Subscription or Pay-as-You-Go: Optimally Purchasing IaaS Instances in Public Clouds

Yang, Shengsong; Pan, Li; Wang, Qingyang; Liu, Shijun; Zhang, Shuo

doi:10.1109/icws.2018.00035

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…where 𝐸𝐸𝑥𝑥𝑡𝑡𝑇𝑇𝑖𝑖𝑚𝑚𝐸𝐸�𝑡𝑡 𝑗𝑗𝑖𝑖 � is the execution time of 𝑡𝑡 𝑗𝑗 processing in 𝑣𝑣 𝑖𝑖 . This value can be calculated by (2).…”

Section: Problem Definition and Formulation Of Objective Functionmentioning

confidence: 99%

“…Cloud computing can also process many different services depending on the service and working platforms that are needed by the users [1]. Cloud computing is a combination of distributed and parallel computing with the sharing of resources such as software and hardware that will be utilized as "pay-as-you-go" [2]. To use it, users do not need to purchase any platform or software; they only have an internet connection to access and pay for their usage.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning

Kruekaew

Kimpan

2022

IEEE Access

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Workload balancing in cloud computing is still challenging problem, especially in Infrastructure as a Service (IaaS) in the cloud model. A problem that should not occur during cloud access is a host or server being overloaded or underloaded, which may affect the processing time or may result in a system crash. Therefore, to prevent these problems, an appropriate schedule of access should be considered so that the system can distribute tasks across all available resources, which is called load balancing. The load balancing technique should ensure that all Virtual Machines (VMs) are used appropriately. In this paper, an independent task scheduling approach in cloud computing is proposed using a Multi-objective task scheduling optimization based on the Artificial Bee Colony Algorithm (ABC) with a Q-learning algorithm, which is a reinforcement learning technique that helps the ABC algorithm work faster, called the MOABCQ method. The proposed method aims to optimize scheduling and resource utilization, maximize VM throughput, and create load balancing between VMs based on makespan, cost, and resource utilization, which are limitations of concurrent considerations. Performance analysis of the proposed method was compared using CloudSim with the existing load balancing and scheduling algorithms: Max-Min, FCFS, HABC_LJF, Q-learning, MOPSO, and MOCS algorithms in three datasets: Random, Google Cloud Jobs (GoCJ), and Synthetic workload. The experimental results indicated that the algorithms used MOABCQ approach outperformed the other algorithms in terms of reducing makespan, reducing cost, reducing degree of imbalance, increasing throughput and average resource utilization.INDEX TERMS Cloud computing, hybrid artificial bee colony algorithm, multi-objective task, Q-learning, task scheduling.

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Section: Problem Definition and Formulation Of Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning

Kruekaew

Kimpan

2022

IEEE Access

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“…Depending on the service required, the cloud services are categorized into four categories; they are "Platform as a service (PaaS)" [3], "Infrastructure as a service (IaaS)" [4], "Expert as a service (EaaS)" [5], and "Software as a service (SaaS)" [6]. Cloud computing enables the parallel and distributed computing by proving both hardware and software resources on a shared basis like as "Payas-you-go" [7]. To uses the resources, the users need an internet connection and they have to purchase any service or platform.…”

Section: Introductionmentioning

confidence: 99%

Multiple Resource Attributes and Conditional Logic Assisted Task Scheduling in Cloud Computing

2023

IJIES

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From the past few decades, the cloud computing has been arisen as an extensively used platform to provide storage, compute and analytics services to organizations and end users on the basis of pay-as-you-use. This enabled the organizations as well as individuals to access the larger set of resources without establishing a costly and high performance computing platform. However, the major issue in cloud computing is the task scheduling which is declared as NP-hard problem and most of the researchers applied meta-heuristic algorithms to solve it. However, they experienced a slower convergence speed which has a direct impact on the efficiency of cloud computing environment. To achieve a faster convergence along with efficient quality of Service, this paper proposes a simple and effective task scheduling mechanism based on multiple resource attributes and conditional logic. This method considers totally four resource attributes such as Resource reaction time resource location, resource availability and resource reliability rate. Based on these four attributes, the proposed method constructs an index called as task scheduling index (TSI) and assigns tasks for resources based on their TSI value. The TSI is constructed through the proposed conditional logic and high priority is given for resources those have higher TSI. For experimental validation, we used two benchmark datasets such as GOCJ and synthetic dataset. Three performance metrics namely Makespan, Throughput and convergence speed are measured and compared with state-of-the-art methods like PSO, GA and GWO. On an average, the reduced Makespan of proposed method is 48.22% and 46.87% for GOCJ and Synthetic datasets respectively.

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“…At the same time, NFV alleviates the reliance on network devices such as firewalls and proxies to provide network functionality. Therefore, for many users, NFV is considered an economically appealing platform that allows users to pay only for the services they use, according to the pay-as-you-go payment model [16,17]. Due to these capabilities, many cloud service providers have recently migrated their network services and applications to NFV.…”

Section: Introductionmentioning

confidence: 99%

Efficient Placement of Service Function Chains in Cloud Computing Environments

2021

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The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.

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Subscription or Pay-as-You-Go: Optimally Purchasing IaaS Instances in Public Clouds

Cited by 11 publications

References 11 publications

Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning

Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning

Multiple Resource Attributes and Conditional Logic Assisted Task Scheduling in Cloud Computing

Efficient Placement of Service Function Chains in Cloud Computing Environments

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