Performance Of Cloud Infrastructure Research Articles

Neural network inspired efficient scalable task scheduling for cloud infrastructure

The rapid development of Cloud Computing in the 21st Century is landmark occasion, not only in the field of technology, but also in the field of engineering and services. The development in cloud architecture and services has enabled fast and easy transfer of data from one unit of a network to other. Cloud services support the latest transport services like smart cars, smart aviation services and many others. In the current trend, smart transport services depend on the performance of cloud Infrastructure and its services. Smart cloud services derive real time computing and allows it to make smart decision. For further improvement in cloud services, cloud resource optimization is a vital cog that defines the performance of cloud. Cloud services have certainly aimed to make the optimum use of all available resources to the become as cost efficient and time efficient as possible. One of the issues that still occur in multiple Cloud Environments is a failure in task execution. While there exist multiple methods to tackle this problem in task scheduling, in the recent times, the use of smart scheduling techniques has come to prominence. In this work, we aim to use the Harmony Search Algorithm and neural networks to create a fault aware system for optimal usage of cloud resources. Cloud environments are in general expected to be free of any errors or faults but with time and experience, we know that no system can be faultless. With our approach, we are looking to create the best possible time-efficient system for faulty environments, Where the result shows that the proposed harmony search-inspired ANN model provides least execution time, number of task failures, power consumption and high resource utilization as compared to recent Red fox and Crow search inspired models.

Optimizing Performance of Cloud Infrastructure Through Effective Resource Scheduling

Cloud computing has emerged as a very promising technology that has garnered significant interest from both industry professionals and academic researchers. Cloud computing service models refer to the various types of services that are provided, including hardware and software infrastructure, platforms for application development, testing, and deployment, as well as enterprise software that is readily available for usage through subscription. Public cloud computing involves the delegation of IT infrastructure, storage, or applications to an external service provider. The presence of a cloud infrastructure also signifies the existence of geographically distributed computing resources. The utilisation of resources in conjunction with cloud computing is not exclusive to large-scale organisations, as it may be employed by entities of any size. Numerous services are offered based on a fee-for-use model, rendering them cost-effective for organisations of various sizes. Cloud service providers are obligated to provide consumers with cloud services on demand, as there is a growing need for such services. This requirement stems from the necessity to decrease the size of large data volumes, which in turn leads to cost savings in maintaining extensive storage systems. The overall effectiveness of cloud computing environments is directly related to the operational performance of cloud infrastructure. This phenomenon holds substantial significance in the realm of optimization, since it enhances the overall efficiency of the underlying cloud architecture. The proposed technique exhibits a significant effectiveness in enhancing cloud performance, as it manifests improvements for both service providers and cloud customers.
 Key Words: Cloud Computing, Task Scheduling, Cloud Infrastructure, Resource Scheduling, Performance Analysis and Infrastructure as Service.

Fault aware intelligent resource allocation using Big Bang-Big Crunch trained neural network for cloud infrastructure

This Ongoing COVID-19 epidemic situation, which has resulted in the loss of lives and economics. In this scenario, social distancing is the only way to prevent ourselves. In such a scenario to boost the economy, a globally large number of industries and businesses have shifted their system to cloud-like education, shipping, training and many more globally. To support this transition cloud services are the only solution to provide reliable and secure services to the user to sustain their business. Due to this, the load over the existing cloud infrastructure has drastically increased. So it is the responsibility of the cloud to manage the load over the existing infrastructure to maintain reliability and serve high-quality services to the user. Task allocation in the cloud is one of the key features to optimize the performance of cloud infrastructure. In this work, we have proposed a prediction-based technique using a pre-trained neural network to find a reliable resource for a task based on previous training and history of cloud and its performance to optimize the performance under the overloaded and under loaded situation. The main aim of this work is to reduce the fault and provide high performance by reducing scheduling time, execution time and network load. The proposed model uses the Big Bang Big Crunch algorithm to generated huge datasets for training our neural model. The accuracy of the BB-BC-ANN model is improved with 98% accuracy.

An Efficient Resource Scheduling Framework for Infrastructure Performance Enhancement in Cloud Environment

On-demand cloud services must be provided to customers at any time by ways of cloud service providers due to cloud demand. It is obligatory for cloud service providers to lessen large volumes of data, thereby it can reduce costs for maintaining large storage systems.Infrastructure level performance is an important problem which directly affects the overall working of cloud computing environment. The objective of our framework is enhancing the performance of cloud infrastructure. Proposed approach demonstrates high effective in cloud performance enhancement, as it displays enhancement in both the service providers as well as for cloud users.

An Automated Approach to Cloud Performance Benchmarking

Cloud infrastructure has been supplied for many years as an on-demand service across numerous different providers. The different instance types are provided with hardware specifications, but no concrete performance metrics. Benchmarking virtual machines is one method to contrast and compare between the different services offered across several providers. This paper presents a tool which permits the automated collection of cloud infrastructure performance information, to compare against the cost of these services.

Task scheduling in cloud environment: A multi-objective ABC framework

Cloud computing is an emerging distributed, low cost computing paradigm with a large collection of heterogeneous autonomous systems. It provides, on demand, flexible and scalable services to customers through a pay per use basis. The overall performance of cloud infrastructure depends on task assignment and scheduling. Efficient task scheduling reduces power consumption of the cloud infrastructure and increase the profit of service providers by reducing processing time of the user job. This research focuses on efficient task scheduling using multi-objective Artificial Bee Colony Algorithm (TA-ABC). The proposed algorithm optimizes the energy, cost, resource utilization and processing time of the cloud environment. The results obtained by TA-ABC is also simulated by an open source cloud platform (CloudSim). Further, the proposed algorithm (TA-ABC) provide an optimal balance results for multiple objectives and the results are comparable to the state-of-the-art existing scheduling algorithms.

Identifying Overloaded Servers and Managing Dynamic Placement of Virtual machines in Cloud

Cloud computing is becoming one of the most popular commercial infrastructure due to its little maintenance expense and on demand resource utilization. Cloud computing possesses many kinds of technical challenges such as fault tolerance, reliability, availability, integrity etc. due to its complex and distributed nature. But the main problem related to all those is overload incurred by Virtual Machines (VM). So, load balancing is one of the most significant issues that can help to gain rapid performance of cloud infrastructure. This research proposes algorithms for detecting failed servers due to overloaded VMs. The failure detection algorithm checks server status after a predefined time interval. This algorithm gives proactive technique to deal with overloaded VMs. When any failure in the server is found, the resource balancing algorithm migrates its VMs to an adequate healthy Physical Machine (PM). To distribute workload evenly, the resource utilization skew is measured. This VM to PM mapping is done in a way that every PM will do almost equal amount of work. General Terms Cloud computing

Smart CloudBench—A framework for evaluating cloud infrastructure performance

Cloud migration allows organizations to benefit from reduced operational costs, improved flexibility, and greater scalability, and enables them to focus on core business goals. However, it also has the flip side of reduced visibility. Enterprises considering migration of their IT systems to the cloud only have a black box view of the offered infrastructure. While information about server pricing and specification is publicly available, there is limited information about cloud infrastructure performance. Comparison of alternative cloud infrastructure offerings based only on price and server specification is difficult because cloud vendors use heterogeneous hardware resources, offer different server configurations, apply different pricing models and use different virtualization techniques to provision them. Benchmarking the performance of software systems deployed on the top of the black box cloud infrastructure offers one way to evaluate the performance of available cloud server alternatives. However, this process can be complex, time-consuming and expensive, and cloud consumers can greatly benefit from tools that can automate it. Smart CloudBench is a generic framework and system that offers automated, on-demand, real-time and customized benchmarking of software systems deployed on cloud infrastructure. It provides greater visibility and insight into the run-time behavior of cloud infrastructure, helping consumers to compare and contrast available offerings during the initial cloud selection phase, and monitor performance for service quality assurance during the subsequent cloud consumption phase. In this paper, we first discuss the rationale behind our approach for benchmarking the black box cloud infrastructure. Then, we propose a generic architecture for benchmarking representative applications on the heterogeneous cloud infrastructure and describe the Smart CloudBench benchmarking workflow. We also present simple use case scenarios that highlight the need for tools such as Smart CloudBench.