With the promise of low-cost access to flexible and elastic resources, enterprises are increasingly migrating their existing workloads into the Cloud. Yet, the heterogeneity of the workloads and existing configuration of legacy IT infrastructure make it challenging to enable a one-click, seamless migration process. There are multiple tools available for migrating servers based on their existing configurations and multiple ways of dealing with data synchronization (post migration). In this paper, we present a Cloud Migration Orchestrator (CMO), based on business process management (BPM) approach to provide a systematic framework to automate and coordinate migration activities. CMO coordinates the process of migration, starting from discovery, provisioning, network configuration, execution of migration, cutover and validation. CMO integrates multiple migration technologies, to support different migration scenarios. We present and discuss our results from a preliminary deployment of CMO to migrate 25 VMware instances and discuss how this approach improves the effectiveness of migration, and seamlessly coordinates activities required to be executed.

Location-based services are growing in popularity due to the ubiquity of smartphone users. The relevance of location-based query results is very important, especially for mobile phones with limited screen size. Location-based data frequently changes; this introduces challenges in indexing and ranking places. The growing popularity of mobile social networks, such as Twitter, FourSquare and Facebook Places, presents an opportunity to build better location-based services by leveraging user interactions on these networks. In this paper, we present SocialTelescope, a location-based service that automatically compiles, indexes and ranks locations, based on user interactions with locations in mobile social networks. We implemented our system as a location-based search engine that uses geo-tweets by Twitter users to learn about places. We evaluated the coverage and relevance of our system by comparing it against current state-of-the-art approaches including page-rank (Google Local Search), expert-based (Zagat) and user-review based (Yelp). Our results show that a crowd-sourced location-based service returns results that match those returned by current approaches in relevance, at a substantially lower cost.

3D virtual world software is becoming a popular medium for entertainment, social interaction and commerce. To the best of our knowledge, there is no system available to facilitate the bridging between Web applications and virtual world systems in the form of information sharing, data collection and control propagation. As a result, user experience in a Web interface is not sensitive to state changes of virtual world avatars or objects. Similarly, a virtual world environment does not provide Web context-rich user experience. We address this issue and propose a bridging and context sharing architecture between the Web and virtual world applications such that Web applications can control, monitor and collect information from artifacts in the virtual worlds, and vice versa. We also implemented this architecture using existing Web and virtual world technologies. Based on this implementation, we illustrate some novel applications and present a user study to illustrate the value of the system.

Content transformation (or transcoding) proxies have been recently proposed to tailor Web content to device characteristics of Web clients. In this paper, we address the problem of distributing the computational load caused by object transcoding throughout a collaborative proxy system organized in a hierarchical network. We evaluate through simulation the impact of load distribution and caching policies on users' response time. We nd that the simple global policy that captures the proxy load information along the request path can provide reasonably good load sharing, and that, to e ectively share the load, it is necessary to provide the edge proxies a mechanism to push up some transcoding load. On the caching policy, we examine policies that allow di erent versions of an object to be cached. Our study shows that the demand based caching policy which has the transcoding proxy cache the transcoded version performs better than the coverage based caching policy that caches the more detailed version and the anticipatory caching policy that caches both of these versions. 1.

Traditional query process for time-series data transforms data from the time domain into the frequency domain.It is less controllable by the end users, therefore isn't well suited for queries that find patterns with many dynamically specified user constraints.For these queries we present a method to search timeseries data which first transforms time sequences into symbol strings using change ratio between contiguous data points in time series.Next, a suffix tree is built to index all suffixes of the symbol strings.The focus of this paper is to demonstrate how this method can adapt to the processing of the dynamically constrained time-series queries.

We present the BUS (Bandwidth-sensitive Update Scheduling) method for WWW push proxies that actively sets different update schedules for various WWW push channels. Based on open protocols describing push objects, BUS optimizes the currency of the objects it delivers to the clients under the constraint of the available bandwidth. As a result, push traffic overflow is minimized with adequate gateway bandwidth reserved for critical non-push traffic. The BUS method also provides a mechanism to monitor client interest and conducts a dynamic proxy update for given channels if the client requests for these channels increase suddenly. In contrast to fixed schedule approaches, this dynamic update mechanism can better capture the dynamic changes (such as the financial activities) that are of interest to many clients.

Efficient path computation is essential for applications such as intelligent transportation systems (ITS) and network routing. In ITS navigation systems, many path requests can be submitted over the same, typically huge, transportation network within a small time window. While path precomputation (path view) would provide an efficient path query response, it raises three problems which must be addressed: 1) precomputed paths exceed the current computer main memory capacity for large networks; 2) disk-based solutions are too inefficient to meet the stringent requirements of these target applications; and 3) path views become too costly to update for large graphs (resulting in out-of-date query results). We propose a hierarchical encoded path view (HEPV) model that addresses all three problems. By hierarchically encoding partial paths, HEPV reduces the view encoding time, updating time and storage requirements beyond previously known path precomputation techniques, while significantly minimizing path retrieval time. We prove that paths retrieved over HEPV are optimal. We present complete solutions for all phases of the HEPV approach, including graph partitioning, hierarchy generation, path view encoding and updating, and path retrieval. In this paper, we also present an in-depth experimental evaluation of HEPV based on both synthetic and real GIS networks. Our results confirm that HEPV offers advantages over alternative path finding approaches in terms of performance and space efficiency.

Investigates optimization strategies for processing path queries with embedded spatial constraints, such as avoiding areas with certain characteristics. To resolve complex spatial constraints during path finding, we consider two decisions: (1) the spatial relation operations (e.g. intersection) between areas and links can be pre-processed or intermixed with path-finding, and (2) areas satisfying the query constraint can be pre-filtered or dynamically selected during path-finding. Based on these two decisions, we propose and implement the resulting four integrated query processing strategies, utilizing state-of-the-art technologies such as spatial joins for intersect computation, R-tree access structure for spatial overlap searching, and spatial clustering for efficient path searching. In this paper, we also report an experimental evaluation to show which strategies perform best in different scenarios.

The development of a cost model for predicting the performance of spatial joins has been identified in the literature as an important and difficult problem. The authors present the first cost model that can predict the performance of spatial joins using R-trees. Based on two existing R-trees (join targets), the model first estimates the number of expected I/Os for the join process by assuming a zero buffer size. The method for this estimation extends the cost model for R-tree window queries (developed by Kamel and Faloutsos (1993) and by Pagel et al. (1993)) to also handle spatial joins (which are more complex). In the context of spatial join processing, this number of zero-buffer expected I/Os is not practical for performance prediction in a buffered environment. To model the buffer impact, they use an (exponential) distribution function to measure the probability that a bufferless I/O would cause a page fault in a buffered environment. Based on this probability and the zero-buffer expected I/O cost, the estimated number of I/Os for an R-tree join can then be computed. The comparisons between the predictions from the cost model and the actual results from the experiments based on real GIS maps show that the average relative error ratio is about 10% with a maximum of about 20% for a wide range of buffer sizes. Therefore, our model is a useful tool for the query optimization of spatial join queries.

In this paper, we present an experimental evaluation of graph clustering strategies in terms of their effectiveness in optimizing I/O for path query processing in digital map databases.Clustering optimization is attractive because it does not incurs any run-time cost, and is complimentary to many of the existing techniques in path query optimization.We first propose a novel graph clustering technique, called Spatial Partition Clustering (SPC), that creates balanced partitions of links based on the spatial proximity of their origin nodes.We then select three alternative clustering techniques from the literature, namely two-way partitioning, approximately topological clustering, and random clustering, to compare their performance in path query processing with SPC.Experimental evahration indicatesthat our SPC performs the best for the high-locality graphs (such as GIS maps), whereas the two-way partitioning approach performs the best for no-locality random graphs.

Efficient path query processing is a key requirement for advanced database applications including GIS (Geographic Information Systems) and ITS (Intelligent Transportation Systems).We study the problem in the context of automobile navigation systems where a large number of path requests can be submitted over the tmrtsportation network within a short period of time.To guarantee efficient responsefor path queries, we employ a path view materiatization strategy for precomputing the best paths.We tackle the following three issues: (1) memory-resident solutions quickly exceed cr.nrentcomputer storage capacity for networks of thousands of nodes, (2) diskbased solutions have been found inefficient to meet the stringent performance requirements, and (3) path views become too costly to update for large graphs.We propose the HEPV (Hierarchical Encoded Path View) approach that addressesthese problems while guaranteeing the optimality of path retrieval.Our experimental fesuks reveaf that HEPV is more efficient than previously known path finding approaches.