5,490 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
A Case for Cooperative and Incentive-Based Coupling of Distributed Clusters
Research interest in Grid computing has grown significantly over the past
five years. Management of distributed resources is one of the key issues in
Grid computing. Central to management of resources is the effectiveness of
resource allocation as it determines the overall utility of the system. The
current approaches to superscheduling in a grid environment are non-coordinated
since application level schedulers or brokers make scheduling decisions
independently of the others in the system. Clearly, this can exacerbate the
load sharing and utilization problems of distributed resources due to
suboptimal schedules that are likely to occur. To overcome these limitations,
we propose a mechanism for coordinated sharing of distributed clusters based on
computational economy. The resulting environment, called
\emph{Grid-Federation}, allows the transparent use of resources from the
federation when local resources are insufficient to meet its users'
requirements. The use of computational economy methodology in coordinating
resource allocation not only facilitates the QoS based scheduling, but also
enhances utility delivered by resources.Comment: 22 pages, extended version of the conference paper published at IEEE
Cluster'05, Boston, M
A Taxonomy for Congestion Control Algorithms in Vehicular Ad Hoc Networks
One of the main criteria in Vehicular Ad hoc Networks (VANETs) that has
attracted the researchers' consideration is congestion control. Accordingly,
many algorithms have been proposed to alleviate the congestion problem,
although it is hard to find an appropriate algorithm for applications and
safety messages among them. Safety messages encompass beacons and event-driven
messages. Delay and reliability are essential requirements for event-driven
messages. In crowded networks where beacon messages are broadcasted at a high
number of frequencies by many vehicles, the Control Channel (CCH), which used
for beacons sending, will be easily congested. On the other hand, to guarantee
the reliability and timely delivery of event-driven messages, having a
congestion free control channel is a necessity. Thus, consideration of this
study is given to find a solution for the congestion problem in VANETs by
taking a comprehensive look at the existent congestion control algorithms. In
addition, the taxonomy for congestion control algorithms in VANETs is presented
based on three classes, namely, proactive, reactive and hybrid. Finally, we
have found the criteria in which fulfill prerequisite of a good congestion
control algorithm
Causative Cyberattacks on Online Learning-based Automated Demand Response Systems
Power utilities are adopting Automated Demand Response (ADR) to replace the
costly fuel-fired generators and to preempt congestion during peak electricity
demand. Similarly, third-party Demand Response (DR) aggregators are leveraging
controllable small-scale electrical loads to provide on-demand grid support
services to the utilities. Some aggregators and utilities have started
employing Artificial Intelligence (AI) to learn the energy usage patterns of
electricity consumers and use this knowledge to design optimal DR incentives.
Such AI frameworks use open communication channels between the
utility/aggregator and the DR customers, which are vulnerable to
\textit{causative} data integrity cyberattacks. This paper explores
vulnerabilities of AI-based DR learning and designs a data-driven attack
strategy informed by DR data collected from the New York University (NYU)
campus buildings. The case study demonstrates the feasibility and effects of
maliciously tampering with (i) real-time DR incentives, (ii) DR event data sent
to DR customers, and (iii) responses of DR customers to the DR incentives
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