Virtualization is being widely used in large-scale computing environments, such as clouds, data centers, and grids, to provide application portability and facilitate resource multiplexing while retaining application isolation. In many existing virtualized platforms, it has been found that the network bandwidth often becomes the bottleneck resource, causing both high network contention and reduced performance for communication and data-intensive applications. In this paper, we present a decentralized affinity-aware migration technique that incorporates heterogeneity and dynamism in network topology and job communication patterns to allocate virtual machines on the available physical resources. Our technique monitors network affinity between pairs of VMs and uses a distributed bartering algorithm, coupled with migration, to dynamically adjust VM placement such that communication overhead is minimized. Our experimental results running the Intel MPI benchmark and a scientific application on a 7-node Xen cluster show that we can get up to 42% improvement in the runtime of the application over a no-migration technique, while achieving up to 85% reduction in network communication cost. In addition, our technique is able to adjust to dynamic variations in communication patterns and provides both good performance and low network contention with minimal overhead.

Large-scale donation-based distributed infrastructures need to cope with the inherent unreliability of participant nodes. A widely-used work scheduling technique in such environments is to redundantly schedule the out sourced computations to a number of nodes. We present the design and implementation of RIDGE, a reliability aware system which uses a node's prior performance and behavior to make more effective scheduling decisions. We have implemented RIDGE on top of the BOINC distributed computing infrastructure and have evaluated its performance on a live test bed consisting of 120 PlanetLab nodes. Our experimental results show that RIDGE is able to match or surpass the throughput of the best vanilla BOINC configuration under different reliability environments, by automatically adapting to the characteristics of the underlying environment. In addition, RIDGE is able to provide much lower work unit makes pans compared to BOINC, which indicates its desirability in service-oriented environments with time constraints.

This paper addresses the inherent unreliability and instability of worker nodes in large-scale donation-based distributed infrastructures such as peer-to-peer and grid systems. We present adaptive scheduling techniques that can mitigate this uncertainty and significantly outperform current approaches. In this work, we consider nodes that execute tasks via donated computational resources and may behave erratically or maliciously. We present a model in which reliability is not a binary property, but a statistical one based on a node's prior performance and behavior. We use this model to construct several reputation-based scheduling algorithms that employ estimated reliability ratings of worker nodes for efficient task allocation. Our scheduling algorithms are designed to adapt to changing system conditions, as well as nonstationary node reliability. Through simulation, we demonstrate that our algorithms can significantly improve throughput while maintaining a very high success rate of task completion. Our results suggest that reputation-based scheduling can handle a wide variety of worker populations, including nonstationary behavior, with overhead that scales well with system size. We also show that our adaptation mechanism allows the application designer fine-grain control over the desired performance metrics.

This paper presents a design and analysis of scheduling techniques to cope with the inherent unreliability and instability of worker nodes in large-scale donation-based distributed infrastructures such as P2P and Grid systems. In particular, we focus on nodes that execute tasks via donated computational resources and may behave erratically or maliciously. We present a model in which reliability is not a binary property but a statistical one based on a node’s prior performance and behavior. We use this model to construct several reputation-based scheduling algorithms that employ estimated reliability ratings of worker nodes for efficient task allocation. Through simulation of a BOINC-like distributed computing infrastructure, we demonstrate that our algorithms can significantly improve throughput, while maintaining a very high success rate of task completion.