250 research outputs found
A Reconfigurable High-Performance Optical Data Center Architecture
Optical data center network architectures are becoming attractive because of
their low energy consumption, large bandwidth, and low cabling complexity.
In\cite{Xu1605:PODCA}, an AWGR-based passive optical data center architecture
(PODCA) is presented. Compared with other optical data center architectures,
e.g., DOS \cite{ye2010scalable}, Proteus \cite{singla2010proteus}, and Petabit
\cite{xia2010petabit}, PODCA can save up to 90 on power consumption and
88 in cost. Also, average latency can be low as 9 s at close to
100 throughput. However, PODCA is not reconfigurable and cannot optimize
the network topology to dynamic traffic.
In this paper, we present a novel, scalable and flexible reconfigurable
architecture called RODCA. RODCA is built on and augments PODCA with a flexible
localized intra-cluster optical network. With the reconfigurable intra-cluster
network, racks with mutually large traffic can be located within the same
cluster, and share the large bandwidth of the intra-cluster network. We present
an algorithm for DCN topology reconfiguration, and present simulation results
to demonstrate the effectiveness of reconfiguration
Impact of Wavelength and Modulation Conversion on Transluscent Elastic Optical Networks Using MILP
Compared to legacy wavelength division multiplexing networks, elastic optical
networks (EON) have added flexibility to network deployment and management.
EONs can include previously available technology, such as signal regeneration
and wavelength conversion, as well as new features such as finer-granularity
spectrum assignment and modulation conversion. Yet each added feature adds to
the cost of the network. In order to quantify the potential benefit of each
technology, we present a link-based mixed-integer linear programming (MILP)
formulation to solve the optimal resource allocation problem. We then propose a
recursive model in order to either augment existing network deployments or
speed up the resource allocation computation time for larger networks with
higher traffic demand requirements than can be solved using an MILP. We show
through simulation that systems equipped with signal regenerators or wavelength
converters require a notably smaller total bandwidth, depending on the topology
of the network. We also show that the suboptimal recursive solution speeds up
the calculation and makes the running-time more predictable, compared to the
optimal MILP
On the Optimality of Scheduling Dependent MapReduce Tasks on Heterogeneous Machines
MapReduce is the most popular big-data computation framework, motivating many
research topics. A MapReduce job consists of two successive phases, i.e. map
phase and reduce phase. Each phase can be divided into multiple tasks. A reduce
task can only start when all the map tasks finish processing. A job is
successfully completed when all its map and reduce tasks are complete. The task
of optimally scheduling the different tasks on different servers to minimize
the weighted completion time is an open problem, and is the focus of this
paper. In this paper, we give an approximation ratio with a competitive ratio
, where is the number of servers and is the
task-skewness product. We implement the proposed algorithm on Hadoop framework,
and compare with three baseline schedulers. Results show that our DMRS
algorithm can outperform baseline schedulers by up to
Chronos: A Unifying Optimization Framework for Speculative Execution of Deadline-critical MapReduce Jobs
Meeting desired application deadlines in cloud processing systems such as
MapReduce is crucial as the nature of cloud applications is becoming
increasingly mission-critical and deadline-sensitive. It has been shown that
the execution times of MapReduce jobs are often adversely impacted by a few
slow tasks, known as stragglers, which result in high latency and deadline
violations. While a number of strategies have been developed in existing work
to mitigate stragglers by launching speculative or clone task attempts, none of
them provides a quantitative framework that optimizes the speculative execution
for offering guaranteed Service Level Agreements (SLAs) to meet application
deadlines. In this paper, we bring several speculative scheduling strategies
together under a unifying optimization framework, called Chronos, which defines
a new metric, Probability of Completion before Deadlines (PoCD), to measure the
probability that MapReduce jobs meet their desired deadlines. We systematically
analyze PoCD for popular strategies including Clone, Speculative-Restart, and
Speculative-Resume, and quantify their PoCD in closed-form. The result
illuminates an important tradeoff between PoCD and the cost of speculative
execution, measured by the total (virtual) machine time required under
different strategies. We propose an optimization problem to jointly optimize
PoCD and execution cost in different strategies, and develop an algorithmic
solution that is guaranteed to be optimal. Chronos is prototyped on Hadoop
MapReduce and evaluated against three baseline strategies using both
experiments and trace-driven simulations, achieving 50% net utility increase
with up to 80% PoCD and 88% cost improvements
Trading Off Computation with Transmission in Status Update Systems
This paper is motivated by emerging edge computing applications in which
generated data are pre-processed at the source and then transmitted to an edge
server. In such a scenario, there is typically a tradeoff between the amount of
pre-processing and the amount of data to be transmitted. We model such a system
by considering two non-preemptive queues in tandem whose service times are
independent over time but the transmission service time is dependent on the
computation service time in mean value. The first queue is in M/GI/1/1 form
with a single server, memoryless exponential arrivals, general independent
service and no extra buffer to save incoming status update packets. The second
queue is in GI/M/1/2* form with a single server receiving packets from the
first queue, memoryless service and a single data buffer to save incoming
packets. Additionally, mean service times of the first and second queues are
dependent through a deterministic monotonic function. We perform stationary
distribution analysis in this system and obtain closed form expressions for
average age of information (AoI) and average peak AoI. Our numerical results
illustrate the analytical findings and highlight the tradeoff between average
AoI and average peak AoI generated by the tandem nature of the queueing system
with dependent service times
Optimizing Information Freshness Through Computation-Transmission Tradeoff and Queue Management in Edge Computing
Edge computing applications typically require generated data to be
preprocessed at the source and then transmitted to an edge server. In such
cases, transmission time and preprocessing time are coupled, yielding a
tradeoff between them to achieve the targeted objective. This paper presents
analysis of such a system with the objective of optimizing freshness of
received data at the edge server. We model this system as two queues in tandem
whose service times are independent over time but the transmission service time
is monotonically dependent on the computation service time in mean value. This
dependence captures the natural decrease in transmission time due to lower
offloaded computation. We analyze various queue management schemes in this
tandem queue where the first queue has a single server, Poisson packet
arrivals, general independent service and no extra buffer to save incoming
status update packets. The second queue has a single server receiving packets
from the first queue and service is memoryless. We consider the second queue in
two forms: (i) No data buffer and (ii) One unit data buffer and last come first
serve with discarding. We analyze various non-preemptive as well as preemptive
cases. We perform stationary distribution analysis and obtain closed form
expressions for average age of information (AoI) and average peak AoI. Our
numerical results illustrate analytical findings on how computation and
transmission times could be traded off to optimize AoI and reveal a consequent
tradeoff between average AoI and average peak AoI.Comment: arXiv admin note: substantial text overlap with arXiv:1907.0092
On Age and Value of Information in Status Update Systems
Motivated by the inherent value of packets arising in many cyber-physical
applications (e.g., due to precision of the information content or an alarm
message), we consider status update systems with update packets carrying values
as well as their generation time stamps. Once generated, a status update packet
has a random initial value and a deterministic deadline after which it is not
useful (ultimate staleness). In our model, value of a packet decreases in time
(even after reception) starting from its generation to ultimate staleness when
it vanishes. The value of information (VoI) at the receiver is additive in that
the VoI is the sum of the current values of all packets held by the receiver.
We investigate various queuing disciplines under potential dependence between
value and service time and provide closed form expressions for average VoI at
the receiver. Numerical results illustrate the average VoI for different
scenarios and the contrast between average age of information (AoI) and average
VoI
Relative Age of Information: A New Metric for Status Update Systems
In this paper, we introduce a new data freshness metric, relative Age of
Information (rAoI), and examine it in a single server system with various
packet management schemes. The (classical) AoI metric was introduced to measure
the staleness of status updates at the receiving end with respect to their
generation at the source. This metric addresses systems where the timings of
update generation at the source are absolute and can be designed separately or
jointly with the transmission schedules. In many decentralized applications,
transmission schedules are blind to update generation timing, and the
transmitter can know the timing of an update packet only after it arrives. As
such, an update becomes stale after a new one arrives. The rAoI metric measures
how fresh the data is at the receiver with respect to the data at the
transmitter. It introduces a particularly explicit dependence on the arrival
process in the evaluation of age. We investigate several queuing disciplines
and provide closed form expressions for rAoI and numerical comparisons
Waiting before Serving: A Companion to Packet Management in Status Update Systems
In this paper, we explore the potential of server waiting before packet
transmission in improving the Age of Information (AoI) in status update
systems. We consider a non-preemptive queue with Poisson arrivals and
independent general service distribution and we incorporate waiting before
serving in two packet management schemes: M/GI/1/1 and M/GI/1/. In
M/GI/1/1 scheme, the server waits for a deterministic time immediately after a
packet enters the server. In M/GI/1/ scheme, depending on idle or busy
system state, the server waits for a deterministic time before starting service
of the packet. In both cases, if a potential newer arrival is captured existing
packet is discarded. Different from most existing works, we analyze AoI
evolution by indexing the incoming packets, which is enabled by an alternative
method of partitioning the area under the evolution of instantaneous AoI to
calculate its time average. We obtain expressions for average and average peak
AoI for both queueing disciplines with waiting. Our numerical results
demonstrate that waiting before service can bring significant improvement in
average age, particularly, for heavy-tailed service distributions. This
improvement comes at the expense of an increase in average peak AoI. We
highlight the trade-off between average and average peak AoI generated by
waiting before serving
On double-link failure recovery in WDM optical networks
Abstract — Network survivability is a crucial requirement in high-speed optical networks. Typical approaches of providing survivability have considered the failure of a single component such as a link or a node. In this paper, we consider a failure model in which any two links in the network may fail in an arbitrary order. Three loopback methods of recovering from double-link failures are presented. The first two methods require the identification of the failed links, while the third one does not. However, precomputing the backup paths for the third method is more difficult than for the first two. A heuristic algorithm that pre-computes backup paths for links is presented. Numerical results comparing the performance of our algorithm with other approaches suggests that it is possible to achieve  ¢¡£¡¥¤ recovery from double-link failures with a modest increase in backup capacity. Index Terms—Wavelength division multiplexing (WDM), loopback recovery, restoration, double-link failures, 3-edge-connected graph. I
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