454,426 research outputs found
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
Beltway: Getting Around Garbage Collection Gridlock
We present the design and implementation of a new garbage collection framework that significantly generalizes existing copying collectors. The Beltway framework exploits and separates object age and incrementality. It groups objects in one or more increments on queues called belts, collects belts independently, and collects increments on a belt in first-in-first-out order. We show that Beltway configurations, selected by command line options, act and perform the same as semi-space, generational, and older-first collectors, and encompass all previous copying collectors of which we are aware. The increasing reliance on garbage collected languages such as Java requires that the collector perform well. We show that the generality of Beltway enables us to design and implement new collectors that are robust to variations in heap size and improve total execution time over the best generational copying collectors of which we are aware by up to 40%, and on average by 5 to 10%, for small to moderate heap sizes. New garbage collection algorithms are rare, and yet we define not just one, but a new family of collectors that subsumes previous work. This generality enables us to explore a larger design space and build better collectors
Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development
Mobile devices and platforms have become an established target for modern
software developers due to performant hardware and a large and growing user
base numbering in the billions. Despite their popularity, the software
development process for mobile apps comes with a set of unique, domain-specific
challenges rooted in program comprehension. Many of these challenges stem from
developer difficulties in reasoning about different representations of a
program, a phenomenon we define as a "language dichotomy". In this paper, we
reflect upon the various language dichotomies that contribute to open problems
in program comprehension and development for mobile apps. Furthermore, to help
guide the research community towards effective solutions for these problems, we
provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference
on Program Comprehension (ICPC'18
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