Geo-distributed clouds provide an intriguing platform to deploy online social network (OSN) services. To leverage the potential of clouds, a major concern of OSN providers is optimizing the monetary cost spent in usin...
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Geo-distributed clouds provide an intriguing platform to deploy online social network (OSN) services. To leverage the potential of clouds, a major concern of OSN providers is optimizing the monetary cost spent in using cloud resources while considering other important requirements, including providing satisfactory quality of service (QoS) and data availability to OSN users. In this paper, we study the problem of cost optimization for the dynamic OSN on multiple geo-distributed clouds over consecutive time periods while meeting predefined QoS and data availability requirements. We model the cost, the QoS, as well as the data availability of the OSN, formulate the problem, and design an algorithm named. We carry out extensive experiments with a large-scale real-world Twitter trace over 10 geo-distributed clouds all across the US. Our results show that, while always ensuring the QoS and the data availability as required, can reduce much more one-time cost than the state-of-the-art methods, and it can also significantly reduce the accumulative cost when continuously evaluated over 48 months, with OSN dynamics comparable to real-world cases.
Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter-datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational c...
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Bulk data transfers, such as backups and propagation of bulky updates, account for a large portion of the inter-datacenter traffic. These bulk transfers consume massive bandwidth and further increase the operational cost of datacenters. The advent of store-and-forward transfer mode offers the opportunity for cloud provider companies to transfer bulk data by utilizing dynamic leftover bandwidth resources. In this paper, we study the multiple bulk data transfers scheduling problem in inter-datacenter networks with dynamic link capacities. To improve the network utilization while guaranteeing fairness among requests, we employ the max-min fairness and aim at computing the lexicographically maximized solution. Leveraging the time-expanded technique, the problem in dynamic networks is formulated as a static multi-flow model. Then, we devise an optimal algorithm to solve it simultaneously from routing assignments and bandwidth allocation. To further reduce the computational cost, we propose to select an appropriate number of disjoint paths for each request. Extensive simulations are conducted on a real datacenter topology and prove that (i) benefiting from max-min fairness, the network utilization is significantly improved while honoring each individual performance;(ii) a small number of disjoint paths per request are sufficient to obtain the near optimal allocation within practical execution time. Copyright (c) 2013 John Wiley & Sons, Ltd.
Bulk data migration between datacenters is often a critical step in deploying new services, improving reliability under failures, or implementing various cost reduction strategies for cloud companies. These bulk amoun...
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Bulk data migration between datacenters is often a critical step in deploying new services, improving reliability under failures, or implementing various cost reduction strategies for cloud companies. These bulk amounts of transferring data consume massive bandwidth, and further cause severe network congestion. Leveraging the temporal and spacial characteristics of inter-datacenter bulk data traffic, in this paper, we investigate the Multiple Bulk Data Transfers Scheduling (MBDTS) problem to reduce the network congestion. Temporally, we apply the store-and-forward transfer mode to reduce the peak traffic load on the link. Spatially, we propose to lexicographically minimize the congestion of all links among datacenters. To solve the MBDTS problem, we first model it as an optimization problem, and then propose a novel Elastic Time-Expanded Network technique to represent the time-varying network status as a static one with a reasonable expansion cost. Using this transformation, we reformulate the problem as a Linear Programming (LP) model, and obtain the optimal solution through iteratively solving the LP model. We have conducted extensive simulations on a real network topology. The results prove that our algorithm can significantly reduce the network congestion as well as balance the entire network traffic with practical computational costs. (C) 2014 Elsevier B.V. All rights reserved.
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