An Efficient Energy-Aware Tasks Scheduling with Deadline-Constrained in Cloud Computing (original) (raw)
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EATS: Energy-Aware Tasks Scheduling in Cloud Computing Systems
Procedia Computer Science, 2016
The increasing cost in power consumption in data centers, and the corresponding environmental threats have raised a growing demand in energy-efficient computing. Despite its importance, little work was done on introducing models to manage the consumption efficiently. With the growing use of Cloud Computing, this issue becomes very crucial. In a Cloud Computing, the services run in a data center on a set of clusters that are managed by the Cloud computing environment. The services are provided in the form of a Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The amount of energy consumed by the underutilized and overloaded computing systems may be substantial. Therefore, there is a need for scheduling algorithms to take into account the power consumption of the Cloud for energy-efficient resource utilization. On the other hand, Cloud computing is seen as crucial for high performance computing; for instance for the purpose of Big Data processing, and that should not be much compromised for the sake of reducing energy consumption. In this work, we derive an energy-aware tasks scheduling (EATS) model, which divides and schedules a big data in the Cloud. The main goal of EATS is to increase the application efficiency and reduce the energy consumption of the underlying resources. The power consumption of a computing server was measured under different working load conditions. Experiments show that the ratio of energy consumption at peak performance compared to an idle state is 1.3. This shows that resources must be utilized correctly without scarifying performance. The results of the proposed approach are very promising and encouraging. Hence, the adoption of such strategies by the cloud providers result in energy saving for data centers.
GreenSched: An intelligent energy aware scheduling for deadline-and-budget constrained cloud tasks
Simulation Modelling Practice and Theory, 2018
The constant growth of the energy crisis within the ICT Sector has persistently gained importance thereby prompting endeavors to curb growing energy demands and associated expenditures. This paper attempts to propose an intelligent energy aware task allocation and resource provisioning technique running in GreenSched model. The GreenSched model tends to exploit the heterogeneity of tasks and multi-core capacity of the varied nodes in the cloud environment and attempts to proactively schedule the deadline-and budget-constrained tasks on identified less energy consuming or energy aware nodes. It implements a Forward-only Counter Propagation Network (CPN) based intelligent scheduler unit that runs a scheduling technique to identify the best nodes for the task allocation process, one with least energy consumption and deadline-and budget-fulfilling capability. The nodes are clustered and classified by comparing their energy consumption values. The proposed algorithm has been implemented using the CloudSim toolkit and Kohonen and CP-ANN Toolbox with the help of Matlab TM platform. The experimental results exhibit that the proposed technique offers reduced energy consumption along with an overall improvement in the performance by meeting the deadline-and-budget constraints imposed by the users.
IJERT-A Survey of the Impact of Task Scheduling Algorithms on Energy-Efficiency in Cloud Computing
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/a-survey-of-the-impact-of-task-scheduling-algorithms-on-energy-efficiency-in-cloud-computing https://www.ijert.org/research/a-survey-of-the-impact-of-task-scheduling-algorithms-on-energy-efficiency-in-cloud-computing-IJERTV3IS10624.pdf Cloud computing is a recent and upcoming technology which includes various areas. One among them is energy conservation. Maintaining the efficiency of energy has become a major problem with increased usage of devices consuming more energy. Each and every person has a separate system in the current world. Many efforts have been taken to minimize energy consumption. In this paper, task scheduling is taken as the factor to reduce consumption of energy. Tasks can be assigned and scheduled based on the algorithms and so energy can be conserved.
An Energy-aware Real-time Task Scheduling Approach in a Cloud Computing Environment
Journal of AI and Data Mining, 2021
Interest in cloud computing has grown considerably over recent years, primarily due to scalable virtualized resources. So, cloud computing has contributed to the advancement of real-time applications such as signal processing, environment surveillance and weather forecast where time and energy considerations to perform the tasks are critical. In real-time applications, missing the deadlines for the tasks will cause catastrophic consequences; thus, real-time task scheduling in cloud computing environment is an important and essential issue. Furthermore, energy-saving in cloud data center, regarding the benefits such as reduction of system operating costs and environmental protection is an important concern that is considered during recent years and is reducible with appropriate task scheduling. In this paper, we present an energy-aware task scheduling approach, namely EaRTs for real-time applications. We employ the virtualization and consolidation technique subject to minimizing the ...
Task Scheduling Techniques for Energy Efficiency in the Cloud
EAI Endorsed Transactions on Energy Web
Energy efficiency is a key goal in cloud datacentre since it saves money and complies with green computing standards. When energy efficiency is taken into account, task scheduling becomes much more complicated and crucial. Execution overhead and scalability are major concerns in current research on energy-efficient task scheduling. Machine learning has been widely utilized to solve the problem of energy-efficient task scheduling, however, it is usually used to anticipate resource usage rather than selecting the schedule. The bulk of machine learning approaches are used to anticipate resource consumption, and heuristic or metaheuristic algorithms utilize these predictions to choose which computer resource should be assigned to a certain activity. As per the knowledge and research, none of the algorithms have independently used machine learning to make an energy-efficient scheduling decision. Heuristic or meta-heuristic approaches, as well as approximation algorithms, are frequently u...
Energy-Efficient Task Scheduling in Cloud Environment
IRJET, 2022
Cloud data centers consume large amounts of cooling power. Previous research was primarily based on the provision of mission plans, optimizing both computational power and overall performance gains. However, with the development of cloud facts, user needs are changing and cannot be met by traditional scheduling algorithms. One of the necessities is to maintain the value of cooling the facts in the middle as much as possible. According to literature reviews, the value of cooling power is half a million dollars. Executing value in a cloud environment requires a good mission planning approach. Temperature affects not only reliability, but also the overall performance, performance, and cost of embedded systems. This section describes the heat-aware task assignments and set of rules for cloud data centers. Mission plans are built in ways that not only reduce computational costs but also reduce cooling. Rigorous simulations were performed and compared to state-of-the-art graphics algorithms. Experimental results show that the rules of thermal task planning are superior to other strategies. This project aims to provide cloud data centers with a heat-aware task scheduling approach and improve data center performance by adopting task scheduling techniques. The basic motivation is to provide a valuable and powerful green algorithm through the fact center.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/task-scheduling-techniques-for-minimizing-energy-consumption-and-response-time-in-cloud-computing https://www.ijert.org/research/task-scheduling-techniques-for-minimizing-energy-consumption-and-response-time-in-cloud-computing-IJERTV3IS070485.pdf Cloud computing which provides services "on demand" is gaining increasing popularity. However, one of the most challenging problems in cloud computing is to minimize the power consumption in the data centers. The subject of Green Cloud Computing has emerged with the objective of reducing energy consumption. While reducing the energy consumption is of importance to Cloud Service Providers, performing the computation in minimum possible time (makespan) is of interest to users. In this paper we propose a technique to achieve the twin objective of minimizing the energy consumption as well as reducing the makespan of tasks. A method has been proposed and its effectiveness verified by simulating on CloudSim[21]. Results presented in this paper show the advantages of the proposed technique.
Energy Efficient Task Scheduling in Cloud Data Center
International Journal of Distributed and Cloud Computing, 2018
Cloud computing is emerging as a necessary need for the IT industry in order to reduce the setup and operational cost of its infrastructure. There is a huge requirement of computing resources to satisfy customer demands. A minute delay in a service may result in a measurable amount of loss for an organization. Response time is a major metric for evaluating performance of cloud applications. Cloud data centers form backbone of cloud computing. Data centers consume enormous amount of energy. Server racks have processing units, storage and network interface. Energy is dissipated at the server racks and cooling units. Various task scheduling algorithms and virtual machine scheduling algorithms have been proposed to measure the loss in performance but the energy loss is kept at the lowest priority. The paper is focused on discussing about the two techniques that maintain a scheduled routine for tasks arriving in a data center through a simulation scenario. VM-specific scheduling of tasks is done for assignment of the tasks to single or multiple virtual machines. Comparison of the two techniques, time-shared and space-shared technique is also done to give the reader a clear view about the situation in which both techniques are used. Future work is also discussed in the same context.
A Survey on Energy-Aware Scheduling Techniques in Cloud Computing Environment
Energy efficiency is one of the most primarily focused research issue in the cloud computing environment. Cloud offers various services (hardware/software) to the vast cloud consumers through internet. High end data centers are deployed by the cloud service provider (CSP) at the back end to provide cloud services to consumers. Datacenters consume high amount of energy which increases its operational expenditure as well as results in strong environment footprints. Therefore, efficient energy utilization in the datacenters is one of the primary research issue. To achieve the energy efficiency in cloud, various energy aware scheduling strategies are proposed in literature. This paper presents a survey of the proposed energy aware scheduling strategies in literature. The survey highlights the proposed energy efficiency strategy followed in each paper, its scope and results.
Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers
Journal of Cloud Computing: Advances, Systems and Applications, 2015
In this paper, we introduce a model of task scheduling for a cloud-computing data center to analyze energy-efficient task scheduling. We formulate the assignments of tasks to servers as an integer-programming problem with the objective of minimizing the energy consumed by the servers of the data center. We prove that the use of a greedy task scheduler bounds the constraint service time whilst minimizing the number of active servers. As a practical approach, we propose the most-efficient-server-first task-scheduling scheme to minimize energy consumption of servers in a data center. Most-efficient-server-first schedules tasks to a minimum number of servers while keeping the data-center response time within a maximum constraint. We also prove the stability of most-efficient-server-first scheme for tasks with exponentially distributed, independent, and identically distributed arrivals. Simulation results show that the server energy consumption of the proposed most-efficient-server-first scheduling scheme is 70 times lower than that of a random-based task-scheduling scheme.