RACS: A framework for Resource Aware Cloud computing

2013

Cloud Computing (CC) is emerging as the next generation platform which would facilitate the user on pay as you use mode as per requirement. It provides a number of benefits which could not otherwise be realized. The primary aim of CC is to provide efficient access to remote and geographically distributed resources. A scheduling algorithm is needed to manage the access to the different resources. There are different types of resource scheduling technologies in CC environment. These are implemented at different levels based on different parameters like cost, performance, resource utilization, time, priority, physical distances, throughput, bandwidth, resource availability etc. In this research paper various types of resource allocation scheduling algorithms that provide efficient cloud services have been surveyed and analyzed. Based on the study of different algorithms, a classification of the scheduling algorithms on the basis of selected features has been presented. 2013 Elixir All ...

In this paper we will present a reliable scheduling algorithm in cloud computing environment. In this algorithm we create a new algorithm by means of a new technique and with classification and considering request and acknowledge time of jobs in a qualification function. By evaluating the previous algorithms, we understand that the scheduling jobs have been performed by parameters that are associated with a failure rate. Therefore in the proposed algorithm, in addition to previous parameters, some other important parameters are used so we can gain the jobs with different scheduling based on these parameters. This work is associated with a mechanism. The major job is divided to sub jobs. In order to balance the jobs we should calculate the request and acknowledge time separately. Then we create the scheduling of each job by calculating the request and acknowledge time in the form of a shared job. Finally efficiency of the system is increased. So the real time of this algorithm will be improved in comparison with the other algorithms. Finally by the mechanism presented, the total time of processing in cloud computing is improved in comparison with the other algorithms.

Recently, lot of interest have been put forth by researchers to improve workload scheduling in cloud platform. However, execution of scientific workflow on cloud platform is time consuming and expensive. As users are charged based on hour of usage, lot of research work have been emphasized in minimizing processing time for reduction of cost. However, the processing cost can be reduced by minimizing energy consumption especially when resources are heterogeneous in nature; very limited work have been done considering optimizing cost with energy and processing time parameters together in meeting task quality of service (QoS) requirement. This paper presents cost and performance aware workload scheduling (CPA-WS) technique under heterogeneous cloud platform. This paper presents a cost optimization model through minimization of processing time and energy dissipation for execution of task. Experiments are conducted using two widely used workflow such as Inspiral and CyberShake. The outcome shows the CPA-WS significantly reduces energy, time, and cost in comparison with standard workload scheduling model.

Advances in Science and Technology Research Journal

Cloud Computing offers the avant-garde services at a stretch that are too attractive for any cloud user to ignore. With its growing application and popularization, IT companies are rapidly deploying distributed data centers globally, posing numerous challenges in terms of scheduling of resources under different administrative domains. This perspective brings out certain vital factors for efficient scheduling of resources providing a wide genre of characteristics, diversity in context of level of service agreements and that too with user-contingent elasticity. In this paper, a comprehensive survey of research related to various aspects of cloud resource scheduling is provided. A comparative analysis of various resource scheduling techniques focusing on key performance parameters like Energy efficiency, Virtual Machine allocation and migration, Cost-effectiveness and Service-Level Agreement is also presented.

International Journal of Electrical and Computer Engineering (IJECE), 2020

Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list. 1. INTRODUCTION Nowadays, modern technology is witnessing a wide range of development in scientific applications and research areas. Cloud computing is considered one of the most important research fields that received a lot of attention from developers and designers. It is an innovative technology that uses central remote servers and internet networks to share information/applications and store important data to become available for users [1, 2]. Cloud computing allows consumers to share and access for daily applications/sensitive information without coordination and synchronization mode. These applications provide important data and control information to users at suitable time and without any delay [3, 4]. Cloud computing utilizes computing resources and network terminals to provide important data, information, and knowledge for users at the appropriate time. In other words, users can access the same resource at different times and place i.e. share data. The main aspect of cloud computing does not need pre-established communication for any request from resources. This aspect makes cloud computing occupy a large area of scientific research and modern applications in our daily life. The cloud computing permits access to several online services, social data, information and resources that can be utilized for exchange between computer devices on demand. In order to allocate computing resources quickly and efficiently, resource allocation tasks must be scheduled [5-8]. The basic structure of cloud computing is shown in Figure 1.

International Journal of Grid and Distributed Computing

Cloud Computing is a business model that based on "pay as you go" principle. It is used to provide the IT services to the user in the flexible and dynamic manner with minimal management effort. The most important feature of the Cloud Computing is the ability to dynamically schedule the application on the best resource according to the load. According to the work in this paper, a task schedule on the Cloud environment has been proposed. The principle of the proposed algorithm is to allocate the incoming task on the best resource during the runtime of some tasks based on measuring the current situation of each resource with respect to its availability level according to its processing power, cost, and the number of running tasks to know its fitness to receive the incoming task, then choosing the best one to the incoming task. To evaluate the performance of the proposed algorithm, a comparative study has been done between this proposed algorithm, Round Ribbon (RR) algorithm, and Minimum Completion Time (MCT) algorithm. The experimental results show that the proposed algorithm outperforms the RR, and MCT algorithms by reducing make-span and the cost of the running tasks.

— Cloud computing is a new class of network based computing that provides the computing resources as a service over a network to the customers on their demand. The unique concept of cloud computing creates new opportunities for Business and IT enterprises to achieve their goals. In cloud computing, usually there are many jobs have to be executed with the available resources to achieve optimal performance, minimal total time for completion, shortest response time, and efficient utilization of resources etc. To achieve these objectives and high performance, it is important to design and develop a multi objective scheduling algorithm. Hence it is most challenging to schedule the tasks along with satisfying the user's Quality of Service requirements. The paper aims to improve the performance of CPU, memory and network operations by reducing the load of a virtual machine (VM) by using Load Balancing Method. Finally, it optimizes the resource utilization by using Resource Aware Scheduling Algorithm.

Ciência e Natura

Cloud computing is the latest distributed technology providing a rich environment of dynamically shared resources through virtualization, which can fulfill the requirements of users by allocating resources to programs. Any program in a cloud environment is delivered by workflows which are a series of interlinked tasks to accomplish a goal. One of the most important tasks in cloud computing is correct mapping of tasks onto resources. It is essential to schedule processes in distributed systems such as cloud, since it leaves a tremendous impact on the system performance. This is done by scheduling algorithms. Therefore, it is crucial to present and adopt an efficient algorithm in the cloud environment. This article attempted to examine the parameters effective in the efficiency of scheduling algorithms including deadline, cost constraint, balanced loading, power consumption and fault tolerance. Additionally, the performances of several algorithms were briefly discussed.

International Journal of Advanced Research in Computer Science, 2014

Cloud computing is an emerging technique that describes new class of network. Cloud computing share resources connected to each other via a link and uses concept of distributed computing, grid computing, utility computing and virtualization. Scheduling in cloud computing is interest of area to schedule processes that is a difficult task. Scheduling in cloud computing is to manage and control processes. Basically selection of scheduling method depends on user requirement, choosing the right scheduling method can cause maximum packet transmission, can control packet loss and can increase CPU utilization. This paper describes different scheduling method used in cloud computing such that First Come First Serve (FCFS), Priority Queue (PQ), Round Robin (RR), Multi Level Feedback Queue (MLFQ), Multi Level Queue (MLQ). Keywords: Cloud Computing, Scheduling, Job, Resources, Processes, Task.

International Journal of Scientific Research in Science, Engineering and Technology

Cloud Computing is becoming a dominant trend in providing information technology (IT) services. The cloud comprises many hardware and software resources today, and more people are switching to such services. Users' requests for cloud resources must incur a minimum amount of load on the system while getting a rapid response. In the cloud today, there is too much computational power. Load balancing makes it possible for various components of the cloud computing environment to work efficiently. To balance client requests to available resources so that the system is not overloaded, and the requested resources are delivered as quickly as possible, an effective load balancing strategy is essential. In this research article, we have presented a critical analysis of various existing cloud load balancing and scheduling algorithms. Several task scheduling approaches have been proposed in the literature review, but there appears to be a lack of scheduling algorithms for real-time task work...