scholarly journals Dynamic Resource Allocation in the Cloud with Near-Optimal Efficiency

2021 ◽  
Author(s):  
Sebastian Perez-Salazar ◽  
Ishai Menache ◽  
Mohit Singh ◽  
Alejandro Toriello
Keyword(s):  
Service Level Agreement ◽  
Synthetic Data ◽  
Service Level ◽  
Dynamic Resource Allocation ◽  
Cloud Provider ◽  
Optimal Resource ◽  
Cloud Environments ◽  
Dynamic Resource ◽  
Optimal Efficiency ◽  
Demand Patterns

Motivated by maximizing spot instances in cloud shared systems, in this work, we consider the problem of taking advantage of unused resources in highly dynamic cloud environments while preserving users’ performance. We introduce an online model for sharing resources that captures basic properties of cloud systems, such as unpredictable users’ demand patterns, very limited feedback from the system, and service level agreement (SLA) between the users and the cloud provider. We provide a simple and efficient algorithm for the single-resource case. For any demand patterns, our algorithm guarantees near-optimal resource utilization as well as high users’ performance compared with their SLA baseline. In addition to this, we validate empirically the performance of our algorithm using synthetic data and data obtained from Microsoft’s systems.

Dynamic Resource Allocation Method for Load Balance Scheduling Over Cloud Data Center Networks

2021 ◽  
Author(s):  
Sakshi Chhabra ◽  
Ashutosh Kumar Singh
Keyword(s):  
Resource Allocation ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Dynamic Resource Allocation ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Dynamic Resource

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.

scholarly journals EFFICIENT RESOURCE MANAGEMENT ON CONTAINER AS A SERVICE

2021 ◽  
Author(s):  
Paul ChanHyung Park
Keyword(s):  
Resource Management ◽  
Service Level Agreement ◽  
Service Level ◽  
Fixed Number ◽  
Fine Tuning ◽  
Management Capability ◽  
Management Scheme ◽  
Generic Management ◽  
Efficient Resource ◽  
Dynamic Resource

Docker has been widely adopted as a platform solution for microservice. As the popularity of microservice increases, the importance of fine-tuning the efficiency of resource management in the Docker platform also increases. While Docker’s out-of-box resource management solution provides some generic management capability, more work is required to improve resource utilization and enforce Service Level Agreement (SLA) for critical services. In this research, an efficient Docker resource management scheme, called Adaptive SLA Enforcement, is designed and implemented. For the sake of comparison, we also study and implement three simpler schemes: 1) Fixed Number of Containers, 2) Dynamic Resource Management without SLA Enforcement, 3) Strict SLA Enforcement. We found that the Adaptive SLA Enforcement scheme can deliver efficient resource management with SLA enforcement, thus successfully addressing the deficiencies of the other three schemes.

scholarly journals EFFICIENT RESOURCE MANAGEMENT ON CONTAINER AS A SERVICE

2021 ◽  
Author(s):  
Paul ChanHyung Park
Keyword(s):  
Resource Management ◽  
Service Level Agreement ◽  
Service Level ◽  
Fixed Number ◽  
Fine Tuning ◽  
Management Capability ◽  
Management Scheme ◽  
Generic Management ◽  
Efficient Resource ◽  
Dynamic Resource

Docker has been widely adopted as a platform solution for microservice. As the popularity of microservice increases, the importance of fine-tuning the efficiency of resource management in the Docker platform also increases. While Docker’s out-of-box resource management solution provides some generic management capability, more work is required to improve resource utilization and enforce Service Level Agreement (SLA) for critical services. In this research, an efficient Docker resource management scheme, called Adaptive SLA Enforcement, is designed and implemented. For the sake of comparison, we also study and implement three simpler schemes: 1) Fixed Number of Containers, 2) Dynamic Resource Management without SLA Enforcement, 3) Strict SLA Enforcement. We found that the Adaptive SLA Enforcement scheme can deliver efficient resource management with SLA enforcement, thus successfully addressing the deficiencies of the other three schemes.

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 DBaaS Multitenancy, Auto-tuning and SLA Maintenance in Cloud Environments: a Brief Survey

2018 ◽  
Vol 11 (2) ◽  
pp. 30-42
Author(s):  
Vinicius Da Silveira Segalin ◽  
Carina Friedrich Dorneles ◽  
Mario Antonio Ribeiro Dantas
Keyword(s):  
Data Storage ◽  
Service Providers ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Resources Management ◽  
Database As A Service ◽  
Cloud Environments ◽  
And Performance ◽  
The One

Cloud computing is a paradigm that presents many advantages to both costumers and service providers, such as low upfront investment, pay-per-use and easiness of use, delivering/enabling scalable services using Internet technologies. Among many types of services we have today, Database as a Service (DBaaS) is the one where a database is provided in the cloud in all its aspects. Examples of aspects related to DBaaS utilization are data storage, resources management and SLA maintenance. In this context, an important feature, related to it, is resource management and performance, which can be done in many different ways for several reasons, such as saving money, time, and meeting the requirements agreed between client and provider, that are defined in the Service Level Agreement (SLA). A SLA usually tries to protect the costumer from not receiving the contracted service and to ensure that the provider reaches the profit intended. In this paper it is presented a classification based on three main parameters that aim to manage resources for enhancing the performance on DBaaS and guarantee that the SLA is respected for both user and provider sides benefit. The proposal is based upon a survey of existing research work efforts.

A new popularity-based data replication strategy in cloud systems

2021 ◽  
Vol 17 (2) ◽  
pp. 159-177
Author(s):  
Abdenour Lazeb ◽  
Riad Mokadem ◽  
Ghalem Belalem
Keyword(s):  
Service Level Agreement ◽  
Data Replication ◽  
Evaluation Study ◽  
Service Level ◽  
Cloud Provider ◽  
Computing Systems ◽  
Data Intensive ◽  
Replication Strategy ◽  
Context Data ◽  
Replication Technique

Data-intensive cloud computing systems are growing year by year due to the increasing volume of data. In this context, data replication technique is frequently used to ensure a Quality of service, e.g., performance. However, most of the existing data replication strategies just reproduce the same number of replicas on some nodes, which is certainly not enough for more accurate results. To solve these problems, we propose a new data Replication and Placement strategy based on popularity of User Requests Group (RPURG). It aims to reduce the tenant response time and maximize benefit for the cloud provider while satisfying the Service Level Agreement (SLA). We demonstrate the validity of our strategy in a performance evaluation study. The result of experimentation shown robustness of RPURG.

Optimizing the Migration of Virtual Machines in Cloud Data Centers

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
Time Simulation ◽  
Cloud Data Centers ◽  
Dynamic Resource

Dynamic resource allocation of cloud data centers is implemented with the use of virtual machine migration. Selected virtual machines (VM) should be migrated on appropriate destination servers. This is a critical step and should be performed according to several criteria. It is proposed to use the criteria of minimum resource wastage and service level agreement violation. The optimization problem of the VM placement according to two criteria is formulated, which is equivalent to the well-known main assignment problem in terms of the structure, necessary conditions, and the nature of variables. It is suggested to use the Hungarian method or to reduce the problem to a closed transport problem. This allows the exact solution to be obtained in real time. Simulation has shown that the proposed approach outperforms widely used bin-packing heuristics in both criteria.

Sign in / Sign up

Export Citation Format

Share Document