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A review energy-efficient task scheduling algorithms in cloud computing

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Saleh Atiewi
added 2 research items
Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.
Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.
Saleh Atiewi
added 2 research items
Global warming, which is currently one of the greatest environmental challenges, is caused by carbon emissions. A report from the Energy Information Administration indicates that approximately 98% of CO2 emissions can be attributed to energy consumption. The trade-off between efficient and ecologically sound operation represents a major challenge faced by many organizations at present. In addition, numerous companies are currently compelled to pay a carbon tax for the resources they use and the environmental impact of their products and services. Therefore, an energy consumption system can generate actual financial payback. Green information technology involves various approaches, including power management, recycling, telecommunications, and virtualization. This paper focuses on comparing and evaluating techniques used for reducing energy consumption in virtualized environments. We first highlight the impact of virtualization techniques on minimizing energy consumption in cloud computing. Then we present an experimental comparative study between two common energy-efficient task scheduling algorithms in cloud computing (i.e., the green scheduler, the power saver scheduler). These algorithms are discussed briefly and analyzed. The three metrics used to evaluate the task scheduling algorithms are (1) total power consumption, (2) data center load, and (3) virtual machine load. This work aims to gauge and subsequently improve energy consumption efficiency in virtualized environments.
Cloud computing is a fascinating and profitable area in modern distributed computing. Aside from providing millions of users the means to use offered services through their own computers, terminals, and mobile devices, cloud computing presents an environment with low cost, simple user interface, and low power consumption by employing server virtualisation in its offered services (e.g., Infrastructure as a Service). The pool of virtual machines found in a cloud computing data centre (DC) must run through an efficient task scheduling algorithm to achieve resource utilisation and good quality of service, thus ensuring the positive effect of low energy consumption in the cloud computing environment. In this paper, we present an energy-efficient scheduling algorithm for a cloud computing DC using the dynamic voltage frequency scaling technique. The proposed scheduling algorithm can efficiently reduce the energy consumption for executing jobs by increasing resource utilisation. GreenCloud simulator is used to simulate our algorithm. Experimental results show that, compared with other algorithms, our algorithm can increase server utilisation, reduce energy consumption, and reduce execution time.
Muder Almi'ani
added 2 research items
With dramatic increase in the mobile technology, the mobile cellular phones have played a significant role in the several fields particularly, education, military, business and health. The deployment of mobile phone in the Government has been of high interest of research community to improve fundamental functions, efficient information provision and augmenting the quality of service QoS provisioning. A lot of research is conducted for service improvement in Mobile Government(MGovernment), but there is marginal research is available for security particularly privacy preserving of M-Government. In this paper, the authors propose privacy preserving framework for M-Government to secure an administration interface to avoid possible security threats. The proposed framework is verified using mathematical formulation. Furthermore, the proposed framework is tested using Java Platform. The experimental results confirm the reliability, efficiency and QoS provision of the privacy preserving framework.
Saleh Atiewi
added 2 research items
Cloud computing is an interesting and beneficial area in modern distributed computing. It enables millions of users to use the offered services through their own devices or terminals. Cloud computing offers an environment with low cost, ease of use and low power consumption by utilizing server virtualization in its offered services (e.g., Infrastructure as a Service). The pool of Virtual Machines (VMs) in a cloud computing Data Center (DC) needs to be managed through an efficient task scheduling algorithm to maintain quality of service and resource utilization and thus ensure the positive impact of energy consumption in the cloud computing environment. In this study, an experimental comparative study is carried out among three task scheduling algorithms in cloud computing, namely, random resource selection, round robin and green scheduler. Based on the analysis of the simulation result, we can conclude which algorithm is the best for scheduling in terms of energy and performance of VMs. The evaluation of these algorithms is based on three metrics: Total power consumption, DC load and VM load. A number of experiments with various aims are completed in this empirical comparative study. The results showed that there is no algorithm that is superior to the others. Each has its own pros and cons. Based on the simulation performed, the green scheduler gives the best performance with respect to energy consumption. On the other hand, the random scheduler showed the best performance with respect to both VM and DC load. The round robin scheduler gives better VM and DC load than the green scheduler but have more energy consumption than both random and green schedulers. However, since the RR scheduler distributes the tasks fairly, the network traffic is balanced and neither the server nor the network node will get overloaded or congested.
Cloud computing is a model for delivering information technology services, wherein resources are retrieved from the Internet through web-based tools and applications instead of a direct connection to a server. The capability to provision and release cloud computing resources with minimal management effort or service provider interaction led to the rapid increase of the use of cloud computing. Therefore, balancing cloud computing resources to provide better performance and services to end users is important. Load balancing in cloud computing means balancing three important stages through which a request is processed. The three stages are data center selection, virtual machine scheduling, and task scheduling at a selected data center. User task scheduling plays a significant role in improving the performance of cloud services. This paper presents a review of various energy-efficient task scheduling methods in a cloud environment. A brief analysis of various scheduling parameters considered in these methods is also presented. The results show that the best power-saving percentage level can be achieved by using both DVFS and DNS.