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Scheduling independent tasks on a parallel platform is a widely-studied problem, in particular when the goal is to minimize the total execution time, or makespan (P | | C max problem in Graham's notations). Also, many applications do not consist of sequential tasks, but rather parallel tasks, either rigid, with a fixed degree of parallelism, or moldable, with a variable degree of parallelism (i.e., for which we can decide at the execution on how many processors they are executed). Furthermore, since the energy consumption of data centers is a growing concern, both from an environmental and economical point of view, minimizing the energy consumption of a schedule is a main challenge to be addressed. One can then decide, for each task, on how many processors it is executed, and at which speed the processors are operated, with the goal to minimize the total energy consumption. We further focus on co-schedules, where tasks are partitioned into shelves, and we prove that the problem of minimizing the energy consumption remains NP-complete when static energy is consumed during the whole duration of the application. We are however able to provide an optimal algorithm for the schedule within one shelf, i.e., for a set of tasks that start at the same time. Several approximation results are derived, both with discrete and continuous speed models, and extensive simulations are performed to show the performance of the proposed algorithms. • A model for a theoretical analysis of the energy consumption of a schedule for moldable and rigid tasks. • Most presented scheduling problems are NP-complete. • There exist approximation algorithms for the presented problems (from 1.59 to 3 depending on the problem). • An experimental study of the trade-off between the time complexity of the scheduler and the energy consumed by the schedule. [ABSTRACT FROM AUTHOR]

Energy is a considerable portion of the cost of a cloud data center. In addition to energy, carbon emission tax imposes another cost on data centers, which have different policies in different cities around the world. Most of the cloud data centers are geographically distributed, which can have a huge advantage in reducing such costs. In addition, in recent years, cloud federation, in which multiple cloud providers share their IT infrastructures voluntarily, could play a crucial role in minimizing the energy consumption of cloud data centers. On the other hand, improper VM placement results frequent VM migration, constant turning off/on physical machines, service quality degradation and energy consumption increase. To tackle these weaknesses, we propose a multi-objective algorithm, Called OUR-ACS, to minimize data centers' energy consumption, carbon emission, and the total expenses (energy cost + carbon tax) in a federated environment considering both initial VM placement and VM consolidation approaches. We evaluate the efficiency of our proposed algorithm by using the ClousSim Plus simulator toolkit using real-world datasets. Compared to the competing algorithms, our simulation results indicate that our proposed algorithm could reduce the total energy consumption, carbon emission, and the total costs of multiple cloud data centers by an average of 37.6%, 41%, and 25%, respectively, in a federated environment. • We propose a novel multi-objective cloud federation algorithm considering to minimize three objectives. • We have identified a new problem in VM migration called Migration lock-in status. • We employ new heuristics to make a tradeoff between servers' power consumption and their resource utilization simultaneously. • We conduct a comprehensive simulation under an IaaS cloud infrastructure using a real-world datasets. [ABSTRACT FROM AUTHOR]

The diverse needs of customers drive cloud providers to incorporate more GPU-enabled services. It is known that users barely utilize GPUs. Hence, GPU virtualization techniques are employed to enable GPU sharing and increase resource utilization. Among GPU virtualization techniques, API remoting leverages disaggregation for GPU provisioning, allowing GPUs to be accessed remotely by non-resident VMs. It enables a VM to access any available GPU in the data center which, in turn, provides more flexibility to reach a better placement. It also enables users to choose VM and vGPU instances separately based on computational needs as GPU-enabled VM instances with fixed configurations may not proportionally utilize allocated resources. However, an inefficient VM or vGPU placement may result in poor performance and increase in energy consumption and rejection ratio. There is also a network communication overhead for remotely allocated vGPUs. The main challenge is how to place VMs and vGPUs such that rejection ratio is decreased and the negative performance impacts caused by GPU sharing and remote access are kept at a minimum. In this work, we formally define the joint problem of VM and vGPU placement based on API remoting as a multi-objective ILP model and introduce gVMP as a heuristic to solve it. The proposed method is compared against state-of-the-art heuristics and commercial solutions that are based on API remoting and GRID technologies for the placement of GPU-enabled VMs. We study the effectiveness of the proposed gVMP algorithm for low and high arrival rates in slow and fast networks. Our results show that under different scenarios and in comparison to state-of-the-art policies, our heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively. • The joint problem of VM and vGPU placement based on API remoting is formulated as a multi-objective ILP model. • The proposed heuristic reduces rejection ratio and negative performance impacts of GPU sharing and remote vGPU accesses. • The results are compared against state-of-the-art and commercial solutions for API remoting and GRID technologies. • The effectiveness is studied for low and high arrival rates in slow and fast networks under different scenarios. • The heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively. [ABSTRACT FROM AUTHOR]

The vigorous development and the increasing popularity of cloud computing highlight the necessity of reducing data center energy consumption and the environmental impact of carbon dioxide emissions. For geographically distributed data centers, cloud servers are connected to the conventional power grid and in addition they are supported by an attached renewable energy source. Since the carbon footprint rate of energy consumption has dynamic differences in space, reducing energy consumption does not mean decrease carbon emission, which indicates that energy consumption and carbon footprint need to be synergistically optimized. In this paper, an energy and carbon-aware algorithm for virtual machine placement is proposed. The goal is to obtain a virtual machine allocation scheme that aims to achieve the trade-off between energy consumption and carbon emissions by improving renewable energy utilization. The experimental results show that the proposed approach is more energy-efficient and greener, which can also maximize the renewable energy utilization with 73.11% while ensuring the SLA violation with 0.2% in comparison to the baseline algorithms. • Propose a novel energy-aware VM placement approach in cloud data centers. • Introduce renewable energy for managing energy consumption and carbon footprint. • Design renewable energy generation prediction method based on EEMD and TCN. • Demonstrate the superiority of the proposed algorithm with five baseline algorithms. [ABSTRACT FROM AUTHOR]

• We propose a new approach to achieve balance and optimization in resource exploitation and reduce energy consumption in the Cloud. • We propose two successive algorithms of FCA and Fuzzy-FCA to adapt our data according to their type (exact or fuzzy). • We use a mixed environment by integrating the MEC architecture with the cloud to minimize end-to-end latency. • We offer a feasible solution to properly place network services at VNFs. • We apply Fuzzy FCA to place and consolidate the VNFs based on a classification very effective. Virtual Network Function (VNF) is one of the pillars of a Cloud network that separates network functions and their dedicated hardware devices, such as routers, firewalls, and load balancers, to host their services on virtual machines. The VNF is responsible for network services that run on virtual machines and can connect each of them alone or organize themselves into a single enclosure to use all the resources available in that enclosure. This flexibility allows physical and virtual resources to be used in a way that ensures control over power consumption, balance in resource use, and minimizing costs and latency. In order to consolidate VNF groups into a minimum number of Virtual Machine (VM) with estimation of the association relation to a measure of confidence under the context of possibility theory, we propose a new Fuzzy-FCA approach for VNF placement based on Formal Concept Analysis (FCA) and fuzzy logic in mixed environment based on cloud data centers and Multiple access Edge Computing (MEC) architecture. Thus, the inclusion of this architecture in the cloud environment ensures the distribution of compute resources to the end user in order to reduce end-to-end latency. To confirm the effectiveness of our solution, we compared it to one of the best algorithms studied in the literature, namely the MultiSwarm algorithm. The results of the series of experiments carried out show the feasibility and efficiency of our algorithm. Indeed, the harvested results confirm the capability of maximizing and balancing the use of resources, of minimizing the latency and the cost of energy consumption. The performance of our solution in terms of average latency represents 16%, a slight increase compared to MultiSwarm, and an average gain, in runtime, of 49%, compared to the same algorithm. [ABSTRACT FROM AUTHOR]

Elephant flows (elephants) refer to the sequences of packets that contribute only 10% of the total volume but consume over 90% of the network bandwidth. They often cause network congestion and should be efficiently managed. Present cloud data centers often involve host- and switch-based approaches to detect and schedule elephants, but suffer (1) each host and switch in the network needs to be customized, and (2) dynamic models and advanced policies are difficult to be applied. Software Defined Cloud (SDC) addresses these issues by enabling controller-based approaches. With the aid of Machine Learning (ML) technologies, SDC can achieve learning-based models, flexible deployment, and early detection and schedule of elephants for the optimization of network performance and energy usage in a dynamic and intelligent manner. On this purpose, this article emphases the significance of models describing elephants, surveys the mechanisms that may apply to model, detect, and schedule elephants for SDC to optimize the network performance and energy usage. To the best of our knowledge, this work is the first effort that reviews the techniques in all these related subtopics simultaneously in the context of energy cloud. • This study surveys how to optimize the network performance and energy usage for SDC. • Review the techniques in related subtopics simultaneously in the context of energy cloud and machine learning. • A survey mechanism is developed based on the modeling, detecting, and scheduling elephant flows of SDC. [ABSTRACT FROM AUTHOR]

In the age of the information explosion, the energy demand for cloud data centers has increased markedly; hence, reducing the energy consumption of cloud data centers is essential. Dynamic virtual machine VM consolidation, as one of the effective methods for reducing energy energy consumption is extensively employed in large cloud data centers. It achieves the energy reductions by concentrating the workload of active hosts and switching idle hosts into low-power state; moreover, it improves the resource utilization of cloud data centers. However, the quality of service (QoS) guarantee is fundamental for maintaining dependable services between cloud providers and their customers in the cloud environment. Therefore, reducing the power costs while preserving the QoS guarantee are considered as the two main goals of this study. To efficiently address this problem, the proposed VM consolidation approach considers the current and future utilization of resources through the host overload detection (UP-POD) and host underload detection (UP-PUD). The future utilization of resources is accurately predicted using a Gray-Markov-based model. In the experiment, the proposed approach is applied for real-world workload traces in CloudSim and were compared with the existing benchmark algorithms. Simulation results show that the proposed approaches significantly reduce the number of VM migrations and energy consumption while maintaining the QoS guarantee. • The proposed Virtual Machine consolidation approach reduces the power costs while preserving the quality of service (QoS) guarantee. • Utilization of resources is accurately predicted using a new model. • In the experiment, the proposed approach is applied for real-world workload traces in CloudSim and were compared with the existing benchmark algorithms. • Simulation results show that the proposed approaches significantly reduce the number of VM migrations and energy consumption while maintaining the QoS guarantee. [ABSTRACT FROM AUTHOR]

Cloud computing offers attractive features for both service providers and customers. Users benefit from the pay-as-you-go model by saving expenditures and service providers are deploying their services to cloud data centers to reduce their maintenance efforts. However, due to the fast growth of cloud data centers, the energy consumed by the data centers can lead to a huge amount of carbon emission with environmental impacts, and the carbon intensity of different locations are varied among different power plants according to the sources of energy. Thus, in this paper, to address the carbon emission problem of data centers, we consider shifting the workloads among multi-cloud located in different time zones. We also formulate the energy usage and carbon emission of data centers and model the solar power corresponding to the locations. This helps to reduce the usage of brown energy and maximize the utilization of renewable energy at different locations. We propose an approach for managing carbon footprint and renewable energy for multiple data centers at California, Virginia, and Dublin, which are in different time zones. The results show that our proposed approaches that apply workload shifting can reduce around 40% carbon emission in comparison to the baseline while ensuring the average response time of user requests. • Model considering power consumption, renewable energy and carbon footprint. • Algorithm for managing the renewable energy and carbon footprint for data centers. • Evaluations on the impacts of requests executed in different time zones. [ABSTRACT FROM AUTHOR]

The computing power of supercomputers and data centers has noticeably grown during the last decades at the cost of an ever increasing energy demand. The need for energy (and power) of these facilities has finally limited the evolution of high performance computing, making that many researchers are concerned not only about performance but also about energy efficiency. However, despite the many concerns about energy consumption, the search for computing power continues. In this regard, the research on exascale systems, able to deliver 1018 floating point operations per second, has reached a widely consensus that these systems should operate within a maximum power budget of 20 megawatts. Many efficiency improvements are necessary for achieving this goal. One of these improvements is the usage of ARM low-power processors, as the Mont-Blanc project proposes. In this paper we analyze the combined use of ARM processors with the rCUDA remote GPU virtualization middleware as a way to improve efficiency even more. Results show that it is possible to speed up applications by almost 8x while reducing energy consumption up to 35% when rCUDA is used to access high-end GPUs. These improvements are achieved while maintaining a feasible average power consumption level for future exascale systems. • Suitability of rCUDA remote GPU virtualization middleware for future exascale. • Performance, power and energy consumption measurements. • Almost 8x application speed up while reducing energy consumption up to 35% • Maintaining a feasible average power consumption level for future exascale systems. [ABSTRACT FROM AUTHOR]

Resource management in cloud computing consists of allocating processing resources, storage, and network to a set of software applications. Resource providers focus on performance and utilization of resources considering the constraints of service level agreement. Resource performance is achieved by virtualization techniques, which share infrastructure of the resource provider between different virtual machines. This study proposes a novel algorithm based on learning automata, which improves resource utilization and reduces energy consumption. The proposed algorithm considers changes in the user demanded resources to predict the PM, which may suffer from overload. Due to preventing server overload, the proposed algorithm improves PMs’ utilization, reduces the number of migrations, and shuts down idle servers to reduce the energy consumption of the data center. The proposed algorithm is simulated in CloudSim simulator; the 10-day processor information of a real PlanetLab cloud infrastructure system are used for workload data. Performance of the proposed algorithm is compared with existing algorithms such as DVFS, NPA, and the threshold algorithm in terms of energy consumption and the number of shut down PMs. Simulation results indicate that the proposed algorithm outperforms other algorithms with 175.48 Kwh, 0.00326 in energy consumption, SLA violation respectively. [ABSTRACT FROM AUTHOR]

SuperMUC, deployed at the Leibniz Supercomputing Centre, is the first High-Temperature (ASHRAE W4 chiller-less) DirectLiquid Cooled (HT-DLC) Petascale supercomputer installed worldwide. Chiller-less direct liquid cooling can save data centers a substantial amount of energy by reducing data center cooling overheads. An essential question remains unanswered — how to determine an optimal operational environment for balancing scientific discovery with the energy consumption of both the supercomputer and the cooling infrastructure? This paper shows, for the first time, how the new technologies (HT-DLC and chiller-less cooling) influence the performance and energy/power efficiency of large-scale HPC applications and how different inlet temperatures affect the overall system power consumption and the HT-DLC efficiency. [ABSTRACT FROM AUTHOR]

Energy saving in large-scale video sharing data centers is an important yet daunting challenge due to the conflicting goal of providing real-time guarantees. Simple energy reduction techniques can result in excessive delay and severely affect the quality-of-service. This paper aims to optimize energy consumption while ensuring service delay constraints in data centers that provide large-scale video-sharing services. However, this broader goal requires three challenges that must be holistically addressed rather than in isolation. First, we propose a generic model to accurately characterize the disk behavior in a VSS by taking into account the unique characteristic of parallel video workloads. Second, the paper proposes a prediction-based algorithm that formulates and solves a constrained optimization problem for determining optimal selections of disk power modes in VSSs. Third, two novel caching algorithms are proposed that achieve additional energy saving through optimizing cache utilization. Experiments reveal that the proposed 3-component scheme achieves a significant amount of energy saving under the same delay level as compared to traditional energy management schemes. [ABSTRACT FROM AUTHOR]