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With the growth of the cloud computing market, the number and scale of cloud data centers are expanding rapidly. While cloud data centers provide a large amount of computing power, generating tremendous energy consumption has become a fundamental issue in the financial and environmental fields. Improving quality of service and reducing energy costs are fundamental challenges for next-generation cloud data centers. Task scheduling in cloud data centers grows increasingly complex due to the heterogeneity of computing resources, intricate dependencies of jobs and rising expenses resulting from high energy consumption. Efficiently utilizing computing resources is crucial, so it is necessary to develop optimal strategies for job scheduling. This paper proposes a reinforcement learning-based task scheduler (E2DSched) for online scheduling of randomly arriving directed acyclic graph jobs in cloud data centers. E2DSched divides the scheduling process into three layers: task selection layer, server selection layer and frequency control layer. It achieves joint optimization of energy consumption and quality of service through three-layer cooperation. Finally, we compare E2DSched with various other algorithms, and the results show that E2DSched can provide excellent service with less energy consumption. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) separates the forwarding plane and control plane of the network equipment, adopts a centralized control mode to simplify network deployment and improve network management efficiency and realizes the network flexible control and management of traffic through programmable open interfaces. At present, it has been widely used in domestic and international data centre networks. With the explosive growth of the scale of data centres and the increase in user requirements for service quality, load balancing and congestion control of data centres have become significant issues in current research. After studying and analysing data centre SDN architecture and load balancing problems in detail, certain experts proposed an SDN load balancing algorithm, based on improved ant colony optimization-load balancing (IACO-LB). Firstly, the overall framework of SDN load balancing in data centres is studied, which is mainly divided into three parts: basic network equipment, OpenFlow protocol and controller. Among them, the controller constitutes the core of the entire load balancing system, including four modules: network topology awareness, status collection, the core of the load balancing algorithm and the flow table distribution. Then, an SDN load balancing algorithm, based on the improved ant colony optimization (IACO) is proposed to achieve dynamic load balancing of the SDN. The algorithm fully considers the performance parameters of network links and servers, and its design is based on the principle of selecting links and servers with low utilization. The evaluation methods of server module and link module are designed and the ant colony algorithm is used to find the global optimal solution. In order to prevent the algorithm from falling into local optimum, the Kent chaotic model is adopted to disturb the transition probability of the ant colony, by improving the basic ant colony algorithm. Finally, a network topology model was established in MATLAB to carry out simulation experiments. The results show that, compared with the equivalent multi-path algorithm and path server traffic scheduling algorithm, IACO-LB can effectively solve the load balancing problem of SDN and can dynamically adjust the routing scheme, according to the changes in network link traffic and server utilization. The algorithm converges quickly and can achieve a better global load balancing scheme. [ABSTRACT FROM AUTHOR]

How to improve resource utilization of cloud data centers (CDCs) and ensure users' quality of service (QoS) through efficient virtual machine (VM) scheduling is an urgent problem. Especially when service reliability is taken into consideration, the problem becomes more challenging. However, existing related researches mostly ignore the influence of reliability factors, such as failures and recoveries of computing nodes (CNs), which cannot reflect the realistic situations of real-life CDCs. Therefore, this paper investigates the problem of fault tolerance-aware VM scheduling and formulates it as a multi-objective optimization model with multiple QoS constraints. The proposed model tries to minimize users' total expenditure and, at the same time, maximize the successful execution rate of their businesses. To solve the proposed optimization model, a greedy-based best fit decreasing (GBFD) algorithm is then developed. The GBFD algorithm adopts a cost efficiency factor whose definition is according to the characteristics of CNs, to select a suitable CN for each VM request. Finally, extensive experiments are conducted to verify the feasibility of the proposed models and algorithm based on both the real-world CDC cluster data sets and the simulation ones. The results show that, first, as expected, fault tolerance significantly influences the performance criteria of VM scheduling and second, in most cases, the developed algorithm can decrease users' expenditure, increase success rate for executing their business and improve their overall satisfactions. Specifically, under real-world CDC cluster scenario, GBFD algorithm can increase the overall satisfaction of all cloud users by 38.3%, 20.9% and 14.6%, respectively, compared with the other three ones. Thus, the developed algorithm can perform better under fault tolerance-aware cloud environments. [ABSTRACT FROM AUTHOR]

New hierarchical crossbar switch architectures, such as Omni-Path (OPA) and Cray X2, have appeared to improve packet latency, reduce overall cost and increase fault tolerance of the high-performance interconnection networks in supercomputing and data center systems. These and other interconnect technologies (Infiniband or 40/100 Gigabit Ethernet) include support to provide quality of service (QoS) to the applications. In this paper, we show how this QoS support can be enabled to achieve bandwidth and/or latency differentiation in Omni-Path interconnection networks, as a representative case of hierarchical switches. To do that, three different table-based schedulers are used. We include the description of these schedulers and a comparative study by using the results obtained when we evaluate them with Hiperion, a simulation tool that implements an OPA model. [ABSTRACT FROM AUTHOR]

High-performance computing requires numerous virtual machines (VMs) to meet the demand for services of the end-users. Such computation generates high intra-network traffic among the services running in different VMs. Thus, to achieve a high throughput of the application, VMs are to be deployed in physical machines in a way that reduces the communication cost and delays and thereby meets the high quality of services demanded by the users. On the other hand, the service providers aim to consolidate VMs in a minimum number of active physical machines to reduce their operational costs. Finding an optimal solution to minimize the cost of either the deployment or communication, by themselves are NP-Hard problems. Moreover, an attempt to optimize one disregarding the other may give a solution that is better in terms of cost but inferior in terms of throughput (or vice versa). Hence, we formulate the VM placement problem as a multi-objective optimization problem and propose an Ant Colony Optimization-based VM consolidation algorithm to find a solution in real-time. We have also presented a performance comparison of the proposed algorithm with existing mono-objective and multi-objective algorithms. [ABSTRACT FROM AUTHOR]

The growing interest in cloud-based services has made the cost of energy a major concern for cloud service providers. On the other hand, there is an increasing global concern on the carbon footprints of data centers. Shifting from fossil fuels to renewable energy sources is essential to reduce operational costs in data centers while keeping carbon and greenhouse emissions to a minimum. However, considering dynamic electricity pricing, dynamic PUE of data centers, intermittent renewable energy sources and the heterogeneity in workloads and data centers, determining the best data center to host a given VM request is very challenging. Moreover, the fragmentation caused by the placement which increases the energy cost may add to this complexity. This paper presents GreenPacker, a renewable- and fragmentation-aware geographical load balancing method to minimize the energy cost of data centers. This is done by introducing a cost metric that quantifies the suitability of a data center based on its green energy availability, electricity price, PUE, and the fragmentation caused by the placement. Comprehensive analyses using various workload scenarios show that the proposed algorithm can achieve up to 7.2% energy cost savings while maintaining the quality of service at an acceptable level, compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

The large-scale virtualized Cloud data centers consume huge amount of electrical energy leading to high operational costs and emission of greenhouse gases. Virtual machine (VM) consolidation has been found to be a promising approach to improve resource utilization and reduce energy consumption of the data center. However, aggressive consolidation of VMs tends to increase the number of VM migrations and leads to over-utilization of hosts. This in turn affects the quality of service (QoS) of the applications running in the VMs. Thus, reduction in energy consumption and at the same time ensuring proper QoS to the Cloud users are one of the major challenges among the researchers. In this paper, we have proposed an energy efficient and QoS-aware VM consolidation technique in order to address this problem. We have used Markov chain-based prediction approach to identify the over-utilized and under-utilized hosts in the data center. We have also proposed an efficient VM selection and placement policy based on linear weighted sum approach to migrate the VMs from over-utilized and under-utilized hosts considering both energy and QoS. Extensive simulations using real-world traces and comparison with state-of-art strategies show that our VM consolidation approach substantially reduces energy consumption within a data center while delivering suitable QoS. [ABSTRACT FROM AUTHOR]

With the development of information technology, there is a need for computational works everywhere and every time. Thus, people should be able to carry out their heavy computations without having the burden of purchasing expensive hardware and software. Cloud computing is an attractive solution to such needs, but the high energy consumption of physical machines in a Cloud data center is a matter of great concern. Therefore, some of the low-loaded machines can be turned off or switched into low energy mode using server consolidation approaches. In this paper, a Stochastic Process-Based Dynamic Server Consolidation (SB-DSC) policy is developed to reduce the total cost of data centers while satisfying the required quality of service. A novel algorithm, which we call it Stochastic Process-Based BFD (SBBFD), is employed in SB-DSC policy to perform virtual machine placements over time. SBBFD overcomes most drawbacks of other algorithms proposed in the literature. The simulation results on real workload data show that SB-DSC leads to a noticeable reduction in total cost in terms of power consumption, SLA violations, number of mode switching and number of migrations. [ABSTRACT FROM AUTHOR]

With the development of cloud computing, energy consumption has become a major and costly problem in data centers. To improve the energy efficiency of data centers, we analyze the influence factors of energy consumption and discover that reducing the idle servers can effectively cut down the energy consumption of data centers. Then the load demand forecasting algorithm using weighted random forests is proposed. And time factor matching coefficient obtained by considering the day type and the time span is employed to calculate the weights. To enhance the forecasting performance, an error correction strategy is also introduced into the forecasting model. The experimental results show that these strategies further improve the prediction accuracy, and the root-mean-square error is 2.6–4.1% lower than other forecasting algorithms. We finally design an adaptive scheduling technology that utilizes short-term prediction of load demand. This technology adaptively adjusts the scale of the data center cluster based on the forecast results. The simulation results indicate that the technology can reduce 12.5% energy consumption while ensuring the service quality. [ABSTRACT FROM AUTHOR]

Virtual machine (VM) migration is a process of migrating VMs from one physical server to another. It provides several benefits to a data center in a variety of scenarios including improved performance, fault tolerance, manageability load balancing and power management. However, VM’s migration leads to performance degradation and service-level agreement (SLA) violations which cannot be ignored, particularly if critical business goals are to be met. In this paper, we propose an algorithm for VM’s placement and migration that considers different users quality of service requirement, in order to decrease energy consumption and SLA violations due to under utilization of data centers. The proposed work mainly focuses on a novel heuristics-based energy-aware resource allocation to allocate the user’s tasks in the form of cloudlets to the cloud resources that consumes minimal energy. In addition to that, it is incorporated with load balancing and constraint-based scheduling mechanism. The proposed work is implemented using the service-oriented-based architecture, and the same has been simulated using the CloudSim toolkit. In this paper, we compared our work with non-power-aware (NPA), dynamic voltage and frequency scaling (DVFS), single-threshold (ST) policies and minimization migration policy (MMP). The experiment results indicate that our approach saves about 83% of power comparing to the NPA system and 77% comparing to a system that apply only DVFS. However, if we compare these algorithms, which allow dynamic consolidation of VMs such as ST, it saves 53%, and finally, if we compare to MMP, it saves power between 22 and 38%. Similarly if we compare number of VM migration comparing to ST, it reduces 23 and 73% compared to MMP polices. [ABSTRACT FROM AUTHOR]

The ability to deliver acceptable levels of quality of service is crucial for cloud systems, and this requires performance as well as availability analysis. Existing modeling attempts mainly focus on pure performance analysis; however, the software and hardware components of cloud infrastructures may have limited reliability. In this study, analytical models are presented for performability evaluation of cloud centers. A novel approximate solution approach is introduced which allows consideration of large numbers of servers. The challenges for analytical modeling of cloud systems mentioned in the literature are considered. The analytical models and solutions, therefore, are capable of considering large numbers of facility nodes typically up to orders of hundreds or thousands, and able to incorporate various traffic loads while evaluating quality of service for cloud centers together with server availabilities. The results obtained from the analytical models are presented comparatively with the results obtained from discrete event simulations for validation. [ABSTRACT FROM AUTHOR]

In this paper, we develop the optimal minimum-energy scheduler for the adaptive joint allocation of the task sizes, computing rates, communication rates and communication powers in virtualized networked data centers (VNetDCs) that operate under hard per-job delay-constraints. The considered VNetDC platform works at the Middleware layer of the underlying protocol stack. It aims at supporting real-time stream service (such as, for example, the emerging big data stream computing (BDSC) services) by adopting the software-as-a-service (SaaS) computing model. Our objective is the minimization of the overall computing-plus-communication energy consumption. The main new contributions of the paper are the following ones: (i) the computing-plus-communication resources are jointly allotted in an adaptive fashion by accounting in real-time for both the (possibly, unpredictable) time fluctuations of the offered workload and the reconfiguration costs of the considered VNetDC platform; (ii) hard per-job delay-constraints on the overall allowed computing-plus-communication latencies are enforced; and, (iii) to deal with the inherently nonconvex nature of the resulting resource optimization problem, a novel solving approach is developed, that leads to the lossless decomposition of the afforded problem into the cascade of two simpler sub-problems. The sensitivity of the energy consumption of the proposed scheduler on the allowed processing latency, as well as the peak-to-mean ratio (PMR) and the correlation coefficient (i.e., the smoothness) of the offered workload is numerically tested under both synthetically generated and real-world workload traces. Finally, as an index of the attained energy efficiency, we compare the energy consumption of the proposed scheduler with the corresponding ones of some benchmark static, hybrid and sequential schedulers and numerically evaluate the resulting percent energy gaps. [ABSTRACT FROM AUTHOR]

A high-quality service for applications in cloud computing environments is guaranteed by making efficient use of resources in data centers. Applications should be allocated to resources suitable for the load of data centers to achieve this. The complex and dynamic nature of the load prevents the proper selection of one of multiple data centers and fails to meet the demands of resources in applications. An incorrect data center selection seriously lowers resource utilization in the data center and accordingly deteriorates the quality of services for applications. This paper proposes a neuro-fuzzy inference-based prediction scheme to select one of multiple data centers in accordance with application workloads. This scheme is used to aggressively capture the time-varying load of data centers by learning and predicting the availability of resources therein. Therefore, it predicts not only the present load but also the future load of data centers in the process of determining a suitable data center. By an autonomic control for data center selection, our scheme can also provide load balancing between data centers. Moreover, we present performance evaluations with experiments based on Xen testbeds to demonstrate the effectiveness of our scheme. The experimental results show that our scheme is superior to other selection schemes with regard to the entire and changed loads of data centers. [ABSTRACT FROM AUTHOR]