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Register a new userA Load Balancing Algorithm for Resource Allocation in IEEE 802.15.4e Networks
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Added by
Katina Kralevska
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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The recently created IETF 6TiSCH working group combines the high reliability and low-energy consumption of IEEE 802.15.4e Time Slotted Channel Hopping with IPv6 for industrial Internet of Things. We propose a distributed link scheduling algorithm, called Local Voting, for 6TiSCH networks that adapts the schedule to the network conditions. The algorithm tries to equalize the link load (defined as the ratio of the queue length over the number of allocated cells) through cell reallocation. Local Voting calculates the number of cells to be added or released by the 6TiSCH Operation Sublayer (6top). Compared to a representative algorithm from the literature, Local Voting provides simultaneously high reliability and low end-to-end latency while consuming significantly less energy. Its performance has been examined and compared to On-the-fly algorithm in 6TiSCH simulator by modeling an industrial environment with 50 sensors.
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Support of real-time applications that impose strict requirements on packet loss ratio and latency is an essential feature of the next generation Wi-Fi networks. Initially introduced in the 802.11ax amendment to the Wi-Fi standard, uplink OFDMA seems to be a promising solution for supported low-latency data transmission from the numerous stations to an access point. In this paper, we study how to allocate OFDMA resources in an 802.11ax network and propose an algorithm aimed at providing the delay less than one millisecond and reliability up to 99.999% as required by numerous real-time applications. We design a resource allocation algorithm and with extensive simulation, show that it decreases delays for real-time traffic by orders of magnitude, while the throughput for non-real-time traffic is reduced insignificantly.
We consider the scheduling and resource allocation problem in AP-initiated uplink OFDMA transmissions of IEEE 802.11ax networks. The uplink OFDMA resource allocation problem is known to be non-convex and difficult to solve in general. However, due to the special subcarrier allocation model of IEEE 802.11ax, the utility maximization problem involving the instantaneous rates of stations can be formulated as an assignment problem, and hence can be solved using the Hungarian method. In this paper, we address the more general problem of stochastic network utility maximization. Specifically, we maximize the utility of long-term average rates of stations subject to average rate and power constraints using Lyapunov optimization. The resulting resource allocation policies perform arbitrarily close to optimal and have polynomial time complexity. An important advantage of the proposed framework is that it can be used along with the target wake time mechanism of IEEE 802.11ax to provide guarantees on the average power consumption and/or achievable rates of stations whenever possible. Two key applications of such a design approach are power-constrained IoT networks and battery-powered sensor networks. We complement the theoretical study with computer simulations that evaluate our approach against other existing methods.
To address the rising demand for strong packet delivery guarantees in networking, we study a novel way to perform graph resource allocation. We first introduce allocation graphs, in which nodes can independently set local resource limits based on physical constraints or policy decisions. In this scenario we formalize the distributed path-allocation (PAdist) problem, which consists in allocating resources to paths considering only local on-path information -- importantly, not knowing which other paths could have an allocation -- while at the same time achieving the global property of never exceeding available resources. Our core contribution, the global myopic allocation (GMA) algorithm, is a solution to this problem. We prove that GMA can compute unconditional allocations for all paths on a graph, while never over-allocating resources. Further, we prove that GMA is Pareto optimal with respect to the allocation size, and it has linear complexity in the input size. Finally, we show with simulations that this theoretical result could be indeed applied to practical scenarios, as the resulting path allocations are large enough to fit the requirements of practically relevant applications.
This paper presents a modified proportional fairness (PF) criterion suitable for mitigating the textit{rate anomaly} problem of multirate IEEE 802.11 Wireless LANs employing the mandatory Distributed Coordination Function (DCF) option. Compared to the widely adopted assumption of saturated network, the proposed criterion can be applied to general networks whereby the contending stations are characterized by specific packet arrival rates, $lambda_s$, and transmission rates $R_d^{s}$. The throughput allocation resulting from the proposed algorithm is able to greatly increase the aggregate throughput of the DCF while ensuring fairness levels among the stations of the same order of the ones available with the classical PF criterion. Put simply, each station is allocated a throughput that depends on a suitable normalization of its packet rate, which, to some extent, measures the frequency by which the station tries to gain access to the channel. Simulation results are presented for some sample scenarios, confirming the effectiveness of the proposed criterion.
Load Balancing plays a vital role in modern data centers to distribute traffic among instances of network functions or services. State-of-the-art load balancers such as Silkroad dispatch traffic obliviously without considering the real-time utilization of service instances and therefore can lead to uneven load distribution and suboptimal performance. In this paper, we design and implement Spotlight, a scalable and distributed load balancing architecture that maintains connection-to-instance mapping consistency at the edge of data center networks. Spotlight uses a new stateful flow dispatcher which periodically polls instances load and dispatches incoming connections to instances in proportion to their available capacity. Our design utilizes distributed control plane and in-band flow dispatching and thus scales horizontally in data center networks. Through extensive flow-level simulation and packet-level experiments on a testbed, we demonstrate that compared to existing methods Spotlight distributes the traffic more efficiently and has near-optimum performance in terms of overall service utilization. Moreover, Spotlight is not sensitive to utilization polling interval and therefore can be implemented with low polling frequency to reduce the amount of control traffic. Indeed, Spotlight achieves the mentioned performance improvements using O(100ms) polling interval.
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