A RESOURCE ALLOCATION SCHEME FOR CLOUD-BASED IOT APPLICATIONS BASED ON AN ENERGY-EFFICIENT MAKESPAN TASK SCHEDULING ALGORITHM

The massive growth of cloud computing leads to huge amounts of energy consumption and release of carbon footprints as data centers are housed by a large number of servers. Consequently, the cloud service providers are looking for eco-friendly solutions to reduce energy consumption and carbon emissions. As a result, task scheduling has drawn attention, in which efficient resource utilization and minimum energy consumption take into great consideration. This is an exigent issue, especially for the heterogeneous environment. In this work, we put forward an energy-efficient task scheduling algorithm (ETSA) to address the demerits associated with task consolidation and scheduling. The proposed algorithm ETSA takes into account the completion time and total utilization of a task on the resources, and follows a normalization procedure to make a scheduling decision. We evaluate the proposed algorithm ETSA to measure energy efficiency and makespan in the heterogeneous environment. The experimental results are compared with recent algorithms, namely random, round robin, dynamic cloud list scheduling, energy-aware task consolidation, energy-conscious task consolidation and MaxUtil. The proposed algorithm ETSA provides an elegant trade-off between energy efficiency and makespan than the existing algorithms.

In addition to cutting down on energy use, the green cloud-based service also drastically cuts down on operating expenses. Strongly coupled data centers demand controlled energy, constant performance, and overall optimization of excess energy consumption in order to do considerable calculations, which in turn give entire processing influence from a wide collection of resources. An energy-saving scheduling method is the focus of the research, which makes use of green cloud technologies. In recent years, cloud computing has been integrated into several domains, including computing, research, industry, and business. The need for specialized hardware along with additional resources is rendered obsolete by the provision of many services across the internet through cloud computing. Problems with energy efficiency, resource heterogeneity, and resource usage are only a few of the issues that cloud computing systems encounter. Consolidation approaches like as task scheduling as well as virtual machines (VMs) are used to address these concerns. There has been a plethora of research on task scheduling. Researchers have examined the issue using a variety of metrics and aims. The essay delves into the topic of virtualized cloud data centers' energy usage and effective resource use. Task categorization and thresholds form the basis of the proposed method, which aims to improve resource use and scheduling efficiency. Every company needs a highly efficient structure that is both flexible and homogeneous across all of their cloud environments. This research aims to reduce energy consumption in green cloud systems by using a hybrid scheduling strategy that combines a minimal completion time with a priority-based weighted round-robin. An Artificial Neural Network (ANN) based load balancing method is presented in this study. ANN makes demand predictions and then distributes resources accordingly. This method uses less energy than the cautious over-provisioning strategy since it constantly adjusts the number of active servers based on current demand. In addition, a server that is operating at a high utilization may handle more task with the same amount of power, but it will use more power overall. At last, we take a look at the current state of load balancing in cloud computing and compare it to others using different metrics.

The information technology (IT) and mobile computing industries are now in the development stages of cloud computing (CC). Instead of being purchased, resources such as software, CPUs, memory, I/O hardware, and others are used and charged as needed. The massive expansion of CC necessitates enormous energy consumption, or data centers house a diverse spectrum of computers. Consequently, cloud service providers are exploring low-cost strategies for reducing energy use and carbon emissions. Therefore, work planning has garnered great attention and critical consideration about effective resources and bad energy consumption. This paper proposes a machine learning technique called short-term or Long-Term Memory (LSTM) for efficient power task scheduling to address growing carbon or energy emissions. The recommended strategy for scheduling considers the finish time or exclusive usage of a resource task, as well as the standardizing process. The Novel Black Window is used to reduce weight and improve the performance of LTSM. The simulated analysis is used to evaluate the efficiency of the LSTM-NBW in aspects of makes pan, power consumption, task completion time, and resource utilization. The findings show that the suggested model only obtained 400KWh more for the 80kB user job than the original LSTM model.

Cloud computing and big data: Technologies and applications

Cloud computing is growing massively in the world of information technology. As a result of which the number of servers and virtual machines is increasing day by day. It leads to huge energy consumption and carbon emission. In this regard, task scheduling has drawn the attention of cloud service providers to reduce energy consumption and increase cloud utilization. This is a crucial issue in a heterogeneous cloud environment. In this paper, energy consumption issues of cloud datacenters are addressed and an Energy-Aware Cloud Task Scheduling (EACTS) algorithm is proposed. It is taking the concept from traditional min-min, max-min, and sufferage heuristics and merge with the energy model. These heuristics are implemented on the heterogeneous cloud environment. EACTS energy model focuses on the estimation of energy consumption in cloud datacenters. The proposed algorithms estimated the makespan, cloud utilization, and energy consumption for a benchmark dataset. The experimental results obtained from EACTS algorithm provides a valid arbitration between energy efficiency and makespan. It has shown the comparison between above mentioned algorithms for various scheduling parameters.

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.

ABSTRACTTwo of the most essential elements of cloud resource management systems are resource management as well as scheduling. Due to the heterogeneity of resources, their interdependencies, and the unpredictable nature of load in a cloud environment, cloud Resource Scheduling (RS) is the most important issue to handle effectively. One of the most challenging tasks in cloud computing is RS, where resources must be assigned to the necessary tasks or jobs in accordance with the necessary Quality of Service (QoS) of the cloud applications. This study suggests a unique method for scheduling cloud resources based on a virtual data center and machine learning model. The genetic reinforcement kernel swarm optimization methodology and cloud data centers are deployed here. The suggested network analysis aims to balance energy usage and SLA. The suggested framework is assessed by doing experiments on the Google Cluster dataset, Planet Lab, and Bitbrains VM traces, as well as three real‐world workload datasets. Execution cost, Execution time, Makespan, Energy consumption, Resource utilization, and Scalability are factors that were examined here. The proposed framework is superior to several performance indicators compared to state‐of‐the‐art techniques. The project's methodology addresses the issues of load imbalance and excessive migration expenses.

Multi objective trust aware task scheduling algorithm in cloud computing using whale optimization

Cyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms.

With ever increasing usage of IT infrastructure around the globe, the increase in energy consumption and carbon emission is a worrisome situation for everyone because of its direct effect on environment. The environment is being dangerously impacted by Information Technology infrastructure extensive use and its waste. Cloud computing has emerged as one step further solution to deploy all virtualized IT resources as per need on self-service basis as a rental method for the users. Different cloud deployment methods have emerged according to the need of users, institutions and communities. In last decade virtualization has been a milestone in enabling optimal usage of information technology resources. Cloud computing has provided a platform for deploying everything as a Service in IT, which has changed the economics of IT based business and organizations from Capital Expenditure to Operational Expenditure. For enabling Cloud services we first need to understand our existing infrastructure. In second step we should be able to virtualize it for optimal usage. Then with the help of existing cloud applications we can deliver and deploy information technology as a resource at various levels such as infrastructure, platform, and software. Keywords; cloud computing, carbon emission, energy consumption, IT waste, virtualization. The Advancement in the field of information technology has enabled us to optimize the use of physical infrastructure with the help of virtualization, most of which is only used in specific applications like vitalizing a local area network on switch or vitalizing a compute resource to run more than one operating system on a desktop. The benefits of same has not been completely implemented and utilized in industrial environments where we require a large amount of IT resources. The advantage of using cloud is to enable complete utilization of Information technology resources, where most of the resources are underutilized. The cost involved in maintenance and management of those resources is huge which can be saved due to centralized management of private cloud infrastructure. We create private cloud keeping in mind. • Availability and performance requirements for the cloud • Infrastructure sizing, performance and continual availability requirements • The need for data security or isolation • The appropriate user interface With advent of IT as a service in cloud computing technology energy consumption and related carbon emissions and waste are considerably lesser than in-house implementation. Technology in Cloud Computing creates an able structure for sharing resources with the help of virtualization among many; it greatly decreases or abolishes the need for most products, equipment, utilities and facilities. The results are more sustainable IT infrastructures and processes which help in keeping environment safe. • Highest resource consumption and effectiveness • Reduced energy consumption by up to 95 % with the help of virtualization which optimizes the use of physical infrastructure and thereby helps IT to be delivered as a service. • Displaced utility costs Workstation utility costs are cut by 90% due to change of capital expenditure into operational expenditure in industries or organizations across the world. • Reduced electronic waste as No more batteries and minimal software/hardware waste. • Reduced waste overall due to consumption of computational power as service, no more as infrastructure. • Reduced ecological impact of workstations as compared to thin clients as anything related to computation can be provided as a service on a client connected with internet to the cloud provider.

In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths. Task scheduling is developed using modified artificial ecosystem optimization (AEO), combined with negative swarm operators, Salp Swarm Algorithm (SSA), in order to competitively optimize their capabilities during the exploitation phase of the optimal search process. In addition, the proposed strategy, Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm (EAEOSSA), attempts to find the most suitable solution. The optimization that combines cost and energy for multi-objective task scheduling optimization problems. The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well. A comparison was made between the proposed strategy and other strategies in terms of time, cost, energy, and productivity. Experimental results showed that the proposed strategy improved energy consumption, cost, and time over other algorithms. Simulation results demonstrate that the proposed algorithm increases the average cost, average energy consumption, and mean service time in most scenarios, with average reductions of up to 21.15% in cost and 25.8% in energy consumption.

With the application of cloud computing services in more and more fields, it will undertake more computing tasks and storage tasks. The problem of high energy consumption in data centers will become more serious. Virtualization technology is very important in cloud computing, which can improve the utilization rate of resources. At the same time, it has flexibility in resource scheduling and can integrate multiple virtual machines to achieve power efficiency. Using online virtual machine migration technology for energy-saving planning in cloud environment is a hot research topic in academic circles. The scheduling strategy proposed in this paper can reduce the server downtime and the number of server hosts, so as to achieve the maximum use of resources. The main research work of this paper includes the following aspects: Firstly, the energy consumption in the process of virtual machine energy-saving design is modeled, and the relationship between energy consumption and resource usage under different load conditions is analyzed, and the problems are abstracted, e.g., packaging box problem. Secondly, this paper uses genetic algorithm to solve the problem of high energy consumption. Finally, based on the target allocation scheme of virtual machines obtained by the above method, the migration problem of virtual machines is abstracted as the problem of finding the maximum weighted independent set of graphs. And a greedy algorithm is designed to solve this problem. In this paper, CloudSim simulation platform is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed algorithm can reduce data energy consumption and avoid frequent migration of virtual machines.

Energy-efficient resource allocation in cloud infrastructure using L3F-MGA and E-ANFIS

Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review