Computing and Information Systems - Research Publications

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    Holistic resource management for sustainable and reliable cloud computing: An innovative solution to global challenge
    Gill, SS ; Garraghan, P ; Stankovski, V ; Casale, G ; Thulasiram, RK ; Ghosh, SK ; Ramamohanarao, K ; Buyya, R (Elsevier Inc., 2019-09-01)
    Minimizing the energy consumption of servers within cloud computing systems is of upmost importance to cloud providers toward reducing operational costs and enhancing service sustainability by consolidating services onto fewer active servers. Moreover, providers must also provision high levels of availability and reliability, hence cloud services are frequently replicated across servers that subsequently increases server energy consumption and resource overhead. These two objectives can present a potential conflict within cloud resource management decision making that must balance between service consolidation and replication to minimize energy consumption whilst maximizing server availability and reliability, respectively. In this paper, we propose a cuckoo optimization-based energy-reliability aware resource scheduling technique (CRUZE) for holistic management of cloud computing resources including servers, networks, storage, and cooling systems. CRUZE clusters and executes heterogeneous workloads on provisioned cloud resources and enhances the energy-efficiency and reduces the carbon footprint in datacenters without adversely affecting cloud service reliability. We evaluate the effectiveness of CRUZE against existing state-of-the-art solutions using the CloudSim toolkit. Results indicate that our proposed technique is capable of reducing energy consumption by 20.1% whilst improving reliability and CPU utilization by 17.1% and 15.7% respectively without affecting other Quality of Service parameters.
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    BULLET: Particle Swarm Optimization Based Scheduling Technique for Provisioned Cloud Resources
    Gill, SS ; Buyya, R ; Chana, I ; Singh, M ; Abraham, A (SPRINGER, 2018-04)
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    Failure Management for Reliable Cloud Computing: A Taxonomy, Model, and Future Directions
    Gill, SS ; Buyya, R (Institute of Electrical and Electronics Engineers, 2020-05-01)
    The next generation of cloud computing must be reliable to fulfil the end-user requirements, which are changing dynamically. Presently, cloud providers are facing challenges to ensure the reliability of their services. In this paper, we propose a comprehensive taxonomy of failure management in cloud computing. The taxonomy is used to investigate the existing techniques for reliability that need careful attention and investigation, as proposed by several academic and industry groups. Further, the existing techniques have been compared based on the common characteristics and properties of failure management as implemented in commercial and open-source solutions. A conceptual model for reliable cloud computing has been proposed, along with a discussion on future research directions. Moreover, a case study of astronomy workflow is presented for reliable execution in the cloud environment.
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    RADAR: Self-configuring and self-healing in resource management for enhancing quality of cloud services
    Gill, SS ; Chana, I ; Singh, M ; Buyya, R (Wiley, 2019-01-10)
    Cloud computing utilizes heterogeneous resources that are located in various datacenters to provide an efficient performance on a 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 a 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 delivers better outcomes in terms of execution cost, resource contention, execution time, and SLA violation while it delivers reliable services.