11 research outputs found
Remedy: Network-Aware Steady State VM Management for Data Centers
Abstract. Steady state VM management in data centers should be network-aware so that VM migrations do not degrade network performance of other flows in the network, and if required, a VM migration can be intelligently orchestrated to decongest a network hotspot. Recent research in network-aware management of VMs has focused mainly on an optimal network-aware initial placement of VMs and has largely ignored steady state management. In this context, we present the design and implementation of Remedy. Remedy ranks target hosts for a VM migration based on the associated cost of migration, available bandwidth for mi-gration and the network bandwidth balance achieved by a migration. It models the cost of migration in terms of additional network traffic generated during mi-gration. We have implemented Remedy as an OpenFlow controller application that detects the most congested links in the network and migrates a set of VMs in a network-aware manner to decongest these links. Our choice of target hosts ensures that neither the migration traffic nor the flows that get rerouted as a result of migration cause congestion in any part of the network. We validate our cost of migration model on a virtual software testbed using real VM migrations. Our simulation results using real data center traffic data demonstrate that selective network aware VM migrations can help reduce unsatisfied bandwidth by up to 80-100%
Distributed Computation of Large-scale Graph Problems
Motivated by the increasing need for fast distributed processing of
large-scale graphs such as the Web graph and various social networks, we study
a message-passing distributed computing model for graph processing and present
lower bounds and algorithms for several graph problems. This work is inspired
by recent large-scale graph processing systems (e.g., Pregel and Giraph) which
are designed based on the message-passing model of distributed computing.
Our model consists of a point-to-point communication network of machines
interconnected by bandwidth-restricted links. Communicating data between the
machines is the costly operation (as opposed to local computation). The network
is used to process an arbitrary -node input graph (typically )
that is randomly partitioned among the machines (a common implementation in
many real world systems). Our goal is to study fundamental complexity bounds
for solving graph problems in this model.
We present techniques for obtaining lower bounds on the distributed time
complexity. Our lower bounds develop and use new bounds in random-partition
communication complexity. We first show a lower bound of rounds
for computing a spanning tree (ST) of the input graph. This result also implies
the same bound for other fundamental problems such as computing a minimum
spanning tree (MST). We also show an lower bound for
connectivity, ST verification and other related problems.
We give algorithms for various fundamental graph problems in our model. We
show that problems such as PageRank, MST, connectivity, and graph covering can
be solved in time, whereas for shortest paths, we present
algorithms that run in time (for -factor
approx.) and in time (for -factor approx.)
respectively.Comment: In Proceedings of SODA 201
Achieving Adaptation Through Live Virtual Machine Migration in Two-Tier Clouds
This thesis presents a model-driven approach for application deployment and management in two-tier heterogeneous cloud environments. For application deployment, we introduce the architecture, the services and the domain specific language that abstract common features of multi-cloud deployments. By leveraging the architecture and the language, application deployers author a deployment model that captures the high-level structure of the application. The deployment model is then translated into deployment workflows on specific clouds. As a use case, we introduce a live VM migration framework that maintains the application quality of services through VM migrations across two tier-clouds. The proposed framework can monitor the performance of the applications and their underlying infrastructure and plan and executes VM migrations to eliminate hotspots in a datacenter. We evaluate both the application deployment architecture and the live migration on public clouds
Minimum congestion mapping in a cloud
We study a basic resource allocation problem that arises in cloud computing environments. The physical network of the cloud is represented as a graph with vertices representing servers and edges corresponding to communication links. A workload is a set of processes with processing requirements and mutual communication requirements. The workloads arrive and depart over time, and the resource allocator must map each workload upon arrival to the physical network. We consider the objective of minimizing the congestion. We show that solving a subproblem (\sf SingleMap) about mapping a single workload to the physical graph essentially suffices for solving the general problem. In particular, an -approximation algorithm for \sf SingleMap gives an competitive algorithm for the general problem, where is the number of nodes in the physical network and is the maximum to minimum workload duration ratio. We then consider the \sf SingleMap problem for two natural classes of workloads, namely depth- trees and complete-graph workloads. For depth- trees, we give an time -approximation algorithm based on a strong LP relaxation inspired by the Sherali--Adams hierarchy. For complete graphs, we give a polylogarithmic approximation algorithm using Räcke decompositions