The deferred acceptance algorithm proposed by Gale and Shapley (1962) has had a profound influence on market design, both directly, by being adapted into practical matching mechanisms, and, indirectly, by raising new theoretical questions. Deferred acceptance algorithms are at the basis of a number of labor market clearinghouses around the world, and have recently been implemented in school choice systems in Boston and New York City. In addition, the study of markets that have failed in ways that can be fixed with centralized mechanisms has led to a deeper understanding of some of the tasks a marketplace needs to accomplish to perform well. In particular, marketplaces work well when they provide thickness to the market, help it deal with the congestion that thickness can bring, and make it safe for participants to act effectively on their preferences. Centralized clearinghouses organized around the deferred acceptance algorithm can have these properties, and this has sometimes allowed failed markets to be reorganized.

Deferred Acceptance Algorithms: History, Theory, Practice, and Open Questions

Benchmarking is an essential aspect of any database management system (DBMS) effort. Despite several recent advancements, such as pre-configured cloud database images and database-as-a-service (DBaaS) offerings, the deployment of a comprehensive testing platform with a diverse set of datasets and workloads is still far from being trivial. In many cases, researchers and developers are limited to a small number of workloads to evaluate the performance characteristics of their work. This is due to the lack of a universal benchmarking infrastructure, and to the difficulty of gaining access to real data and workloads. This results in lots of unnecessary engineering efforts and makes the performance evaluation results difficult to compare. To remedy these problems, we present OLTP-Bench, an extensible "batteries included" DBMS benchmarking testbed. The key contributions of OLTP-Bench are its ease of use and extensibility, support for tight control of transaction mixtures, request rates, and access distributions over time, as well as the ability to support all major DBMSs and DBaaS platforms. Moreover, it is bundled with fifteen workloads that all differ in complexity and system demands, including four synthetic workloads, eight workloads from popular benchmarks, and three workloads that are derived from real-world applications. We demonstrate through a comprehensive set of experiments conducted on popular DBMS and DBaaS offerings the different features provided by OLTP-Bench and the effectiveness of our testbed in characterizing the performance of database services.

/pdf/oltp-bench-an-extensible-testbed-for-benchmarking-relational-4emh028gkv.pdf

OLTP-Bench: an extensible testbed for benchmarking relational databases

Load-balancing algorithms in cloud computing

Assurance requirements were included in the disposal regulations to compensate in a qualitative manner for the inherent uncertainties in projecting the behavior of natural and engineered components of the WIPP for many thousands of years (50 FR 38072). Section 194.42 is one of the six assurance requirements in the Compliance Criteria. Section 194.42 specifically addresses requirements for monitoring the disposal system during preand post-closure operations. This requirement distinguishes between preand post-closure monitoring because of the differences in monitoring techniques used to access the repository during operations (pre­ closure) and after the repository has been backfilled and sealed (post-closure). The purpose of monitoring is to confirm that the repository is behaving as predicted.

https://repository.kallipos.gr/bitstream/11419/859/1/02_chapter_A1.pdf

Monitoring στη ΜΕΘ

Agriculture at the crossroads.

Today’s Recommender system is a relatively new area of research in machine learning. The recommender system’s main idea is to build relationship between the products, users and make the decision to select the most appropriate product to a specific user. There are four main ways that recommender systems produce a list of recommendations for a user – content-based, Collaborative, Demographic and hybrid filtering. In content-based filtering the model uses specifications of an item in order to recommend additional items with similar properties. Collaborative filtering uses past behavior of the user like items that a user previously viewed or purchased, In summation to any ratings the user gave those items rate and similar conclusions made by other user’s items list. To predicts items that the user may find interesting. Demographic filtering is view user profile data like age category, gender, education and living area to find similarities with other profiles to get a new recommender list. Hybrid filtering combines all three filtering techniques. This paper introduces survey about recommendation systems, techniques, challenges the face recommender systems and list some research papers solve these challenges.

Recommender Systems Challenges and Solutions Survey

Cloud computing appeared for huge data because of its ability to provide users with on-demand, reliable, flexible, and low-cost services. With the increasing use of cloud applications, data security protection has become an important issue for the cloud. In this work, the proposed approach was used to improve the performance of encryption /Decryption file by using AES and OTP algorithms integrated on Hadoop. Where files are encrypted within the HDFS and decrypted within the Map Task. Encryption /Decryption in previous works used AES algorithm, the size of the encrypted file increased by 50% from the original file size. The proposed approach improved this ratio as the size of the encrypted file increased by 20% from the original file size. Also, we have compared this approach with the previously implemented method, we implement this new approach to secure HDFS, and some experimental studies were conducted to verify its effectiveness.

An approach for big data security based on Hadoop distributed file system

Cloud computing is considered a booming trend in the world of information technology which depends on the idea of computing on demand. Cloud computing platform is a set of scalable data servers, providing computing and storage services. The cloud storage is a relatively basic and widely applied service which can provide users with stable, massive data storage space. Our research concerns with searching in content of different kind of files in the cloud based on ontology, this approach resolves the weaknesses that existed in Google File System that depends on metadata. In this paper, we are proposing new cloud storage architecture based on ontology that can store and retrieve files in the cloud based on its content. Our new architecture was tested on Cloud Storage Simulator and the result shows that the new architecture has better scalability, fault tolerance and performance.

/pdf/ocss-ontology-cloud-storage-system-sm224g6g5q.pdf

OCSS: Ontology Cloud Storage System

Exploiting coarse-grained reused-based opportunities in Big Data multi-query optimization

Cloud computing is a new generation of computing based on the layered model which provides different services to cloud customer. Conceptually, cloud computing offers a scalable platform for Big Data Analytic Applications (BDAAs) which can elastically provision resources based on data growth and complexity of analytic applications. This complexity of analysis increases with vast volumes of data in big enterprises which prefer effective and fast decision-making. Therefore, the importance of Service Level Agreement (SLA) is appeared which clarifies the roles between a customer (i.e., cloud user or Big Data analyst) and a provider for particular service provision comes. The main components of the SLAs are Quality of Service parameters which must be monitored to achieve a set of Service Level Objectives (SLOs) and detect violations. Indeed, many SLA management approaches have been developed as solutions for preventing SLA violations to avoid costly penalties. Consequently, many interesting solutions developed works of SLA violation management in cloud technology and cloud-hosted BDAAs. A survey about the existed works in terms of idea, strengths and weaknesses is introduced in this paper. Meanwhile, the challenges and new research directions in this area which require further investigation will be discussed to provide a comprehensive overview and big-picture.

/pdf/a-survey-on-sla-management-for-cloud-computing-and-cloud-4qnydukwi1.pdf

Mohamed Helmy Khafagy

Papers

Recommender Systems Challenges and Solutions Survey

An approach for big data security based on Hadoop distributed file system

OCSS: Ontology Cloud Storage System

Exploiting coarse-grained reused-based opportunities in Big Data multi-query optimization

A Survey on SLA Management for Cloud Computing and Cloud-Hosted Big Data Analytic Applications