Pervasive Cloud Controller for Geotemporal Inputs

Lučanin, Dražen; Brandić, Ivona

doi:10.1109/tcc.2015.2464794

Cited by 12 publications

(7 citation statements)

References 38 publications

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“…In [34], the authors propose a new pervasive cloud controller for dynamic resource reallocation in cloud environments. The proposed system adapts to volatile time and location-dependent factors, while considering the QoS impact of too frequent migrations and the data quality limits of time series forecasting methods.…”

Section: System Model and Related Workmentioning

confidence: 99%

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Alsarhan

Itradat

Al‐Dubai

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP's profit under varying cloud environment conditions.

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Section: System Model and Related Workmentioning

confidence: 99%

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Alsarhan

Itradat

Al‐Dubai

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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“…An energy consumption model was devised, and a corresponding energy-aware resource allocation algorithm was proposed for virtual machine scheduling. The energy-efficient management of geographically-distributed data centers was also the subject of [34]. The authors focused on the significant impact of "geotemporal input", i.e., the time-and location-dependent factors that may impact energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Reducing the Operational Cost of Cloud Data Centers through Renewable Energy

Laganá¹,

Mastroianni²,

Meo

et al. 2018

Algorithms

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The success of cloud computing services has led to big computing infrastructures that are complex to manage and very costly to operate. In particular, power supply dominates the operational costs of big infrastructures, and several solutions have to be put in place to alleviate these operational costs and make the whole infrastructure more sustainable. In this paper, we investigate the case of a complex infrastructure composed of data centers (DCs) located in different geographical areas in which renewable energy generators are installed, co-located with the data centers, to reduce the amount of energy that must be purchased by the power grid. Since renewable energy generators are intermittent, the load management strategies of the infrastructure have to be adapted to the intermittent nature of the sources. In particular, we consider EcoMultiCloud , a load management strategy already proposed in the literature for multi-objective load management strategies, and we adapt it to the presence of renewable energy sources. Hence, cost reduction is achieved in the load allocation process, when virtual machines (VMs) are assigned to a data center of the considered infrastructure, by considering both energy cost variations and the presence of renewable energy production. Performance is analyzed for a specific infrastructure composed of four data centers. Results show that, despite being intermittent and highly variable, renewable energy can be effectively exploited in geographical data centers when a smart load allocation strategy is implemented. In addition, the results confirm that EcoMultiCloud is very flexible and is suited to the considered scenario.

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“…This contribution addresses RQ 2. It was previously published in [126] and is presented in Chapter 5 of this thesis. SCIENTIFIC CONTRIBUTION 3.…”

Section: Rq2 -Sc2 Rq1 -Sc1mentioning

confidence: 99%

A Cloud Controller for Performance-Based Pricing

Lucanin

Pietri²,

Brandić³

et al. 2015

2015 IEEE 8th International Conference on Cloud Computing

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Abstract-New dynamic cloud pricing options are emerging with cloud providers offering resources as a wide range of CPU frequencies and matching prices that can be switched at runtime. On the other hand, cloud providers are facing the problem of growing operational energy costs. This raises a trade-off problem between energy savings and revenue loss when performing actions such as CPU frequency scaling. Although existing cloud controllers for managing cloud resources deploy frequency scaling, they only consider fixed virtual machine (VM) pricing. In this paper we propose a performance-based pricing model adapted for VMs with different CPU-boundedness properties. We present a cloud controller that scales CPU frequencies to achieve energy cost savings that exceed service revenue losses. We evaluate the approach in a simulation based on real VM workload, electricity price and temperature traces, estimating energy cost savings up to 32% in certain scenarios. I. INTRODUCTIONWith the wide range of VM types, heterogeneous infrastructure and different computing environments including VMs and containers [1], estimating the performance of provisioned resources is becoming increasingly challenging [2]. New cloud pricing schemes are emerging where resources are priced based on the delivered performance. For example, the CPU frequency provided to a VM at runtime determines the price [3], with high CPU frequencies being more expensive. We call this model performance-based pricing and it is used in production by cloud providers, such as ElasticHosts [4]. Though this approach mainly targets users, it could also be used by cloud providers to control their energy consumption. Energy consumption of data centers is becoming a major issue, accounting for 1.5% of global electricity usage [5]. Furthermore, modern clouds may consist of geographically-distributed data centers influenced by dynamic local factors, such as real-time electricity prices [6] and temperature-dependent cooling [7], that we call geotemporal inputs.We call the subsystem of the cloud, that determines the actions to allocate and manage the VMs, a cloud controller. Adapting the cloud controller to geotemporal inputs through actions, such as CPU frequency scaling, raises a trade-off problem between the potential energy savings and service revenue losses incurred under performance-based pricing. This is the challenge at the core of this paper.Frequency scaling is a power management technique commonly used to lower the operating frequency of hardware resources in order to reduce power consumption [8]. However, frequency reduction may degrade the performance of resources. Depending on the workload characteristics, workload

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Pervasive Cloud Controller for Geotemporal Inputs

Cited by 12 publications

References 38 publications

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Reducing the Operational Cost of Cloud Data Centers through Renewable Energy

A Cloud Controller for Performance-Based Pricing

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