From the July-December 2013 issue
Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers
By Carlo Mastroianni, Michela Meo, Giuseppe Papuzzo
Power efficiency is one of the main issues that will drive the design of data centers, especially of those devoted to provide Cloud computing services. In virtualized data centers, consolidation of Virtual Machines (VMs) on the minimum number of physical servers has been recognized as a very efficient approach, as this allows unloaded servers to be switched off or used to accommodate more load, which is clearly a cheaper alternative to buy more resources. The consolidation problem must be solved on multiple dimensions, since in modern data centers CPU is not the only critical resource: depending on the characteristics of the workload other resources, for example, RAM and bandwidth, can become the bottleneck. The problem is so complex that centralized and deterministic solutions are practically useless in large data centers with hundreds or thousands of servers.
NOTE: We seek submission of papers that present new, original and innovative ideas for the "first" time in TCC (Transactions on Cloud Computing). That means, submission of "extended versions" of already published works (e.g., conference/workshop papers) is not encouraged unless they contain significant number of "new and original" ideas/contributions along with more than 49% brand "new" material.
News and Announcements
Introduction to the IEEE Transactions on Cloud Computing by Rajkumar Buyya
Welcome to the IEEE Transactions on Cloud Computing (TCC). It is my privilege and honor to serve as the inaugural Editor-in-Chief of TCC. I would like to thank the IEEE and the world-wide Cloud Computing community for giving me the opportunity to serve them. Let me first share some of the open opportunities and challenges in Cloud Computing and then introduce the transactions and its progress. Read more. (PDF)
Welcome Message by Jon Rokne
I am delighted to introduce the first issue of the IEEE Transactions on Cloud Computing. Cloud computing is the new paradigm for distributed and shared computing that has been embraced by researchers, practitioners, and industry. The impact of cloud implementations on how computing is performed is profound. It reduces acquisition cost, maintenance cost, and has transformed the way that IT professionals and computer users handle their work. While there are many publications that cover cloud issues from an industry point of view, the IEEE Computer Society recognizes the need for a respected transactions that publishes research in the field of cloud computing. The new journal will help to fill this void by publishing high-quality, peer-reviewed papers, covering topics such as cloud security and privacy, cloud standards and protocols, cloud development tools, cloud software, cloud backup and recovery, cloud interoperability, cloud applications management, cloud data analytics, mobile cloud, private clouds, liability issues for data loss on clouds, cloud education and skill sets, and cloud applications in commerce, education, and industry. Read more. (PDF)
Rajkumar Buyya, director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, has been named editor in chief of IEEE Transactions on Cloud Computing, IEEE Computer Society's newest peer-reviewed journal.
Buyya, a professor of computer science and software engineering at University of Melbourne, is also founding CEO of a university spinoff called Manjrasoft Pty Ltd., which has developed innovative software technologies for cloud computing utilized by high-profile organizations such as China Southern Railways and Indian Space Research Organization (ISRO).
Well known in the cloud computing community, Buyya was 2009 recipient of the IEEE Medal for Excellence in Scalable Computing in recognition of his significant contribution to the scalable computing community. In particular, he was recognized for pioneering the economic paradigm for utility-oriented distributed computing platforms such as grids and clouds, and serving as chair of the Technical Committee on Scalable Computing. Among his many other awards are the IEEE Computer Society's Richard Merwin Award in 1999 and a Distinguished Service Award in 2009. Read more...
Call for Papers
Special Issue on Economics and Market Mechanisms for Cloud Computing
In the past decade there has been a stream of interdisciplinary research between computer science and economics. Cloud computing is one of the successful and representative examples in this interdisciplinary research. On the one hand, cloud is an emerging computing market where cloud providers and users are the players. Those players share, trade and consume computing resource in the cloud. On the other hand, economic mechanisms (such as auctions and tiered pricing) have been designed for shaping cloud computing into a diversifying pay-as-you-go paradigm. As cloud computing is still an emerging and evolving paradigm, challenges and opportunities co-exist for new research directions and applications for economics and market mechanisms for cloud computing. As the complexity, heterogeneity and scale of resources appear, it will be increasingly important to develop economics and market mechanisms for managing, trading and pricing those resources. Further, business ventures operating across multiple Clouds may need to set and oblige policy driven schemes, which may become prohibitively expensive for ordinary users. In addition, pricing models, trust and security based research are added issues to be addressed. For example, new hardware components (e.g., GPUs and SSDs) are being added to the cloud, which poses new issues for pricing models. Thus, there is a need to fundamentally address all the above-mentioned issues. IEEE Transaction on Cloud Computing seeks original manuscripts for a Special Issue on the theme - Economics and Market Mechanisms for Cloud Computing scheduled to appear in the January issue of 2015.
Submission deadline: May 28, 2014. View PDF.
Special Issue on Scientific Cloud Computing
Computational and Data-Driven Sciences have become the third and fourth pillar of scientific discovery in addition to experimental and theoretical sciences. Scientific Computing has already begun to change how science is done, enabling scientific breakthroughs through new kinds of experiments that would have been impossible only a decade ago. It is the key to solving "grand challenges" in many domains and providing breakthroughs in new knowledge, and it comes in many shapes and forms: high-performance computing (HPC) which is heavily focused on compute-intensive applications; high-throughput computing(HTC) which focuses on using many computing resources over long periods of time to accomplish its computational tasks; many-task computing (MTC) which aims to bridge the gap between HPC and HTC by focusing on using many resources over short periods of time; and data-intensive computing which is heavily focused on data distribution, data-parallel execution, and harnessing data locality by scheduling of computations close to the data. Today's "Big Data" trend is generating datasets that are increasing exponentially in both complexity and volume, making their analysis, archival, and sharing one of the grand challenges of the 21st century. Not surprisingly, it becomes increasingly difficult to design and operate large scale systems capable of addressing these grand challenges.
This journal Special Issue on Scientific Cloud Computing in the IEEE Transaction on Cloud Computing will provide the scientific community a dedicated forum for discussing new research, development, and deployment efforts in running these kinds of scientific computing workloads on Cloud Computing infrastructures. This special is sue will focus on the use of cloud-based technologies to meet new compute-intensive and data-intensive scientific challenges that are not well served by the current supercomputers, grids and HPC clusters. The special issue will aim to address questions such as: What architectural changes to the current cloud frameworks (hardware, operating systems, networking and/or programming models) are needed to support science? Dynamic information derived from remote instruments and coupled simulation, and sensor ensembles that stream data for real-time analysis are important emerging techniques in scientific and cyber-physical engineering systems. How can cloud techn ologies enable and adapt to these new scientific approaches dealing with dynamism? How are scientists using clouds? Are there scientific HPC/HTC/MTC workloads that are suitable candidates to take advantage of emerging cloud computing resources with high efficiency? Commercial public clouds provide easy access to cloud infrastructure for scientists. What are the gaps in commercial cloud offerings and how can they be adapted for running existing and novel eScience applications? What benefits exist by adopting the cloud model, over clusters, grids, or supercomputers? What factors are limiting clouds use or would make them more usable/efficient?
Submission deadline: July 31, 2014. View PDF.
Special Issue on Big Data Computing on Clouds
Big data is an emerging paradigm applied to datasets whose size or complexity is beyond the ability of commonly used computer software and hardware tools. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions and web logs; and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision making (Veracity). Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce. As estimated by IDC, by 2020, about 40% data globally would be touched with Cloud Computing. Besides, Cloud Computing provides strong storage, computation and distributed capability in support of Big Data processing. Therefore, there is a strong demand to investigate various challenges about how to support Big Data processing by facilitating Cloud Computing potential. This special issue will focus on this challenging topic.
Submission deadline: November 15, 2014. View PDF.
General Call for Papers
General call for papers. View PDF.
TCC is financially cosponsored by:
TCC is technically cosponsored by: