Trace-driven Simulation Study Research Articles

Load-optimization in Reconfigurable Data-center Networks: Algorithms and Complexity of Flow Routing

Emerging reconfigurable data centers introduce unprecedented flexibility in how the physical layer can be programmed to adapt to current traffic demands. These reconfigurable topologies are commonly hybrid, consisting of static and reconfigurable links, enabled by e.g., an Optical Circuit Switch (OCS) connected to top-of-rack switches in Clos networks. Even though prior work has showcased the practical benefits of hybrid networks, several crucial performance aspects are not well understood. For example, many systems enforce artificial segregation of the hybrid network parts, leaving money on the table. In this article, we study the algorithmic problem of how to jointly optimize topology and routing in reconfigurable data centers, in order to optimize a most fundamental metric, maximum link load. The complexity of reconfiguration mechanisms in this space is unexplored at large, especially for the following cross-layer network-design problem: given a hybrid network and a traffic matrix, jointly design the physical layer and the flow routing in order to minimize the maximum link load. We chart the corresponding algorithmic landscape in our work, investigating both un-/splittable flows and (non-)segregated routing policies. A topological complexity classification of the problem reveals NP-hardness in general for network topologies that are trees of depth at least two, in contrast to the tractability on trees of depth one. We moreover prove that the problem is not submodular for all these routing policies, even in multi-layer trees. However, networks that can be abstracted by a single packet switch (e.g., nonblocking Fat-Tree topologies) can be optimized efficiently, and we present optimal polynomial-time algorithms accordingly. We complement our theoretical results with trace-driven simulation studies, where our algorithms can significantly improve the network load in comparison to the state-of-the-art.

Distributed Self-Adjusting Tree Networks

The performance of many data-centric cloud applications critically depends on the performance of the underlying datacenter network. Reconfigurable optical technologies have recently introduced a novel opportunity to improve datacenter network performance, by allowing to dynamically adjust the network topology according to the demand. However, the vision of self-adjusting networks raises the fundamental question how such networks can be efficiently operated in a scalable and distributed manner. This article presents <inline-formula><tex-math notation="LaTeX">$DiSplayNet$</tex-math></inline-formula> , the first fully distributed self-adjusting network. <inline-formula><tex-math notation="LaTeX">$DiSplayNet$</tex-math></inline-formula> relies on algorithms that perform decentralized and concurrent topological adjustments to account for changes in the demand. We propose two natural metrics to evaluate the performance of distributed self-adjusting networks, the <i>amortized work</i> (the cost of routing on and adjusting the network) and the <i>makespan</i> (the time it takes to serve a set of communication requests). We present a rigorous formal analysis of the work and makespan of <inline-formula><tex-math notation="LaTeX">$DiSplayNet$</tex-math></inline-formula> , which can be seen as an interesting generalization of analyses known from sequential self-adjusting datastructures. We complement our theoretical contribution with an extensive trace-driven simulation study, shedding light on the opportunities and limitations of leveraging spatial and temporal locality and concurrency in self-adjusting networks.

Load-Optimization in Reconfigurable Networks

Emerging reconfigurable data centers introduce the unprecedented flexibility in how the physical layer can be programmed to adapt to current traffic demands. These reconfigurable topologies are commonly hybrid, consisting of static and reconfigurable links, enabled by e.g. an Optical Circuit Switch (OCS) connected to top-of-rack switches in Clos networks. Even though prior work has showcased the practical benefits of hybrid networks, several crucial performance aspects are not well understood. In this paper, we study the algorithmic problem of how to jointly optimize topology and routing in reconfigurable data centers with a known traffic matrix, in order to optimize a most fundamental metric, maximum link load. We chart the corresponding algorithmic landscape by investigating both un-/splittable flows and (non-)segregated routing policies. We moreover prove that the problem is not submodular for all these routing policies, even in multi-layer trees, where a topological complexity classification of the problem reveals that already trees of depth two are intractable. However, networks that can be abstracted by a single packet switch (e.g., nonblocking Fat-Tree topologies) can be optimized efficiently, and we present optimal polynomialtime algorithms accordingly. We complement our theoretical results with trace-driven simulation studies, where our algorithms can significantly improve the network load in comparison to the state of the art.

Velocity Optimization of Pure Electric Vehicles with Traffic Dynamics and Driving Safety Considerations

As Electric Vehicles (EVs) become increasingly popular, their battery-related problems (e.g., short driving range and heavy battery weight) must be resolved as soon as possible. Velocity optimization of EVs to minimize energy consumption in driving is an effective alternative to handle these problems. However, previous velocity optimization methods assume that vehicles will pass through traffic lights immediately at green traffic signals. Actually, a vehicle may still experience a delay to pass a green traffic light due to a vehicle waiting queue in front of the traffic light. Also, as velocity optimization is for individual vehicles, previous methods cannot avoid rear-end collisions. That is, a vehicle following its optimal velocity profile may experience rear-end collisions with its frontal vehicle on the road. In this article, for the first time, we propose a velocity optimization system that enables EVs to immediately pass green traffic lights without delay and to avoid rear-end collisions to ensure driving safety when EVs follow optimal velocity profiles on the road. We collected real driving data on road sections of US-25 highway (with two driving lanes in each direction and relatively low traffic volume) to conduct extensive trace-driven simulation studies. Results show that our velocity optimization system reduces energy consumption by up to 17.5% compared with real driving patterns without increasing trip time. Also, it helps EVs to avoid possible collisions compared with existing collision avoidance methods.

Truthful online double auction based dynamic resource provisioning for multi-objective trade-offs in IaaS clouds.

Auction designs have recently been adopted for static and dynamic resource provisioning in IaaS clouds, such as Microsoft Azure and Amazon EC2. However, the existing mechanisms are mostly restricted to simple auctions, single-objective, offline setting, one-sided interactions either among cloud users or cloud service providers (CSPs), and possible misreports of cloud user’s private information. This paper proposes a more realistic scenario of online auctioning for IaaS clouds, with the unique characteristics of elasticity for time-varying arrival of cloud user requests under the time-based server maintenance in cloud data centers. We propose an online truthful double auction technique for balancing the multi-objective trade-offs between energy, revenue, and performance in IaaS clouds, consisting of a weighted bipartite matching based winning-bid determination algorithm for resource allocation and a Vickrey–Clarke–Groves (VCG) driven algorithm for payment calculation of winning bids. Through rigorous theoretical analysis and extensive trace-driven simulation studies exploiting Google cluster workload traces, we demonstrate that our mechanism significantly improves the performance while promising truthfulness, heterogeneity, economic efficiency, individual rationality, and has a polynomial-time computational complexity.

CloudHeat

Datacenters are major energy consumers and dissipate an enormous amount of waste heat. Simple outdoor discharging of datacenter heat is energy-consuming and environmentally unfriendly. By harvesting datacenter waste heat and selling to the district heating system (DHS), both energy cost compensation and environment protection can be achieved. To realize such benefits in practice, an efficient market mechanism is required to incentivize the participation of datacenters. This work proposes CloudHeat, an online reverse auction mechanism for the DHS to solicit heat bids from datacenters. To minimize long-term social operational cost of the DHS and the datacenters, we apply a RFHC approach for decomposing the long-term problem into a series of one-round auctions, guaranteeing a small loss in competitive ratio. The one-round optimization is still NP-hard, and we employ a randomized auction framework to simultaneously guarantee truthfulness, polynomial running time, and an approximation ratio of 2. The performance of CloudHeat is validated through theoretical analysis and trace-driven simulation studies.

A Shapley-Value Mechanism for Bandwidth On Demand between Datacenters

Recent studies in cloud resource allocation and pricing have focused on computing and storage resources but not network bandwidth. Cloud users nowadays customarily deploy services across multiple geo-distributed datacenters, with significant inter-datacenter traffic generated, paid by cloud providers to ISPs. An effective bandwidth allocation and charging mechanism is needed between the cloud provider and the cloud users. Existing volume based static charging schemes lack market efficiency. This work presents the first dynamic pricing mechanism for inter-datacenter on-demand bandwidth, via a Shapley value based auction. Our auction is expressive enough to accept bids as a flat bandwidth rate plus a time duration, or a data volume with a transfer deadline. We start with an offline auction, design an optimal end-to-end traffic scheduling approach, and exploit the Shapley value in computing payments. Our auction is truthful, individual rational, budget balanced and approximately efficient in social welfare. An online version of the auction follows, where decisions are made instantly upon the arrival of each user's realtime transmission demand. We propose an efficient online traffic scheduling algorithm, and approximate the offline Shapley value based payments on the fly. We validate our mechanism design with solid theoretical analysis, as well as trace-driven simulation studies.

Effective opportunistic dissemination of spatio-temporal contents in mobile environments

Dissemination of spatio-temporally valid content from content providers to consumers is critical in certain application contexts as data items could lose their validity across time and space. Content sharing in challenged opportunistic environments remains a research challenge as existing solutions fail to exploit dissemination patterns across spatio-temporal limits. In this paper, we propose spatio-temporal reachability graphs to depict reachability of time- and space-sensitive content in opportunistic mobile environments. Furthermore, we develop an analytical framework to estimate content distribution in such environments and validate its feasibility over long-term datasets. We perform extensive trace-driven simulation studies to determine content dissemination properties of environments with known mobility patterns. The analytical framework estimates dissemination ratio, optimizes parameter setting, and tests transmission capacities of opportunistic environments. Proposed scheme is useful to content providers as well as receivers.

Online Auctions in IaaS Clouds: Welfare and Profit Maximization With Server Costs

Auction design has recently been studied for dynamic resource bundling and virtual machine (VM) provisioning in IaaS clouds, but is mostly restricted to one-shot or offline setting. This paper targets a more realistic case of online VM auction design, where: 1) cloud users bid for resources into the future to assemble customized VMs with desired occupation durations, possibly located in different data centers; 2) the cloud provider dynamically packs multiple types of resources on heterogeneous physical machines (servers) into the requested VMs; 3) the operational costs of servers are considered in resource allocation; and 4) both social welfare and the cloud provider’s net profit are to be maximized over the system running span. We design truthful, polynomial time auctions to achieve social welfare maximization and/or the provider’s profit maximization with good competitive ratios. Our mechanisms consist of two main modules: 1) an online primal-dual optimization framework for VM allocation to maximize the social welfare with server costs, and for revealing the payments through the dual variables to guarantee truthfulness and 2) a randomized reduction algorithm to convert the social welfare maximizing auctions to ones that provide a maximal expected profit for the provider, with competitive ratios comparable to those for social welfare. We adopt a new application of Fenchel duality in our primal-dual framework, which provides richer structures for convex programs than the commonly used Lagrangian duality, and our optimization framework is general and expressive enough to handle various convex server cost functions. The efficacy of the online auctions is validated through careful theoretical analysis and trace-driven simulation studies.

Online Stochastic Buy-Sell Mechanism for VNF Chains in the NFV Market

With the recent advent of network functions virtualization (NFV), enterprises and businesses are looking into network service provisioning through the service chains of virtual network functions (VNFs), instead of relying on dedicated hardware middleboxes. Accompanying this trend, an NFV market is emerging, where NFV service providers create VNF instances, assemble VNF service chains, and sell them for the use of customers, using resources (computing, bandwidth) that they own or rent from other resource suppliers. Efficient service chain provisioning and pricing mechanisms are still missing, to charge assembled service chains according to demand and the supply of resources at any time. We propose an online stochastic auction mechanism for on-demand service chain provisioning and pricing at an NFV provider. Our auction takes in buy bids for service chains from multiple customers and sell bids from various resource suppliers to supplement the NFV provider’s geo-distributed resource pool, with resource occupation/contribution durations. We extend online primal-dual optimization framework for handling both buyers and sellers, with a new competitive analysis. The online mechanism maximizes the expected social welfare of the NFV ecosystem (the NFV provider, customers and resource suppliers) with a good competitive ratio as compared with the expected offline optimal social welfare, while guaranteeing truthfulness in bidding, individual rationality for both buyers and sellers, and polynomial time for computation. We evaluate our mechanism through trace-driven simulation studies, and demonstrate a close-to-offline-optimal performance in expected social welfare under realistic settings.

An Online Auction Framework for Dynamic Resource Provisioning in Cloud Computing

Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and allocating resources in cloud computing. This work, to the authors’ knowledge, represents the first online combinatorial auction designed for the cloud computing paradigm, which is general and expressive enough to both: 1) optimize system efficiency across the temporal domain instead of at an isolated time point; and 2) model dynamic provisioning of heterogeneous virtual machine (VM) types in practice. The final result is an online auction framework that is truthful, computationally efficient, and guarantees a competitive ratio $\approx 3.30$ in social welfare in typical scenarios. The framework consists of three main steps: 1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with a small additive loss in competitive ratio; 2) a randomized subframework that applies primal-dual optimization for translating a centralized cooperative social welfare approximation algorithm into an auction mechanism, retaining the competitive ratio while adding truthfulness; and 3) a primal-dual algorithm for approximating the one-shot optimization with a ratio close to $e$ . We also propose two extensions: 1) a binary search algorithm that improves the average-case performance; 2) an improvement to the online auction framework when a minimum budget spending fraction is guaranteed, which produces a better competitive ratio. The efficacy of the online auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework can be instructive in auction design for other related problems .

Online Auctions in IaaS Clouds

Auction design has recently been studied for dynamic resource bundling and VM provisioning in IaaS clouds, but is mostly restricted to the one-shot or offline setting. This work targets a more realistic case of online VM auction design, where: (i) cloud users bid for resources into the future to assemble customized VMs with desired occupation durations; (ii) the cloud provider dynamically packs multiple types of resources on heterogeneous physical machines (servers) into the requested VMs; (iii) the operational costs of servers are considered in resource allocation; (iv) both social welfare and the cloud provider's net profit are to be maximized over the system running span. We design truthful, polynomial time auctions to achieve social welfare maximization and/or the provider's profit maximization with good competitive ratios. Our mechanisms consist of two main modules: (1) an online primal-dual optimization framework for VM allocation to maximize the social welfare with server costs, and for revealing the payments through the dual variables to guarantee truthfulness; and (2) a randomized reduction algorithm to convert the social welfare maximizing auctions to ones that provide a maximal expected profit for the provider, with competitive ratios comparable to those for social welfare. We adopt a new application of Fenchel duality in our primal-dual framework, which provides richer structures for convex programs than the commonly used Lagrangian duality, and our optimization framework is general and expressive enough to handle various convex server cost functions. The efficacy of the online auctions is validated through careful theoretical analysis and trace-driven simulation studies.

An online auction framework for dynamic resource provisioning in cloud computing

Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and resource allocation in cloud computing. This work, to the authors' knowledge, represents the first online combinatorial auction designed in the cloud computing paradigm, which is general and expressive enough to both (a) optimize system efficiency across the temporal domain instead of at an isolated time point, and (b) model dynamic provisioning of heterogeneous Virtual Machine (VM) types in practice. The final result is an online auction framework that is truthful, computationally efficient, and guarantees a competitive ratio ~ e + 1 over e -1 ~ 3.30 in social welfare in typical scenarios. The framework consists of three main steps: (1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with an additive loss of 1 over e -1 in competitive ratio, (2) a randomized auction sub-framework that applies primal-dual optimization for translating a centralized co-operative social welfare approximation algorithm into an auction mechanism, retaining a similar approximation ratio while adding truthfulness, and (3) a primal-dual update plus dual fitting algorithm for approximating the one-shot optimization with a ratio λ close to e. The efficacy of the online auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework, as well as its three independent modules, can be instructive in auction design for other related problems.

Mobile Data Offloading through Opportunistic Communications and Social Participation

3G networks are currently overloaded, due to the increasing popularity of various applications for smartphones. Offloading mobile data traffic through opportunistic communications is a promising solution to partially solve this problem, because there is almost no monetary cost for it. We propose to exploit opportunistic communications to facilitate information dissemination in the emerging Mobile Social Networks (MoSoNets) and thus reduce the amount of mobile data traffic. As a case study, we investigate the target-set selection problem for information delivery. In particular, we study how to select the target set with only k users, such that we can minimize the mobile data traffic over cellular networks. We propose three algorithms, called Greedy, Heuristic, and Random, for this problem and evaluate their performance through an extensive trace-driven simulation study. Our simulation results verify the efficiency of these algorithms for both synthetic and real-world mobility traces. For example, the Heuristic algorithm can offload mobile data traffic by up to 73.66 percent for a real-world mobility trace. Moreover, to investigate the feasibility of opportunistic communications for mobile phones, we implement a proof-of-concept prototype, called Opp-off, on Nokia N900 smartphones, which utilizes their Bluetooth interface for device/service discovery and content transfer.

Correct router interface modeling (abstracts only)

The aim of this paper is to determine how to model router interface in order to accurately predict packet drops. There is an enormous amount of research on traffic models reported, however, a model of router interface has not gained proper consideration yet. Our experiments reveal that an incorrect model of the router interface can result in a significant disparity between drop probabilities measured on a physical interface and derived from a trace-driven simulation study. In this paper an accurate model of the router interface (Cisco IOS-based routers with a non-distributed architecture) was presented.

Online availability upgrades for parity-based RAIDs through supplementary parity augmentations

In this article, we propose a simple but powerful online availability upgrade mechanism, S upplementary P arity A ugmentations( SPA ), to address the availability issue in parity-based RAID systems. The basic idea of SPA is to store and update the supplementary parity units on one or a few newly augmented spare disks for online RAID systems in the operational mode, thus achieving the goals of improving the reconstruction performance while tolerating multiple disk failures and latent sector errors simultaneously. By applying the exclusive OR operations appropriately among supplementary parity, full parity, and data units, SPA can reconstruct the data on the failed disks with a fraction of the original overhead that is proportional to the supplementary parity coverage, thus significantly reducing the overhead of data regeneration and decreasing recovery time in parity-based RAID systems. Our extensive trace-driven simulation study shows that SPA can significantly improve the reconstruction performance of the RAID5 and RAID5+0 systems, at an acceptable performance overhead imposed in the operational mode. Moreover, our reliability analytical modeling and sequential Monte-Carlo simulation demonstrate that SPA is consistently more than double the MTTDL of the RAID5 system and improves the reliability of the RAID5+0 system noticeably.

A peer-to-peer IO buffering service based on RAM-grid

The performance of IO-intensive applications is determined by the hit ratio of local disk cache and the IO latency of missed disk accesses. To improve the IO performance, in this paper we propose PIB, a peer-to-peer IO buffering service based on RAM-grid (a new grid system aiming at memory resource sharing in WAN). The PIB service acts as a two-level disk cache, which buffers obsolete blocks in idle nodes on the internet for IO-intensive applications. It reduces the IO latency of missed disk accesses by means of the speed advantage of network over disks, and improves the hit ratio of local cache based on accurate identification of IO patterns. The effectiveness of our proposals is demonstrated through the trace driven simulation studies.

Optimal Routing for Wireless Mesh Networks With Dynamic Traffic Demand

Wireless mesh networks have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broad-band Internet access. Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best routing solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. This paper presents an integrated framework for wireless mesh network routing under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By studying the traces collected at wireless access points, we first present a traffic estimation method which predicts future traffic demand based on its historical data using time-series analysis. This method provides not only the mean value of the future traffic demand estimation but also its statistical distribution. We further investigate the optimal routing strategies for wireless mesh network which take these two forms of traffic demand estimations as inputs. The goal is to balance the traffic load so that minimum congestion will be incurred. This routing objective could be transformed into the throughput optimization problem where the throughput of aggregated flows is maximized subject to fairness constraints that are weighted by the traffic demands. Based on linear programming, we present two routing algorithms which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate the traffic dynamics in mesh network routing.

Retransmission schemes for streaming internet multimedia

This paper presents a trace-driven simulation study of two classes of retransmission timeout (RTO) estimators in the context of real-time streaming over the Internet. We explore the viability of employing retransmission timeouts in NACK-based (i.e., rate-based) streaming applications to support multiple retransmission attempts per lost packet. The first part of our simulation is based on trace data collected during a number of real-time streaming tests between dialup clients in all 50 states in the U.S. (including 653 major U.S. cities) and a backbone video server. The second part of the study is based on streaming tests over DSL and ISDN access links. First, we define a generic performance measure for assessing the accuracy of hypothetical RTO estimators based on the samples of the round-trip delay (RTT) recorded in the trace data. Second, using this performance measure, we evaluate the class of TCP-like estimators and find the optimal estimator given our performance measure. Third, we introduce a new class of estimators based on delay jitter and show that they significantly outperform TCP-like estimators in NACK-based applications with low-frequency RTT sampling. Finally, we show that high-frequency sampling of the RTT completely changes the situation and makes the class of TCP-like estimators as accurate as the class of delay-jitter estimators.

A trace-driven simulation study of two-level cache systems

This paper presents a trace-driven simulation study to examine the performance of 2-level cache system using different combinations of replacement policies. The replacement schemes considered are: LRU-LRU, LRU-FIFO, FIFO-LRU and FIFO-FIFO. The address streams that drive the simulator were collected by a SUN SPARC-2 workstation running three different application programs: Fast Fourier Transform (FFT), Binary Search and Quick Sort application programs. The software tracing technique was used to collect address traces. It was found that the LRU-LRU replacement combination has the best performance followed by LRU-FIFO, FIFO-LRU and FIFO-FIFO, respectively. The simulation results have shown that for a fixed total memory size of the two caches, the larger the level-1 memory, the smaller the total effective access time, t eff, for the entire memory hierarchy. Finally, the performance of the two-level cache system was found to be more sensitive to the block(line)size variation than the number of sets variation.