the proposed harmony search-inspired ANN model provides least execution time, number of task failures, power consumption and high resource utilization as compared to recent Red fox and Crow search inspired models.

Magnets to Adversaries—An Analysis of the Attacks on Public Cloud Servers

Security adversaries are always constantly looking for targets to exploit. The mechanism of exploitation used by security adversaries varies significantly. Many focus on easy compromises as mere pivots to extend their attacks from these exploited systems to continue accomplishing their original goals. The cloud environment is a highly susceptible target for adversaries and provides a solid mechanism for observing adversary behavior. The sheer volume of attacks on the cloud provides insights into the attacker’s objectives and attack patterns, which can be leveraged for protecting infrastructure. This work deep dives into the practices used by adversaries on the commonly exposed protocols in the Amazon Web Services (AWS), Microsoft Azure (Azure), Google Cloud Platform (GCP), and Oracle Cloud Infrastructure (OCI) platforms. A robust honeypot model is documented that compares attacker behavior across various ports and protocols running in multiple cloud environments. This work illustrates that adversary activity is highly versatile in the public cloud environment, with an average of 700 new and unique IP addresses found attacking honeypot infrastructure daily. Further, this article illustrates the security safeguards a typical organization can leverage to mitigate the threats from these adversaries constantly probing insecure targets on the cloud platform.

Scalable Zero-Trust Architectures for Enhancing Security in Multi-Cloud SaaS Platforms

The recent advancement in cloud computing technology has led organizations to embrace multi-cloud systems, wherein they utilize services from many cloud service providers. The increasing reliance on Software as a Service (SaaS) platforms across multiple cloud environments has underscored the need for robust security frameworks that can effectively address contemporary cyber threats. This review paper explores the scalable Zero-Trust Architectures (ZTA) as a vital security measure for multi-cloud SaaS platforms. By advocating a security paradigm that assumes no implicit trust, regardless of network location, ZTA provides a framework for continuous verification of users and devices accessing sensitive resources. We examine a number of ZTA architectural components, including micro-segmentation, identity and access management, and real-time threat detection. Furthermore, we emphasise the importance of ZTA in improving data security, compliance, and overall resilience against complex assaults, as well as the difficulties and advantages of integrating ZTA within multi-cloud environments. The analysis indicate that a scalable Zero-Trust approach can significantly improve security posture while maintaining operational efficiency across diverse cloud infrastructures

Smart City IoT Data Management with Proactive Middleware

With the increased emergence of cloud-based services, users are frequently perplexed as to which cloud service to use and whether it will be beneficial to them. The user must compare various services, which can be a time-consuming task if the user is unsure of what they might need for their application. This paper proposes a middleware solution for storing Internet of Things (IoT) data produced by various sensors, such as traffic, air quality, temperature, and so on, on multiple cloud service providers depending on the type of data. Standard cloud computing technologies become insufficient to handle the data as the volume of data generated by smart city devices grows. The middleware was created after a comparative study of various existing middleware. The middleware uses the concept of the federal cloud for the purpose of storing data. The middleware solution described in this paper makes it easier to distribute and classify IoT data to various cloud environments based on its type. The middleware was evaluated using a series of tests, which revealed its ability to properly manage smart city data across multiple cloud environments. Overall, this research contributes to the development of middleware solutions that can improve the management of IoT data in settings such as smart cities.

Context-Aware Offloading for IoT Application using Fog-Cloud Computing

It is difficult to run delay-sensitive applications and the cloud simultaneously due to performance metrics such as latency, energy consumption, bandwidth, and response time exceeding threshold levels. This is the case even though advanced networks and technologies are being used. The middleware layer of the Internet of Things (IoT) architecture appears to be a promising solution that could be used to deal with these issues while still meeting the need for high task offloading criterion. The research that is being proposed recommends implementing Fog Computing (FC) as smart gateway in middleware so that it can provide services the edge of the networks. Applications that are sensitive to delays would then be able to be provided in an efficient manner as a result of this. A smart gateway is proposed as solution for taking the offloading decision based on the context of data, which offers a hybrid approach in order to make decisions regarding offloading that are efficient and effective. A solution that uses machine-learning reasoning techniques to make offloading decisions, in multiple fog-based cloud environments. Feature selection, and classification are used as a learning method and are also ensembled as hybrid logistic regression-based learning to provide the best offloading solution. It works by learning the contextual information of data and identify the cases to make the decision of offloading. The proposed model offers a solution that is both energy and time efficient, with an overall accuracy of approximately 80 percent. With the proposed intelligent offloading approach, it is expected that Internet of Things applications will be able to meet the requirement for low response time and other performance characteristics.

BPPIR: Blockchain-assisted privacy-preserving similarity image retrieval over multiple clouds

With the progress of the times and the development of electronic products, more and more images are captured and stored on multiple cloud servers, which are more likely to exhibit dishonesty because of interdependence rather than semi-honesty and thus how to achieve search over multiple clouds becomes intractable. Moreover, most of the existing research works on content-based image retrieval so far do not consider dishonest clouds and thereby do not support multi-cloud retrieval. On the one hand, a central search server is generally introduced for the convenience of retrieval in the system with multiple cloud servers. However, the central search server may return false or incomplete search results for possible commercial profit, which reduces the retrieval accuracy. On the other hand, cloud servers may destroy the image integrity by deleting or tampering images to save computing and bandwidth resources, resulting in the loss of important information for the image owner and incorrect decryption of the image for the user. In order to address these two problems, we propose a blockchain-assisted privacy-protected similarity image retrieval in the multiple cloud environment, which can not only ensure the accuracy of the retrieval results, but also can verify the integrity of images. The performance of this scheme is evaluated on a real-world dataset, and the results show the high retrieval precision and efficiency of this scheme.

System Observability and Monitoring for Multi Cloud Native Network Service based Function

Abstract: The simplicity and agility of public Cloud infrastructure is driving large service based and application oriented Enterprises to host their business functions as Cloud Native Services and Applications. These Enterprises often host their applications and services in multiple cloud infrastructures either due to business, compliance and functionality requirements. On these cloud infrastructure platforms, applications and services are primarily designed and deployed as microservices which are small and independent services and functions. Each service microservice is typically self contained and interacts with other services using predefined Service based interfaces or APIs. As these services could be hosted in multiple cloud environments on infrastructures such as virtual machines and containers, there’s a need to provide a holistic visibility of services across many infrastructures, form factors and service interfaces. Keywords: Microservice, API, Virtual Machine, Visibility, Multicloud.

Reconnoitering the significance of security using multiple cloud environments for conveyance applications with blowfish algorithm

In recent years the process of transportation needs a highly effective traffic system in order to monitor all consumer goods as many goods are left out at different locations. To handle such moving cases cloud platform is highly helpful as with respect to geographical location the goods are mapped in correct form. However incorporation of single cloud platform does not provide sufficient amount of storage about all goods thus a multiple cloud platform is introduced in proposed system. As multiple cloud platform is provided the security features of each data base system is also checked and enhanced using encryption keys. Moreover for proper operating conditions of multiple cloud platforms an analytical model is designed that synchronizes necessary data at end system. The defined analytical model focuses on solving multiple objectives that are related to critical energy problems where demand problems are reduced. Further the encryption process is carried out using Improved BlowFish Algorithm (IBFA) by allocating proper resources with decryption keys. To validate the effectiveness of proposed method five scenarios are considered where all scenario outcomes proves to be much higher than existing models by an average of 43%.

BGSA: Broker Guided Service Allocation in Federated Cloud

Cloud brokering has emerged a new service paradigm because of the proliferation of a large number of cloud service providers. Cloud brokering attracts users because it aggregates and extends computing services from multiple cloud service providers. Service providers offer services with varying capabilities, technologies and pricing models which creates a heterogeneous service environment. Cloud users are also increasing day by day with varied requirements. Users prefer services from multiple service providers in order to fulfill their money and application requirements. Therefore, service allocation becomes one of most challenging task to fulfill all the requirements of users within resource constraints. Service allocation problem in multiple cloud environment has been studied as combinatorial optimization problem. Broker guided service allocation increases application resilience, provides unified billing and simplifies service management. In this paper we have proposed a broker guided service allocation (BGSA) model for Federated Cloud. BGSA employs a genetic algorithm based technique for service allocation. BGSA optimizes service allocation and derives well distributed set of non-dominated solutions. Simulation based evaluation of BGSA has been done and obtained results are compared with other two multi-objective algorithms. Observed results improve both execution time and total utilization.

Security for Multi Cloud Using Server less Registering Approach

These days, in any application development, security for specific area has become crucial job in the service access environment. Since clients needs to utilize the unique services and resources in distributed computing environment. Here the security administrations and cloud portal frameworks have been highly advanced based on the client necessities. However cloud offers a lot of resources through the global service vendors and Multicloud technologies are rapidly in use, but still the cloud requires security enhancement. Applications become complex and have attacks when deployed on multiclouds .So it is very important factor to protect the data and resources from the hackers. In multiple cloud environments it is possible to control all the applications, user resources, secret information and other confidential user process level with the help of server less approach. The server less computing approach is a sort of Distributed computing execution model through which Cloud Service provider will allocate the resource to the client in a dynamic manner .This paper represents what is Multi cloud, advantages of Multicloud, Why Security issue with Multi cloud, How server less is different from monolith services and Security Approaches to multi cloud with server less computing.

Wiera: Policy-Driven Multi-Tiered Geo-Distributed Cloud Storage System

Multi-tiered geo-distributed cloud storage systems must tame complexity at many levels: uniform APIs for storage access, supporting flexible storage policies that meet a wide array of application metrics, determining an optimal data placement, handling uncertain network dynamics and access dynamism, and operating across many levels of heterogeneity both within and across data-centers (DCs). In this paper, we present an integrated solution called Wiera. Wiera enables the specification of data management policies both within a local DC and across DCs. Such policies enable the user to optimize for cost, performance, reliability, durability, and consistency, and to express their tradeoffs. In addition, Wiera determines an optimal data placement for the user to meet their desired tradeoffs easily in such an environment. A key aspect of Wiera is first-class support for dynamism due to network, workload, and access patterns changes. As far as we know, Wiera is the first geo-distributed cloud storage system which handles dynamism actively at run-time. Wiera allows <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unmodified applications</i> to reap the benefits of flexible data/storage policies by externalizing the policy specification. We show how Wiera enables a rich specification of dynamic policies using a concise notation and describe the design and implementation of the system. We have implemented a Wiera prototype on multiple cloud environments, AWS and Azure, that illustrates potential benefits from managing dynamics and in using multiple cloud storage tiers both within and across DCs.

HealthyBroker: A Trustworthy Blockchain-Based Multi-Cloud Broker for Patient-Centered eHealth Services

Delivering electronic health care (eHealth) services across multi-cloud providers to implement patient-centric care demands a trustworthy brokering architecture. Specifically, such an architecture should aggregate relevant medical information to allow informed decision-making. It should also ensure that this information is complete and authentic and that no one has tampered with it. Brokers deployed in eHealth services may fall short of meeting such criteria due to two key behaviors. The first involves violating international health-data protection laws by allowing user anonymity and limiting user access rights. Second, brokers claiming to provide trustworthy transactions between interested parties usually rely on user feedback, an approach vulnerable to manipulation by malicious users. This paper addresses these data security and trust challenges by proposing HealthyBroker, a novel, trust-building brokering architecture for multiple cloud environments. This architecture is designed specifically for patient-centric cloud eHealth services. It enables care-team members to complete eHealth transactions securely and access relevant patient data on a “need-to-know” basis in compliance with data-protection laws. HealthyBroker also protects against potential malicious behavior by assessing the trust relationship and tracking it using a neutral, tamper-proof, distributed blockchain ledger. Trust is assessed based on two strategies. First, all transactions and user feedback are tracked and audited in a distributed ledger for transparency. Second, only feedback coming from trustworthy parties is taken into consideration. HealthyBroker was tested in a simulated eHealth multi-cloud environment. The test produced better results than a benchmark algorithm in terms of data accuracy, service time, and the reliability of feedback received as measured by three malicious behavior models (naïve, feedback isolated, and feedback collective). These results demonstrate that HealthyBroker can provide care teams with a trustworthy, transparent ecosystem that can facilitate information sharing and well-informed decisions for patient-centric care.

When IoT Meets DevOps: Fostering Business Opportunities

The Internet of Things (IoT) is the new digital revolution for the near-future society, the second after the creation of the Internet itself. The software industry is converging towards the large-scale deployment of IoT devices and services, and there’s broad support from the business environment for this engineering vision. The Development and Operations (DevOps) project management methodology, with continuous delivery and integration, is the preferred approach for achieving and deploying applications to all levels of the IoT architecture. In this paper we also discuss the promising trend of associating devices with microservices, which are further encapsulated into functional packages called containers. Docker is considered the market leader in container-based service delivery, though other important software companies are promoting this concept as part of the technology solution for their IoT customers. In the experimental section we propose a three-layer IoT model, business-oriented, and distributed over multiple cloud environments, comprising the Physical, Fog/Edge, and Application layers.&#x0D; &#x0D; &#x0D; Keywords: Internet-of-Things, software technologies, project management, business environment Headings

Bandwidth Provisioning for Virtual Machine Migration in Cloud: Strategy and Application

Physical resources are highly virtualized in today's datacenter-based cloud-computing networks. Servers, for example, are virtualized as Virtual Machines (VMs). Through abstraction of physical resources, server virtualization enables migration of VMs over the interconnecting network. VM migration can be used for load balancing, energy conservation, disaster protection, etc. Migration of a VM involves iterative memory copy and network re-configuration. Memory states are transferred in multiple phases to keep the VM alive during the migration process, with a small downtime for switchover. Significant network resources are consumed during this process. Migration also results in undesirable performance impacts. Suboptimal network bandwidth assignment, inaccurate pre-copy iterations, and high end-to-end network delay in wide-area networks (WAN) can exacerbate the performance degradation. In this study, we devise strategies to find suitable bandwidth and pre-copy iteration count to optimize different performance metrics of VM migration over a WAN. First, we formulate models to measure network resource consumption, migration duration, and migration downtime. Then, we propose a strategy to determine appropriate migration bandwidth and number of pre-copy iterations, and perform numerical experiments in multiple cloud environments with large number of migration requests. Results show that our approach consumes less network resources when compared with maximum and minimum-bandwidth provisioning strategies while using an order of magnitude less bandwidth than maximum-bandwidth strategy. It also achieves significantly lower migration duration than minimum-bandwidth scheme.

Design of devops solution for managing multi cloud distributed environment

DevOps is the emerging field in the area of IT infrastructure management and cloud automation. Organization are spending time and money in terms of continuous improvement and optimization in their infrastructure management and finding to avenues for efficiency and produc-tivity improvement, reduce redundancy and optimise cost in the area of development and deployment of their enterprise application. More importantly, organisation have multiple environment to manage and hence need to have a mechanism to continuously monitor and optimize operational task and improve collaboration with various teams across organization. In addition, many companies are migrating or migrated on to cloud, some are having multiple cloud environments across regions, and hence managing it is even more difficult and need to have a mechanism to address the key challenges and risk in managing devOps in multicloud. . In this article, we would like to discuss the key challenges and how it can be addressed in a multi cloud environment and proposing an integrated solution addressing all challenges with right set of tools and framework design which enables devops in multi cloud efficiently.

Enhanced Adaptive Learning Mechanism for Cloud Selection

Estimation of cloud services in a distributed computing environment is taking more interests. There is a wealth of developing cloud benefit assets that makes it difficult for the user to select the best administration related to own applications in an evolving multiple cloud environment, particularly for online processing applications. To make clients to choose their interested cloud adequately, we need a model which holds the cloud profits, and hence dynamic cloud benefit determination procedure named Dynamic Cloud Selection (DCS) is adapted. In this procedure of selected services, every cloud benefit business deals with some group of cloud administrations, and executes the DCS method. This paper studies the cloud selection and proposed a way to improve the cloud selection based on related measures. The measures are reliability, response time, throughput, availability, utilization, resilience, scalability, and elasticity. The system is contrived to enhance the cloud benefit choice powerfully and to restore the best administration result to the client. These measures are used to form best selection strategy. User memory requirement is also considered to improve the preferred task. Experimental results proved that using this new strategy, best cloud selection is made efficiently.

Security Enhancement in Multi Clouds Using Serverless Computing Approach

Nowadays, in any application specific domain security plays a vital role in the service access environment. Because customers needs to make use of distinct services and resources in an attacks free surface. In cloud computing environment, the security services and portal systems have been highly progressed based on the user requirements. However, cloud offers plenty of resources through the service vendor globally; still the multi clouds problems are not resolved completely. So, it is very essential to protect the data and other resources from the intruders are a most primary factor. The proposed work will establish a security layer on the multiple cloud environments by introducing the concept of “Serverless Computing”. The serverless computing is a kind of cloud computing execution model through which the cloud service provider (CSP) can manage the allocation of system resources in a dynamic manner. This mechanism will follow the property of utility computing. This approach can be used to hide the operations on entire cloud server management end and capacity planning decisions and also uses no provisioned services. Any type of vulnerabilities is taken care of by the cloud service provider. In this process, attacks rate are compared with the traditional security architectures and each component is an entry point to the serverless application. With this approach, customers can control and monitor the workloads by using IDS / IPS technique.

Abstract 378: The Cancer Genome Collaboratory

Abstract The Cancer Genome Collaboratory is an academic compute cloud designed to enable computational research on the world’s largest and most comprehensive cancer genome dataset, the International Cancer Genome Consortium (ICGC). The ICGC is on target to categorize the genomes of 25,000 tumors by 2018. A subproject of ICGC, the PanCancer Analysis of Whole Genomes (PCAWG) alone has generated over 800TB of harmonized sequence alignments, variants and interpreted data from over 2,800 cancer patients. A dataset of this size requires months to download and significant resources to store and process. By making the ICGC data available in cloud compute form in the Collaboratory, researchers can bring their analysis methods to the cloud, yielding benefits from the high availability, scalability and economy offered by cloud services, avoiding a large investment in static compute resources and essentially eliminating the time needed to download the data. To facilitate the computational analysis on the ICGC data, the Collaboratory has developed software solutions that are optimized for typical cancer genomics workloads, including well tested and accurate genome aligners and somatic variant calling pipelines. We have developed a simple to use, but fast and secure, data transfer tool that imports genomic data from cloud object storage into the user’s compute instances. Because a growing number of cancer datasets have restrictions on their storage locations, it is important to have software solutions that are interoperable across multiple cloud environments. We have successfully demonstrated interoperability across The Cancer Genome Atlas (TCGA) dataset hosted at University of Chicago’s Bionimbus Protected Data Cloud, the ICGC dataset hosted at the Collaboratory, and ICGC datasets stored in the Amazon Web Services (AWS) S3 storage. Lastly, we have developed a non-intrusive user authorization system that allows the Collaboratory to authenticate against the ICGC Data Access Compliance Office (DACO) when researchers require access to controlled tier data. We anticipate that our software solutions will be implemented on additional commercial and academic clouds. The Collaboratory is actively growing, with a target hardware infrastructure of over 3000 CPU cores and 15 petabytes of raw storage. As of November 2016, the Collaboratory holds information on 2,000 ICGC PCAWG donors (500TB total). We anticipate expanding the Collaboratory to host the entire ICGC dataset of 25,000 donors (approximately 5PB) and to extend its data management and analysis facilities across multiple clouds. During the current closed beta phase, the Collaboratory has been successfully utilized by multiple research groups, most notably PCAWG project researchers who analyzed thousands of genomes at scale over a few weeks’ time. The Collaboratory will open to the public during the second quarter of 2017. We invite cancer researchers to learn more about our cloud resources at cancercollaboratory.org, and apply for access to the Collaboratory. Citation Format: Christina K. Yung, George L. Mihaiescu, Bob Tiernay, Junjun Zhang, Francois Gerthoffert, Andy Yang, Jared Baker, Guillaume Bourque, Paul C. Boutros, Bartha M. Knoppers, BF Francis Ouellette, Cenk Sahinalp, Sohrab P. Shah, Vincent Ferretti, Lincoln D. Stein. The Cancer Genome Collaboratory [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 378. doi:10.1158/1538-7445.AM2017-378