Distributed smart and connected community applications of the present and future will continuously sense perishable data on which they make decisions. We say the data are perishable when they represent information sensed at a past point in time, possibly at a distant part of the distributed application, and for which the ground truth could have since changed. This paper discusses actions the sensing source, the network, and the controller can take to make the best possible decisions given that we know only a delayed version of the truth. Some of these actions to preserve freshness of data are based on known real-time techniques such as Time Sensitive Networking. To these we add Information Theory applied on a message-by-message basis and cross-layer coordination between content and network handling. Both the unexpectedness (surprisal) and timeliness (freshness) properties of perishable information can be encoded using a Shannon information entropy framework. Examples of smart community systems of the future for which these concepts are relevant are given.

EdgeNet is the prototype of a scalable, sustainable general-purpose testbed for very wide area distributed systems and extremely low-latency distributed services. In this, it is aimed at the same experimenters and systems that formed the core usage of previous, highly-successful wide-area testbeds such as PlanetLab [1], G-Lab [2], V-Node [5], GENI [4], and SAVI [3], and it incorporates many of the features that characterized those previous testbeds. EdgeNet's goal is to achieve the usability and research value of the previous generations of wide area testbed, whilst offering radical improvements in the scalability and sustainability of those systems. It achieves this scalability and sustainability through a strategy of using industry-standard open-source software as the basis of its software stack, and by a strategy of hardwarefree, bottom-up, site-driven deployment. EdgeNet follows the Seattle [6] and PlanetIgnite [9] strategy of permitting sites to join the testbed with purely local action.

We describe the Ignite Distributed Collaborative Scientific Visualization System (IDCVS), a system which permits real-time interaction and visual collaboration around large data sets, with an initial emphasis on scientific data. The IDCVS offers such a collaborative environment, with real-time interaction on any device between users separated across the wide area. It provides seamless interaction and immediate updates even under heavy load and when users are widely separated: the design goal was to fetch a data set consisting of 30,000 points from a server and render it within 150ms, for a user anywhere in the world, and reflect changes made by a user in one location to all other users within a bound provided by network latency. The system was demonstrated successfully on a significant worldwide air pollution data set, with values on 10, 25, 50, and 100km worldwide grids, monthly over an 18-year period. It was demonstrated on a wide variety of clients, including laptop, tablet, and smartphone.

The virtual waiting time technique is introduced as a solution to the problem of a controller distributing work to servers of different speeds. The servers are considered to be part of a distributed system without feedback. The virtual waiting time technique is shown to minimize the average completion time for a job distributed by the controller. The virtual waiting time technique does not depend on any arrival distribution and is applicable to any service time distribution. The performance of the technique is examined for different arrival and service time distributions.