9,404 research outputs found
Energy-Aware Profiling for Cloud Computing Environments
Cloud Computing has changed the way in which people use the IT resources today. Now, instead of buying their own IT resources, they can use the services offered by Cloud Computing with reasonable costs based on a "pay-per-use" model. However, with the wide adoption of Cloud Computing, the costs for maintaining the Cloud infrastructure have become a vital issue for the providers, especially with the large input of energy costs to underpin these resources. Thus, this paper proposes a system architecture that can be used for profiling the resources usage in terms of the energy consumption. From the profiled data, the application developers can enhance their energy-aware decisions when creating or optimising the applications to be more energy efficient. This paper also presents an adapted existing Cloud architecture to enable energy-aware profiling based on the proposed system. The results of the conducted experiments show energy-awareness at physical host and virtual machine levels
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On Optimal and Fair Service Allocation in Mobile Cloud Computing
This paper studies the optimal and fair service allocation for a variety of
mobile applications (single or group and collaborative mobile applications) in
mobile cloud computing. We exploit the observation that using tiered clouds,
i.e. clouds at multiple levels (local and public) can increase the performance
and scalability of mobile applications. We proposed a novel framework to model
mobile applications as a location-time workflows (LTW) of tasks; here users
mobility patterns are translated to mobile service usage patterns. We show that
an optimal mapping of LTWs to tiered cloud resources considering multiple QoS
goals such application delay, device power consumption and user cost/price is
an NP-hard problem for both single and group-based applications. We propose an
efficient heuristic algorithm called MuSIC that is able to perform well (73% of
optimal, 30% better than simple strategies), and scale well to a large number
of users while ensuring high mobile application QoS. We evaluate MuSIC and the
2-tier mobile cloud approach via implementation (on real world clouds) and
extensive simulations using rich mobile applications like intensive signal
processing, video streaming and multimedia file sharing applications. Our
experimental and simulation results indicate that MuSIC supports scalable
operation (100+ concurrent users executing complex workflows) while improving
QoS. We observe about 25% lower delays and power (under fixed price
constraints) and about 35% decrease in price (considering fixed delay) in
comparison to only using the public cloud. Our studies also show that MuSIC
performs quite well under different mobility patterns, e.g. random waypoint and
Manhattan models
Energy-aware dynamic pricing model for cloud environments
Energy consumption is a critical operational cost for Cloud providers. However, as commercial providers typically use fixed pricing schemes that are oblivious about the energy costs of running virtual machines, clients are not charged according to their actual energy impact. Some works have proposed energy-aware cost models that are able to capture each client’s real energy usage. However, those models cannot be naturally used for pricing Cloud services, as the energy cost is calculated after the termination of the service, and it depends on decisions taken by the provider, such as the actual placement of the client’s virtual machines. For those reasons, a client cannot estimate in advance how much it will pay. This paper presents a pricing model for virtualized Cloud providers that dynamically derives the energy costs per allocation unit and per work unit for each time period. They account for the energy costs of the provider’s static and dynamic energy consumption by sharing out them according to the virtual resource allocation and the real resource usage of running virtual machines for the corresponding time period. Newly arrived clients during that period can use these costs as a baseline to calculate their expenses in advance as a function of the number of requested allocation and work units. Our results show that providers can get comparable revenue to traditional pricing schemes, while offering to the clients more proportional prices than fixed-price models.Peer ReviewedPostprint (author's final draft
TANGO: Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation
The paper is concerned with the issue of how software systems actually use
Heterogeneous Parallel Architectures (HPAs), with the goal of optimizing power
consumption on these resources. It argues the need for novel methods and tools
to support software developers aiming to optimise power consumption resulting
from designing, developing, deploying and running software on HPAs, while
maintaining other quality aspects of software to adequate and agreed levels. To
do so, a reference architecture to support energy efficiency at application
construction, deployment, and operation is discussed, as well as its
implementation and evaluation plans.Comment: Part of the Program Transformation for Programmability in
Heterogeneous Architectures (PROHA) workshop, Barcelona, Spain, 12th March
2016, 7 pages, LaTeX, 3 PNG figure
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