A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm

Abstract

Cloud computing is one of the most important computing patterns that use a pay-as-you-go manner to process data and execute applications. Therefore, numerous enterprises are migrating their applications to cloud environments. Not only do intensive applications deal with enormous quantities of data, but they also demonstrate compute-intensive properties very frequently. The dynamicity, coupled with the ambiguity between marketed resources and resource requirement queries from users, remains important issues that hamper efficient discovery in a cloud environment. Cloud service discovery becomes a complex problem because of the increase in network size and complexity. Complexity and network size keep increasing dynamically, making it a complex NP-hard problem that requires effective service discovery approaches. One of the most famous cloud service discovery methods is the Ant Colony Optimization (ACO) algorithm; however, it suffers from a load balancing problem among the discovered nodes. If the workload balance is inefficient, it limits the use of resources. This paper solved this problem by applying an Inverted Ant Colony Optimization (IACO) algorithm for load-aware service discovery in cloud computing. The IACO considers the pheromones’ repulsion instead of attraction. We design a model for service discovery in the cloud environment to overcome the traditional shortcomings. Numerical results demonstrate that the proposed mechanism can obtain an efficient service discovery method. The algorithm is simulated using a CloudSim simulator, and the result shows better performance. Reducing energy consumption, mitigate response time, and better Service Level Agreement (SLA) violation in the cloud environments are the advantages of the proposed method.

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

“…Following the training procedure, the trained model could investigate the provided testing samples and, as a result, accurately anticipate the outcome [ 26 , 27 ]. Nevertheless, Machine Learning (ML) approaches have recently been recognized as a strategy for helping in disease identification and making an accurate diagnosis in less time [ 28 , 29 ]. As previously stated, ML approaches are an important component of AI, which alludes to the intelligence demonstrated by machines [ 30 , 31 ].…”

Nature‐inspired virtual machine placement mechanisms: A systematic review

“…The introduction of VMP in the data center introduces additional issues for service delivery. Because of excessive communication traffic and time, current placement methodologies for various cloud services, like video streaming and storage, are unable to provide network demanding services 20 . Hence, VMP in terms of link capacity is critical for cloud data centers 21,22 …”

“…Because of excessive communication traffic and time, current placement methodologies for various cloud services, like video streaming and storage, are unable to provide network demanding services. 20 Hence, VMP in terms of link capacity is critical for cloud data centers. 21,22 The VMP problem can be formulated into bin-packing models.…”

A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm

“…Cloud computing has become a popular service because of the rapid advancement of information and communication technology [1,2]. Cloud computing and data-storage systems use several technologies to link and manage required resources across several devices [3,4].…”