Metascheduling and Heuristic Co-Allocation Strategies in Distributed Computing

In this paper, we address problems of efficient computing in distributed systems with non-dedicated resources including utility grid. There are global job flows from external users along with resource owner's local tasks upon the resource non-dedication condition. Competition for resource reservation between independent users, local and global job flows substantially complicates scheduling and the requirement to provide the necessary quality of service. A metascheduling concept, justified in this work, assumes a complex combination of job flow dispatching and application-level scheduling methods for parallel jobs, as well as resource sharing and consumption policies established in virtual organizations and based on economic principles. We introduce heuristic slot selection and co-allocation strategies for parallel jobs. They are formalized by given criteria and implemented by algorithms of linear complexity on an available slots number

Leveraging Physical Layer Capabilites: Distributed Scheduling in Interference Networks with Local Views

In most wireless networks, nodes have only limited local information about the state of the network, which includes connectivity and channel state information. With limited local information about the network, each node's knowledge is mismatched; therefore, they must make distributed decisions. In this paper, we pose the following question - if every node has network state information only about a small neighborhood, how and when should nodes choose to transmit? While link scheduling answers the above question for point-to-point physical layers which are designed for an interference-avoidance paradigm, we look for answers in cases when interference can be embraced by advanced PHY layer design, as suggested by results in network information theory. To make progress on this challenging problem, we propose a constructive distributed algorithm that achieves rates higher than link scheduling based on interference avoidance, especially if each node knows more than one hop of network state information. We compare our new aggressive algorithm to a conservative algorithm we have presented in [1]. Both algorithms schedule sub-networks such that each sub-network can employ advanced interference-embracing coding schemes to achieve higher rates. Our innovation is in the identification, selection and scheduling of sub-networks, especially when sub-networks are larger than a single link.Comment: 14 pages, Submitted to IEEE/ACM Transactions on Networking, October 201