Efficient Task Scheduling and Fair Load Distribution Among Federated Clouds

The federated cloud is the future generation of cloud computing, allowing sharing of computing and storage resources, and servicing of user tasks among cloud providers through a centralized control mechanism. However, a great challenge lies in the efficient management of such federated clouds and fair distribution of the load among heterogeneous cloud providers. In our proposed approach, called QPFS_MASG, at the federated cloud level, the incoming tasks queue are partitioned in order to achieve a fair distribution of the load among all cloud providers of the federated cloud. Then, at the cloud level, task scheduling using the Modified Activity Selection by Greedy (MASG) technique assigns the tasks to different virtual machines (VMs), considering the task deadline as the key factor in achieving good quality of service (QoS). The proposed approach takes care of servicing tasks within their deadline, reducing service level agreement (SLA) violations, improving the response time of user tasks as well as achieving fair distribution of the load among all participating cloud providers. The QPFS_MASG was implemented using CloudSim and the evaluation result revealed a guaranteed degree of fairness in service distribution among the cloud providers with reduced response time and SLA violations compared to existing approaches. Also, the evaluation results showed that the proposed approach serviced the user tasks with minimum number of VMs.

Analysis and Design of Advance Scalable QoS Based Resource Provisioning Framework

In today’s growing cloud world, where users are continuously demanding a large number of services or resources at the same time, cloud providers aim to meet their needs while maintaining service quality, an ideal QoS-based resource provisioning is required. In the consideration of the quality-of-service parameters, it is essential to place a greater emphasis on the scalability attribute, which aids in the design of complex resource provisioning frameworks. This study aims to determine how much work is done in light of scalability as the most important QoS attribute. We first conducted a detailed survey on similar QoS-based resource provisioning proposed frameworks/techniques in this article, which discusses QoS parameters with increasingly growing cloud usage expectations. Second, this paper focuses on scalability as the main QOS characteristic, with types, issues, review questions and research gaps discussed in detail, revealing that less work has been performed thus far. We will try to address scalability and resource provisioning problems with our proposed advance scalable QoS-based resource provisioning framework by integrating new modules resource scheduler, load balancer, resource tracker, and cloud user budget tracker in the resource provisioning process. Cloud providers can easily achieve scalability of resources while performing resource provisioning by integrating the working specialty of these sub modules.

Automated Testing of Data Survivability and Restorability

Regular data backups are fundamental for protection against cyber-attacks and damage to infrastructure. To ensure a successful restoration, backed up data must be tested regularly for restorability to the company’s current environment. Cloud providers generally test their backedup data, but a testing framework is also required for locally stored files and databases. The paper proposes an automated test framework that validates the continued usability of backed up data for target restoration environments. The framework tests backups of Excel files, MySQL and Postgres databases, PDF documents and flat files.

Pricing strategies of an oligopolist in federated cloud markets

In this paper, we study how an oligopolist influences the coalition structure in federated cloud markets. Specifically, we use cooperative game theory to model the circumstances under which a cloud provider prefers to join a cloud federation vis-a-vis consider taking a price offer made by an oligopolist. We consider two price offering strategies for an oligopolist: non-adaptive and adaptive. In non-adaptive strategy, an oligopolist makes a price offer to all the cloud providers simultaneously. It can be noted that the oligopolist can buy-out all the cloud providers by making a price offer which is equal to a core allocation and the total price offer made by the oligopolist is equal to the value of the grand coalition. In adaptive strategy, the oligopolist approaches the cloud providers one after another in a sequential manner. We show that by using the adaptive strategy, the oligopolist can buy-out all the cloud providers at a total price offer which is less than that of the non-adaptive strategy.

An educational toolkit for teaching cloud computing

In an educational context, experimenting with a real cloud computing platform is very important to let students understand the core concepts, methodologies and technologies of cloud computing. However, API heterogeneity of cloud providers complicates the experimentation by forcing students to focus on the use of different APIs, and by hindering the jointly use of different platforms. In this paper, we present EasyCloud, a toolkit enabling the easy and effective use of different cloud platforms. In particular, we describe its features, architecture, scalability, and use in our cloud computing courses, as well as the pedagogical insights we learnt over the years.

VirIoT: A Cloud of Things That Offers IoT Infrastructures as a Service

Many cloud providers offer IoT services that simplify the collection and processing of IoT information. However, the IoT infrastructure composed of sensors and actuators that produces this information remains outside the cloud; therefore, application developers must install, connect and manage the cloud. This requirement can be a market barrier, especially for small/medium software companies that cannot afford the infrastructural costs associated with it and would only prefer to focus on IoT application developments. Motivated by the wish to eliminate this barrier, this paper proposes a Cloud of Things platform, called VirIoT, which fully brings the Infrastructure as a service model typical of cloud computing to the world of Internet of Things. VirIoT provides users with virtual IoT infrastructures (Virtual Silos) composed of virtual things, with which users can interact through dedicated and standardized broker servers in which the technology can be chosen among those offered by the platform, such as oneM2M, NGSI and NGSI-LD. VirIoT allows developers to focus their efforts exclusively on IoT applications without worrying about infrastructure management and allows cloud providers to expand their IoT services portfolio. VirIoT uses external things and cloud/edge computing resources to deliver the IoT virtualization services. Its open-source architecture is microservice-based and runs on top of a distributed Kubernetes platform with nodes in central and edge data centers. The architecture is scalable, efficient and able to support the continuous integration of heterogeneous things and IoT standards, taking care of interoperability issues. Using a VirIoT deployment spanning data centers in Europe and Japan, we conducted a performance evaluation with a two-fold objective: showing the efficiency and scalability of the architecture; and leveraging VirIoT’s ability to integrate different IoT standards in order to make a fair comparison of some open-source IoT Broker implementations, namely Mobius for oneM2M, Orion for NGSIv2, Orion-LD and Scorpio for NGSI-LD.

AI - Based Framework for Private Cloud Computing

Artificial Intelligence (AI) systems are computational simulations that are "trained" using information and expert input to duplicate a professional's choice given the same data. Only using one private cloud storage service to store information can cause a variety of issues for the system administrator. Knowledge providers, scalability, efficiency, privacy, and the potential of vendor support are examples of such concerns. Distributing information across several cloud storage services, comparable to how data is dispersed between various physical disk drives to increase error detection and increase productivity, is a possible approach. Moreover, because multiple private cloud providers have varying pricing strategies and service quality, maximizing the efficiency and profitability of many cloud providers at the same time is difficult. Based on access permission behaviors, this study presents a methodology for dynamically modifying network management rules across several cloud providers. The goal of this research is to look into how to reduce both the estimated cost and delay periods for numerous cloud providers. The architecture was put into practice in a cloud storage systems emulator, which simulated the complexity and effectiveness of numerous cloud providers in a real-world context. In particular, the architecture was evaluated in a variety of cloud storage environments. The outcomes of the platform's testing revealed that many cloud methods were successful.

A Review on Security Aspects and Countermeasures for Cloud Computing

For large-scale companies or people that desire a range of system services at a cheap cost, cloud computing is now the most popular phenomena to use. Personal information is frequently kept in a public cloud that is open to the public. This fundamental raises a number of concerns about cloud providers' flexible services, including confidentiality, persistence, and endurance. The paper aims to better understand cloud components, security concerns, and dangers, as well as developing solutions that might help minimise cloud vulnerabilities. It is a well-known truth that the cloud has been a viable hosting platform since 2008; nevertheless, the view of cloud security is that it requires major changes in order to achieve higher rates of adaptability at the corporate scale. Many of the difficulties affecting cloud computing need to be rectified immediately. The industry has made tremendous progress in combating cloud computing risks, but there is still work to be done to reach the level of maturity that traditional/on-premise hosting has.

Agent-based Multi-tier SLA Negotiation for Intercloud

The evolving intercloud enables idle resources to be traded among cloud providers to facilitate optimizing utilization and to improve the cost-effectiveness of service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, where cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, this paper proposed a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent’s behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent’s behavior. Moreover, this information can pass and guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can not only improve the performance of negotiation, but also enforce global consistency to improve the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and gives full play to the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.