scholarly journals A Research on an Efficient Cloud Scheduling with a Geo Microarray Data Set

2019 ◽  
Vol 9 (1) ◽  
pp. 2946-2952
Keyword(s):  
Load Balancing ◽  
Virtual Machines ◽  
Learning Algorithm ◽  
Resource Provisioning ◽  
Hole Filling ◽  
Gene Expressions ◽  
Data Set ◽  
Dynamic Resource Provisioning ◽  
Flow Equilibrium ◽  
Micro Array

Investigations on micro-array organisms for various researches have made a non discrete dealing of thousands of gene expressions achievable. For any applications, the results would be more accurate only when maximum count is analyzed within a predictable time and it is one of the unseen challenges in the field of bio medicine. The purpose of this data analysis is to regulate and control the activities of thousands of genes in our body. This paper develops a scheduling analysis of how effectively gene molecular patterns are taken into experimentation. This motivated our investigation in a new dimension for a cloud environment. This paper is about applying our previous works such as Workflow Shuffling and Hole Filling Algorithm (WSHF) [13], Agent Centric Enhanced Reinforcement learning algorithm (AGERL) [14], Heuristic Flow Equilibrium based Load Balancing (HFEL) [15] and Dynamic Resource Provisioning and Load Balancing (DRBLHS) [16] algorithms collaboratively for a Gene Express Omnibus dataset as a case study. The gene data’s plays an important role in monitoring the human activities and how well, the data has been processed in the cloud with minimum budget, time and minimum virtual machines. Finally, the efficiency of the system is analyzed in terms of resource utilization, completion time, response time, throughput and VM Migration time

A Value Based Dynamic Resource Provisioning Model in Cloud

2013 ◽  
Vol 3 (2) ◽  
pp. 35-46 ◽  
Author(s):  
Sandeep K. Sood
Keyword(s):  
Resource Utilization ◽  
Virtual Machines ◽  
Fault Tolerant ◽  
Resource Provisioning ◽  
Resource Usage ◽  
Resource Manager ◽  
Usage Rate ◽  
Computing Paradigm ◽  
A Value ◽  
Dynamic Resource Provisioning

Cloud computing has become an innovative computing paradigm, which aims at providing reliable, customized, Quality of Service (QoS) and guaranteed computing infrastructures for users. Efficient resource provisioning is required in cloud for effective resource utilization. For resource provisioning, cloud provides virtualized computing resources that are dynamically scalable. This property of cloud differentiates it from the traditional computing paradigm. But the initialization of a new virtual instance causes a several minutes delay in the hardware resource allocation. Furthermore, cloud provides a fault tolerant service to its clients using the virtualization. But, in order to attain higher resource utilization over this technology, a technique or a strategy is needed using which virtual machines can be deployed over physical machines by predicting its need in advance so that the delay can be avoided. To address these issues, a value based prediction model in this paper is proposed for resource provisioning in which a resource manager is used for dynamically allocating or releasing a virtual machine depending upon the resource usage rate. In order to know the recent resource usage rate, the resource manager uses sliding window to analyze the resource usage rate and to predict the system behavior in advance. By predicting the resource requirements in advance, a lot of processing time can be saved. Earlier, a server has to perform all the calculations regarding the resource usage that in turn wastes a lot of processing power thus decreasing its overall capacity to handle the incoming request. The main feature of the proposed model is that a lot of load is being shifted from the individual server to the resource manager as it performs all the calculations and therefore the server is free to handle the incoming requests to its full capacity.

scholarly journals Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

2020 ◽  
Vol 10 (7) ◽  
pp. 2323
Author(s):  
T. Renugadevi ◽  
K. Geetha ◽  
K. Muthukumar ◽  
Zong Woo Geem
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Power Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Resource Provisioning ◽  
Total Power

Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

scholarly journals Experience with dynamic resource provisioning of the CMS online cluster using a cloud overlay

2019 ◽  
Vol 214 ◽  
pp. 07017
Author(s):  
Jean-Marc Andre ◽  
Ulf Behrens ◽  
James Branson ◽  
Philipp Brummer ◽  
Olivier Chaze ◽  
...  
Keyword(s):  
Virtual Machines ◽  
Hadron Collider ◽  
A Priori ◽  
Resource Provisioning ◽  
Static Mode ◽  
Level Trigger ◽  
Network Connection ◽  
Use Of Resources ◽  
Dynamic Resource Provisioning ◽  
High Level

The primary goal of the online cluster of the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) is to build event data from the detector and to select interesting collisions in the High Level Trigger (HLT) farm for offline storage. With more than 1500 nodes and a capacity of about 850 kHEPSpecInt06, the HLT machines represent similar computing capacity of all the CMS Tier1 Grid sites together. Moreover, it is currently connected to the CERN IT datacenter via a dedicated 160 Gbps network connection and hence can access the remote EOS based storage with a high bandwidth. In the last few years, a cloud overlay based on OpenStack has been commissioned to use these resources for the WLCG when they are not needed for data taking. This online cloud facility was designed for parasitic use of the HLT, which must never interfere with its primary function as part of the DAQ system. It also allows to abstract from the different types of machines and their underlying segmented networks. During the LHC technical stop periods, the HLT cloud is set to its static mode of operation where it acts like other grid facilities. The online cloud was also extended to make dynamic use of resources during periods between LHC fills. These periods are a-priori unscheduled and of undetermined length, typically of several hours, once or more a day. For that, it dynamically follows LHC beam states and hibernates Virtual Machines (VM) accordingly. Finally, this work presents the design and implementation of a mechanism to dynamically ramp up VMs when the DAQ load on the HLT reduces towards the end of the fill.

scholarly journals Threshold based VM Placement Technique for Load Balanced Resource Provisioning using Priority Scheme in Cloud Computing

2021 ◽  
Vol 13 (5) ◽  
pp. 01-18
Author(s):  
Mayank Sohani ◽  
Dr. S. C. Jain
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Virtual Machines ◽  
Resource Scheduling ◽  
Resource Provisioning ◽  
Scheduling Model ◽  
Dynamic Resource Provisioning ◽  
Two Factors ◽  
Cloud Framework ◽  
Resource Cost

The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.

A Value Based Dynamic Resource Provisioning Model in Cloud

2013 ◽  
Vol 3 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Sandeep K. Sood
Keyword(s):  
Resource Utilization ◽  
Virtual Machines ◽  
Fault Tolerant ◽  
Resource Provisioning ◽  
Resource Usage ◽  
Resource Manager ◽  
Usage Rate ◽  
Computing Paradigm ◽  
A Value ◽  
Dynamic Resource Provisioning

Cloud computing has become an innovative computing paradigm, which aims at providing reliable, customized, Quality of Service (QoS) and guaranteed computing infrastructures for users. Efficient resource provisioning is required in cloud for effective resource utilization. For resource provisioning, cloud provides virtualized computing resources that are dynamically scalable. This property of cloud differentiates it from the traditional computing paradigm. But the initialization of a new virtual instance causes a several minutes delay in the hardware resource allocation. Furthermore, cloud provides a fault tolerant service to its clients using the virtualization. But, in order to attain higher resource utilization over this technology, a technique or a strategy is needed using which virtual machines can be deployed over physical machines by predicting its need in advance so that the delay can be avoided. To address these issues, a value based prediction model in this paper is proposed for resource provisioning in which a resource manager is used for dynamically allocating or releasing a virtual machine depending upon the resource usage rate. In order to know the recent resource usage rate, the resource manager uses sliding window to analyze the resource usage rate and to predict the system behavior in advance. By predicting the resource requirements in advance, a lot of processing time can be saved. Earlier, a server has to perform all the calculations regarding the resource usage that in turn wastes a lot of processing power thus decreasing its overall capacity to handle the incoming request. The main feature of the proposed model is that a lot of load is being shifted from the individual server to the resource manager as it performs all the calculations and therefore the server is free to handle the incoming requests to its full capacity.

scholarly journals Secure Dynamic Resource Provisioning Cost by Optimized Placement of Virtual Machines in Cloud Computing

2013 ◽  
Vol 4 (1) ◽  
pp. 88-93
Author(s):  
Aarthee S ◽  
Venkatesan R
Keyword(s):  
Cloud Computing ◽  
Service Management ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Cloud Service ◽  
Resource Provisioning ◽  
On Demand ◽  
Dynamic Resource Provisioning ◽  
The Cost

Cloud computing provides pay-as-you-go computing resources and accessing services are offered from data centers all over the world as the cloud. Consumers may find that cloud computing allows them to reduce the cost of information management as they are not required to own their servers and can use capacity leased from third parties or cloud service providers. Cloud consumers can successfully reduce total cost of resource provisioning using Optimal Cloud Resource Provisioning (OCRP) algorithm in cloud computing environment. The two provisioning plans are reservation and on-demand, used for computing resources which is offered by cloud providers to cloud consumers. The cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since a cloud consumer has to pay to provider in advance. This project proposes that the OCRP algorithm associated with rule based resource manager technique is used to increase the scalability of cloud on-demand services by dynamic placement of virtual machines to reduce the cost and also endow with secure accessing of resources from data centers and parameters like virtualized platforms, data or service management are monitored in the cloud environment.

scholarly journals Lightweight dynamic integration of opportunistic resources

2020 ◽  
Vol 245 ◽  
pp. 07040
Author(s):  
Max Fischer ◽  
Eileen Kuehn ◽  
Manuel Giffels ◽  
Matthias Jochen Schnepf ◽  
Andreas Petzold ◽  
...  
Keyword(s):  
Virtual Machines ◽  
Resource Provisioning ◽  
Third Party ◽  
New Approach ◽  
Dynamic Resource Provisioning ◽  
Key Concepts ◽  
Resource Requirements ◽  
Computing Grid ◽  
Dynamic Resource ◽  
Heterogeneous Resources

To satisfy future computing demands of the Worldwide LHC Computing Grid (WLCG), opportunistic usage of third-party resources is a promising approach. While the means to make such resources compatible with WLCG requirements are largely satisfied by virtual machines and containers technologies, strategies to acquire and disband many resources from many providers are still a focus of current research. Existing meta-schedulers that manage resources in the WLCG are hitting the limits of their design when tasked to manage heterogeneous resources from many diverse resource providers. To provide opportunistic resources to the WLCG as part of a regular WLCG site, we propose a new meta-scheduling approach suitable for opportunistic, heterogeneous resource provisioning. Instead of anticipating future resource requirements, our approach observes resource usage and promotes well-used resources. Following this approach, we have developed an inherently robust meta-scheduler, COBalD, for managing diverse, heterogeneous resources given unpredictable resource requirements. This paper explains the key concepts of our approach, and discusses the benefits and limitations of our new approach to dynamic resource provisioning compared to previous approaches.

Agile Support Vector Machine for Energy-efficient Resource Allocation in IoT-oriented Cloud using PSO

2022 ◽  
Vol 22 (1) ◽  
pp. 1-35
Author(s):  
Muhammad Junaid ◽  
Adnan Sohail ◽  
Fadi Al Turjman ◽  
Rashid Ali
Keyword(s):  
Resource Allocation ◽  
Support Vector Machine ◽  
Load Balancing ◽  
Virtual Machines ◽  
Critical Factor ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Support Vector ◽  
Cloud Infrastructure ◽  
Data Formats

Over the years cloud computing has seen significant evolution in terms of improvement in infrastructure and resource provisioning. However the continuous emergence of new applications such as the Internet of Things (IoTs) with thousands of users put a significant load on cloud infrastructure. Load balancing of resource allocation in cloud-oriented IoT is a critical factor that has a significant impact on the smooth operation of cloud services and customer satisfaction. Several load balancing strategies for cloud environment have been proposed in the past. However the existing approaches mostly consider only a few parameters and ignore many critical factors having a pivotal role in load balancing leading to less optimized resource allocation. Load balancing is a challenging problem and therefore the research community has recently focused towards employing machine learning-based metaheuristic approaches for load balancing in the cloud. In this paper we propose a metaheuristics-based scheme Data Format Classification using Support Vector Machine (DFC-SVM), to deal with the load balancing problem. The proposed scheme aims to reduce the online load balancing complexity by offline-based pre-classification of raw-data from diverse sources (such as IoT) into different formats e.g. text images media etc. SVM is utilized to classify “n” types of data formats featuring audio video text digital images and maps etc. A one-to-many classification approach has been developed so that data formats from the cloud are initially classified into their respective classes and assigned to virtual machines through the proposed modified version of Particle Swarm Optimization (PSO) which schedules the data of a particular class efficiently. The experimental results compared with the baselines have shown a significant improvement in the performance of the proposed approach. Overall an average of 94% classification accuracy is achieved along with 11.82% less energy 16% less response time and 16.08% fewer SLA violations are observed.

Queue Based Q-Learning for Efficient Resource Provisioning in Cloud Data Centers

2015 ◽  
Vol 11 (4) ◽  
pp. 37-54 ◽  
Author(s):  
A. Meera ◽  
S. Swamynathan
Keyword(s):  
Reinforcement Learning ◽  
Virtual Machine ◽  
Optimal Allocation ◽  
Virtual Machines ◽  
Learning Algorithm ◽  
Resource Provisioning ◽  
Time Step ◽  
Q Learning ◽  
Learning Agent ◽  
Efficient Resource

Cloud Computing is a novel paradigm that offers virtual resources on demand through internet. Due to rapid demand to cloud resources, it is difficult to estimate the user's demand. As a result, the complexity of resource provisioning increases, which leads to the requirement of an adaptive resource provisioning. In this paper, the authors address the problem of efficient resource provisioning through Queue based Q-learning algorithm using reinforcement learning agent. Reinforcement learning has been proved in various domains for automatic control and resource provisioning. In the absence of complete environment model, reinforcement learning can be used to define optimal allocation policies. The proposed Queue based Q-learning agent analyses the CPU utilization of all active Virtual Machines (VMs) and detects the least loaded virtual machine for resource provisioning. It detects the least loaded virtual machines through Inter Quartile Range. Using the queue size of virtual machines it looks ahead by one time step to find the optimal virtual machine for provisioning.

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