Abstract-Current data centers usually operate under poor utilization due to resource fragmentation. The hierarchical nature of data centers places a limit on the achievable aggregate bandwidth in the backbone. Suboptimal virtual machine placement also introduces unnecessary cross network traffic. In this paper, we solve a joint tenant (i.e., server or virtual machine) placement and route selection problem by exploiting multipath routing capability and dynamic virtual machine migration. These two complementary degrees of freedom: placement and routing, are mutually-dependent, and their joint optimization turns out to substantially improve data center efficiency. We propose (i) an offline algorithm that solves a static problem given a network snapshot, and (ii) an online solution for a dynamic environment with changing traffic. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm that requires a very small number of virtual machine migrations. Performance evaluation that employs the synthesized data center traffic traces, on various topologies and under a spectrum of elephant and mice workloads, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics used in today's data centers.
Smart grids are capable of two-way communication between individual user devices and the electricity provider, enabling providers to create a control-feedback loop using timedependent pricing. By charging users more in peak and less in off-peak hours, the provider can induce users to shift their consumption to off-peak periods, thus relieving stress on the power grid and the cost incurred from large peak loads. We formulate the electricity provider's cost minimization problem in setting these prices by considering consumers' device-specific scheduling flexibility and the provider's cost structure of purchasing electricity from an electricity generator. Consumers' willingness to shift their device usage is modeled probabilistically, with parameters that can be estimated from real data. We develop an algorithm for computing day-ahead prices, and another algorithm for estimating and refining user reaction to the prices. Together, these two algorithms allow the provider to dynamically adjust the offered prices based on user behavior. Numerical simulations with data from an Ontario electricity provider show that our pricing algorithm can significantly reduce the cost incurred by the provider.
CUBIC is a congestion control protocol for TCP (transmission control protocol) and the current default TCP algorithm in Linux. The protocol modifies the linear window growth function of existing TCP standards to be a cubic function in order to improve the scalability of TCP over fast and long distance networks. It also achieves more equitable bandwidth allocations among flows with different RTTs (round trip times) by making the window growth to be independent of RTT -- thus those flows grow their congestion window at the same rate. During steady state, CUBIC increases the window size aggressively when the window is far from the saturation point, and the slowly when it is close to the saturation point. This feature allows CUBIC to be very scalable when the bandwidth and delay product of the network is large, and at the same time, be highly stable and also fair to standard TCP flows. The implementation of CUBIC in Linux has gone through several upgrades. This paper documents its design, implementation, performance and evolution as the default TCP algorithm of Linux.
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