Workload Consolidation for Cloud Data Centers with Guaranteed QoS Using Request Reneging

Homsi, Soamar; Liu, Shuo; Chaparro-Baquero, Gustavo A.; Bai, Ou; Ren, Shaolei; Quan, Gang

doi:10.1109/tpds.2016.2642941

Cited by 32 publications

(11 citation statements)

References 28 publications

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“…As Cloud computing is a promising technology, rapidly growing and appealing for industries, there is a large corpus of literature devoted to this topic. A large number of studies consider resource management in a single data center [6], [7], [43], [52], [53], [54], [55] while other studies focus on a more complex scenario where multiple data centers (and possibly providers) are involved [5], [56], [57], [58], [59]. For example, [5] focuses on placing cloud services over a distributed infrastructure by introducing affinity (from a QoS point of view) between services and geographic regions, data centers, up to single hosts.…”

Section: Related Workmentioning

confidence: 99%

“…The techniques used to approach the resource allocation problem propose a plethora of heuristics ranging from Ant Colony Optimization [6] to Genetic Algorithms [63] or rely on hierarchical approaches [43], [57], [64] for scalability reasons. A different approach is to focus more on a mathematical formulation of the problem [52], [53], [60], [65], [66], aiming to a closed-form solution or relying on external solvers. In the proposed approach a mathematical formulation of the problem (with a multi-class queuing system inspired by [66]) is merged with the proposal of an algorithm that takes into account also the scalability issues in the solution of the underlying optimization problem.…”

Section: Related Workmentioning

confidence: 99%

“…Several aspects of the service requests are taken into account in the present research. For example, as in [52], the presence of timeouts is considered but, rather than proposing a mechanism for adapting the timeout to avoid overload, the timeout is considered as a parameter of the experimental scenario. The effect of timeout values is analyzed to demonstrate the stability of the proposed solution for different setups.…”

Section: Related Workmentioning

confidence: 99%

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A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Ardagna

Ciavotta

Lancellotti

et al. 2021

IEEE Trans. Cloud Comput.

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Cloud Computing is emerging as a major trend in ICT industry. However, as with any new technology, new major challenges lie ahead, one of them concerning the resource provisioning. Indeed, modern Cloud applications deal with a dynamic context that requires a continuous adaptation process in order to meet satisfactory Quality of Service (QoS) but even the most titled Cloud platform provide just simple rule-based tools; the rudimentary autoscaling mechanisms that can be carried out may be unsuitable in many situations as they do not prevent SLA violations, but only react to them. In addition, these approaches are inherently static and cannot catch the dynamic behavior of the application and are unsuitable to manage multi-Cloud/data center deployments required for mission critical services. This situation calls for advanced solutions designed to provide Cloud resources in a predictive and dynamic way. This work presents capacity allocation algorithms, whose goal is to minimize the total execution cost while satisfying some constraints on the average response time of multi-Cloud based applications. This paper proposes a joint load balancing and receding horizon capacity allocation techniques, which can be employed to handle multiple classes of requests. An extensive evaluation of the proposed solution against an Oracle with perfect knowledge of the future and well-known heuristics proposed in the literature is provided. The analysis shows that our solution outperforms the heuristics producing results very close to the optimal ones, and reducing the number of QoS violations (in the worst case QoS constraints violation rate is 4.259% versus up to 17.245% of other approaches and can easily reduced by roughly a factor of four by exploiting the receding horizon approach). Furthermore, a sensitivity analysis over two different time scales indicates that finer grained time scales are more appropriate for spiky workloads. Analytical results are validated through simulation, which also analyzes the impact of Cloud environment random perturbations. Finally, experiments on a prototype environment demonstrate the effectiveness of the proposed approach under real workloads.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Ardagna

Ciavotta

Lancellotti

et al. 2021

IEEE Trans. Cloud Comput.

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“…Therefore, it becomes the major concern to design energy efficient resource management strategies [5]. Low resource utilization [6] is a major factor in the high power consumption of data centers. It reported that physical servers' average CPU utilization is only 10% to 50% at most of the time in the datacenters [4].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Resource Selection and Allocation Strategy for Virtual Machine Consolidation in Cloud Datacenters

Chang

Luo

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYVirtual Machine Placement (VMP) plays an important role in ensuring efficient resource provisioning of physical machines (PMs) and energy efficiency in Infrastructure as a Service (IaaS) data centers. Efficient server consolidation assisted by virtual machine (VM) migration can promote the utilization level of the servers and switch the idle PMs to sleep mode to save energy. The trade-off between energy and performance is difficult, because consolidation may cause performance degradation, even service level agreement (SLA) violations. A novel residual available capacity (RAC) resource model is proposed to resolve the VM selection and allocation problem from the cloud service provider (CSP) perspective. Furthermore, a novel heuristic VM selection policy for server consolidation, named Minimized Square Root available Resource (MISR) is proposed. Meanwhile, an efficient VM allocation policy, named Balanced Selection (BS) based on RAC is proposed. The effectiveness validation of the BS-MISR combination is conducted on CloudSim with real workloads from the CoMon project. Evaluation results of experiments show that the proposed combination BS-MISR can significantly reduce the energy consumption, with an average of 36.35% compared to the Local Regression and Minimum Migration Time (LR-MMT) combination policy. Moreover, the BS-MISR ensures a reasonable level of SLAs compared to the benchmarks.

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“…licenses, cybersecurity investment, etc.) of cloud clusters [126], [170]. Nevertheless, optimal way to deal with the different behaviors of cybersecurity attackers [177].…”

Section: Introduction To the Research Problemmentioning

confidence: 99%

Cloud Workload Allocation Approaches for Quality of Service Guarantee and Cybersecurity Risk Management

Homsi¹

Self Cite

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Workload Consolidation for Cloud Data Centers with Guaranteed QoS Using Request Reneging

Cited by 32 publications

References 28 publications

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

Energy Efficient Resource Selection and Allocation Strategy for Virtual Machine Consolidation in Cloud Datacenters

Cloud Workload Allocation Approaches for Quality of Service Guarantee and Cybersecurity Risk Management

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