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Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art
Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP
A Domain Specific Approach to High Performance Heterogeneous Computing
Users of heterogeneous computing systems face two problems: firstly, in
understanding the trade-off relationships between the observable
characteristics of their applications, such as latency and quality of the
result, and secondly, how to exploit knowledge of these characteristics to
allocate work to distributed computing platforms efficiently. A domain specific
approach addresses both of these problems. By considering a subset of
operations or functions, models of the observable characteristics or domain
metrics may be formulated in advance, and populated at run-time for task
instances. These metric models can then be used to express the allocation of
work as a constrained integer program, which can be solved using heuristics,
machine learning or Mixed Integer Linear Programming (MILP) frameworks. These
claims are illustrated using the example domain of derivatives pricing in
computational finance, with the domain metrics of workload latency or makespan
and pricing accuracy. For a large, varied workload of 128 Black-Scholes and
Heston model-based option pricing tasks, running upon a diverse array of 16
Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both
the makespan and accuracy are generally within 10% of the run-time performance.
When these models are used as inputs to machine learning and MILP-based
workload allocation approaches, a latency improvement of up to 24 and 270 times
over the heuristic approach is seen.Comment: 14 pages, preprint draft, minor revisio
Hedonic Coalition Formation for Distributed Task Allocation among Wireless Agents
Autonomous wireless agents such as unmanned aerial vehicles or mobile base
stations present a great potential for deployment in next-generation wireless
networks. While current literature has been mainly focused on the use of agents
within robotics or software applications, we propose a novel usage model for
self-organizing agents suited to wireless networks. In the proposed model, a
number of agents are required to collect data from several arbitrarily located
tasks. Each task represents a queue of packets that require collection and
subsequent wireless transmission by the agents to a central receiver. The
problem is modeled as a hedonic coalition formation game between the agents and
the tasks that interact in order to form disjoint coalitions. Each formed
coalition is modeled as a polling system consisting of a number of agents which
move between the different tasks present in the coalition, collect and transmit
the packets. Within each coalition, some agents can also take the role of a
relay for improving the packet success rate of the transmission. The proposed
algorithm allows the tasks and the agents to take distributed decisions to join
or leave a coalition, based on the achieved benefit in terms of effective
throughput, and the cost in terms of delay. As a result of these decisions, the
agents and tasks structure themselves into independent disjoint coalitions
which constitute a Nash-stable network partition. Moreover, the proposed
algorithm allows the agents and tasks to adapt the topology to environmental
changes such as the arrival/removal of tasks or the mobility of the tasks.
Simulation results show how the proposed algorithm improves the performance, in
terms of average player (agent or task) payoff, of at least 30.26% (for a
network of 5 agents with up to 25 tasks) relatively to a scheme that allocates
nearby tasks equally among agents.Comment: to appear, IEEE Transactions on Mobile Computin
Decentralized dynamic task allocation for UAVs with limited communication range
We present the Limited-range Online Routing Problem (LORP), which involves a
team of Unmanned Aerial Vehicles (UAVs) with limited communication range that
must autonomously coordinate to service task requests. We first show a general
approach to cast this dynamic problem as a sequence of decentralized task
allocation problems. Then we present two solutions both based on modeling the
allocation task as a Markov Random Field to subsequently assess decisions by
means of the decentralized Max-Sum algorithm. Our first solution assumes
independence between requests, whereas our second solution also considers the
UAVs' workloads. A thorough empirical evaluation shows that our workload-based
solution consistently outperforms current state-of-the-art methods in a wide
range of scenarios, lowering the average service time up to 16%. In the
best-case scenario there is no gap between our decentralized solution and
centralized techniques. In the worst-case scenario we manage to reduce by 25%
the gap between current decentralized and centralized techniques. Thus, our
solution becomes the method of choice for our problem
Exact and heuristic allocation of multi-kernel applications to multi-FPGA platforms
FPGA-based accelerators demonstrated high energy efficiency compared to GPUs and CPUs. However, single FPGA designs may not achieve sufficient task parallelism. In this work, we optimize the mapping of high-performance multi-kernel applications, like Convolutional Neural Networks, to multi-FPGA platforms. First, we formulate the system level optimization problem, choosing within a huge design space the parallelism and number of compute units for each kernel in the pipeline. Then we solve it using a combination of Geometric Programming, producing the optimum performance solution given resource and DRAM bandwidth constraints, and a heuristic allocator of the compute units on the FPGA cluster.Peer ReviewedPostprint (author's final draft
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