IEEE Transactions on Emerging Topics in Computing

Covering aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions

From the October-December 2014 issue

Distributed Client-Server Assignment for Online Social Network Applications

By Thuan Duong-Ba, Thinh Nguyen, Bella Bose and Duc A. Tran

Featured article thumbnail imageWe study the problem of assigning users to servers with an emphasis on the distributed algorithmic solutions. Typical online social network applications, such as Facebook and Twitter, are built on top of an infrastructure of servers that provides the services on behalf of the users. For a given communication pattern among users, the loads of the servers depend critically on how the users are assigned to the servers. A good assignment will reduce the overall load of the system while balancing the loads among the servers. Unfortunately, this optimal assignment problem is NP-hard. Therefore, we investigate three heuristic algorithms for solving the user server assignment problem: 1) the centralized simulated annealing (CSA) algorithm; 2) the distributed simulated annealing (DSA) algorithm; and 3) the distributed perturbed greedy search (DPGS). The CSA algorithm produces good solution in the fastest time, however it relies on timely accurate global system information, and is practical only for small and static systems. In contrast, the two distributed algorithms, DSA and DPGS, exploit local information at each server during their search for the optimal assignment, and thus can scale well with the number of users and servers as well as adapting to the system dynamics. Simulation results show that the performance of the distributed algorithms, specifically the DPGS algorithm, is very competitive with that of the centralized algorithm while providing the advantage of naturally adapting to time-varying communication patterns of users.

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Editorials and Announcements


  • Beginning in 2015, IEEE Transactions on Emerging Topics in Computing has moved to our hybrid open access publishing model. Authors can now select between either Traditional manuscript submission or Open Access (author-pays OA) manuscript submission. Learn more.

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  • We are pleased to announce that Fabrizio Lombardi, a professor at Northeastern University, Boston, has been appointed as the inaugural EIC for the IEEE Transactions on Emerging Technologies in Computing, effective immediately. Dr. Lombardi is an IEEE fellow, a member of the Computer Society Board of Governors, and is a past EIC and Associate EIC of the IEEE Transactions on Computers.


Call for Papers

Special Issue on Advances in Mobile Cloud Computing

Submission deadline: March 1, 2015. View PDF.

There is a phenomenal burst of research activities in mobile cloud computing, which extends cloud computing functions, services, and results to the world of future mobile communications applications, and the paradigm of cloud computing and virtualization to mobile networks. Mobile applications demand greater resources and improved interactivity for better user experience. Resources in cloud computing platforms such as Amazon, Google AppEngine and Microsoft Azure are a natural fit to remedy the lack of local resources in mobile devices. The availability of cloud computing resources on a pay-as-you-go basis, the advances in network virtualization, software defined networks, and the emergence of advanced wireless networks such as cloud-based radio access networks (C-RANs) create a new space of rich research problems. The objective of this special section is to cover the most recent research and development on the technologies for mobile cloud computing. This special section is to offer a venue for industry and academia to show case their recent progresses and potential research directions on the mobile cloud computing technologies.

Special Issue on Emerging Trends in Education

Submission deadline: March 1, 2015. View PDF.


Technological advancements, such as those seen in cloud computing, mobile devices, and big, open and linked data, to name just a few, bring with them great opportunities for broadening the reach of, and enriching, the educational experience. For instance, virtual learning environments are becoming commonplace in the communication between students and teachers, who can use a plethora of web-based tools and applications to publish assignments and submit them for grading, perform automatic assessment, etc. At the same time, mobile computing is contributing at expanding the reach of learning content and frameworks, which are becoming accessible in an always-on and ready-to-go fashion. By leveraging on smartphones and tablets, new pedagogical tools are being implemented which exploit their innovative interaction capabilities and the rich set of sensors to create immersive and interactive experiences not previously possible. Furthermore, the massive volume of information produced in these tools and environments opens greater possibilities including the sharing, analysis, and visualization of education data patterns and their trends. Although there are many different visions for education in the future, great efforts will be needed to reach a profound integration between the technologies that are already well-established and those that are considered as emerging. By building on a solid scientific and methodological foundation where theory and practice converge, this special issue aims to present both the current trends that characterize the learning and teaching domains of today as well the expected evolution that will shape the education of tomorrow.

Special Issue on Big Data Benchmarks, Performance Optimization, and Emerging Hardware

Submission deadline: June 1, 2015. View PDF.

Big data are emerging as a strategic property of nations and organizations. There are driving needs to generate values from big data. However, the sheer volume of big data requires significant storage capacity, transmission bandwidth, computation, and power consumption. It is expected that systems with unprecedented scales can resolve the problems caused by varieties of big data with daunting volumes. Nevertheless, without big data benchmarks, it is very difficult for big data owners to make a decision on which system is best for meeting with their specific requirements. They also face challenges on how to optimize the systems for specific or even comprehensive workloads. Meanwhile, researchers are also working on innovative data management systems, hardware architectures, and operating systems to improve performance in dealing with big data. This focus of this special issue will be on architecture and system support for big data systems.

Special Issue on Methods and Techniques for Processing Streaming Big Data in Datacentre Clouds

Submission deadline: June 1, 2015. View PDF.

Internet of Things (IoT) is a part of Future Internet and comprises many billions of Internet connected Objects (ICOs) or ‘things’ where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes. ICOs can include sensors, RFIDs, social media, actuators (such as machines/equipments fitted with sensors) as well as lab instruments (e.g., high energy physics synchrotron), and smart consumer appliances (smart TV, smart phone, etc.). The IoT vision has recently given rise to IoT big data applications that are capable of producing billions of data stream and tens of years of historical data to support timely decision making. Some of the emerging IoT big data applications, e.g. smart energy grids, syndromic bio-surveillance, environmental monitoring, emergency situation awareness, digital agriculture, and smart manufacturing, need to process and manage massive, streaming, and multi-dimensional (from multiple sources) data from geographically distributed data sources.

Despite recent technological advances of the data-intensive computing paradigms (e.g. the MapReduce paradigm, workflow technologies, stream processing engines, distributed machine learning frameworks) and datacentre clouds, large-scale reliable system-level software for IoT big data applications are yet to become commonplace. As new diverse IoT applications begin to emerge, there is a need for optimized techniques to distribute processing of the streaming data produced by such applications across multiple datacentres that combine multiple, independent, and geographically distributed software and hardware resources. However, the capability of existing data-intensive computing paradigms is limited in many important aspects such as: (i) they can only process data on compute and storage resources within a centralised local area network, e.g., a single cluster within a datacentre. This leads to unsatisfied Quality of Service (QoS) in terms of timeliness of decision making, resource availability, data availability, etc. as application demands increase; (ii) they do not provide mechanisms to seamlessly integrate data spread across multiple distributed heterogeneous data sources (ICOs); (iii) lack support for rapid formulation of intuitive queries over streaming data based on general purpose concepts, vocabularies and data discovery; and (iv) they do not provide any decision making support for selecting optimal data mining and machine algorithms, data application programming frameworks, and NoSQL database systems based on nature of the big data (volume, variety, and velocity). Furthermore, adoption of existing datacentre cloud platform for hosting IoT applications is yet to be realised due to lack of techniques and software frameworks that can guarantee QoS under uncertain big data application behaviours (data arrival rate, number of data sources, decision making urgency, etc.), unpredictable datacentre resource conditions (failures, availability, malfunction, etc.) and capacity demands (bandwidth, memory, storage, and CPU cycles). It is clear that existing data intensive computing paradigms and related datacentre cloud resource provisioning techniques fall short of the IoT big data challenge or do not exist.

Special Issue on Approximate and Stochastic Computing Circuits, Systems and Algorithms

Submission deadline: September 1, 2015. View PDF.

The last decade has seen renewed interest in non-traditional computing paradigms. Several (re-)emerging paradigms are aimed at leveraging the error resiliency of many systems by releasing the strict requirement of exactness in computing. This special issue of TETC focuses on two specific lines of research, known as approximate and stochastic computing.

Approximate computing is driven by considerations of energy efficiency. Applications such as multimedia, recognition, and data mining are inherently error-tolerant and do not require perfect accuracy in computation. The results of signal processing algorithms used in image and video processing are ultimately left to human perception. Therefore, strict exactness may not be required and an imprecise result may suffice. In these applications, approximate circuits aim to improve energy-efficiency by maximally exploiting the tolerable loss of accuracy and trading it for energy and area savings.

Stochastic computing is a paradigm that achieves fault-tolerance and area savings through randomness. Information is represented by random binary bit streams, where the signal value is encoded by the probability of obtaining a one versus a zero. The approach is applicable for data intensive applications such as signal processing where small fluctuations can be tolerated but large errors are catastrophic. In such contexts, it offers savings in computational resources and provides tolerance to errors. This fault tolerance scales gracefully to high error rates. The focus of this special issue will be on the novel design and analysis of approximate and stochastic computing circuits, systems, algorithms and applications.

Special Issue/Section on Low-Power Image Recognition

Submission deadline: September 1, 2015. View PDF.

Digital images have become integral parts of everyday life. It is estimated that 10 million images are uploaded to social networks each hour and 100 hours of video uploaded for sharing each minute. Sophisticated image / video processing has fundamentally changed how people interact. For example, automatic classification or tagging can mediate how photographs are disseminated to friends. Many of today’s images are captured using smartphones, and cameras in smartphones can be used for a wide range of imaging applications, from high-fidelity location estimation to posture analysis. Image processing is computationally intense and can consume significant amounts of energy on mobile systems. This special issue focuses on the intersection of image recognition and energy conservation. Papers should describe energy efficient systems that perform object detection and recognition in images.

Special Issue/Section on Defect and Fault Tolerance in VLSI and Nanotechnology Systems

Submission deadline: December 1, 2015. View PDF.

The continuous scaling of CMOS devices as well as the increased interest in the use of emerging technologies make more and more important the topics related to defect and fault tolerance in VLSI and nanotechnology systems. All aspects of design, manufacturing, test, reliability, and availability that are affected by defects during manufacturing and by faults during system operation, are of interest. The IEEE Transaction on Emerging Topics in Computing (TETC) seeks original manuscripts for a Special Section on Defect and Fault Tolerance in VLSI Systems scheduled to appear in the December issue of 2016.

Special Issue/Section on New Paradigms in Ad Hoc, Sensor and Mesh Networks, From Theory to Practice

Submission deadline: December 1, 2015. View PDF.

Ad hoc, sensor and mesh networks have attracted significant attention by academia and industry in the past decade. In recent years however new paradigms have emerged due to the large increase in number and processing power of smart phones and other portable devices. Furthermore, new applications and emerging technologies have created new research challenges for ad hoc networks. The emergence of new operational paradigms such as Smart Home and Smart City, Body Area Networks and E-Health, Device-to-Device Communications, Machine-to-Machine Communications, Software Defined Networks, the Internet of Things, RFID, and Small Cells require substantial changes in traditional ad hoc networking. The focus of this special issue is on novel applications, protocols and architectures, non-traditional measurement, modeling, analysis and evaluation, prototype systems, and experiments in ad hoc, sensor and mesh networks.

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