An Approach for Detection of Overloaded Host to Consolidate Workload in Cloud Datacenter

2018 ◽  
Vol 10 (2) ◽  
pp. 59-69
Author(s):  
Nimisha Patel ◽  
Hiren Patel
Keyword(s):  
Control Charts ◽  
Computational Cost ◽  
Service Level Agreement ◽  
Service Level ◽  
Common Mechanism ◽  
Threshold Values ◽  
Sla Violation ◽  
Workload Consolidation ◽  
Cloud Datacenter ◽  
Virtual Resources

This article describes the process of workload consolidation through detection of overloaded hosts in Cloud datacenter which leads to saving in energy consumption. Cloud computing is a novice paradigm where virtual resources are provisioned on pay-as-you-go basis. Upon receiving users' job requirement, it is mapped onto virtual resources running on hosts in datacenter. To achieve workload consolidation, it is required to detect the overloaded hosts. Overloaded host detection is carried out for balancing workload, creating a list of overloaded hosts which will be useful while placing VMs (by not putting a VM on already overloaded host) to reduce Service Level Agreement (SLA) violation and while checking the underloaded host, the overloaded hosts are omitted to reduce computational cost. Most common mechanism to detect overloaded hosts is to calculate upper threshold values based on hosts' utilization statically or dynamically. Most researchers recommend dynamic calculation of threshold values. In this research, the authors propose to use moving range (MR) method of variables control charts to calculate upper threshold. The experimentation results show that MR performs better in terms of reduction in SLA violation, minimization in VM migration.

scholarly journals VM Consolidation Plan for Improving the Energy Efficiency of Cloud

2021 ◽  
Vol 21 (3) ◽  
pp. 145-159
Author(s):  
Satveer ◽  
Mahendra Singh Aswal
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Sla Violation ◽  
Dynamic Consolidation ◽  
Cloud Datacenter ◽  
Cloud Users ◽  
Vm Consolidation

Abstract Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumption of each VM to select the required VMs for migration from the overloaded Physical Machine (PM). The proposed VM allocation algorithm estimates the net increase in Imbalance Utilization Value (IUV) and power consumption of a PM, in advance before allocating the VM. The analysis of simulation results suggests that the proposed dynamic consolidation plan outperforms other state of arts.

Resource Allocation Policies in Cloud Computing Environment

2017 ◽  
pp. 115-132 ◽  
Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
On Demand ◽  
Different Types ◽  
Cloud Datacenter

The basic concept of cloud computing is based on “Pay per Use”. The user can use the remote resources on demand for computing on payment basis. The on-demand resources of the user are provided according to a Service Level Agreement (SLA). In real time, the tasks are associated with a time constraint for which they are called deadline based tasks. The huge number of deadline based task coming to a cloud datacenter should be scheduled. The scheduling of this task with an efficient algorithm provides better resource utilization without violating SLA. In this chapter, we discussed the backfilling algorithm and its different types. Moreover, the backfilling algorithm was proposed for scheduling tasks in parallel. Whenever the application environment is changed the performance of the backfilling algorithm is changed. The chapter aims implementation of different types of backfilling algorithms. Finally, the reader can be able to get some idea about the different backfilling scheduling algorithms that are used for scheduling deadline based task in cloud computing environment at the end.

scholarly journals An Effiecient Approach for Resource Auto-Scaling in Cloud Environments

2016 ◽  
Vol 6 (5) ◽  
pp. 2415
Author(s):  
Bahar Asgari ◽  
Mostafa Ghobaei Arani ◽  
Sam Jabbehdari
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Sla Violation ◽  
Markov Decision ◽  
Cloud Environments ◽  
Auto Scaling

<p>Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches.</p>

scholarly journals Adaptive Mechanism Model for the Prevention of SLA Violation in the Context of COPD Patient Monitoring

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1575
Author(s):  
Konan-Marcelin Kouamé ◽  
Hamid Mcheick ◽  
Hicham Ajami
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Research Area ◽  
Patient Data ◽  
Chronic Obstructive ◽  
Control Mechanisms ◽  
Medical Monitoring ◽  
Mechanism Model ◽  
Sla Violation

In this paper, we introduce a new kind of Service Level Agreement(SLA) Template to better control dynamically quality of medical monitoring platform service. Our approach is based on Health care system and Health Information Technology (HIT) research area, specifically the field of telemonitoring system for patients who suffer from chronic obstructive pulmonary disease (COPD). According to WHO statistics, COPD is the third leading cause of death worldwide. To this end, several solutions or platforms exist today to monitor COPD. Most of these platforms manage large volume of patient data. This can bring about quality and lost data problems. To address these issues, control mechanisms must be proposed and designed to improve the quality of service (QoS) on these platforms. A platform with continuously monitored QoS can save patients’ lives and reduce data quality risk. In this article, we propose an ontology that uses SLAs data from COPD monitoring platforms with dynamic data from a patient context. We dynamically calculate the number of patient data incidents and the number of service request incidents from two dynamic contexts: SLA and the patient context. If the number of incidents is higher than what is expected in the SLA, then alerts are sent to the interface parties in real time. Finally, the contribution of this article is the proposed virtual SLA template to better control SLA violation and improve quality of medical monitoring platforms services.

scholarly journals Workload Consolidation using Task Scheduling Strategy Based on Genetic Algorithm in Cloud Computing

2017 ◽  
Vol 10 (1) ◽  
pp. 60-65
Author(s):  
Ronak Vihol ◽  
Hiren Patel ◽  
Nimisha Patel
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Service Level Agreement ◽  
Dynamic Environment ◽  
Service Level ◽  
Upper Limits ◽  
First Come First Serve ◽  
Workload Consolidation ◽  
Lower Limits

Offering “Computing as a utility” on pay per use plan, Cloud computing has emerged as a technology of ease and flexibility for thousands of users over last few years. Distribution of dynamic workload among available servers and efficient utilization of existing resources in datacenter is one of the major concerns in Cloud computing. The load balancing issue needs to take into consideration the utilization of servers, i.e. the resultant utilization should not exceed the preset upper limits to avoid service level agreement (SLA) violation and should not fall beneath stipulated lower limits to avoid keeping some servers in active use. Scheduling of workload is regarded as an optimization problem that considers many varying criterion such as dynamic environment, priority of incoming applications, their deadlines etc. to improve resource utilization and overall performance of Cloud computing. In this work, a Genetic Algorithm (GA) based novel load balancing mechanism is proposed. Though not done in this work, in future, we aim to compare performance of proposed algorithms with existing mechanisms such as first come first serve (FCFS), Round Robin (RR) and other search algorithms through simulations.

scholarly journals QoS Design Consideration for Enterprise and Provider’s Network at Ingress and Egress Router for VoIP protocols

2016 ◽  
Vol 6 (1) ◽  
pp. 235
Author(s):  
Manjur Kolhar ◽  
Mosleh M Abualhaj ◽  
Faiza Rizwan
Keyword(s):  
Denial Of Service ◽  
Service Level Agreement ◽  
Service Level ◽  
Virtual Private Network ◽  
Multiprotocol Label Switching ◽  
Service Levels ◽  
Sla Violation ◽  
Mpls Vpn ◽  
End To End ◽  
Private Network

Compliance with the Service Level Agreement (SLA) metric is a major challenge in a Multiprotocol Label Switching Virtual Private Network (MPLS VPN) because mandatory models must be maintained on both sides of the MPLS VPN in order to achieve end-to-end service levels. The end-to-end service of an MPLS VPN can be degraded owing to various issues such as distributed denial of service (DDoS), and Random Early Detection (RED) that prevents congestion and differentiates between legitimate and illegitimate user traffic. In this study, we propose a centralized solution that uses a SLA Violation Detector (SLAVD) and intrusion detection to prevent SLA violation.

scholarly journals VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

2019 ◽  
Vol 8 (2) ◽  
pp. 3444-3449
Keyword(s):  
Energy Consumption ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Environment ◽  
Total Energy Consumption ◽  
Vm Migration ◽  
Allocation Policy ◽  
Sla Violation ◽  
Violation Rate

Cloud computing, a metered based technology provides the services using virtualized technology over the internet. In the cloud environment, to improve the performance (such as utilization of the resources, energy minimization) extreme number of virtual machines (VMs) can be installed on the servers as per their resource capacity. In this way, servers can be overloaded. Overloaded servers consume more energythan normal status servers. VM migration (VMM) is an efficient technique to become a server in a normal state. VMM technique is used to consolidate the resources to increase resource utilization (RU) and reduceenergy usage. In the VMM technique, selection of VM such as which VM is migrated from one server to another server and allocation of VM on servers is an important aspect. Appropriate VM selection declines the numeral of VMMs and increasesenergy efficiency. Appropriate VM allocation declines the server to become overloaded. In this paper, the VM selection and allocation strategy is presented. CloudSim toolkit is used to verify the strength of proposed VM selection and allocation algorithm. Proposed VM Selection algorithm (MaMT) performs better than existing MiMT algorithm in terms of total energy consumption, number of hosts shut down, number of VMM, and average Service Level Agreement (SLA) violation rate. MaMT algorithm with resource aware provisioning (RAP) and MiMT+RAP algorithm combines both VM selection and allocation policies. RAP algorithm used both energy and RU parameters while allocating VM to the server.MaMTreduces the energy consumption up to 7.25% and reduces the SLA violation rate up-to 2.6% in comparison to MiMT algorithm. When VM selection and allocation policies combines together than more system performance is improved. MaMT+RAPreduces the energy consumption up to6.76% and reduces the SLA violation rate up-to 0.22% in comparison to MaMT algorithm.MiMT+RAPreduces the energy consumption up to15.23% and reduces the SLA violation rate up-to 0.95% in comparison to MiMT algorithm.

QoS Design Consideration for Enterprise and Provider’s Network at Ingress and Egress Router for VoIP protocols

2016 ◽  
Vol 6 (1) ◽  
pp. 235
Author(s):  
Manjur Kolhar ◽  
Mosleh M Abualhaj ◽  
Faiza Rizwan
Keyword(s):  
Denial Of Service ◽  
Service Level Agreement ◽  
Service Level ◽  
Virtual Private Network ◽  
Multiprotocol Label Switching ◽  
Service Levels ◽  
Sla Violation ◽  
Mpls Vpn ◽  
End To End ◽  
Private Network

Compliance with the Service Level Agreement (SLA) metric is a major challenge in a Multiprotocol Label Switching Virtual Private Network (MPLS VPN) because mandatory models must be maintained on both sides of the MPLS VPN in order to achieve end-to-end service levels. The end-to-end service of an MPLS VPN can be degraded owing to various issues such as distributed denial of service (DDoS), and Random Early Detection (RED) that prevents congestion and differentiates between legitimate and illegitimate user traffic. In this study, we propose a centralized solution that uses a SLA Violation Detector (SLAVD) and intrusion detection to prevent SLA violation.

An Effiecient Approach for Resource Auto-Scaling in Cloud Environments

2016 ◽  
Vol 6 (5) ◽  
pp. 2415 ◽  
Author(s):  
Bahar Asgari ◽  
Mostafa Ghobaei Arani ◽  
Sam Jabbehdari
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Sla Violation ◽  
Markov Decision ◽  
Cloud Environments ◽  
Auto Scaling

<p>Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches.</p>

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