scholarly journals Cloud Computing Virtualization of Resources Allocation for Distributed Systems

2020 ◽  
Vol 1 (3) ◽  
pp. 98-105 ◽  
Author(s):  
Hanan Shukur ◽  
Subhi Zeebaree ◽  
Rizgar Zebari ◽  
Diyar Zeebaree ◽  
Omar Ahmed ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Resource Provisioning ◽  
Third Party ◽  
Cloud Provider ◽  
Cloud Data Center ◽  
Resource Allocation Algorithm ◽  
Cloud Data

Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques.  The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

scholarly journals Protecting Data on Mobile Cloud Computing

2019 ◽  
Vol 8 (11) ◽  
pp. 3453-3459
Keyword(s):  
Cloud Computing ◽  
Mobile Computing ◽  
Mobile Devices ◽  
Data Center ◽  
Mobile Cloud Computing ◽  
Third Party ◽  
Battery Life ◽  
Mobile Cloud ◽  
Cloud Data Center ◽  
Cloud Data

Mobile Cloud Computing is a combination of general Cloud Computing and Mobile Computing in which we have to access resources from the remote cloud data center with the help of mobile electronics and peripherals like mobile smartphones, laptops, gadgets, etc. via Cellular Technology or Wireless Communication. Mobile devices have lots of resource constraints like storage capacity, processing speed, and battery life. Hence through simple mobile computing software and programming, we cannot manipulate on mobile devices of cloud data center information. Because of such kinds of difficulty, we have to process information or data through external mobile devices. Accessing and processing of data with the help of Trusted Third Party Agency (TPA) outside the cloud data center and mobile devices have lots of security challenges. To make cloud data secure over outside resources, lots of terminologies and theory are put forward by various researchers. In this paper, we will analyze their theory and its limitations and offer our security algorithm proposal. In this thesis article, we analyze the security framework for storing data on Cloud Server by Mobile and limitation of this process. Also, we review the theory of how data can be secure our data on cloud administrators

scholarly journals Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

2021 ◽  
Vol 17 (3) ◽  
pp. 155014772199721
Author(s):  
Mueen Uddin ◽  
Mohammed Hamdi ◽  
Abdullah Alghamdi ◽  
Mesfer Alrizq ◽  
Mohammad Sulleman Memon ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Cloud Data Center ◽  
Server Consolidation ◽  
Cloud Data ◽  
Task Scheduling Algorithm ◽  
Cloud Data Centers

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization

2016 ◽  
Vol 8 (2) ◽  
pp. 43-57 ◽  
Author(s):  
Li Mao ◽  
De Yu Qi ◽  
Wei Wei Lin ◽  
Bo Liu ◽  
Ye Da Li
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Resource Scheduling ◽  
Constraint Satisfaction Problems ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource

With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.

scholarly journals Adapted Convolutional Neural Networks and Long Short-Term Memory for Host Utilization prediction in Cloud Data Center

2021 ◽  
Author(s):  
ARIF ullah ◽  
Irshad Ahmed Abbasi ◽  
Muhammad Zubair Rehman ◽  
Tanweer Alam ◽  
Hanane Aznaoui
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Cloud Computing ◽  
Data Center ◽  
Short Term Memory ◽  
Short Term ◽  
Cloud Data Center ◽  
Term Memory ◽  
Cloud Data ◽  
Long Short Term Memory

Abstract Infrastructure service model provides different kinds of virtual computing resources such as networking, storage service, and hardware as per user demands. Host load prediction is an important element in cloud computing for improvement in the resource allocation systems. Hosting initialization issues still exist in cloud computing due to this problem hardware resource allocation takes serval minutes of delay in the response process. To solve this issue prediction techniques are used for proper prediction in the cloud data center to dynamically scale the cloud in order for maintaining a high quality of services. Therefore in this paper, we propose a hybrid convolutional neural network long with short-term memory model for host prediction. In the proposed hybrid model, vector auto regression method is firstly used to input the data for analysis which filters the linear interdependencies among the multivariate data. Then the enduring data are computed and entered into the convolutional neural network layer that extracts complex features for each central processing unit and virtual machine usage components after that long short-term memory is used which is suitable for modeling temporal information of irregular trends in time series components. In all process, the main contribution is that we used scaled polynomial constant unit activation function which is most suitable for this kind of model. Due to the higher inconsistency in data center, accurate prediction is important in cloud systems. For this reason in this paper two real-world load traces were used to evaluate the performance. One is the load trace in the Google data center, while the other is in the traditional distributed system. The experiment results show that our proposed method achieves state-of-the-art performance with higher accuracy in both datasets as compared with ARIMA-LSTM, VAR-GRU, VAR-MLP, and CNN models.

A Novel Multi-Dimension Resource Recycling Mechanism for Cloud Data Centers

2015 ◽  
Vol 7 (2) ◽  
pp. 50-64 ◽  
Author(s):  
Hong-Yi Chang ◽  
Tu-Liang Lin ◽  
Cheng-Kai Huang
Keyword(s):  
Resource Allocation ◽  
Cloud Service ◽  
Allocation Mechanism ◽  
Cloud Data Center ◽  
Resource Allocation Algorithm ◽  
Cloud Data ◽  
Resource Recycling ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Cloud Servers

Cloud servers can be started with ineffective resource arrangement, and extra costs are produced if unnecessary servers are started. This is a substantial fee for the cloud service provider. Therefore, each cloud data center needs an efficient resource allocation mechanism to prevent unnecessary cloud servers from being started. In general, resource allocation problems can be classified into either one-dimension or multi-dimension aspects. In 2013, the authors have proposed a multi-dimension resource allocation algorithm that can improve the utilization of cloud servers by as much as 97%. However, most of previous studies are more concerned with solving the resource allocation problem. In fact, after a period of time, the applications will finish their jobs, and resources been occupied on the servers will be released, thus decreasing the utilization of cloud servers. Therefore, this paper proposes a novel multi-dimension resource recycling algorithm to optimize the server resource again, to recycle the excess cloud resources and to reduce the unnecessary operating cloud servers.

scholarly journals Research of Task Management and Resource Allocation in Cloud Computing

2019 ◽  
Vol 8 (6S4) ◽  
pp. 938-941
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Performance Comparison ◽  
Task Management ◽  
Management Framework

This paper presents the task management framework in which tasks are identified, analysed and grouped based on the user QoS requirements. As a next step, cost and time based scheduling algorithm is employed to select the suitable pair of resources and tasks. The performance of the proposed algorithm is tested with execution time and cost allocated to the resources. Two existing algorithms are considered along with proposed algorithm for performance comparison. The results proved the efficiency of the proposed algorithm.

scholarly journals Virtual Machine Consolidation for Stochastic Load Balancing in Cloud Data Center Management

2021 ◽  
Vol 12 (2) ◽  
pp. 2593-2602
Author(s):  
M. Murugesan
Keyword(s):  
Cloud Computing ◽  
Resource Provisioning ◽  
Virtual Space ◽  
Cloud Services ◽  
Enabling Technology ◽  
Network Resources ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Cloud Resource ◽  
Stochastic Load

Cloud computing is able to managing a massive quantity of growing work for the use of enterprise clients in a specified way Virtualization, which makes assumptions the network resources and makes it simple to control, is an important enabling technology for cloud computing. Computing is being used in the proposed work to distribute cloud services tailored to the needs and to promote the smart grid principle. “Skewness” concept was delivered here wherein equal was reducing to combine workloads to enhance the usage of the server. The complexities of on-demand allocation of resources arise from managing customer demands. As a result, the use of vms technologies has proved to be helpful in terms of resource provisioning. The use of virtualized environments is expected to reduce primarily consist connection speed while also executing tasks in accordance with cloud resource availability. This implementation can be use local negotiation based VM consolidation mechanism to predict each job request and reduce overloads to create virtual space at the time of multiple requests. The proposed system implement co-location approach to combine unused small spaces to create new virtual space for improves the performance of server. Also implement self-destruction approach to eliminate the invalid data based on time to live property.  The proposed framework is executed in genuine time with effective asset allotment. In this system to begin with broaden a forecast show which will gauge the parcel sizes of decrease commitments at runtime. And it can detect information skewness in real time and allocate extra asses for mordant of large walls that help us complete faster.

Performance Comparison of Energy-Aware Task Scheduling with GA and CRO Algorithms in Cloud Environment

2014 ◽  
Vol 596 ◽  
pp. 204-208 ◽  
Author(s):  
Lin Wu ◽  
Yu Jing Wang ◽  
Chao Kun Yan
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Performance Comparison ◽  
Energy Awareness ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Total Energy Consumption ◽  
Cloud Data

With energy problem of cloud data center is becoming more and more serious, the BoT scheduling algorithm only considering the timespan is not applicable to the cloud computing environment. In order to explore the energy-aware task scheduling algorithm performance, this paper validates simulation experiments with GA algorithms and CRO algorithms, to optimize the makespan as the main objective, to optimize energy consumption indicators for the secondary objective. Experiments show that, GA algorithms and CRO algorithm can be applied to different scenarios, while optimizing makespan, but also to some extent reduce the total energy consumption of the system can be used as task scheduling strategy cloud environments.Keyword: Cloud Computing, Task Scheduling, Energy-awareness, CRO algorithm, GA algorithm

scholarly journals Optimization model for QoS based task scheduling in cloud computing environment

2020 ◽  
Vol 18 (2) ◽  
pp. 1081
Author(s):  
Sirisha Potluri ◽  
Katta Subba Rao
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Model ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Cloud Services ◽  
Allocation Model ◽  
Task Scheduling Algorithm ◽  
And Task

Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.

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