The Scope of Supreme Court Review in Obscenity Cases

Performance of many P2P systems depends on the ability  to construct a ran- dom overlay network among the nodes. Current state-of-the-art techniques for constructing random overlays have an implicit  requirement that any two nodes in the system should always be able to communicate and establish a link be- tween them.  However, this is not the case in some of the environments where distributed systems are required to be deployed,  e.g, Decentralized Online So- cial Networks, Wireless networks, or networks with limited connectivity because of NATs/firewalls,  etc. In such restricted networks, every node is able to com- municate with only a predefined set of nodes and thus, the existing solutions for constructing random overlays are not applicable.In this thesis we propose a gossip based peer sampling service capable of running on top of such restricted networks and producing an on-the-fly random overlay.  The service provides ev- ery participating node with a set of uniform random nodes from the network, as well as efficient routing paths for reaching those nodes via the restricted net- work. We perform extensive experiments on four real-world networks and show that  the resulting overlays rapidly converge to random overlays. The results also exhibit that the constructed random overlays have self healing behaviour under churn and catastrophic failures

Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters

Cloud data centers require an operating system to manage resources and satisfy operational requirements and management objectives. The growth of popularity in cloud services causes the appearance of a new spectrum of services with sophisticated workload and resource management requirements. Also, data centers are growing by addition of various type of hardware to accommodate the ever-increasing requests of users. Nowadays a large percentage of cloud resources are executing data-intensive applications which need continuously changing workload fluctuations and specific resource management. To this end, cluster computing frameworks are shifting towards distributed resource management for better scalability and faster decision making. Such systems benefit from the parallelization of control and are resilient to failures. Throughout this thesis we investigate algorithms, protocols and techniques to address these challenges in large-scale data centers. We introduce a distributed resource management framework which consolidates virtual machine to as few servers as possible to reduce the energy consumption of data center and hence decrease the cost of cloud providers. This framework can characterize the workload of virtual machines and hence handle trade-off energy consumption and Service Level Agreement (SLA) of customers efficiently. The algorithm is highly scalable and requires low maintenance cost with dynamic workloads and it tries to minimize virtual machines migration costs. We also introduce a scalable and distributed probe-based scheduling algorithm for Big data analytics frameworks. This algorithm can efficiently address the problem job heterogeneity in workloads that has appeared after increasing the level of parallelism in jobs. The algorithm is massively scalable and can reduce significantly average job completion times in comparison with the-state of-the-art. Finally, we propose a probabilistic fault-tolerance technique as part of the scheduling algorithm

Gossip-based peer sampling in Social overlays

Performance of many P2P systems depends on the ability to construct a ran-dom overlay network among the nodes. Current state-of-the-art techniques forconstructing random overlays have an implicit requirement that any two nodesin the system should always be able to communicate and establish a link be-tween them. However, this is not the case in some of the environments wheredistributed systems are required to be deployed, e.g, Decentralized Online So-cial Networks, Wireless networks, or networks with limited connectivity becauseof NATs/rewalls, etc. In such restricted networks, every node is able to com-municate with only a predened set of nodes and thus, the existing solutionsfor constructing random overlays are not applicable.In this thesis we propose agossip based peer sampling service capable of running on top of such restrictednetworks and producing an on-the-y random overlay. The service provides ev-ery participating node with a set of uniform random nodes from the network,as well as ecient routing paths for reaching those nodes via the restricted net-work. We perform extensive experiments on four real-world networks and showthat the resulting overlays rapidly converge to random overlays. The resultsalso exhibit that the constructed random overlays have self healing behaviourunder churn and catastrophic failures

Gossip-based peer sampling in Social overlays

Performance of many P2P systems depends on the ability to construct a ran-dom overlay network among the nodes. Current state-of-the-art techniques forconstructing random overlays have an implicit requirement that any two nodesin the system should always be able to communicate and establish a link be-tween them. However, this is not the case in some of the environments wheredistributed systems are required to be deployed, e.g, Decentralized Online So-cial Networks, Wireless networks, or networks with limited connectivity becauseof NATs/rewalls, etc. In such restricted networks, every node is able to com-municate with only a predened set of nodes and thus, the existing solutionsfor constructing random overlays are not applicable.In this thesis we propose agossip based peer sampling service capable of running on top of such restrictednetworks and producing an on-the-y random overlay. The service provides ev-ery participating node with a set of uniform random nodes from the network,as well as ecient routing paths for reaching those nodes via the restricted net-work. We perform extensive experiments on four real-world networks and showthat the resulting overlays rapidly converge to random overlays. The resultsalso exhibit that the constructed random overlays have self healing behaviourunder churn and catastrophic failures

Gossip based peer sampling in social overlays

Performance of many P2P systems depends on the ability  to construct a ran- dom overlay network among the nodes. Current state-of-the-art techniques for constructing random overlays have an implicit  requirement that any two nodes in the system should always be able to communicate and establish a link be- tween them.  However, this is not the case in some of the environments where distributed systems are required to be deployed,  e.g, Decentralized Online So- cial Networks, Wireless networks, or networks with limited connectivity because of NATs/firewalls,  etc. In such restricted networks, every node is able to com- municate with only a predefined set of nodes and thus, the existing solutions for constructing random overlays are not applicable.In this thesis we propose a gossip based peer sampling service capable of running on top of such restricted networks and producing an on-the-fly random overlay.  The service provides ev- ery participating node with a set of uniform random nodes from the network, as well as efficient routing paths for reaching those nodes via the restricted net- work. We perform extensive experiments on four real-world networks and show that  the resulting overlays rapidly converge to random overlays. The results also exhibit that the constructed random overlays have self healing behaviour under churn and catastrophic failures