Delay versus Stickiness Violation Trade-offs for Load Balancing in Large-Scale Data Centers

Most load balancing techniques implemented in current data centers tend to rely on a mapping from packets to server IP addresses through a hash value calculated from the flow five-tuple. The hash calculation allows extremely fast packet forwarding and provides flow `stickiness', meaning that all packets belonging to the same flow get dispatched to the same server. Unfortunately, such static hashing may not yield an optimal degree of load balancing, e.g., due to variations in server processing speeds or traffic patterns. On the other hand, dynamic schemes, such as the Join-the-Shortest-Queue (JSQ) scheme, provide a natural way to mitigate load imbalances, but at the expense of stickiness violation. In the present paper we examine the fundamental trade-off between stickiness violation and packet-level latency performance in large-scale data centers. We establish that stringent flow stickiness carries a significant performance penalty in terms of packet-level delay. Moreover, relaxing the stickiness requirement by a minuscule amount is highly effective in clipping the tail of the latency distribution. We further propose a bin-based load balancing scheme that achieves a good balance among scalability, stickiness violation and packet-level delay performance. Extensive simulation experiments corroborate the analytical results and validate the effectiveness of the bin-based load balancing scheme

Optimal Network Control in Partially-Controllable Networks

The effectiveness of many optimal network control algorithms (e.g., BackPressure) relies on the premise that all of the nodes are fully controllable. However, these algorithms may yield poor performance in a partially-controllable network where a subset of nodes are uncontrollable and use some unknown policy. Such a partially-controllable model is of increasing importance in real-world networked systems such as overlay-underlay networks. In this paper, we design optimal network control algorithms that can stabilize a partially-controllable network. We first study the scenario where uncontrollable nodes use a queue-agnostic policy, and propose a low-complexity throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances the original MaxWeight algorithm with an explicit learning of the policy used by uncontrollable nodes. Next, we investigate the scenario where uncontrollable nodes use a queue-dependent policy and the problem is formulated as an MDP with unknown queueing dynamics. We propose a new reinforcement learning algorithm, called Truncated Upper Confidence Reinforcement Learning (TUCRL), and prove that TUCRL achieves tunable three-way tradeoffs between throughput, delay and convergence rate

Survivability in Time-varying Networks

Time-varying graphs are a useful model for networks with dynamic connectivity such as vehicular networks, yet, despite their great modeling power, many important features of time-varying graphs are still poorly understood. In this paper, we study the survivability properties of time-varying networks against unpredictable interruptions. We first show that the traditional definition of survivability is not effective in time-varying networks, and propose a new survivability framework. To evaluate the survivability of time-varying networks under the new framework, we propose two metrics that are analogous to MaxFlow and MinCut in static networks. We show that some fundamental survivability-related results such as Menger's Theorem only conditionally hold in time-varying networks. Then we analyze the complexity of computing the proposed metrics and develop several approximation algorithms. Finally, we conduct trace-driven simulations to demonstrate the application of our survivability framework to the robust design of a real-world bus communication network

Effects of Different Dietary Lipid Sources on Spawning Performance, Egg and Larval Quality, and Egg Fatty Acid Composition in Tongue Sole Cynoglossus semilaevis

A 60-day feeding experiment was conducted to investigate the effects of dietary lipid sources on reproduction of Cynoglossus semilaevis. Experimental diets were formulated with similar proximate compositions but different lipid sources (6.5%): fish oil (FO), soybean oil (SO) and olive oil (OO). The results showed that the relative fecundity in group FO and OO was significantly higher than that in group SO. Group OO showed a significantly higher buoyant egg rate than group FO and SO. The hatching rate and larval survival rate at 7 days post hatching were the highest in group FO, followed by group OO, and group SO recorded the lowest values. Group FO showed significantly higher egg diameter and larval survival activity index (SAI) and significantly lower larval deformity rate compared to group SO and OO. Fatty acid compositions of eggs reflected closely those of the diets. These results showed that the olive oil supplement in diets for tongue sole positively influenced the broodstock fecundity and buoyant egg rate though fish oil resulted in the highest hatching rate and best larval quality among the tested oils. The dietary soybean oil supplement reduced the spawning performance, and egg and larval quality

Survivability of time-varying networks

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 81-83).Time-varying graphs are a useful model for networks with dynamic connectivity such as mmWave networks and vehicular networks, yet, despite their great modeling power, many important features of time-varying graphs are still poorly understood. In this thesis, we study the survivability properties of time-varying networks against unpredictable interruptions. We first show that the traditional definition of survivability is not effective in time-varying networks and propose a new survivability framework. To evaluate survivability of time-varying networks under the new framework, we propose two metrics that are analogous to MaxFlow and MinCut in static networks. We show that some fundamental survivability-related results such as Menger's Theorem only conditionally hold in timevarying networks. Then we analyze the complexity of computing the proposed metrics and develop several approximation algorithms. Finally, we conduct trace-driven simulations to demonstrate the application of our survivability framework in the robust design of a real-world bus communication network.by Qingkai Liang.S.M

Network optimization in adversarial environments

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.Cataloged from PDF version of thesis. "There are no tables included in this thesis. Please disregard the list of tables"--Disclaimer Notice page.Includes bibliographical references (pages 193-197).Stochastic models have been dominant in network optimization theory for over two decades, due to their analytical tractability. However, an increasing number of real-world networked systems exhibit complex behaviors that cannot be captured by the simple stochastic models, such as networks under malicious attacks and networks with uncontrollable nodes. In this thesis, we study efficient network control policies that can optimize network performance in different types of complex environments. First, we investigate network optimization under adversarial dynamics, where the evolution of network conditions follows some non-stationary and possibly adversarial process. Such an adversarial network dynamics model can be used to capture many real-world scenarios, such as networks under Distributed Denial-of-Service (DDoS) attacks or ad-hoc networks with unpredictable mobility. We focus on two network control problems: (1) achieving network stability, and (2) maximizing network utility subject to stability constraints. New adversarial network models are developed to characterize the adversary's behavior, and the notion of regret is used to measure network performance in adversarial environments. We provide lower bounds on the regret performance that could be achieved by any causal control policies and analyze the performance of several network control policies (e.g., MaxWeight and Drift-plus-Penalty). It is proved that these policies are throughput-optimal and achieve good utility-delay tradeoffs even under adversarial dynamics. Second, we study network optimization in a partially-controllable environment where a subset of nodes are uncontrollable and adopt a stationary but unknown control policy. Such a partially-controllable model is of increasing importance in real-world networked systems such as overlay-underlay networks and uncooperative wireless networks. We consider the problem of stabilizing a partially-controllable network. It is shown that many well-known network control algorithms (e.g., MaxWeight) may fail to stabilize the network when some nodes adopt non-stabilizing policies. We first study the scenario where uncontrollable nodes use a queue-agnostic policy and propose a low-complexity throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances the original MaxWeight algorithm with an explicit learning of the policy used by uncontrollable nodes. Next, we investigate the scenario where uncontrollable nodes use a queue-dependent policy and the problem is formulated as an MDP with unknown queueing dynamics. We propose a new reinforcement learning algorithm, called Truncated Upper Confidence Reinforcement Learning (TUCRL), and prove that TUCRL achieves tunable three-way tradeoffs between throughput, delay and convergence rate under mild conditions. Finally, we focus on network optimization with adversarial uncontrollable nodes where the sequence of actions taken by uncontrollable nodes may be non-stationary and adversarial. We first investigate the network stability problem and develop a lower bound on the total queue length that can be achieved by any causal policy. We prove that the Tracking-MaxWeight (TMW) algorithm can achieve network stability under any given sequence of actions of adversarial nodes whenever possible. Next, we study the network utility maximization problem and provide a lower bound on the utility-delay tradeoff. We develop the Tracking Drift-plus-Penalty (TDP) algorithm that achieves tunable utility-delay tradeoffs.by Qingkai Liang.Ph. D

Coflow scheduling in input-queued switches: Optimal delay scaling and algorithms

A coflow is a collection of parallel flows belonging to the same job. It has the all-or-nothing property: a coflow is not complete until the completion of all its constituent flows. In this paper, we focus on optimizing coflow-level delay, i.e., the time to complete all the flows in a coflow, in the context of an N × N input-queued switch. In particular, we develop a throughput-optimal scheduling policy that achieves the best scaling of coflow-level delay as N → ∞. We first derive lower bounds on the coflow-level delay that can be achieved by any scheduling policy. It is observed that these lower bounds critically depend on the variability of flow sizes. Then we analyze the coflow-level performance of some existing coflow-agnostic scheduling policies and show that none of them achieves provably optimal performance with respect to coflow-level delay. Finally, we propose the Coflow-Aware Batching (CAB) policy which achieves the optimal scaling of coflow-level delay under some mild assumptions.National Science Foundation (U.S.) (Grant CNS-1116209)National Science Foundation (U.S.) (Grant CNS-1617091)United States. Defense Advanced Research Projects Agency. Information Innovation Office (I20)Raytheon CompanyBBN Technologies (Contract No. HROO l l-l 5-C-0097