Game Theoretic Approach for Real-Time Task Scheduling in Cloud Computing Environment

Patra, Manoj Kumar; Sahoo, Sampa; Sahoo, Bibhudatta; Turuk, Ashok Kumar

doi:10.1109/icit48102.2019.00086

Cited by 13 publications

(9 citation statements)

References 12 publications

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“…Also, in another research, 63 the authors introduced a game‐theoretic structure for real‐time job scheduling in the cloud. In this model, the task acts as a player, and the VM acts as a strategy.…”

Section: Review Of Literaturementioning

confidence: 99%

Analysis of QoS aware energy‐efficient resource provisioning techniques in cloud computing

Malla

Sheikh

2022

Int J Communication

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Summary By incorporating on‐demand resources, software and data for collaborative services through the Internet, the conventional Information Technology enterprise has been transformed by cloud computing. Based on the pay‐per‐use approach, Infrastructure, platform or Software resources and servers located across data centres are among the several types of resources offered to consumers in cloud computing. Data centres handle these resources. These resources are constantly provisioned to users based on their availability, demand, and quality requirements. Cloud computing systems are known as one of the largest utilisers of energy resources all over the world. Also, power consumption has become a crucial aspect as most cloud computing systems work on traditional nonrenewable resources of energy. In order to make data centres environment‐friendly, there is a need for optimal approaches to reduce energy consumption and their hazardous effects on the environment. To analyse different available strategies for building and maintaining an energy‐efficient cloud is the main objective of this paper. The paper will comprehensively review several energy‐efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing. Moreover, the present study identifies the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems.

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Section: Review Of Literaturementioning

confidence: 99%

Analysis of QoS aware energy‐efficient resource provisioning techniques in cloud computing

Malla

Sheikh

2022

Int J Communication

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“…It queues jobs based on their priority level into three queues, splits them into two areas based on the kind of MapReduce workload, and then places them in the CPU waiting queue and the I/O job queue, respectively. Patra et al 26 presented a game‐theoretic paradigm for scheduling real‐time tasks in the cloud computing environment in order to minimize overall completion time and total waiting time in this article. Task acts as a player in their game model, the VM acts as a strategy, and the player's payoff is measured by completion time and waiting time.…”

Section: Related Workmentioning

confidence: 99%

“…Next, If determiner function returns true and if min-heap is not empty then the top task is removed from the min heap's root, that is, Line (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36), its execution time is added to total burst time. Next, the current time instant is incremented and this task for run 1 unit of time.…”

Section: Proposed Algorithm For Hybrid Edfmentioning

confidence: 99%

Efficient task scheduling in cloud environment

Rana

Gupta

2022

Int J Communication

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Cloud computing is the provision of on-demand computing resources over the internet and on a pay-as-you-go basis, ranging from software to computation power. Task scheduling and its execution is a fundamental requirement of cloud environment. However, dynamic scheduling of tasks on basis of priority is a challenging area such that the tasks finish before their deadline. Earliest Deadline First (EDF) has been considered in literature for task scheduling to meet the deadlines. However, basic EDF (i.e., which schedules tasks on basis of deadline only)is not suitable for cloud environment. Therefore, this work proposes modified Preemptive EDF (p-EDF) and Non-Preemptive EDF (np-EDF) algorithms considering task priority and cloud provider cost. As both algorithms have their own merits and de-merits, a hybrid EDF is further proposed which makes decision dynamically whether to cause preemption or not, using a Determiner function. The objective of the work is to avoid unnecessary wastage of CPU power and time due to unnecessary preemptions, along with avoiding unnecessary deadline misses such that the high priority task does not wait for the low priority task to end. Simulation results show that the proposed algorithm outperforms other considered benchmark scheme for different performance parameters such as Deadline Miss Count, Preemption Count and average waiting time.

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“…The paper centered around establishing a mathematical model to calculate computing nodes' stability, configuring a game-theoretic cooperative model for the task of scheduling cloud computing, and examining the problem as a multi-stage sequential game. Patra et al presented the task as a player and the VM as a strategy in [27]. A non-cooperative game scheduling and a task balance scheduling algorithm are compared to collect the node's average task processing speed.…”

Section: Related Work: Secure Cloud Resource Managementmentioning

confidence: 99%

Perspective Chapter: Deep Reinforcement Learning for Co-Resident Attack Mitigation in The Cloud

Cui¹,

Homsi²

2022

Artificial Intelligence Annual Volume 2022

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Cloud computing brings convenience and cost efficiency to users, but multiplexing virtual machines (VMs) on a single physical machine (PM) results in various cybersecurity risks. For example, a co-resident attack could occur when malicious VMs use shared resources on the hosting PM to control or gain unauthorized access to other benign VMs. Most task schedulers do not contribute to both resource management and risk control. This article studies how to minimize the co-resident risks while optimizing the VM completion time through designing efficient VM allocation policies. A zero-trust threat model is defined with a set of co-resident risk mitigation parameters to support this argument and assume that all VMs are malicious. In order to reduce the chances of co-residency, deep reinforcement learning (DRL) is adopted to decide the VM allocation strategy. An effective cost function is developed to guide the reinforcement learning (RL) policy training. Compared with other traditional scheduling paradigms, the proposed system achieves plausible mitigation of co-resident attacks with a relatively small VM slowdown ratio.

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Game Theoretic Approach for Real-Time Task Scheduling in Cloud Computing Environment

Cited by 13 publications

References 12 publications

Analysis of QoS aware energy‐efficient resource provisioning techniques in cloud computing

Analysis of QoS aware energy‐efficient resource provisioning techniques in cloud computing

Efficient task scheduling in cloud environment

Perspective Chapter: Deep Reinforcement Learning for Co-Resident Attack Mitigation in The Cloud

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