5,277 research outputs found
Mobile, collaborative augmented reality using cloudlets
The evolution in mobile applications to support advanced interactivity and demanding multimedia features is still ongoing. Novel application concepts (e.g. mobile Augmented Reality (AR)) are however hindered by the inherently limited resources available on mobile platforms (not withstanding the dramatic performance increases of mobile hardware). Offloading resource intensive application components to the cloud, also known as "cyber foraging", has proven to be a valuable solution in a variety of scenarios. However, also for collaborative scenarios, in which data together with its processing are shared between multiple users, this offloading concept is highly promising. In this paper, we investigate the challenges posed by offloading collaborative mobile applications. We present a middleware platform capable of autonomously deploying software components to minimize average CPU load, while guaranteeing smooth collaboration. As a use case, we present and evaluate a collaborative AR application, offering interaction between users, the physical environment as well as with the virtual objects superimposed on this physical environment
DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling
The dynamic provisioning of virtualized resources offered by cloud computing
infrastructures allows applications deployed in a cloud environment to
automatically increase and decrease the amount of used resources. This
capability is called auto-scaling and its main purpose is to automatically
adjust the scale of the system that is running the application to satisfy the
varying workload with minimum resource utilization. The need for auto-scaling
is particularly important during workload peaks, in which applications may need
to scale up to extremely large-scale systems.
Both the research community and the main cloud providers have already
developed auto-scaling solutions. However, most research solutions are
centralized and not suitable for managing large-scale systems, moreover cloud
providers' solutions are bound to the limitations of a specific provider in
terms of resource prices, availability, reliability, and connectivity.
In this paper we propose DEPAS, a decentralized probabilistic auto-scaling
algorithm integrated into a P2P architecture that is cloud provider
independent, thus allowing the auto-scaling of services over multiple cloud
infrastructures at the same time. Our simulations, which are based on real
service traces, show that our approach is capable of: (i) keeping the overall
utilization of all the instantiated cloud resources in a target range, (ii)
maintaining service response times close to the ones obtained using optimal
centralized auto-scaling approaches.Comment: Submitted to Springer Computin
Hybrid SDN Evolution: A Comprehensive Survey of the State-of-the-Art
Software-Defined Networking (SDN) is an evolutionary networking paradigm
which has been adopted by large network and cloud providers, among which are
Tech Giants. However, embracing a new and futuristic paradigm as an alternative
to well-established and mature legacy networking paradigm requires a lot of
time along with considerable financial resources and technical expertise.
Consequently, many enterprises can not afford it. A compromise solution then is
a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN
functionalities are leveraged while existing traditional network
infrastructures are acknowledged. Recently, hSDN has been seen as a viable
networking solution for a diverse range of businesses and organizations.
Accordingly, the body of literature on hSDN research has improved remarkably.
On this account, we present this paper as a comprehensive state-of-the-art
survey which expands upon hSDN from many different perspectives
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