Flavour Enhanced Food Recommendation

We propose a mechanism to use the features of flavour to enhance the quality of food recommendations. An empirical method to determine the flavour of food is incorporated into a recommendation engine based on major gustatory nerves. Such a system has advantages of suggesting food items that the user is more likely to enjoy based upon matching with their flavour profile through use of the taste biological domain knowledge. This preliminary intends to spark more robust mechanisms by which flavour of food is taken into consideration as a major feature set into food recommendation systems. Our long term vision is to integrate this with health factors to recommend healthy and tasty food to users to enhance quality of life.Comment: In Proceedings of 5th International Workshop on Multimedia Assisted Dietary Management, Nice, France, October 21, 2019, MADiMa 2019, 6 page

Eventually-Consistent Federated Scheduling for Data Center Workloads

Data center schedulers operate at unprecedented scales today to accommodate the growing demand for computing and storage power. The challenge that schedulers face is meeting the requirements of scheduling speeds despite the scale. To do so, most scheduler architectures use parallelism. However, these architectures consist of multiple parallel scheduling entities that can only utilize partial knowledge of the data center's state, as maintaining consistent global knowledge or state would involve considerable communication overhead. The disadvantage of scheduling without global knowledge is sub-optimal placements-tasks may be made to wait in queues even though there are resources available in zones outside the scope of the scheduling entity's state. This leads to unnecessary queuing overheads and lower resource utilization of the data center. In this paper, extend our previous work on Megha, a federated decentralized data center scheduling architecture that uses eventual consistency. The architecture utilizes both parallelism and an eventually-consistent global state in each of its scheduling entities to make fast decisions in a scalable manner. In our work, we compare Megha with 3 scheduling architectures: Sparrow, Eagle, and Pigeon, using simulation. We also evaluate Megha's prototype on a 123-node cluster and compare its performance with Pigeon's prototype using cluster traces. The results of our experiments show that Megha consistently reduces delays in job completion time when compared to other architectures.Comment: 26 pages. Submitted to Elsevier's Ad Hoc Networks Journa

Moving To The Cloud: Developing Apps in the New World of Cloud Computing

Moving to the Cloud provides an in-depth introduction to cloud computing models, cloud platforms, application development paradigms, concepts and technologies. The authors particularly examine cloud platforms that are in use today. They also describe programming APIs and compare the technologies that underlie them. The basic foundations needed for developing both client-side and cloud-side applications covering compute/storage scaling, data parallelism, virtualization, MapReduce, RIA, SaaS and Mashups are covered. Approaches to address key challenges of a cloud infrastructure, such as scalab

Buffer Management Policy for an On-Demand Video Server

In an on-demand video server environment, multimedia objects (e.g. movies) are very large and are read sequentially. Hence it is not economical to cache the entire object. However, caching random fractions of a multimedia object is not beneficial. This is due to the stringent response time requirements where continuous availability of a stream has to be guaranteed; whereas caching random fractions will result in unpredictable load on the disks. Therefore, traditional buffer management policies such as LRU are not effective. In addition, the sequential access implies pages brought in by a stream can be reused by a closely following stream and subsequently discarded, thus buffering only a fraction of the entire object. In this paper, we propose a buffer management policy called the interval caching policy based on the above idea that identifies certain streams and temporarily buffers the pages brought in by those streams. We study the efficacy of this technique for reducing disk overload..

Scheduling Policies for an On-Demand Video Server with Batching

In an on-demand video server environment, clients make requests for movies to a centralized video server. Due to the stringent response time requirements, continuous delivery of a video stream to the client has to be guaranteed by reserving sufficient resources required to deliver a stream. Hence there is a hard limit on the number of streams that can be simultaneously delivered by a server. The server can satisfy multiple requests for the same movie using a single disk I/O stream by sending the same data pages to multiple clients (using the multicast facility if present in the system). This can be achieved by batching requests for the same movie that arrive within a short duration of time. In this paper, we consider various policies for selecting the movie to be multicast. The choice of a policy depends very much on the customer waiting time tolerance before reneging. We show that an FCFS policy that schedules the movie with the longest outstanding request can perform better than the ..

Moving to the cloud : developing apps in the new world of cloud computing / Dinkar Sitaram and Geetha Manjunath ; technical editor, David R. Deily.

Includes bibliographical references and index.Book Fair 2013.xxviii, 448 p.