Cloud computing is considered to facilitate a more cost-effective way to deploy scientific workflows.The individual tasks of a scientific work-flow necessitate a diversified number of large states that are spatially l...Cloud computing is considered to facilitate a more cost-effective way to deploy scientific workflows.The individual tasks of a scientific work-flow necessitate a diversified number of large states that are spatially located in different datacenters,thereby resulting in huge delays during data transmis-sion.Edge computing minimizes the delays in data transmission and supports the fixed storage strategy for scientific workflow private datasets.Therefore,this fixed storage strategy creates huge amount of bottleneck in its storage capacity.At this juncture,integrating the merits of cloud computing and edge computing during the process of rationalizing the data placement of scientific workflows and optimizing the energy and time incurred in data transmission across different datacentres remains a challenge.In this paper,Adaptive Cooperative Foraging and Dispersed Foraging Strategies-Improved Harris Hawks Optimization Algorithm(ACF-DFS-HHOA)is proposed for optimizing the energy and data transmission time in the event of placing data for a specific scientific workflow.This ACF-DFS-HHOA considered the factors influencing transmission delay and energy consumption of data centers into account during the process of rationalizing the data placement of scientific workflows.The adaptive cooperative and dispersed foraging strategy is included in HHOA to guide the position updates that improve population diversity and effectively prevent the algorithm from being trapped into local optimality points.The experimental results of ACF-DFS-HHOA confirmed its predominance in minimizing energy and data transmission time incurred during workflow execution.展开更多
Big data analytics, the process of organizing and analyzing data to get useful information, is one of the primary uses of cloud services today. Traditionally, collections of data are stored and processed in a single d...Big data analytics, the process of organizing and analyzing data to get useful information, is one of the primary uses of cloud services today. Traditionally, collections of data are stored and processed in a single datacenter. As the volume of data grows at a tremendous rate, it is less efficient for only one datacenter to handle such large volumes of data from a performance point of view. Large cloud service providers are deploying datacenters geographically around the world for better performance and availability. A widely used approach for analytics of gee-distributed data is the centralized approach, which aggregates all the raw data from local datacenters to a central datacenter. However, it has been observed that this approach consumes a significant amount of bandwidth, leading to worse performance. A number of mechanisms have been proposed to achieve optimal performance when data analytics are performed over geo-distributed datacenters. In this paper, we present a survey on the representative mechanisms proposed in the literature for wide area analytics. We discuss basic ideas, present proposed architectures and mechanisms, and discuss several examples to illustrate existing work. We point out the limitations of these mechanisms, give comparisons, and conclude with our thoughts on future research directions.展开更多
Software-defined networking(SDN),a new networking paradigm decoupling the software control logic from the data forwarding hardware,promises to enable simpler management,more flexible resource usage and faster deployme...Software-defined networking(SDN),a new networking paradigm decoupling the software control logic from the data forwarding hardware,promises to enable simpler management,more flexible resource usage and faster deployment of network services.It opens network functionality,application programmability,and control-to-data communication interfaces that used to be closed in conventional network devices,offering endless opportunities but also challenges for both existing players and newcomers in the market.Through a comprehensive and comparative exploratory of SDN state-of-theart techniques,standardization activities and realistic applications,this article unveils historic and technical insights into the innovations that SDN offers toward an emerging open network eco-system.We closely examine the critical challenges and opportunities when the networking industry is reshaped by SDN.We further shed light on future development directions of SDN in broad application scenarios,ranging from cloud datacenters,network operating systems,and advanced wireless networking.展开更多
Cloud providers(e.g.,Google,Alibaba,Amazon)own large-scale datacenter networks that comprise thousands of switches and links.A loadbalancing mechanism is supposed to effectively utilize the bisection bandwidth.Both Eq...Cloud providers(e.g.,Google,Alibaba,Amazon)own large-scale datacenter networks that comprise thousands of switches and links.A loadbalancing mechanism is supposed to effectively utilize the bisection bandwidth.Both Equal-Cost Multi-Path(ECMP),the canonical solution in practice,and alternatives come with performance limitations or significant deployment challenges.In this work,we propose Closer,a scalable load balancing mechanism for cloud datacenters.Closer complies with the evaluation of technology including the deployment of Clos-based topologies,overlays for network virtualization,and virtual machine(VM)clusters.We decouple the system into centralized route calculation and distributed route decision to guarantee its flexibility and stability in large-scale networks.Leveraging In-band Network Telemetry(INT)to obtain precise link state information,a simple but efficient algorithm implements a weighted ECMP at the edge of fabric,which enables Closer to proactively map the flows to the appropriate path and avoid the excessive congestion of a single link.Closer achieves 2 to 7 times better flow completion time(FCT)at 70%network load than existing schemes that work with same hardware environment.展开更多
Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters.Meanwhile,datacenter infrastructure comprises hardw...Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters.Meanwhile,datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels.In cloud datacenter,Virtual Machines(VMs)need to be allocated on various Physical Machines(PMs)in order to minimize resource wastage and increase energy efficiency.Resource allocation problem is NP-hard.Hence finding an exact solution is complicated especially for large-scale datacenters.In this con text,this paper proposes an Energy-oriented Flower Pollination Algorithm(E-FPA)for VM allocation in cloud datacenter environments.A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM.The allocation uses a strategy called Dynamic Switching Probability(DSP).The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search.It considers a processor,storage,and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs.Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware(GAPA)by 21.8%,Order of Exchange Migration(OEM)ant colony system by 21.5%,and First Fit Decreasing(FFD)by 24.9%.Therefore,E-FPA significantly improves datacenter performance and thus,enhances environmental sustainability.展开更多
文摘Cloud computing is considered to facilitate a more cost-effective way to deploy scientific workflows.The individual tasks of a scientific work-flow necessitate a diversified number of large states that are spatially located in different datacenters,thereby resulting in huge delays during data transmis-sion.Edge computing minimizes the delays in data transmission and supports the fixed storage strategy for scientific workflow private datasets.Therefore,this fixed storage strategy creates huge amount of bottleneck in its storage capacity.At this juncture,integrating the merits of cloud computing and edge computing during the process of rationalizing the data placement of scientific workflows and optimizing the energy and time incurred in data transmission across different datacentres remains a challenge.In this paper,Adaptive Cooperative Foraging and Dispersed Foraging Strategies-Improved Harris Hawks Optimization Algorithm(ACF-DFS-HHOA)is proposed for optimizing the energy and data transmission time in the event of placing data for a specific scientific workflow.This ACF-DFS-HHOA considered the factors influencing transmission delay and energy consumption of data centers into account during the process of rationalizing the data placement of scientific workflows.The adaptive cooperative and dispersed foraging strategy is included in HHOA to guide the position updates that improve population diversity and effectively prevent the algorithm from being trapped into local optimality points.The experimental results of ACF-DFS-HHOA confirmed its predominance in minimizing energy and data transmission time incurred during workflow execution.
文摘Big data analytics, the process of organizing and analyzing data to get useful information, is one of the primary uses of cloud services today. Traditionally, collections of data are stored and processed in a single datacenter. As the volume of data grows at a tremendous rate, it is less efficient for only one datacenter to handle such large volumes of data from a performance point of view. Large cloud service providers are deploying datacenters geographically around the world for better performance and availability. A widely used approach for analytics of gee-distributed data is the centralized approach, which aggregates all the raw data from local datacenters to a central datacenter. However, it has been observed that this approach consumes a significant amount of bandwidth, leading to worse performance. A number of mechanisms have been proposed to achieve optimal performance when data analytics are performed over geo-distributed datacenters. In this paper, we present a survey on the representative mechanisms proposed in the literature for wide area analytics. We discuss basic ideas, present proposed architectures and mechanisms, and discuss several examples to illustrate existing work. We point out the limitations of these mechanisms, give comparisons, and conclude with our thoughts on future research directions.
基金supported in part by agrant from the National Natural Science Foundation of China(NSFC)(Grant Nos.61370232 and 61520106005)
文摘Software-defined networking(SDN),a new networking paradigm decoupling the software control logic from the data forwarding hardware,promises to enable simpler management,more flexible resource usage and faster deployment of network services.It opens network functionality,application programmability,and control-to-data communication interfaces that used to be closed in conventional network devices,offering endless opportunities but also challenges for both existing players and newcomers in the market.Through a comprehensive and comparative exploratory of SDN state-of-theart techniques,standardization activities and realistic applications,this article unveils historic and technical insights into the innovations that SDN offers toward an emerging open network eco-system.We closely examine the critical challenges and opportunities when the networking industry is reshaped by SDN.We further shed light on future development directions of SDN in broad application scenarios,ranging from cloud datacenters,network operating systems,and advanced wireless networking.
基金supported by National Key Research and Development Project of China(2019YFB1802501)Research and Development Program in Key Areas of Guangdong Province(2018B010113001)Open Foundation of Science and Technology on Communication Networks Laboratory(No.6142104180106)。
文摘Cloud providers(e.g.,Google,Alibaba,Amazon)own large-scale datacenter networks that comprise thousands of switches and links.A loadbalancing mechanism is supposed to effectively utilize the bisection bandwidth.Both Equal-Cost Multi-Path(ECMP),the canonical solution in practice,and alternatives come with performance limitations or significant deployment challenges.In this work,we propose Closer,a scalable load balancing mechanism for cloud datacenters.Closer complies with the evaluation of technology including the deployment of Clos-based topologies,overlays for network virtualization,and virtual machine(VM)clusters.We decouple the system into centralized route calculation and distributed route decision to guarantee its flexibility and stability in large-scale networks.Leveraging In-band Network Telemetry(INT)to obtain precise link state information,a simple but efficient algorithm implements a weighted ECMP at the edge of fabric,which enables Closer to proactively map the flows to the appropriate path and avoid the excessive congestion of a single link.Closer achieves 2 to 7 times better flow completion time(FCT)at 70%network load than existing schemes that work with same hardware environment.
文摘Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters.Meanwhile,datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels.In cloud datacenter,Virtual Machines(VMs)need to be allocated on various Physical Machines(PMs)in order to minimize resource wastage and increase energy efficiency.Resource allocation problem is NP-hard.Hence finding an exact solution is complicated especially for large-scale datacenters.In this con text,this paper proposes an Energy-oriented Flower Pollination Algorithm(E-FPA)for VM allocation in cloud datacenter environments.A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM.The allocation uses a strategy called Dynamic Switching Probability(DSP).The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search.It considers a processor,storage,and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs.Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware(GAPA)by 21.8%,Order of Exchange Migration(OEM)ant colony system by 21.5%,and First Fit Decreasing(FFD)by 24.9%.Therefore,E-FPA significantly improves datacenter performance and thus,enhances environmental sustainability.
