Q-aware: Quality of service based cloud resource provisioning

Cluster Computing, 2017

Cloud computing is the future generation of computational services delivered over the Internet. As cloud infrastructure expands, resource management in such a large heterogeneous and distributed environment is a challenging task. In a cloud environment, uncertainty and dispersion of resources encounters problems of allocation of resources. Unfortunately, existing resource management techniques, frameworks and mechanisms are insufficient to handle these environments, applications and resource behaviors. To provide an efficient performance and to execute workloads, there is a need of quality of service (QoS) based autonomic resource management approach which manages resources automatically and provides reliable, secure and cost efficient cloud services. In this paper, we present an intelligent QoS-aware autonomic resource management approach named as CHOPPER (Configuring, Healing, Optimizing and Protecting Policy for Efficient Resource management). CHOPPER offers self-configuration of applications and resources, self-healing by handling sudden failures, selfprotection against security attacks and self-optimization for maximum resource utilization. We have evaluated the perfor-B Sukhpal Singh Gill

Concurrency and Computation: Practice and Experience, 2018

Cloud computing utilizes heterogeneous resources that are located in various datacenters to provide an efficient performance on pay per use basis. However, existing mechanisms, frameworks and techniques for management of resources are inadequate to manage these applications, environments and the behavior of resources. There is a requirement of Quality of Service (QoS) based autonomic resource management technique to execute workloads and deliver cost-efficient and reliable cloud services automatically. In this paper, we present an intelligent and autonomic resource management technique named RADAR. RADAR focuses on two properties of self-management, firstly self-healing that handles unexpected failures and secondly self-configuration of resources and applications. The performance of RADAR is evaluated in the cloud simulation environment and the experimental results show that RADAR delivering better outcomes in terms of execution cost, resource contention, execution time, SLA violation while delivers reliable services.

Journal of Grid Computing, 2016

Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource

Journal of Network and Systems Management, 2017

Cloud resource scheduling requires mapping of cloud resources to cloud workloads. Scheduling results can be optimized by considering Quality of Service (QoS) parameters as inherent requirements of scheduling. In existing literature, only a few resource scheduling algorithms have considered cost and execution time constraints but efficient scheduling requires better optimization of QoS parameters. The main aim of this research paper is to present an efficient strategy for execution of workloads on cloud resources. A particle swarm optimization based resource scheduling technique has been designed named as BULLET which is used to execute workloads effectively on available resources. Performance of the proposed technique has been evaluated in cloud environment. The experimental results show that the proposed technique efficiently reduces execution cost, time and energy consumption along with other QoS parameters.

Operationalizing Multi-Cloud Environments, 2021

The advancements in the use of Internet of Things (IoT) devices is increasing continuously and generating huge amounts of data in a fast manner. Cloud computing is an important paradigm which processes and manages user data effectively. Further, fog and edge computing paradigms are introduced to improve user service by reducing latency and response time. This chapter presents a manifesto for modern fog and edge computing systems based on the current research trends. Further, architectures and applications of fog and edge computing are explained. Moreover, research opportunities and promising future directions are presented with respect to the new paradigms, which will be helpful for practitioners, researchers and academicians to continue their research.

Journal of Grid Computing, 2018

Provisioning of adequate resources to cloud workloads depends on the Quality of Service (QoS) requirements of these cloud workloads. Based on workload requirements (QoS) of cloud users, discovery and allocation of best workload-resource pair is an optimization problem. Acceptable QoS can be offered only if provisioning of resources is appropriately controlled. So, there is a need for a QoS-based resource provisioning framework for the autonomic scheduling of resources to observe the behavior of the services and adjust it dynamically in order to satisfy the QoS requirements. In this paper, framework for selfmanagement of cloud resources for execution of clustered workloads named as SCOOTER is proposed that efficiently schedules the provisioned cloud resources and maintains the Service Level Agreement (SLA) by considering properties of self-management and the maximum possible QoS parameters are required to improve cloud based services. Finally, the performance of SCOOTER has been evaluated in a cloud environment that demonstrates the optimized QoS parameters such as execution cost, energy consumption, execution time, SLA violation rate, fault

Internet of Things, 2020

Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog and Edge paradigms. Not only these emerging paradigms demand reevaluating cloud-native optimizations and exploring Fog and Edge-based solutions, but also the objectives require significant shift from considering only latency to energy, security, reliability and cost. Hence, it is apparent that optimization objectives have become diverse and lately Internet of Things (IoT)-specific born objectives must come into play. This is critical as incorrect selection of metrics can mislead the developer about the real performance. For instance, a latency-aware auto-scaler must be evaluated through latency-related metrics as response time or tail latency; otherwise the resource manager is not carefully evaluated even if it can reduce the cost. Given such challenges, researchers and developers are struggling to explore and utilize the right metrics to evaluate the performance of optimization techniques such as task scheduling, resource provisioning, resource allocation, resource scheduling and resource execution. This is challenging due to (1) novel and multi-layered computing paradigm, e.g., Cloud, Fog and Edge, (2) IoT applications with different requirements, e.g., latency or privacy, and (3) not having a benchmark and standard for the evaluation metrics. In this paper, by exploring the literature, (1) we present a taxonomy of the various real-world metrics to evaluate the performance of cloud, fog, and edge computing; (2) we survey the literature to recognize common metrics and their applications; and (3) outline open issues for future research. This comprehensive benchmark study can significantly assist developers and researchers to evaluate performance under realistic metrics and standards to ensure their objectives will be achieved in the production environments.

Digital Business, 2018

In cloud computing, an application design plays an important role and the efficient structure of an application can increase the energy-efficiency and sustainability of cloud datacenters. To make the infrastructure eco-friendly, energy-efficient and sustainable, there is a need for innovative applications. In this chapter, we comprehensively analyze the challenges in sustainable cloud computing and review the current developments for different applications. We propose a taxonomy of application management for sustainable cloud computing and identified research challenges. We also map the existing related studies to the taxonomy in order to identify current search gaps in the area of application management for sustainable cloud computing. Furthermore, we propose open research challenges for sustainable cloud computing based on the observations.

ACM Computing Surveys, 2016

As computing infrastructure expands, resource management in a large, heterogeneous, and distributed environment becomes a challenging task. In a cloud environment, with uncertainty and dispersion of resources, one encounters problems of allocation of resources, which is caused by things such as heterogeneity, dynamism, and failures. Unfortunately, existing resource management techniques, frameworks, and mechanisms are insufficient to handle these environments, applications, and resource behaviors. To provide efficient performance of workloads and applications, the aforementioned characteristics should be addressed effectively. This research depicts a broad methodical literature analysis of autonomic resource management in the area of the cloud in general and QoS (Quality of Service)-aware autonomic resource management specifically. The current status of autonomic resource management in cloud computing is distributed into various categories. Methodical analysis of autonomic resource ...

Cluster Computing, 2018

Cloud computing delivers computing resources like software and hardware as a service to the users through a network. The main idea of cloud computing is to share the tremendous power of storage, computation and information to the scientific applications. In cloud computing, the user tasks are organized and executed with suitable resources to deliver the services effectively. There are plenty of task allocation techniques that are used to accomplish task scheduling. In order to enhance the task scheduling technique, an efficient task scheduling algorithm is proposed in this paper. Optimization techniques are very popular in solving NP-hard problems. In this proposed technique, user tasks are stored in the queue manager. The priority is calculated and suitable resources are allocated for the task if it is a repeated task. New tasks are analyzed and stored in the on-demand queue. The output of the on-demand queue is given to the Hybrid Genetic-Particle Swarm Optimization (HGPSO) algorithm. To implement HGPSO technique, genetic algorithm and particle swarm optimization algorithm are combined and used. HGPSO algorithm evaluates suitable resources for the user tasks which are in the ondemand queue.

International Journal of Technology and Human Interaction, 2019

The Cloud environment is a large pool of virtually available resources that perform thousands of computational operations in real time for resource provisioning. Allocation and scheduling are two major pillars of said provisioning with quality of service (QoS). This involves complex modules such as: identification of task requirement, availability of resource, allocation decision, and scheduling operation. In the present scenario, it is intricate to manage cloud resources, as Service provider aims to provide resources to users on productive cost and time. In proposed research article, an optimized technique for efficient resource allocation and scheduling is presented. The proposed policy used heuristic based, ant colony optimization (ACO) for well-ordered allocation. The suggested algorithm implementation done using simulation, shows better results in terms of cost, time and utilization as compared to other algorithms.

Computing Algorithms with Applications in Engineering, 2020

Applied Sciences

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Software - Practice and Experience, 2019

Cloud computing is an innovative computing paradigm designed to provide a flexible and low-cost way to deliver information technology services on demand over the Internet. Proper scheduling and load balancing of the resources are required for the efficient operations in the distributed cloud environment. Since cloud computing is growing rapidly and customers are demanding better performance and more services, scheduling and load balancing of the cloud resources have become very interesting and important area of research. As more and more consumers assign their tasks to cloud, service-level agreements (SLAs) between consumers and providers are emerging as an important aspect. The proposed prediction model is based on the past usage pattern and aims to provide optimal resource management without the violations of the agreed service-level conditions in cloud data centers. It considers SLA in both the initial scheduling stage and in the load balancing stage, and it looks into different objectives to achieve the minimum makespan, the minimum degree of imbalance, and the minimum number of SLA violations. The experimental results show the effectiveness of the proposed system compared with other state-of-the-art algorithms.

Journal of King Saud University - Computer and Information Sciences, 2021

As the paradigm shift toward Software as a Service (SaaS) continues to gain the interest of companies and the scientific community, performances must be optimal. Indeed, cloud providers must provide an optimal quality of service (QoS) for their users in order to survive in such a competitive cloud market. Workload forecasting techniques have been proposed in order to improve capacity planning, ensure efficient management of resources and, hence, maintain SLA contracts with end users. In this context, we propose a new approach to predict the number of requests arriving at a SaaS service in order to prepare the virtualized resources necessary to respond to user requests. The method will be implemented in order to simultaneously achieve a twofold benefit: obtain precise forecast results while optimizing response time. In this regard, we have chosen to control the computation time by dynamizing the size of the sliding window associated to the recent history to be analyzed, since the larger the size of the entry in the prediction model, the more the algorithmic complexity increases. Then, the prediction will be established based on the temporal locality principle and the dynamic assignment of weights to different data points in recent history. Moreover, the proposed method can be extended to cover other uses in prediction. Experiments were carried out to assess the performance of the proposed method using two real workload traces and compared to state-of-the-art methods. The proposed method offers a compromise between the execution time and the accuracy of the prediction.