Cloud Federation Research Articles

Global reduction for geo-distributed MapReduce across cloud federation

Geo-distributed Bigdata processing is increasing day by day, resulting in the origins of data that are geographically distributed in different countries and hold datacenters (DCs) across the globe, and also the applications that use different sites to increase reliability, security, and processing performances. Most popular frameworks like Hadoop and Spark are re-designed to process geographically distributed data at their locations. However, these methods still suffer from a large amount of data transfer over the Internet, which prohibits a high processing time and cost for many applications, and in several cases, the output results of the computation are smaller than its inputs. In this paper, we keep the data locality principle for processing data at different locations but ignore the principle of transferring the entire intermediate results to a single global reducer. We propose Geo-MR, an intelligent geo-distributed MapReduce-based framework across federated cloud based on two heuristic algorithms: (i) chosen the best clusters as global reducers to reduce the communication and optimize the transfer on the bandwidth, GResearch. (ii) The second, Geo-MR, ensures the scheduling of only the relevant data to selected global reducers that process the final results. As a baseline, we propose an exact MapReduce scheduling model for benchmarking and to compare and discuss the Geo-MR heuristic algorithm results. The experimental results show that the proposed algorithm Geo-MR can improve resource (bandwidth and VMs of clusters) utilization of the cloud federation and consequently reduce cost and job response time.

Optimizing Cloud Resource Allocation in Federated Environments through Outsourcing Strategies

Cloud computing enables users to access required resources, with the invention of high-end devices leading to exponential increases in cloud resource requests. This poses significant challenges for resource management due to the scale of the cloud and unpredictable user demands. This paper presents an approach to managing resources during peak request periods for virtual machines (VMs) by leveraging cloud federation, outsourcing requests to other federation members. An algorithm is proposed to initiate cloud federation and allocate customer requests within it. The primary objectives are to increase the profit of cloud providers and improve resource utilization. An ensemble algorithm maximizes profit using both the proposed algorithm and three established ones. Experimental results demonstrate that our method outperforms existing approaches in profit, resource utilization, and rejected requests in most scenarios.

Blockchain-Based Solution for Supply Chain Data Integrity

Data plays a crucial role in today's world, guiding business decisions across various computer- assisted activities. Maintaining the integrity of data is vital, as any tampering could have serious consequences for important business decisions. This concern is particularly significant in cloud computing settings, where data owners have limited control over key aspects like physical storage and access control. Blockchain technology has emerged as an intriguing solution to address data integrity concerns. With its inherent properties, blockchain offers promising avenues for ensuring data integrity. However, some challenges need to be overcome, such as low throughput, high latency, and stability issues, which currently limit the practical implementation of blockchain-based solutions. we focus on a case study from the European SUNFISH project, which aims to develop a secure cloud federation platform for the public sector. We examine the specific data integrity requirements in cloud computing environments and identify the research questions that need to be addressed to implement blockchain-based databases effectively. We start by outlining the open research questions and the inherent difficulties associated with addressing them. Then, we propose a preliminary design for a blockchain-based database tailored to cloud computing environments. This design aims to leverage the strengths of blockchain technology while addressing the challenges unique to cloud computing. By addressing these research questions and proposing practical solutions, we aim to pave the way for the adoption of blockchain-based databases in cloud computing environments. This has the potential to enhance data integrity and security, ultimately benefiting organizations operating in the cloud.

SLA Aware Optimized Task Scheduling Model for Faster Execution of Workloads Among Federated Clouds

SLA Aware Optimized Task Scheduling Model for Faster Execution of Workloads Among Federated Clouds

Trustless Collaborative Cloud Federation

Trustless Collaborative Cloud Federation

Secure Federated Cloud Storage Protection Strategy Using Hybrid Heuristic Attribute-Based Encryption With Permissioned Blockchain

Secure Federated Cloud Storage Protection Strategy Using Hybrid Heuristic Attribute-Based Encryption With Permissioned Blockchain

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.

A Research Perspective on Data Management Techniques for Federated Cloud Environment

Cloud computing has given a large scope of improvement in processing, storage and retrieval of data that is generated in huge amount from devices and users. Heterogenous devices and users generates the multidisciplinary data that needs to take care for easy and efficient storage and fast retrieval by maintaining quality and service level agreements. By just storing the data in cloud will not full fill the user requirements, the data management techniques has to be applied so that data adaptiveness and proactiveness characteristics are upheld. To manage the effectiveness of entire eco system a middleware must be there in between users and cloud service providers. Middleware has set of events and trigger based policies that will act on generated data to intermediate users and cloud service providers. For cloud service providers to deliver an efficient utilization of resources is one of the major issues and has scope of improvement in the federation of cloud service providers to fulfill user’s dynamic demands. Along with providing adaptiveness of data management in the middleware layer is challenging. In this paper, the policies of middleware for adaptive data management have been reviewed extensively. The main objectives of middleware are also discussed to accomplish high throughput of cloud service providers by means of federation and qualitative data management by means of adaptiveness and proactiveness. The cloud federation techniques have been studied thoroughly along with the pros and cons of it. Also, the strategies to do management of data has been exponentially explored.

Multi-cloud service provision based on decision tree and two-layer Restricted Monte Carlo Tree Search

Compared with traditional service provision methods, cloud computing has many advantages, such as on-demand services and flexible pricing systems, attracting widespread attention from both academia and industry. However, the abundance of cloud services makes it difficult for users to choose suitable ones. Furthermore, the cloud is usually a dynamically changing execution environment, requiring users to constantly switch service carriers for the best experience. Therefore, to improve the efficiency of service interactions in a multi-cloud environment and cope with the high concurrency demands, this paper proposes a multi-agent and distributed broker-based cloud service provisioning model. Equipped with a decision tree-based user preference learning module, brokers in the proposed model can continuously adjust their resource introduction strategy through training on historical data. In the process of cloud service interaction, brokers select the best cost-effective service for users within a limited time through a two-layer Restricted Monte Carlo Tree Search (RMTCS) algorithm. This paper develops a multi-cloud service interconnection system based on the Libcloud framework. Shielding the different interfaces of different cloud service providers in cloud virtual machine and storage services provides the most suitable services for users and realizes the unified cloud federation service supply. The experiment results prove that the proposed model achieves better performance over the traditional methods and can better meet the service requirements of users.

Prospects for the Formation of the Information Space of the Regional Level in the Digital Economy

The directions of development of socio-economic information systems used for regional management are considered. Systems that need to be upgraded in accordance with modern capabilities and requirements of the digital economy. As a result of the consistent implementation of many private software solutions to achieve individual tasks, the so-called “patchwork” (“niche”) automation has been formed, aimed at creating socio-economic information, which should fundamentally be systematic and consistent. Currently, the sources of statistical data differ in structure, format, composition of indicators and periodicity of the generated data, their state is characterized by the fragmentation of the software systems used, data storage forms and the data itself. Collecting and processing this data requires a lot of money, but the result does not meet the increased requirements of users. The existing initiatives to create a single space of socio-economic information in the regional context are described: the Volgograd Regional Statistical Information and Analytical System (RSIAS), the interdepartmental Information and Analytical System (EMISS), the central analytical platform (CAP), the national data management system (NMS), the federal cloud platform of the state market “Gostech”. The article assesses the efforts being made to integrate information about the regions into a single information space. The proposed ways of integrating economic information into a single information space are considered. A conceptual approach to such integration based on ontologies of the subject area of socio-economic relations is proposed.

A Market-based Framework for Resource Management in Cloud Federation

A Market-based Framework for Resource Management in Cloud Federation

WifiZoneCloud: A Cloud-Based Wi-Fi Hotspot Platform

Today, there are millions of Wi-Fi hotspots available by diverse service providers such as operators, public spaces, enterprises, and even cities. With the developments on OpenRoaming approach, cloud federation, automatic global roaming, efficient user onboarding and network automation have become important requirements for stakeholders within the ecosystem. This study aims to present a cloud based hotspot platform solution called WifiZoneCloud which enriches the limited hotspot capabilities of new generation firewall, access point and similar network devices; provides roaming between different locations of an institution; keeps the use of the network under control and makes the network reportable, and even performs required logging. With diverse authentication tools, the platform ensures high security according to the needs of users. Above all, these operations are completely administered in the cloud environment. The proposed new platform is likely to pave the way for the establishment of the global Wi-Fi network and present valuable opportunities in the 5G era.

A multi-objective cloud energy optimizer algorithm for federated environments

Energy is a considerable portion of the cost of a cloud data center. In addition to energy, carbon emission tax imposes another cost on data centers, which have different policies in different cities around the world. Most of the cloud data centers are geographically distributed, which can have a huge advantage in reducing such costs. In addition, in recent years, cloud federation, in which multiple cloud providers share their IT infrastructures voluntarily, could play a crucial role in minimizing the energy consumption of cloud data centers. On the other hand, improper VM placement results frequent VM migration, constant turning off/on physical machines, service quality degradation and energy consumption increase. To tackle these weaknesses, we propose a multi-objective algorithm, Called OUR-ACS, to minimize data centers' energy consumption, carbon emission, and the total expenses (energy cost + carbon tax) in a federated environment considering both initial VM placement and VM consolidation approaches. We evaluate the efficiency of our proposed algorithm by using the ClousSim Plus simulator toolkit using real-world datasets. Compared to the competing algorithms, our simulation results indicate that our proposed algorithm could reduce the total energy consumption, carbon emission, and the total costs of multiple cloud data centers by an average of 37.6%, 41%, and 25%, respectively, in a federated environment.

A pricing approach for optimal use of computing resources in cloud federation

A pricing approach for optimal use of computing resources in cloud federation

A cost‐aware approach for cloud federation formation

AbstractThe number of computing resources required for high‐volume applications is ever‐increasing. As far as the users' requests generate workload variations over time and the resources of cloud servers that respond to the requests are limited, the quality of services (QoS) of the cloud server is experiencing fluctuations. The cloud federation is a place where the cloud service providers work together to overcome the problem of resource scarcity and poor service quality. As a result, the main aim of forming a cloud federation is to create an optimal one in a way that provides the required resources and QoS for customers and maximum profit for the providers. Since the service providers play a key role in cloud federation formation, and their absence deteriorates the QoS of the federation, it is required to form an optimal cloud federation that provides the best profit for service providers to encourage them to remain in the federation. In the present paper, first, a model will be introduced for evaluating the cloud service providers, inspired by the concepts of macroeconomics. Then a greedy algorithm will be presented for forming an optimal federation. The proposed algorithm uses the evaluation results to rank the service providers and selects the best federation based on this ranking. In addition, the reputation of providers is evaluated based on the proposed method, which is a criterion for introducing the best providers. Furthermore, the surplus shared resources of the providers will be determined using the evaluation results and its optimal value will be proposed to cloud service providers using the proposed method. Finally, a set of tests will be performed on the introduced system.

Queuing-Based Federation and Optimization for Cloud Resource Sharing

Resource sharing can gain economies of scale and increase utilization of cloud infrastructure, a critical challenge of which is how to design efficient resource sharing solutions among self-interested cloud providers. Cloud federation can realize resource sharing, but the existing methods of forming federation need complex computation to guarantee the stability of federation. To address this shortcoming, after analyzing an optimal allocation approach of service requests among clouds, we propose a pareto optimal resource sharing solution named Cloud Light-Federation Sharing (CLFS), in which each cloud can choose its own optimal strategies individually and federation can be formed without complex computation for allocation of service requests and profits. In addition, an optimal resource sharing solution named Cloud Cooperative-Federation Sharing (CCFS) was also designed, in which cloud providers are fully cooperative and have fair profit allocation. The experimental results show that the two federation methods can significantly improve the total utility and decrease the number of dropped jobs. Although the federation rules of Cloud Light-Federation Sharing are simple, its performance is still very close to that of Cloud Cooperative-Federation Sharing.

An improved pricing approach based on combinatorial auction method in cloud environment

Cloud pricing models are essential in the context of the increasing availability of cloud service providers across the Internet and the willingness of users to use high-quality and affordable services. The double-sided combinatorial auction model has been used extensively due to its inherent characteristics. This paper presents a cost-effective pricing method that considers both the forward market and spot market. Moreover, resource requests are workflow or hybrid to support scalability and improve the number of federal cloud response requests. The evaluation results indicate more profit for both buyers and vendors simultaneously. Furthermore, the proposed algorithm reduces the waiting time and the number of pending demands, increasing users' satisfaction relative to cloud services.

Proactive Fault-Tolerance Technique to Enhance Reliability of Cloud Service in Cloud Federation Environment

Cloud federation is a new computing paradigm that has paved the way for cloud service providers (CSPs) to offer their unused resources (virtual machine) to other CSPs when their resource demands are low. Federation also allows CSPs to outsource their resource requests to other CSPs when their computing resources’ demands are high. Thus, in cloud federation environment reliability and availability of services offered by service providers increase as the CSPs are able to share their resources among themselves. Moreover, to maintain the reliability and availability of cloud services offered through federation, it is important that the computational environment of member CSPs within the federation is fault tolerant. Therefore, there is a need for fault tolerant system to guarantee cloud service reliability and availability in cloud federation environment. In this article, we propose a proactive fault tolerance system that preempts faults within the federation on the basis of CPU temperature. The fault tolerance system within the federation is modeled as a multi-objective optimization problem of maximizing profit and minimizing migration cost while redistributing resources (virtual machine) from faulty CSPs to non-faulty CSPs within the federation. To address this issue, we have also proposed an algorithm called Preference Based Fault Management (PBFM) to manage the federation in the event of faults. We perform extensive experiments to evaluate the effectiveness of our proposed mechanism and compare it with two other mechanisms MCAFM (Migration Cost Assured Fault Management) and PAFM (Profit Assured Fault Management). Results show that our proposed mechanism PBFM yields an optimized solution to the general problem of profit and migration cost trade-off in presence of faulty CSPs.

Optimized load balance scheduling algorithm

The cloud computing environment faces several challenges as a federation of clouds, controlling the traffic flow, scalability, and balancing the load on virtual machines that are considered the most crucial issue due to their impact on the execution time, resource utilization, and cost. This paper is interested in some of the existing algorithms that distribute the workload evenly. These algorithms aim to avoid the blind assignment that often results in some over-loaded servers while another node might be under-loaded. In this work a combination of two inspired metaheuristic algorithms BAT and cuckoo search was proposed; the first algorithm can utilize fast exploration using global search, the latter algorithm can avoid trapping into BAT local optimum problem using levy flight with a far random walk. Additonaly, the proposed algorithm could be used to mitigate distributed denial of service (DDoS) attack that aims to cause endless load on the servers and stop the service. Experimental results for five virtual machine (VM), ten VM, with the varying number of tasks showed that the proposed algorithm has better resource utilization and less makespan time in almost all the cases.

Implementation and Validation of the Optimized Deduplication Strategy in Federated Cloud Environment

Cloud computing technology is the culmination of technical advancements in computer networks, hardware and software capabilities that collectively gave rise to computing as a utility. It offers a plethora of utilities to its clients worldwide in a very cost-effective way and this feature is enticing users/companies to migrate their infrastructure to cloud platform. Swayed by its gigantic capacity and easy access clients are uploading replicated data on cloud resulting in an unnecessary crunch of storage in datacenters. Many data compression techniques came to rescue but none could serve the purpose for the capacity as large as a cloud, hence, researches were made to de-duplicate the data and harvest the space from exiting storage capacity which was going in vain due to duplicacy of data. For providing better cloud services through scalable provisioning of resources, interoperability has brought many Cloud Service Providers (CSPs) under one umbrella and termed it as Cloud Federation. Many policies have been devised for private and public cloud deployment models for searching/eradicating replicated copies using hashing techniques. Whereas the exploration for duplicate copies is not restricted to any one type of CSP but to a set of public or private CSPs contributing to the federation. It was found that even in advanced deduplication techniques for federated clouds, due to the different nature of CSPs, a single file is stored at private as well as public group in the same cloud federation which can be handled if an optimized deduplication strategy be rendered for addressing this issue. Therefore, this study has been aimed to further optimize a deduplication strategy for federated cloud environment and suggested a central management agent for the federation. It was perceived that work relevant to this is not existing, hence, in this paper, the concept of federation agent has been implemented and deduplication technique following file level has been used for the accomplishment of this approach.