21,612 research outputs found
A C-DAG task model for scheduling complex real-time tasks on heterogeneous platforms: preemption matters
Recent commercial hardware platforms for embedded real-time systems feature
heterogeneous processing units and computing accelerators on the same
System-on-Chip. When designing complex real-time application for such
architectures, the designer needs to make a number of difficult choices: on
which processor should a certain task be implemented? Should a component be
implemented in parallel or sequentially? These choices may have a great impact
on feasibility, as the difference in the processor internal architectures
impact on the tasks' execution time and preemption cost. To help the designer
explore the wide space of design choices and tune the scheduling parameters, in
this paper we propose a novel real-time application model, called C-DAG,
specifically conceived for heterogeneous platforms. A C-DAG allows to specify
alternative implementations of the same component of an application for
different processing engines to be selected off-line, as well as conditional
branches to model if-then-else statements to be selected at run-time. We also
propose a schedulability analysis for the C-DAG model and a heuristic
allocation algorithm so that all deadlines are respected. Our analysis takes
into account the cost of preempting a task, which can be non-negligible on
certain processors. We demonstrate the effectiveness of our approach on a large
set of synthetic experiments by comparing with state of the art algorithms in
the literature
Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms
There has been significant progress in understanding the parallelism inherent
to iterative sequential algorithms: for many classic algorithms, the depth of
the dependence structure is now well understood, and scheduling techniques have
been developed to exploit this shallow dependence structure for efficient
parallel implementations. A related, applied research strand has studied
methods by which certain iterative task-based algorithms can be efficiently
parallelized via relaxed concurrent priority schedulers. These allow for high
concurrency when inserting and removing tasks, at the cost of executing
superfluous work due to the relaxed semantics of the scheduler.
In this work, we take a step towards unifying these two research directions,
by showing that there exists a family of relaxed priority schedulers that can
efficiently and deterministically execute classic iterative algorithms such as
greedy maximal independent set (MIS) and matching. Our primary result shows
that, given a randomized scheduler with an expected relaxation factor of in
terms of the maximum allowed priority inversions on a task, and any graph on
vertices, the scheduler is able to execute greedy MIS with only an additive
factor of poly() expected additional iterations compared to an exact (but
not scalable) scheduler. This counter-intuitive result demonstrates that the
overhead of relaxation when computing MIS is not dependent on the input size or
structure of the input graph. Experimental results show that this overhead can
be clearly offset by the gain in performance due to the highly scalable
scheduler. In sum, we present an efficient method to deterministically
parallelize iterative sequential algorithms, with provable runtime guarantees
in terms of the number of executed tasks to completion.Comment: PODC 2018, pages 377-386 in proceeding
Datacenter Traffic Control: Understanding Techniques and Trade-offs
Datacenters provide cost-effective and flexible access to scalable compute
and storage resources necessary for today's cloud computing needs. A typical
datacenter is made up of thousands of servers connected with a large network
and usually managed by one operator. To provide quality access to the variety
of applications and services hosted on datacenters and maximize performance, it
deems necessary to use datacenter networks effectively and efficiently.
Datacenter traffic is often a mix of several classes with different priorities
and requirements. This includes user-generated interactive traffic, traffic
with deadlines, and long-running traffic. To this end, custom transport
protocols and traffic management techniques have been developed to improve
datacenter network performance.
In this tutorial paper, we review the general architecture of datacenter
networks, various topologies proposed for them, their traffic properties,
general traffic control challenges in datacenters and general traffic control
objectives. The purpose of this paper is to bring out the important
characteristics of traffic control in datacenters and not to survey all
existing solutions (as it is virtually impossible due to massive body of
existing research). We hope to provide readers with a wide range of options and
factors while considering a variety of traffic control mechanisms. We discuss
various characteristics of datacenter traffic control including management
schemes, transmission control, traffic shaping, prioritization, load balancing,
multipathing, and traffic scheduling. Next, we point to several open challenges
as well as new and interesting networking paradigms. At the end of this paper,
we briefly review inter-datacenter networks that connect geographically
dispersed datacenters which have been receiving increasing attention recently
and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial
Scheduling for Multi-Camera Surveillance in LTE Networks
Wireless surveillance in cellular networks has become increasingly important,
while commercial LTE surveillance cameras are also available nowadays.
Nevertheless, most scheduling algorithms in the literature are throughput,
fairness, or profit-based approaches, which are not suitable for wireless
surveillance. In this paper, therefore, we explore the resource allocation
problem for a multi-camera surveillance system in 3GPP Long Term Evolution
(LTE) uplink (UL) networks. We minimize the number of allocated resource blocks
(RBs) while guaranteeing the coverage requirement for surveillance systems in
LTE UL networks. Specifically, we formulate the Camera Set Resource Allocation
Problem (CSRAP) and prove that the problem is NP-Hard. We then propose an
Integer Linear Programming formulation for general cases to find the optimal
solution. Moreover, we present a baseline algorithm and devise an approximation
algorithm to solve the problem. Simulation results based on a real surveillance
map and synthetic datasets manifest that the number of allocated RBs can be
effectively reduced compared to the existing approach for LTE networks.Comment: 9 pages, 10 figure
QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts
Large inter-datacenter transfers are crucial for cloud service efficiency and
are increasingly used by organizations that have dedicated wide area networks
between datacenters. A recent work uses multicast forwarding trees to reduce
the bandwidth needs and improve completion times of point-to-multipoint
transfers. Using a single forwarding tree per transfer, however, leads to poor
performance because the slowest receiver dictates the completion time for all
receivers. Using multiple forwarding trees per transfer alleviates this
concern--the average receiver could finish early; however, if done naively,
bandwidth usage would also increase and it is apriori unclear how best to
partition receivers, how to construct the multiple trees and how to determine
the rate and schedule of flows on these trees. This paper presents QuickCast, a
first solution to these problems. Using simulations on real-world network
topologies, we see that QuickCast can speed up the average receiver's
completion time by as much as while only using more
bandwidth; further, the completion time for all receivers also improves by as
much as faster at high loads.Comment: [Extended Version] Accepted for presentation in IEEE INFOCOM 2018,
Honolulu, H
PRIORITIZED TASK SCHEDULING IN FOG COMPUTING
Cloud computing is an environment where virtual resources are shared among the many users over network. A user of Cloud services is billed according to pay-per-use model associated with this environment. To keep this bill to a minimum, efficient resource allocation is of great importance. To handle the many requests sent to Cloud by the clients, the tasks need to be processed according to the SLAs defined by the client. The increase in the usage of Cloud services on a daily basis has introduced delays in the transmission of requests. These delays can cause clients to wait for the response of the tasks beyond the deadline assigned. To overcome these concerns, Fog Computing is helpful as it is physically placed closer to the clients. This layer is placed between the client and the Cloud layer, and it reduces the delay in the transmission of the requests, processing and the response sent back to the client greatly. This paper discusses an algorithm which schedules tasks by calculating the priority of a task in the Fog layer. The tasks with higher priority are processed first so that the deadline is met, which makes the algorithm practical and efficient
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