Spider Monkey Optimization based Energy-Efficient Resource Allocation in Cloud Environment

The origin of Cloud computing is from the principle of utility computing, which is characterized as a broadband service providing storage and computational resources. It provides a large variety of processing options and heterogeneous tools, allowing it to meet the needs of a wide range of applications at different levels. As a result, resource allocation and management are critical in cloud computing. In this work, the Spider Monkey Optimization (SMO) is used for attaining an optimized resource allocation. The key parameters considered to regulate the performance of SMO are its application time, migration time, and resource utilization. Energy consumption is another key factor in cloud computation which is also considered in this work. The Green Cloud Scheduling Model (GCSM) is followed in this work for the energy utilization of the resources. This is done by scheduling the heterogeneity tasks with the support of a scheduler unit which schedules and allocates the tasks which are deadline-constrained enclosed to nodes which are only energy-conscious. Assessing these methods is formulated using the cloud simulator programming process. The parameter used to determine the energy efficiency of this method is its energy consumption. The simulated outcome of the proposed approach proves to be effective in response time, makespan, energy consumption along with resource utility corresponding to the existing algorithms.

ESCOVE: Energy-SLA-Aware Edge–Cloud Computation Offloading in Vehicular Networks

The vehicular network is an emerging technology in the Intelligent Smart Transportation era. The network provides mechanisms for running different applications, such as accident prevention, publishing and consuming services, and traffic flow management. In such scenarios, edge and cloud computing come into the picture to offload computation from vehicles that have limited processing capabilities. Optimizing the energy consumption of the edge and cloud servers becomes crucial. However, existing research efforts focus on either vehicle or edge energy optimization, and do not account for vehicular applications’ quality of services. In this paper, we address this void by proposing a novel offloading algorithm, ESCOVE, which optimizes the energy of the edge–cloud computing platform. The proposed algorithm respects the Service level agreement (SLA) in terms of latency, processing and total execution times. The experimental results show that ESCOVE is a promising approach in energy savings while preserving SLAs compared to the state-of-the-art approach.

Utilizing Virtual Crowd for Global Software Development

Software firms are interested in outsourcing and developing of software globally to the virtual crowd for minimizing the product cost and for increasing the software quality. Developments in information technology (IT) have changed the organizational working environment from centralized to disperse development working practices. As a result, companies have recognized the value of virtual world networks that offer benefits such as efficient time management, lower cost of growth, reduced travel costs, and access to larger competent team members to select the right skilled individual. With the wide spread of Web 3.0 applications and improvements in cloud computation technologies, multinational, multiskilled, and diverse crowds carry out the software developmental process. The aim of this research is to select the effective virtual crowd for the development of quality software. The proposed “characteristic-based virtual crowd selection (CBVCS)” method will select the crowd according to their unique characteristics such as their skills, experiences, expertise, and knowledge.

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.

A Generator Based Polynomial with Secret Encryption Scheme for Secure Data Sharing and Privacy in Multi-Party/Federated-Cloud Computation

Now a days exploring and analyzing or mining data in various ways give insights into future for invention and plays a critical role in decision making. For accurate analytical assertion of data, accurate results is essential. So hiding data and at the same time preserving data privacy is necessary to protect externals from attacks. An successful process for sharing sensitive information for data processing, validation and publication should then be deducted. In this paper Polynomial Based Encryption Secret Sharing Scheme (PBESSS) for Multi-Party mechanism is proposed that allows multiple parties to exchange secret data between them at the same time secret data is encrypted so as to protect from untrusted parties. Each party will have stronger protection by selected own polynomial with primitive root number ‘generator’ and the secret data will be in cryptic form and it can be found by each party after final computation of polynomials. This multi-party mechanism can be applied to federated cloud for computation securely.