Scheduling data processing workflows (dataflows) on the cloud is a very complex and challenging task. It is essentially an optimization problem, very similar to query optimization, that is characteristically different from traditional problems in two aspects: Its space of alternative schedules is very rich, due to various optimization opportunities that cloud computing offers; its optimization criterion is at least two-dimensional, with monetary cost of using the cloud being at least as important as query completion time. In this paper, we study scheduling of dataflows that involve arbitrary data processing operators in the context of three different problems: 1) minimize completion time given a fixed budget, 2) minimize monetary cost given a deadline, and 3) find trade-offs between completion time and monetary cost without any a-priori constraints. We formulate these problems and present an approximate optimization framework to address them that uses resource elasticity in the cloud. To investigate the effectiveness of our approach, we incorporate the devised framework into a prototype system for dataflow evaluation and instantiate it with several greedy, probabilistic, and exhaustive search algorithms. Finally, through several experiments that we have conducted with the prototype elastic optimizer on numerous scientific and synthetic dataflows, we identify several interesting general characteristics of the space of alternative schedules as well as the advantages and disadvantages of the various search algorithms. The overall results are quite promising and indicate the effectiveness of our approach.

Presents first-hand experiences from an actual re-engineering project. Business re-engineering is a process affecting not only the software system involved, but the underlying business model as well. Indeed, it is the changed business model along with the new technologies that determine the design of the new system. This paper is a walkthrough of the design of PICSDESK, a prototype incorporating some modern technologies to support the old business model of its predecessor and its evolution to a new business model. PICSDESK may be thought of as an example of a new breed of inventory control systems.

We describe the design and implementation of BERMUDA, which is a loosely coupled system interfacing Prolog to the Britton-Lee Intelligent Database Machine (IDM-500). BERMUDA allows multiple concurrent Prolog processes, possibly running on different machines, to share a database. In addition, it preserves the semantics of Prolog programs and makes the use of the database system transparent to the user. We discuss several architectural issues faced by such systems and the approach adopted for each one in BERMUDA. We also present the performance results of a variety of experiments with the system. These include single-user benchmarks of BERMUDA against stand-alone Prolog and stand-alone IDM, detailed profiling of the costs of the modules of BERMUDA that shows the overhead imposed by the interface, and multiuser benchmarks of BERMUDA that show how the system behaves under heavier load. These experiments demonstrate the effectiveness of loosely coupled systems in general and of BERMUDA specifically. They also help in the identification of some aspects of the design and implementation of BERMUDA that could be improved.>

We investigate the performance of some of the best-known object clustering algorithms on four different workloads based upon the tektronix benchmark. For all four workloads, stochastic clustering gave the best performance for a variety of performance metrics. Since stochastic clustering is computationally expensive, it is interesting that for every workload there was at least one cheaper clustering algorithm that matched or almost matched stochastic clustering. Unfortunately, for each workload, the algorithm that approximated stochastic clustering was different. Our experiments also demonstrated that even when the workload and object graph are fixed, the choice of the clustering algorithm depends upon the goals of the system. For example, if the goal is to perform well on traversals of small portions of the database starting with a cold cache, the important metric is the per-traversal expansion factor, and a well-chosen placement tree will be nearly optimal; if the goal is to achieve a high steady-state performance with a reasonably large cache, the appropriate metric is the number of pages to which the clustering algorithm maps the active portion of the database. For this metric, the PRP clustering algorithm, which only uses access probabilities achieves nearly optimal performance.

article Free Access Share on A stochastic approach for clustering in object bases Authors: Manolis M. Tsangaris Department of Computer Sciences, University of Wisconsin-Madison Department of Computer Sciences, University of Wisconsin-MadisonView Profile , Jeffrey F. Naughton Department of Computer Sciences, University of Wisconsin-Madison Department of Computer Sciences, University of Wisconsin-MadisonView Profile Authors Info & Claims ACM SIGMOD RecordVolume 20Issue 2June 1991pp 12–21https://doi.org/10.1145/119995.115792Published:01 April 1991Publication History 59citation434DownloadsMetricsTotal Citations59Total Downloads434Last 12 Months32Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF