Number Of Service Level Agreement Violations Research Articles

Novel QoS-Guaranteed Orchestration Scheme for Energy-Efficient Mobile Augmented Reality Applications in Multi-Access Edge Computing

In this study, we focus on improving the energy efficiency of multiple mobile augmented reality (MAR) devices (MDs) with different MAR applications, which are connected to a single multi-access edge computing (MEC) server, through the centralized orchestration of the MEC server. To achieve this, a trade-off between the accuracy, latency, and energy consumption of each MD is considered as intertwined costs. Accordingly, we minimize a sum of intertwined costs including the energy consumption, latency and accuracy loss of multiple MDs, while satisfying the maximum latency constraint and the minimum accuracy constraint of each MAR application. With rigorous analysis, we design a theoretical framework for the optimization of MD performance by jointly managing the frame resolution of the image to offload to the MEC server from MDs, and the computation capacity distribution over MDs in the case of the limited computation capacity of the MEC server. Our numerical analysis validates that the proposed scheme significantly reduces the number of service-level agreement violations, such as exceeding the maximum latency constraint, compared with the existed algorithm. Specifically, the proposed work significantly improve costs of service latency, accuracy loss, and energy consumption of MDs for MEC-assisted MAR services compared with baseline schemes.

Petri net model for resource scheduling with auto scaling in elastic cloud

Cloud service vendors offer wide range of services in a pay per use paradigm to the customers. The aim of cloud resource management is to speed up the execution of tasks and efficient usage of computing resources. The main benefit of optimal scheduling policy is reduced makespan, energy as well as cost with minimum number of service level agreement (SLA) violations. In market oriented cloud the service vendors offer vivid variety of purchasing options and also dynamic prices to the customers. In order to incorporate these purchase-promotional offers and dynamic prices, this paper proposes a Petri net model for scheduling the workload across physical servers. Here, SLA requirements considered are CPU speed, memory, makespan and bandwidth with fewer virtual machine migrations. The experimental results indicate that the proposed system efficiently performs dynamic provisioning and elasticity in multiple public clouds with auto scaling that reduces makespan, number of SLA violations, penalty cost and maximises the profit.

Petri net model for resource scheduling with auto scaling in elastic cloud

Cloud service vendors offer wide range of services in a pay per use paradigm to the customers. The aim of cloud resource management is to speed up the execution of tasks and efficient usage of computing resources. The main benefit of optimal scheduling policy is reduced makespan, energy as well as cost with minimum number of service level agreement (SLA) violations. In market oriented cloud the service vendors offer vivid variety of purchasing options and also dynamic prices to the customers. In order to incorporate these purchase-promotional offers and dynamic prices, this paper proposes a Petri net model for scheduling the workload across physical servers. Here, SLA requirements considered are CPU speed, memory, makespan and bandwidth with fewer virtual machine migrations. The experimental results indicate that the proposed system efficiently performs dynamic provisioning and elasticity in multiple public clouds with auto scaling that reduces makespan, number of SLA violations, penalty cost and maximises the profit.

Energy-Aware VM Consolidation in Cloud Data Centers Using Utilization Prediction Model

Virtual Machine (VM) consolidation provides a promising approach to save energy and improve resource utilization in data centers. Many heuristic algorithms have been proposed to tackle the VM consolidation as a vector bin-packing problem. However, the existing algorithms have focused mostly on the number of active Physical Machines (PMs) minimization according to their current resource requirements and neglected the future resource demands. Therefore, they generate unnecessary VM migrations and increase the rate of Service Level Agreement (SLA) violations in data centers. To address this problem, we propose a VM consolidation approach that takes into account both the current and future utilization of resources. Our approach uses a regression-based model to approximate the future CPU and memory utilization of VMs and PMs. We investigate the effectiveness of virtual and physical resource utilization prediction in VM consolidation performance using Google cluster and PlanetLab real workload traces. The experimental results show, our approach provides substantial improvement over other heuristic and meta-heuristic algorithms in reducing the energy consumption, the number of VM migrations and the number of SLA violations.

Dynamic changes of QoS parameters in cloud computing service level agreement

Service level agreement (SLA) in cloud computing can be defined as a contractual agreement signed between cloud user and cloud provider in order to manage the quality of service (QoS) parameters which are the fundamental part of SLAs. The current dynamic SLA monitoring approaches have considered the cloud provider's perspective only. To take into consideration the user's perspective; this research proposes a dynamic SLA monitoring approach by generating new SLA terms based on analysing the SLA penalties. A new proposed trade-off mechanism is applied between monthly cost and QoS guarantee values. Thus; we propose a dynamic model for updating QoS parameters of SLA in cloud environment. Java language is used with a QoS measurement dataset to experiment the proposed approach. The current Static SLA (SSLA) approaches are compared with the proposed Dynamic SLA (DSLA) approach. The results of this experimental study show that the number of SLA violations in DSLA are less than the number of SLA violations in SSLA.

Dynamic changes of QoS parameters in cloud computing service level agreement

Service level agreement (SLA) in cloud computing can be defined as a contractual agreement signed between cloud user and cloud provider in order to manage the quality of service (QoS) parameters which are the fundamental part of SLAs. The current dynamic SLA monitoring approaches have considered the cloud provider's perspective only. To take into consideration the user's perspective; this research proposes a dynamic SLA monitoring approach by generating new SLA terms based on analysing the SLA penalties. A new proposed trade-off mechanism is applied between monthly cost and QoS guarantee values. Thus; we propose a dynamic model for updating QoS parameters of SLA in cloud environment. Java language is used with a QoS measurement dataset to experiment the proposed approach. The current Static SLA (SSLA) approaches are compared with the proposed Dynamic SLA (DSLA) approach. The results of this experimental study show that the number of SLA violations in DSLA are less than the number of SLA violations in SSLA.

Optimal selection of VMs for resource task scheduling in geographically distributed clouds using fuzzy c‐mean and MOLP

SummaryBecause of widespread distribution of resources in the geographically distributed cloud environment, optimal selection of virtual machines (VMs) is one of the most important challenges for the structure of the network. This is due to the high number of data centers and VMs with different qualities of service parameters. Because of redundancy in the VMs and the high number of service parameters, optimal selection of VMs is an NP‐hard problem. Therefore, a method is required, which can suggest the best VMs on the basis of the user's request and on the service‐level agreements (SLAs). This study focuses on four important factors in SLAs: cost, response time, availability, and reliability.In this paper, we propose a four‐tier structure, Observe, Orient, Decide, and Act, where (1) Observe is responsible for continuous monitoring users' requests and characteristics of data centers and VMs, (2) Orient is responsible for clustering data centers using fuzzy c‐means and based of the four quality of services (SLA's factors) and then the selection of the most suitable data center cluster for the VM selection, (3) Decide is responsible for making decision on the most suitable VMs using multiobjective linear programming, and (4) Act is responsible for the execution of the decision. The proposed structure was implemented, and its effectiveness was evaluated through considering the number of SLA violations.

SLA-Based Resource Provisioning for Hosted Software-as-a-Service Applications in Cloud Computing Environments

Cloud computing is a solution for addressing challenges such as licensing, distribution, configuration, and operation of enterprise applications associated with the traditional IT infrastructure, software sales and deployment models. Migrating from a traditional model to the Cloud model reduces the maintenance complexity and cost for enterprise customers, and provides on-going revenue for Software as a Service (SaaS) providers. Clients and SaaS providers need to establish a Service Level Agreement (SLA) to define the Quality of Service (QoS). The main objectives of SaaS providers are to minimize cost and to improve Customer Satisfaction Level (CSL). In this paper, we propose customer driven SLA-based resource provisioning algorithms to minimize cost by minimizing resource and penalty cost and improve CSL by minimizing SLA violations. The proposed provisioning algorithms consider customer profiles and providers' quality parameters (e.g., response time) to handle dynamic customer requests and infrastructure level heterogeneity for enterprise systems. We also take into account customer-side parameters (such as the proportion of upgrade requests), and infrastructure-level parameters (such as the service initiation time) to compare algorithms. Simulation results show that our algorithms reduce the total cost up to 54 percent and the number of SLA violations up to 45 percent, compared with the previously proposed best algorithm.