1,031 research outputs found
Towards Hybrid Cloud-assisted Crowdsourced Live Streaming: Measurement and Analysis
Crowdsourced Live Streaming (CLS), most notably Twitch.tv, has seen explosive
growth in its popularity in the past few years. In such systems, any user can
lively broadcast video content of interest to others, e.g., from a game player
to many online viewers. To fulfill the demands from both massive and
heterogeneous broadcasters and viewers, expensive server clusters have been
deployed to provide video ingesting and transcoding services. Despite the
existence of highly popular channels, a significant portion of the channels is
indeed unpopular. Yet as our measurement shows, these broadcasters are
consuming considerable system resources; in particular, 25% (resp. 30%) of
bandwidth (resp. computation) resources are used by the broadcasters who do not
have any viewers at all. In this paper, we closely examine the challenge of
handling unpopular live-broadcasting channels in CLS systems and present a
comprehensive solution for service partitioning on hybrid cloud. The
trace-driven evaluation shows that our hybrid cloud-assisted design can smartly
assign ingesting and transcoding tasks to the elastic cloud virtual machines,
providing flexible system deployment cost-effectively
Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning
Unmanned Aerial Vehicles (UAVs) have been recently considered as means to
provide enhanced coverage or relaying services to mobile users (MUs) in
wireless systems with limited or no infrastructure. In this paper, a UAV-based
mobile cloud computing system is studied in which a moving UAV is endowed with
computing capabilities to offer computation offloading opportunities to MUs
with limited local processing capabilities. The system aims at minimizing the
total mobile energy consumption while satisfying quality of service
requirements of the offloaded mobile application. Offloading is enabled by
uplink and downlink communications between the mobile devices and the UAV that
take place by means of frequency division duplex (FDD) via orthogonal or
non-orthogonal multiple access (NOMA) schemes. The problem of jointly
optimizing the bit allocation for uplink and downlink communication as well as
for computing at the UAV, along with the cloudlet's trajectory under latency
and UAV's energy budget constraints is formulated and addressed by leveraging
successive convex approximation (SCA) strategies. Numerical results demonstrate
the significant energy savings that can be accrued by means of the proposed
joint optimization of bit allocation and cloudlet's trajectory as compared to
local mobile execution as well as to partial optimization approaches that
design only the bit allocation or the cloudlet's trajectory.Comment: 14 pages, 5 figures, 2 tables, IEEE Transactions on Vehicular
Technolog
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
High Performance Computing (HPC) clouds are becoming an alternative to
on-premise clusters for executing scientific applications and business
analytics services. Most research efforts in HPC cloud aim to understand the
cost-benefit of moving resource-intensive applications from on-premise
environments to public cloud platforms. Industry trends show hybrid
environments are the natural path to get the best of the on-premise and cloud
resources---steady (and sensitive) workloads can run on on-premise resources
and peak demand can leverage remote resources in a pay-as-you-go manner.
Nevertheless, there are plenty of questions to be answered in HPC cloud, which
range from how to extract the best performance of an unknown underlying
platform to what services are essential to make its usage easier. Moreover, the
discussion on the right pricing and contractual models to fit small and large
users is relevant for the sustainability of HPC clouds. This paper brings a
survey and taxonomy of efforts in HPC cloud and a vision on what we believe is
ahead of us, including a set of research challenges that, once tackled, can
help advance businesses and scientific discoveries. This becomes particularly
relevant due to the fast increasing wave of new HPC applications coming from
big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
Many algorithms in workflow scheduling and resource provisioning rely on the
performance estimation of tasks to produce a scheduling plan. A profiler that
is capable of modeling the execution of tasks and predicting their runtime
accurately, therefore, becomes an essential part of any Workflow Management
System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS)
platforms that use clouds for deploying scientific workflows, task runtime
prediction becomes more challenging because it requires the processing of a
significant amount of data in a near real-time scenario while dealing with the
performance variability of cloud resources. Hence, relying on methods such as
profiling tasks' execution data using basic statistical description (e.g.,
mean, standard deviation) or batch offline regression techniques to estimate
the runtime may not be suitable for such environments. In this paper, we
propose an online incremental learning approach to predict the runtime of tasks
in scientific workflows in clouds. To improve the performance of the
predictions, we harness fine-grained resources monitoring data in the form of
time-series records of CPU utilization, memory usage, and I/O activities that
are reflecting the unique characteristics of a task's execution. We compare our
solution to a state-of-the-art approach that exploits the resources monitoring
data based on regression machine learning technique. From our experiments, the
proposed strategy improves the performance, in terms of the error, up to
29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM
International Conference on Utility and Cloud Computin
Optimal association of mobile users to multi-access edge computing resources
Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game
Developing resource consolidation frameworks for moldable virtual machines in clouds
This paper considers the scenario where multiple clusters of Virtual Machines (i.e., termed Virtual Clusters) are hosted in a Cloud system consisting of a cluster of physical nodes. Multiple Virtual Clusters (VCs) cohabit in the physical cluster, with each VC offering a particular type of service for the incoming requests. In this context, VM consolidation, which strives to use a minimal number of nodes to accommodate all VMs in the system, plays an important role in saving resource consumption. Most existing consolidation methods proposed in the literature regard VMs as “rigid” during consolidation, i.e., VMs’ resource capacities remain unchanged. In VC environments, QoS is usually delivered by a VC as a single entity. Therefore, there is no reason why VMs’ resource capacity cannot be adjusted as long as the whole VC is still able to maintain the desired QoS. Treating VMs as “moldable” during consolidation may be able to further consolidate VMs into an even fewer number of nodes. This paper investigates this issue and develops a Genetic Algorithm (GA) to consolidate moldable VMs. The GA is able to evolve an optimized system state, which represents the VM-to-node mapping and the resource capacity allocated to each VM. After the new system state is calculated by the GA, the Cloud will transit from the current system state to the new one. The transition time represents overhead and should be minimized. In this paper, a cost model is formalized to capture the transition overhead, and a reconfiguration algorithm is developed to transit the Cloud to the optimized system state with low transition overhead. Experiments have been conducted to evaluate the performance of the GA and the reconfiguration algorithm
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