6,545 research outputs found
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
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
Experimental Performance Evaluation of Cloud-Based Analytics-as-a-Service
An increasing number of Analytics-as-a-Service solutions has recently seen
the light, in the landscape of cloud-based services. These services allow
flexible composition of compute and storage components, that create powerful
data ingestion and processing pipelines. This work is a first attempt at an
experimental evaluation of analytic application performance executed using a
wide range of storage service configurations. We present an intuitive notion of
data locality, that we use as a proxy to rank different service compositions in
terms of expected performance. Through an empirical analysis, we dissect the
performance achieved by analytic workloads and unveil problems due to the
impedance mismatch that arise in some configurations. Our work paves the way to
a better understanding of modern cloud-based analytic services and their
performance, both for its end-users and their providers.Comment: Longer version of the paper in Submission at IEEE CLOUD'1
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