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.

Trends in pneumatics – digitalization

The paper presents an overview of the latest technology trends in pneumatic automatization at Festo, focusing on digitalization from a component to a system level. The Festo Motion Terminal (VTEM) is valve terminal, designed for digitalization. Unique valve unit design allows valve functions to be defined by a software and to be changed in a running system very quickly, even on the fly. Model based applications on a valve controller offer many advanced functionalities such as: pneumatic servo positioning, force and torque control of pneumatic drives and pressure or flow regulation in pneumatic systems. VTEM native connectivity with higher order controllers adds a possibility to seamlessly integrate it on all levels, from the field to the cloud. User control logic and/or AI algorithms in a combination with digitalized pneumatics allows new services, such as: auto-commissioning, predictive maintenance, increased energy efficiency, automatic leakage detection within pneumatic systems and many others.

A survey on Content Based Image Retrieval in Cloud Environment with Privacy Preservation & Copy Deterrence

Importance of images in day to day life increased tremendously. Therefore Content Based Image Retrieval studied extensively. Cloud computing offers on demand services to cloud user therefore many organizations prefer to use cloud for data storage. To protect images with sensitive or private information needs to be encrypted before being outsourced to cloud. However, this causes difficulties in image retrieval and management. The purpose of this study is to provide privacy preservation and copy deterrence Content Based Image Retrieval method using Lucene Indexing. Keywords: CBIR, Lucene Indexing, Copy Deterrence.

An Efficient Technique for Virtual Machine Clustering and Communications Using Task-Based Scheduling in Cloud Computing

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

Reverse Auction-Based Services Optimization in Cloud Computing Environments

Cloud-based services have been increasingly used to provide on-demand access to a large amount of computing requests, such as data, computing, resources, and so on, in which it is vitally important to correctly select and assign the right resources to a workload or application. This paper presents a novel online reverse auction scheme based on online algorithm for allocating the cloud computing services, which can help the cloud users and providers to build workflow applications in a cloud computing environment. The online reverse auction scheme consists of three parts: online algorithm design, competitive ratio calculation, and performance valuation. The online reverse auction-based algorithm is proposed for the cloud user agent to choose the final winners based on Vickrey–Clarke–Groves (VCG) mechanism and online algorithm (OA). The competitive analysis is applied to calculate the competitive ratio of the proposed algorithm compared with the offline algorithm. This analysis method is significant to measure the performance of proposed algorithm, without the assumption of the distribution of cloud providers’ bids. The results prove that the proposed online reverse auction-based algorithm is the appropriate mechanism because it allows the cloud user agent to make purchase decisions without knowing the future bids. The difference of auction rounds and transaction cost can impressively influence and improve the performance of the proposed reverse auction algorithm.

A Heuristic Approach for Service Allocation in Cloud Computing

Cloud computing systems are considered complex systems, because of the various classes of services offered for users and the big challenges for providers to meet the increasing demands. Thus, service allocation is a critical issue in cloud computing. Fuzzy modeling is one choice to deal with such complexity. In this paper, the authors introduce a new heuristic service allocation model for cloud computing service allocation. Fuzzy sets are used to determine a candidate cloud for providing a service and crisp sets are used to serve requests from a cloud. Supply and demand are used as the fuzzy input to provide the desired heuristic allocation model for the candidate cloud, and a set of parameters are used to determine a cloud user needs.

Secure Key Storage and Access Delegation Through Cloud Storage

Cloud storage is gaining popularity to store and share files. To secure the files, cloud storage providers supply client interfaces with the facility to encrypt the files and upload them into the cloud. When client-side encryption is done, the onus of key management lies with the cloud user. Public key proxy re-encryption mechanisms can be used to distribute the key among stakeholders of the file. However, clients use low powered devices like mobile phones to share their files. Lightweight cryptography operations are needed to carry out the encryption operations. Ring-LWE-based encryption scheme meets this criterion. In this work, a proxy re-encryption scheme is proposed to distribute the file key. The scheme is proved CCA secure under Ring-LWE assumption in the random oracle model. The performance of the scheme is compared with the existing proxy re-encryption schemes which are observed to show better performance for re-encryption and re-key generation.

An Intelligent and Cost-Efficient Resource Consolidation Algorithm in Nanoscale Computing Environments

Because the Internet of things (IoT) and fog computing are prevalent, an efficient resource consolidation scheme in nanoscale computing environments is urgently needed. In nanoscale environments, a great many small devices collaborate to achieve a predefined goal. The representative case would be the edge cloud, where small computing servers are deployed close to the cloud users to enhance the responsiveness and reduce turnaround time. In this paper, we propose an intelligent and cost-efficient resource consolidation algorithm in nanoscale computing environments. The proposed algorithm is designed to predict nanoscale devices’ scheduling decisions and perform the resource consolidation that reconfigures cloud resources dynamically when needed without interrupting and disconnecting the cloud user. Because of the large number of nanoscale devices in the system, we developed an efficient resource consolidation algorithm in terms of complexity and employed the hidden Markov model to predict the devices’ scheduling decision. The performance evaluation shows that our resource consolidation algorithm is effective for predicting the devices’ scheduling decisions and efficiency in terms of overhead cost and complexity.