Cloud providers leverage live migration of virtual machines to reduce energy consumption and allocate resources efficiently in data centers. Each migration decision depends on three questions: when to move a virtual machine, which virtual machine to move and where to move it? Dynamic, uncertain, and heterogeneous workloads running on virtual machines make such decisions difficult. Knowledge-based and heuristics-based algorithms are commonly used to tackle this problem. Knowledge-based algorithms, such as MaxWeight scheduling algorithms, are dependent on the specifics and the dynamics of the targeted Cloud architectures and applications. Heuristics-based algorithms, such as MMT algorithms, suffer from high variance and poor convergence because of their greedy approach. We propose an online reinforcement learning algorithm called Megh. Megh does not require prior knowledge of the workload rather learns the dynamics of workloads as-it-goes. Megh models the problem of energy- and performance-efficient resource management during live migration as a Markov decision process and solves it using a functional approximation scheme. While several reinforcement learning algorithms are proposed to solve this problem, these algorithms remain confined to the academic realm as they face the curse of dimensionality. They are either not scalable in real-time, as it is the case of MadVM, or need an elaborate offline training, as it is the case of Q-learning. These algorithms often incur execution overheads which are comparable with the migration time of a VM. Megh overcomes these deficiencies. Megh uses a novel dimensionality reduction scheme to project the combinatorially explosive state-action space to a polynomial dimensional space with a sparse basis. Megh has the capacity to learn uncertain dynamics and the ability to work in real-time without incurring significant execution overhead. Megh is both scalable and robust. We implement Megh using the CloudSim toolkit and empirically evaluate its performance with the PlanetLab and the Google Cluster workloads. Experiments validate that Megh is more cost-effective, converges faster, incurs smaller execution overhead and is more scalable than MadVM and MMT. An empirical sensitivity analysis explicates the choice of parameters in experiments.

We propose a reinforcement learning algorithm, Megh, for live migration of virtual machines that simultaneously reduces the cost of energy consumption and enhances the performance. Megh learns the uncertain dynamics of workloads as-it-goes. Megh uses a dimensionality reduction scheme to projectthe combinatorially explosive state-action space to a polynomial dimensional space. These schemes enable Megh to be scalable and to work in real-time. We experimentally validate that Megh is more cost-effective and time-efficient than the MadVM and MMT algorithms.

Memory fences are widely used to ensure the correctness for synchronization constructs on machines with relaxed consistency models. However, they are expensive and usually impose over-constrained ordering that causes unnecessary CPU stalls. In this paper, we observe that memory fences in TSO are merely intended to order synchronization variables. Based on this observation, we rethink the hardware-software interface of synchronization constructs on multicore processors and propose a new design called Sync-Order that differentiates synchronization variables (sync-vars) from normal ones. Sync-Order reduces hardware complexity such that the processor only needs to serialize the ordering among sync-vars. Its simplicity makes it easy to be integrated to the directory controller and it supports distributed directory, a missing feature in prior designs. We show that Sync-Order eliminates traditional fences on all sides of synchronization constructs (instead of only one side in prior work) and requires small effort for a programmer or compiler to annotate sync-vars. Our experimental results show that Sync-Order significantly reduces CPU stalls and boosts the performance of a set of synchronization constructs and concurrent data structures by 10 percent; meanwhile, the fence overhead of full applications from SPLASH-2 and PARSEC is reduced from 42 to 3 percent.