Yehia Z Elshater

Designing E-Business applications in an efficient way has become a competitive necessity rather than a competitive advantage. One of the most important goals for many organizations is to satisfy their clients' service level agreements with respect to the response time and throughput. Adopting Service Oriented Architecture (SOA) during design and implementation promotes communication with the external and internal business entities. Web services are one of the popular technologies to achieve SOA solutions. Lookup web services are broadly used by many service consumers to fetch data which are used by their applications. In this paper we focus on how to efficiently build lookup web services using design patterns. Our goal is to improve the response time (latency) and throughput of lookup web services.

- Due to the heterogeneity of the existing platforms, IT Environments became very extremely complex, consequent-ly the communication between the organizations more diffi-cult. The service oriented architecture (SOA) claims that the interactions between different parities will be ...

The growing popularity of cloud computing has magnified the rise of software reuse by facilitating service provisioning over the Internet. At the same time, a new generation of mobile apps has emerged relying on backend services that expand the app functionally, while reducing the overhead on limited mobile resources. The Web service approach promises great flexibility in offering software functionality over the network, while maintaining interoperability between heterogeneous platforms. In addition, recent years have witnessed the rise of user-facing service developments that can be consumed on-the-go with a standard interface, such as Restful Web services. However, the discovery of such services does not match their growing popularity and remain challenging. Users cannot tolerate long latency in finding relevant services to their requests. In this paper, we propose a robust and efficient Web service discovery approach that uses statistical methods and indexing techniques to improve the precision and response time of the discovery process. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art discovery mechanisms and significantly reduces the query response time by at least 77%, while maintaining comparable accuracy.

With almost everything now online, organizations look at the Big Data collected to gain insights for improving their services. In the analytics process, derivation of such insights requires experimenting-with and integrating different analytics techniques, while handling the Big Data high arrival velocity and large volumes. Existing solutions cover bits-and-pieces of the analytics process, leaving it to organizations to assemble their own ecosystem or buy an off-the-shelf ecosystem that can have unnecessary components to them. We build on this point by dividing the Big Data Analytics problem into six main pillars. We characterize and show examples of solutions designed for each of these pillars. We then integrate these six pillars into a taxonomy to provide an overview of the possible state-of-the-art analytics ecosystems. In the process, we highlight a number of ecosystems to meet organizations different needs. Finally, we identify possible areas of research for building future Big...

With almost everything now online, organizations look at the Big Data collected to gain insights for improving their services. In the analytics process, derivation of such insights requires experimenting with and integrating different analytics techniques, while handling the Big Data high arrival velocity and large volumes.Existing solutions cover bits-and-pieces of the analytics process, leaving it to organizations to assemble their own ecosystem or buy an off-the-shelf ecosystem that can have unnecessary components to them. We build on this point by dividing the Big Data Analytics problem into six main pillars. We characterize and show examples of solutions designed for each of these pillars. We then integrate these six pillars into a taxonomy to provide an overview of the possible state-of-the-art analytics ecosystems. In the process, we highlight a number of ecosystems to meet organizations different needs. Finally, we identify possible areas of research for building future Big Data Analytics Ecosystems

IoT scenarios cannot tolerate long latency in finding relevant Web services to consume on the fly or dynamically integrate in IoT applications providing real time services. In this paper, we present a comprehensive Web service discovery approach for large scale IoT deployments. We leverage the information available in the Web service description document to cluster large service repositories into functionally similar groups to reduce the discovery latency and improve precision. Then, we use statistical indexing techniques to generate data structures for each cluster for fast and efficient matching. In this research, we propose and study the performance of four matching algorithms: semantic keyword matching using Normalized Google Distance (NGD), brute force search over Term Frequency-Inverse Document Frequency (TF-IDF) matrix, K-Dimensional (K-D) tree, and Locality Sensitive Hashing (LSH). Our thesis is that indexing-based discovery algorithms (i.e., K-D tree and LSH) provide a much faster response with comparable precision, while NGD and brute force search provide a slightly better accuracy, but at the cost of high latency. Our experimental results show that we can reduce the query latency by up to 5x fold, while achieving comparable precision with the state-of-the-art service discovery mechanisms.

Co-locating the computation as close as possible
to the data is an important consideration in the current data
intensive systems. This is known as data locality problem. In
this paper, we analyze the impact of data locality on YARN,
which is the new version of Hadoop. We investigate YARN delay
scheduler behavior with respect to data locality for a variety of
workloads and configurations. We address in this paper three
problems related to data locality. First, we study the trade-off
between the data locality and the job completion time. Secondly,
we observe that there is an imbalance of resource allocation when
considering the data locality, which may under-utilize the cluster.
Thirdly, we address the redundant I/O operations when different
YARN containers request input data blocks on the same node.
Additionally, we propose YARN Locality Simulator (YLocSim),
a simulator tool that simulates the interactions between YARN
components in a real cluster and reports the data locality
percentages in real time. We validate YLocSim over a real cluster
setup and use it in our study

Designing  E-Business  applications  in  an  efficient
way has become a competitive necessity rather than a competitive
advantage.  One  of  the  most  important  goals  for  many  organi-
zations  is  to  satisfy  their  clients’  service  level  agreements  with
respect  to  the  response  time  and  throughput.  Adopting  Service
Oriented Architecture (SOA) during design and implementation
promotes communication with the external and internal business
entities.  Web  services  are  one  of  the  popular  technologies  to
achieve  SOA  solutions.  Lookup  web  services  are  broadly  used
by  many  service  consumers  to  fetch  data  which  are used  by their  applications.  In  this  paper  we  focus  on  how  to  efficiently build  lookup  web  services  using  design  patterns.  Our  goal  is  to improve  the  response  time  (latency)  and  throughput  of  lookup web services