40,994 research outputs found
Adversarial Task Allocation
The problem of allocating tasks to workers is of long standing fundamental
importance. Examples of this include the classical problem of assigning
computing tasks to nodes in a distributed computing environment, as well as the
more recent problem of crowdsourcing where a broad array of tasks are slated to
be completed by human workers. Extensive research into this problem generally
addresses important issues such as uncertainty and, in crowdsourcing,
incentives. However, the problem of adversarial tampering with the task
allocation process has not received as much attention. We are concerned with a
particular adversarial setting in task allocation where an attacker may target
a specific worker in order to prevent the tasks assigned to this worker from
being completed. We consider two attack models: one in which the adversary
observes only the allocation policy (which may be randomized), and the second
in which the attacker observes the actual allocation decision. For the case
when all tasks are homogeneous, we provide polynomial-time algorithms for both
settings. When tasks are heterogeneous, however, we show the adversarial
allocation problem to be NP-Hard, and present algorithms for solving it when
the defender is restricted to assign only a single worker per task. Our
experiments show, surprisingly, that the difference between the two attack
models is minimal: deterministic allocation can achieve nearly as much utility
as randomized
Adversarial Task Assignment
The problem of assigning tasks to workers is of long-standing fundamental
importance. Examples of this include the classical problem of assigning
computing tasks to nodes in a distributed computing environment, assigning jobs
to robots, and crowdsourcing. Extensive research into this problem generally
addresses important issues such as uncertainty and incentives. However, the
problem of adversarial tampering with the task assignment process has not
received as much attention.
We are concerned with a particular adversarial setting where an attacker may
target a set of workers in order to prevent the tasks assigned to these workers
from being completed. When all tasks are homogeneous, we provide an efficient
algorithm for computing the optimal assignment. When tasks are heterogeneous,
we show that the adversarial assignment problem is NP-Hard, and present an
algorithm for solving it approximately. Our theoretical results are accompanied
by extensive experiments showing the effectiveness of our algorithms.Comment: arXiv admin note: substantial text overlap with arXiv:1709.0035
Energy-Efficient Real-Time Scheduling for Two-Type Heterogeneous Multiprocessors
We propose three novel mathematical optimization formulations that solve the
same two-type heterogeneous multiprocessor scheduling problem for a real-time
taskset with hard constraints. Our formulations are based on a global
scheduling scheme and a fluid model. The first formulation is a mixed-integer
nonlinear program, since the scheduling problem is intuitively considered as an
assignment problem. However, by changing the scheduling problem to first
determine a task workload partition and then to find the execution order of all
tasks, the computation time can be significantly reduced. Specifically, the
workload partitioning problem can be formulated as a continuous nonlinear
program for a system with continuous operating frequency, and as a continuous
linear program for a practical system with a discrete speed level set. The task
ordering problem can be solved by an algorithm with a complexity that is linear
in the total number of tasks. The work is evaluated against existing global
energy/feasibility optimal workload allocation formulations. The results
illustrate that our algorithms are both feasibility optimal and energy optimal
for both implicit and constrained deadline tasksets. Specifically, our
algorithm can achieve up to 40% energy saving for some simulated tasksets with
constrained deadlines. The benefit of our formulation compared with existing
work is that our algorithms can solve a more general class of scheduling
problems due to incorporating a scheduling dynamic model in the formulations
and allowing for a time-varying speed profile. Moreover, our algorithms can be
applied to both online and offline scheduling schemes
A Novel Hybrid Algorithm for Task Graph Scheduling
One of the important problems in multiprocessor systems is Task Graph
Scheduling. Task Graph Scheduling is an NP-Hard problem. Both learning automata
and genetic algorithms are search tools which are used for solving many NP-Hard
problems. In this paper a new hybrid method based on Genetic Algorithm and
Learning Automata is proposed. The proposed algorithm begins with an initial
population of randomly generated chromosomes and after some stages, each
chromosome maps to an automaton. Experimental results show that superiority of
the proposed algorithm over the current approaches
Heterogeneous Coded Computation across Heterogeneous Workers
Coded distributed computing framework enables large-scale machine learning
(ML) models to be trained efficiently in a distributed manner, while mitigating
the straggler effect. In this work, we consider a multi-task assignment problem
in a coded distributed computing system, where multiple masters, each with a
different matrix multiplication task, assign computation tasks to workers with
heterogeneous computing capabilities. Both dedicated and probabilistic worker
assignment models are considered, with the objective of minimizing the average
completion time of all computations. For dedicated worker assignment, greedy
algorithms are proposed and the corresponding optimal load allocation is
derived based on the Lagrange multiplier method. For probabilistic assignment,
successive convex approximation method is used to solve the non-convex
optimization problem. Simulation results show that the proposed algorithms
reduce the completion time by 80% over uncoded scheme, and 49% over an
unbalanced coded scheme.Comment: Submitted for publicatio
Improving Robustness of Heterogeneous Serverless Computing Systems Via Probabilistic Task Pruning
Cloud-based serverless computing is an increasingly popular computing
paradigm. In this paradigm, different services have diverse computing
requirements that justify deploying an inconsistently Heterogeneous Computing
(HC) system to efficiently process them. In an inconsistently HC system, each
task needed for a given service, potentially exhibits different execution times
on each type of machine. An ideal resource allocation system must be aware of
such uncertainties in execution times and be robust against them, so that
Quality of Service (QoS) requirements of users are met. This research aims to
maximize the robustness of an HC system utilized to offer a serverless
computing system, particularly when the system is oversubscribed. Our strategy
to maximize robustness is to develop a task pruning mechanism that can be added
to existing task-mapping heuristics without altering them. Pruning tasks with a
low probability of meeting their deadlines improves the likelihood of other
tasks meeting their deadlines, thereby increasing system robustness and overall
QoS. To evaluate the impact of the pruning mechanism, we examine it on various
configurations of heterogeneous and homogeneous computing systems. Evaluation
results indicate a considerable improvement (up to 35%) in the system
robustness.Comment: IPDPSW '1
Decentralized Computation Offloading and Resource Allocation in Heterogeneous Networks with Mobile Edge Computing
We consider a heterogeneous network with mobile edge computing, where a user
can offload its computation to one among multiple servers. In particular, we
minimize the system-wide computation overhead by jointly optimizing the
individual computation decisions, transmit power of the users, and computation
resource at the servers. The crux of the problem lies in the combinatorial
nature of multi-user offloading decisions, the complexity of the optimization
objective, and the existence of inter-cell interference. Then, we decompose the
underlying problem into two subproblems: i) the offloading decision, which
includes two phases of user association and subchannel assignment, and ii)
joint resource allocation, which can be further decomposed into the problems of
transmit power and computation resource allocation. To enable distributed
computation offloading, we sequentially apply a many-to-one matching game for
user association and a one-to-one matching game for subchannel assignment.
Moreover, the transmit power of offloading users is found using a bisection
method with approximate inter-cell interference, and the computation resources
allocated to offloading users is achieved via the duality approach. The
proposed algorithm is shown to converge and is stable. Finally, we provide
simulations to validate the performance of the proposed algorithm as well as
comparisons with the existing frameworks.Comment: Submitted to IEEE Journa
Energy-Aware Task Partitioning on Heterogeneous Multiprocessor Platforms
Efficient task partitioning plays a crucial role in achieving high
performance at multiprocessor plat forms. This paper addresses the problem of
energy-aware static partitioning of periodic real-time tasks on heterogeneous
multiprocessor platforms. A Particle Swarm Optimization variant based on
Min-min technique for task partitioning is proposed. The proposed approach aims
to minimize the overall energy consumption, meanwhile avoid deadline
violations. An energy-aware cost function is proposed to be considered in the
proposed approach. Extensive simulations and comparisons are conducted in order
to validate the effectiveness of the proposed technique. The achieved results
demonstrate that the proposed partitioning scheme significantly surpasses
previous approaches in terms of both number of iterations and energy savings.Comment: 8 pages, 9 figure
Multiple Workflows Scheduling in Multi-tenant Distributed Systems: A Taxonomy and Future Directions
The workflow is a general notion representing the automated processes along
with the flow of data. The automation ensures the processes being executed in
the order. Therefore, this feature attracts users from various background to
build the workflow. However, the computational requirements are enormous and
investing for a dedicated infrastructure for these workflows is not always
feasible. To cater to the broader needs, multi-tenant platforms for executing
workflows were began to be built. In this paper, we identify the problems and
challenges in the multiple workflows scheduling that adhere to the platforms.
We present a detailed taxonomy from the existing solutions on scheduling and
resource provisioning aspects followed by the survey of relevant works in this
area. We open up the problems and challenges to shove up the research on
multiple workflows scheduling in multi-tenant distributed systems.Comment: Several changes has been done based on reviewers' comments after
first round review. This is a pre-print for paper (currently under second
round review) submitted to ACM Computing Survey
MapReduce Scheduler: A 360-degree view
Undoubtedly, the MapReduce is the most powerful programming paradigm in
distributed computing. The enhancement of the MapReduce is essential and it can
lead the computing faster. Therefore, here are many scheduling algorithms to
discuss based on their characteristics. Moreover, there are many shortcoming to
discover in this field. In this article, we present the state-of-the-art
scheduling algorithm to enhance the understanding of the algorithms. The
algorithms are presented systematically such that there can be many future
possibilities in scheduling algorithm through this article. In this paper, we
provide in-depth insight on the MapReduce scheduling algorithm. In addition, we
discuss various issues of MapReduce scheduler developed for large-scale
computing as well as heterogeneous environment.Comment: Journal Articl
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