83,967 research outputs found
Joint Frequency Regulation and Economic Dispatch Using Limited Communication
We study the performance of a decentralized integral control scheme for joint
power grid frequency regulation and economic dispatch. We show that by properly
designing the controller gains, after a power flow perturbation, the control
achieves near-optimal economic dispatch while recovering the nominal frequency,
without requiring any communication. We quantify the gap between the
controllable power generation cost under the decentralized control scheme and
the optimal cost, based on the DC power flow model. Moreover, we study the
tradeoff between the cost and the convergence time, by adjusting parameters of
the control scheme.
Communication between generators reduces the convergence time. We identify
key communication links whose failures have more significant impacts on the
performance of a distributed power grid control scheme that requires
information exchange between neighbors
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
rDLB: A Novel Approach for Robust Dynamic Load Balancing of Scientific Applications with Parallel Independent Tasks
Scientific applications often contain large and computationally intensive
parallel loops. Dynamic loop self scheduling (DLS) is used to achieve a
balanced load execution of such applications on high performance computing
(HPC) systems. Large HPC systems are vulnerable to processors or node failures
and perturbations in the availability of resources. Most self-scheduling
approaches do not consider fault-tolerant scheduling or depend on failure or
perturbation detection and react by rescheduling failed tasks. In this work, a
robust dynamic load balancing (rDLB) approach is proposed for the robust self
scheduling of independent tasks. The proposed approach is proactive and does
not depend on failure or perturbation detection. The theoretical analysis of
the proposed approach shows that it is linearly scalable and its cost decrease
quadratically by increasing the system size. rDLB is integrated into an MPI DLS
library to evaluate its performance experimentally with two computationally
intensive scientific applications. Results show that rDLB enables the tolerance
of up to (P minus one) processor failures, where P is the number of processors
executing an application. In the presence of perturbations, rDLB boosted the
robustness of DLS techniques up to 30 times and decreased application execution
time up to 7 times compared to their counterparts without rDLB
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