15,014 research outputs found
Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks
Cooperative video caching and transcoding in mobile edge computing (MEC)
networks is a new paradigm for future wireless networks, e.g., 5G and 5G
beyond, to reduce scarce and expensive backhaul resource usage by prefetching
video files within radio access networks (RANs). Integration of this technique
with other advent technologies, such as wireless network virtualization and
multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible
video delivery opportunities, which leads to enhancements both for the
network's revenue and for the end-users' service experience. In this regard, we
propose a two-phase RAF for a parallel cooperative joint multi-bitrate video
caching and transcoding in heterogeneous virtualized MEC networks. In the cache
placement phase, we propose novel proactive delivery-aware cache placement
strategies (DACPSs) by jointly allocating physical and radio resources based on
network stochastic information to exploit flexible delivery opportunities.
Then, for the delivery phase, we propose a delivery policy based on the user
requests and network channel conditions. The optimization problems
corresponding to both phases aim to maximize the total revenue of network
slices, i.e., virtual networks. Both problems are non-convex and suffer from
high-computational complexities. For each phase, we show how the problem can be
solved efficiently. We also propose a low-complexity RAF in which the
complexity of the delivery algorithm is significantly reduced. A Delivery-aware
cache refreshment strategy (DACRS) in the delivery phase is also proposed to
tackle the dynamically changes of network stochastic information. Extensive
numerical assessments demonstrate a performance improvement of up to 30% for
our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure
OS Scheduling Algorithms for Memory Intensive Workloads in Multi-socket Multi-core servers
Major chip manufacturers have all introduced multicore microprocessors.
Multi-socket systems built from these processors are routinely used for running
various server applications. Depending on the application that is run on the
system, remote memory accesses can impact overall performance. This paper
presents a new operating system (OS) scheduling optimization to reduce the
impact of such remote memory accesses. By observing the pattern of local and
remote DRAM accesses for every thread in each scheduling quantum and applying
different algorithms, we come up with a new schedule of threads for the next
quantum. This new schedule potentially cuts down remote DRAM accesses for the
next scheduling quantum and improves overall performance. We present three such
new algorithms of varying complexity followed by an algorithm which is an
adaptation of Hungarian algorithm. We used three different synthetic workloads
to evaluate the algorithm. We also performed sensitivity analysis with respect
to varying DRAM latency. We show that these algorithms can cut down DRAM access
latency by up to 55% depending on the algorithm used. The benefit gained from
the algorithms is dependent upon their complexity. In general higher the
complexity higher is the benefit. Hungarian algorithm results in an optimal
solution. We find that two out of four algorithms provide a good trade-off
between performance and complexity for the workloads we studied
Cellular Offloading via Downlink Cache Placement
In this paper, the downlink file transmission within a finite lifetime is
optimized with the assistance of wireless cache nodes. Specifically, the number
of requests within the lifetime of one file is modeled as a Poisson point
process. The base station multicasts files to downlink users and the selected
the cache nodes, so that the cache nodes can help to forward the files in the
next file request. Thus we formulate the downlink transmission as a Markov
decision process with random number of stages, where transmission power and
time on each transmission are the control policy. Due to random number of file
transmissions, we first proposed a revised Bellman's equation, where the
optimal control policy can be derived. In order to address the prohibitively
huge state space, we also introduce a low-complexity sub-optimal solution based
on an linear approximation of the value function. The approximated value
function can be calculated analytically, so that conventional numerical value
iteration can be eliminated. Moreover, the gap between the approximated value
function and the real value function is bounded analytically. It is shown by
simulation that, with the approximated MDP approach, the proposed algorithm can
significantly reduce the resource consumption at the base station.Comment: Submitted for IEEE ICC 201
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