scholarly journals Reliability aware green workflow scheduling using ε-fuzzy dominance in cloud

2021 ◽  
Author(s):  
Rama Rani ◽  
Ritu Garg
Keyword(s):  
Energy Consumption ◽  
Supply Voltage ◽  
Negative Impact ◽  
Gaussian Elimination ◽  
Scheduling Algorithm ◽  
Operating Frequency ◽  
Future Research ◽  
Workflow Scheduling ◽  
Carbon Footprints ◽  
Cloud Data

AbstractThe enormous energy consumed by cloud data centers (CDCs) increases the carbon footprints, operational cost and decreases the system reliability, so it becomes a great challenge for CDCs providers. Dynamic voltage and frequency scaling (DVFS) is an efficient approach for energy efficiency, which reduces the operating frequency, and supply voltage of the processor during the task’s execution. Recent research shows that scaling of the supply voltage and operating frequency has negative impact on the system’s reliability as it increases transient fault rate of the resources. Thus, the system’s reliability and the energy consumption are two prime concerns in a cloud computing environment that requires attention. Most workflow scheduling algorithms in literature do not consider energy and reliability simultaneously. In this paper, we proposed the ε-fuzzy dominance based reliable green workflow scheduling (FDRGS) algorithm, which optimizes the application’s reliability and energy consumption simultaneously using the ε-fuzzy dominance mechanism. The simulation results obtained using fast Fourier transform (FFT) and gaussian elimination (GE) task graphs manifest that our scheduling algorithm is more efficient in optimizing energy consumption and lifetime system’s reliability jointly than several widely used algorithms. The proposed algorithm will help scientists and engineers for further insight into future research in the area of cloud.

Advances in Dynamic Virtual Machine Management for Cloud Data Centers

2017 ◽  
pp. 91-114
Author(s):  
Rashmi Rai ◽  
G. Sahoo
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Economic Modeling ◽  
User Preferences ◽  
Virtual Machine Migration ◽  
Carbon Footprints ◽  
Cloud Data ◽  
Computing Services

The ever-rising demand for computing services and the humongous amount of data generated everyday has led to the mushrooming of power craving data centers across the globe. These large-scale data centers consume huge amount of power and emit considerable amount of CO2.There have been significant work towards reducing energy consumption and carbon footprints using several heuristics for dynamic virtual machine consolidation problem. Here we have tried to solve this problem a bit differently by making use of utility functions, which are widely used in economic modeling for representing user preferences. Our approach also uses Meta heuristic genetic algorithm and the fitness is evaluated with the utility function to consolidate virtual machine migration within cloud environment. The initial results as compared with existing state of art shows marginal but significant improvement in energy consumption as well as overall SLA violations.

Load and Cost Aware Min-Min Workflow Scheduling Algorithm for Heterogeneous Resources in Fog, Cloud and Edge Scenarios

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Workflow Scheduling ◽  
Heterogeneous Environment ◽  
Cloud Servers ◽  
Heterogeneous Resources

Fog computing and Edge computing are few of the latest technologies which are offered as solution to challenges faced in Cloud Computing. Instead of offloading of all the tasks to centralized cloud servers, some of the tasks can be scheduled at intermediate Fog servers or Edge devices. Though this solves most of the problems faced in cloud but also encounter other traditional problems due to resource-related constraints like load balancing, scheduling, etc. In order to address task scheduling and load balancing in Cloud-fog-edge collaboration among servers, we have proposed an improved version of min-min algorithm for workflow scheduling which considers cost, makespan, energy and load balancing in heterogeneous environment. This algorithm is implemented and tested in different offloading scenarios- Cloud only, Fog only, Cloud-fog and Cloud-Fog-Edge collaboration. This approach performed better and the result gives minimum makespan, less energy consumption along with load balancing and marginally less cost when compared to min-min and ELBMM algorithms

A Threshold Speed Computation Algorithm in Adaptive DVS Scheduling

2014 ◽  
Vol 519-520 ◽  
pp. 1071-1074
Author(s):  
Wei Qiang Sun ◽  
Shan Ren Nie
Keyword(s):  
Energy Consumption ◽  
Power Dissipation ◽  
Supply Voltage ◽  
Scheduling Algorithm ◽  
Dynamic Voltage Scaling ◽  
Computing Systems ◽  
Adaptive Scheme ◽  
Dynamic Voltage ◽  
Computation Algorithm ◽  
Circuit Power

Performance boosting of modern computing systems is constrained by the chip/circuit power dissipation. Dynamic voltage scaling (DVS) has been applied for reducing the energy consumption by dynamically changing the supply voltage. One can apply an adaptive scheme by computing a threshold speed of the supplied voltage, and adopting greedy online DVS scheduling algorithm when the voltage exceeds the threshold while choosing a conservative speed on the contrary. This paper presents an algorithm to determine the threshold speed. The proposed algorithm allows to obtaining the threshold speed for the adaptive DVS scheduling algorithm more efficiently.

Enhanced Parallel Application Scheduling Algorithm with Energy Consumption Constraint in Heterogeneous Distributed Systems

2019 ◽  
Vol 28 (11) ◽  
pp. 1950190 ◽  
Author(s):  
Jinghong Li ◽  
Guoqi Xie ◽  
Keqin Li ◽  
Zhuo Tang
Keyword(s):  
Energy Consumption ◽  
Distributed Systems ◽  
Gaussian Elimination ◽  
Scheduling Algorithm ◽  
Parallel Applications ◽  
Parallel Application ◽  
Heterogeneous Distributed Systems ◽  
Dynamic Voltage ◽  
Cluster Computer Systems ◽  
Consumption Constraints

Energy consumption has always been one of the main design problems in heterogeneous distributed systems, whether for large cluster computer systems or small handheld terminal devices. And as energy consumption explodes for complex performance, many efforts and work are focused on minimizing the schedule length of parallel applications that meet the energy consumption constraints currently. In prior studies, a pre-allocation method based on dynamic voltage and frequency scaling (DVFS) technology allocates unassigned tasks with minimal energy consumption. However, this approach does not necessarily result in minimal scheduling length. In this paper, we propose an enhanced scheduling algorithm, which allocates the same energy consumption for each task by selecting a relatively intermediate value among the unequal allocations. Based on the two real-world applications (Fast Fourier transform and Gaussian elimination) and the randomly generated parallel application, experiments show that the proposed algorithm not only achieves better scheduling length while meeting the energy consumption constraints, but also has better performance than the existing parallel algorithms.

scholarly journals A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Weiwei Lin ◽  
Wentai Wu ◽  
James Z. Wang
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Power Efficiency ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Cloud Data ◽  
Virtual Clusters ◽  
Resource Requirements

Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS). As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.

scholarly journals Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

2022 ◽  
Author(s):  
Tahereh Abbasi-khazaei ◽  
Mohammad Hossein Rezvani
Keyword(s):  
Energy Consumption ◽  
Service Providers ◽  
Cloud Service ◽  
Computing Methods ◽  
Energy Costs ◽  
Vm Placement ◽  
Energy Aware ◽  
Carbon Footprints ◽  
Cloud Data ◽  
Cloud Data Centers

Abstract One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.

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

Resource Scheduling Algorithm of Cloud Computing for Energy Optimization Base on Virtual Machine Migration

2014 ◽  
Vol 926-930 ◽  
pp. 3232-3235
Author(s):  
Liang Hao ◽  
Gang Cui ◽  
Ming Cheng Qu ◽  
Wen De Ke
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Scheduling Algorithm ◽  
Energy Optimization ◽  
Resource Scheduling ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
Use Of Resources

As the growing demand for cloud computing, the scale of cloud data centers increased gradually, so that the energy issues of cloud environments have become increasingly prominent. For the situation of energy consumption serious in cloud computing data center, the resource scheduling algorithm of cloud computing for energy optimization was designed base on the technology of virtual machine migration. The energy consumption of cloud data center was saved effectively through effective use of resources, rational allocation of resources scheduling. Simulation results showed that compared to sequence execution algorithm, utilization of CPU, the average energy consumption, the average time was reduced by 16.63%, 27.26% and 23.72%, respectively.

Applying personalized medicine to adult severe asthma

2021 ◽  
Vol 42 (1) ◽  
pp. e8-e16 ◽  
Author(s):  
Angelica Tiotiu
Keyword(s):  
Personalized Medicine ◽  
Severe Asthma ◽  
Negative Impact ◽  
Target Therapy ◽  
Asthma Management ◽  
Future Research ◽  
Biologic Therapies ◽  
Eosinophilic Asthma ◽  
Definition Of ◽  
Structured Approach

Background: Severe asthma is a heterogeneous disease that consists of various phenotypes driven by different pathways. Associated with significant morbidity, an important negative impact on the quality of life of patients, and increased health care costs, severe asthma represents a challenge for the clinician. With the introduction of various antibodies that target type 2 inflammation (T2) pathways, severe asthma therapy is gradually moving to a personalized medicine approach. Objective: The purpose of this review was to emphasize the important role of personalized medicine in adult severe asthma management. Methods: An extensive research was conducted in medical literature data bases by applying terms such as “severe asthma” associated with “structured approach,” “comorbidities,” “biomarkers,” “phenotypes/endotypes,” and “biologic therapies.” Results: The management of severe asthma starts with a structured approach to confirm the diagnosis, assess the adherence to medications and identify confounding factors and comorbidities. The definition of phenotypes or endotypes (phenotypes defined by mechanisms and identified through biomarkers) is an important step toward the use of personalized medicine in asthma. Severe allergic and nonallergic eosinophilic asthma are two defined T2 phenotypes for which there are efficacious targeted biologic therapies currently available. Non-T2 phenotype remains to be characterized, and less efficient target therapy exists. Conclusion: Despite important progress in applying personalized medicine to severe asthma, especially in T2 inflammatory phenotypes, future research is needed to find valid biomarkers predictive for the response to available biologic therapies to develop more effective therapies in non-T2 phenotype.

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