On the Risk-aware Connection Defragmentation in OCS-based Data-center Networks

A hybrid optical/electrical datacenter network (HOE-DCN) integrates the benefits of electrical packet switching (EPS) and optical circuit switching (OCS) for better architectural scalability. Meanwhile, as each network service usually involves multiple virtual machines (VMs) that form a virtual network (VNT), how to reconfigure the VNTs in an HOE-DCN to adapt to the dynamic network environment becomes an interesting and important problem to tackle. In this work, we optimize the procedure of VNT reconfigurations that remap VNTs to given virtual network embedding (VNE) schemes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , scheduling the required VM migrations and optical cross-connect (OXC) reconfiguration properly such that the overall VNT reconfiguration latency can be minimized. We first assume that all the VM migrations should be scheduled before the OXC reconfiguration to minimize service interruptions, and formulate a mixed integer linear programming (MILP) model to solve the scheduling problem exactly. Then, with the same assumption, we propose a time-efficient heuristic to schedule parallel VM migrations in batches such that the bandwidth competition can be significantly relieved. Finally, we divide the OXC reconfiguration into several progressive steps, and design a joint scheduling algorithm to arrange VM migrations together with the OXC reconfiguration steps.

Increasing demands for distributed machine learning (DML) have posed significant pressure on data-center networks (DCNs). This promotes the adoption of reconfigurable all-optical interconnects (AOI) in DCNs leveraging optical circuit switching (OCS) for better performance on throughput, energy efficiency, and data transfer latency. Despite their benefits, these OCS-based DCNs (ODCNs) still bear limited flexibility due to the larger switching granularity and longer reconfiguration latency of OCS. To address this issue, this work introduces in-network computing (INC) in an ODCN to realize P4INC-AOI, which can orchestrate INC and AOI to explore their mutual benefits for accelerating the training of DML jobs with less AOI reconfigurations. In the control plane of P4INC-AOI, we address the scheduling of concurrent DML jobs by formulating a mixed integer linear programming (MILP) model and proposing a time-efficient heuristic, to allocate multi-dimensional resources and configure AOI for minimizing the longest job completion time (JCT) across workloads. For the data plane, we extend existing in-network gradient aggregation schemes to accelerate DML jobs more efficiently. We first implement P4INC-AOI and verify its performance in a small-scale ODCN testbed, and further justify its effectiveness with large-scale simulations. Our experimental results demonstrate that compared with an ODCN without INC, P4INC-AOI not only cuts down AOI reconfigurations effectively but also reduces the average JCT of DML jobs in ResNet50 and VGG16 by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$46.66\%$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$56.34\%$</tex-math> </inline-formula> , respectively.

Passive Optical Network (PON) deployments have recently been aiming to combine the capacity of metro and access networks in the last mile of the Internet service provisioning. Deployment of PONs by running fiber to the premises introduces the advantage of huge capacity but at the same time, it calls for a robust design in order to avoid long service outage durations in case of network failures where survivable network design is mostly limited to the deployment budget. In this paper, we propose three mixed integer linear programming (MILP) models for various survivability policies to deploy reliable long-reach PONs under the budget limitations. Each MILP model aims to place the ONUs in optimal locations so that the covered area is maximized while availability requirements of the users are satisfied within the deployment budget. We solve the MILP models under the uniform and heterogeneous availability requirement scenarios and show that service availability and coverage introduce a trade-off so as the coverage and deployment cost do. Two out of the three survivability policies can guarantee 99.99% service availability while the third one is able to guarantee 99.999% by running the proposed MILP models. However, the first two schemes are able to cover larger area when compared to the third scheme which is the most reliable protection policy.

Purpose: The main purpose of the paper is to evaluate the impact of diverse personnel policies around personnel promotion in the design of the strategic staff plan for a public university. The strategic staff planning consists in the determination of the size and composition of the workforce for an organization.Design/methodology/approach: The staff planning is solved using a Mixed Integer Linear Programming (MILP) model. The MILP model represents the organizational structure of the university, the personnel categories and capacity decisions, the demand requirements, the required service level and budget restrictions. All these aspects are translated into a set of data, as well as the parameters and constraints building up the mathematical model for optimization. The required data for the model is adopted from a Spanish public university.Findings: The development of appropriate policies for personnel promotion can effectively reduce the number of dismissals while proposing a transition towards different preferable workforce structures in the university.Research limitations/implications: The long term staff plan for the university is solved by the MILP model considering a time horizon of 8 years. For this time horizon, the required input data is derived from current data of the university. Different scenarios are proposed considering different temporal trends for input data, such as in demand and admissible promotional ratios for workers.Originality/value: The literature review reports a lack of formalized procedures for staff planning in universities taking into account, at the same time, the regulations on hiring, dismissals, promotions and the workforce heterogeneity, all considered to optimize workforce size and composition addressing not only an economic criteria, but also the required workforce expertise and the quality in the service offered. This paper adopts a formalized procedure developed by the authors in previous works, and exploits it to assess the impact of various personnel policies in the staff planning for a particular university case, and this is the principal contribution of the paper.

Multi-tenancy is a key feature of modern data centers. It allows for the existence of multiple independent virtual infrastructures, called virtual slices, on top of the same physical infrastructure, each of them specially tailored to the tenants' needs. In such a scenario, an optimal mapping of the virtual slices plays a capital role toward an efficient utilization of the data center network resources, potentially saving costs for the data center owner. However, due to the increasing trend of bringing optics to data center networks, specific virtual slice mapping mechanisms accounting for the particularities of the optical medium (e.g., wavelength continuity constraint) have to be investigated. For this, we present an integer linear programming (ILP) model for optimally mapping a set of virtual slices from different tenants in a hybrid optical circuit switching (OCS)/optical packet switching (OPS) data center network with the aim to minimize the necessary optical transponders to be equipped in the network. Additionally, we also present a lightweight heuristic for the cases in which the ILP model scalability is compromised. The benefits of the proposals are highlighted by benchmarking them against a pure OCS solution through extensive simulations.

To support the selection of food items for FFQs in such a way that the amount of information on all relevant nutrients is maximised while the food list is as short as possible. Selection of the most informative food items to be included in FFQs was modelled as a Mixed Integer Linear Programming (MILP) model. The methodology was demonstrated for an FFQ with interest in energy, total protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, mono- and disaccharides, dietary fibre and potassium. The food lists generated by the MILP model have good performance in terms of length, coverage and R 2 (explained variance) of all nutrients. MILP-generated food lists were 32-40 % shorter than a benchmark food list, whereas their quality in terms of R 2 was similar to that of the benchmark. The results suggest that the MILP model makes the selection process faster, more standardised and transparent, and is especially helpful in coping with multiple nutrients. The complexity of the method does not increase with increasing number of nutrients. The generated food lists appear either shorter or provide more information than a food list generated without the MILP model.

Mixed integer linear programming for the design of solar thermal energy systems with short-term storage

Modeling side slopes in vertical alignment resource road construction using convex optimization

Cellular manufacturing systems (CMS) are production systems that typically comprise a number of manufacturing cells served by a centralized material handling system. Designing such systems includes three major decisions; cell formation (CF), group layout (GL), and group scheduling (GS). Traditionally, these three decisions have been dealt with separately, which has usually lead to less than optimal system performance. In this paper, a new mixed integer linear programming (MILP) model is proposed for the integrated CF, GL and GS problem, to efficiently design and operate CMSs. The model solves the integrated problem, taking into consideration intercellular and intracellular transportation times to determine the optimal cell formation, layout of machines and schedule of parts on the machines, simultaneously. Sequence-dependent set up times are also considered in the model. The performance of the model is tested by solving problems previously introduced in the literature considering two objectives; minimizing the makespan or minimizing the mean flow time in the system. The results show that the proposed model is efficient in solving small to medium-sized problems.

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

The increasing level of customer awareness and application of environmental laws by governments, on the one hand, and the increasing number of competitors, on the other hand, has obliged industry owners to include green activities in the design of the supply chain network. Green activities include any type of action that reduces environmental degradation. Hence, this study seeks to develop a bi-objective, mixed integer linear programming (MILP) model for designing a green supply chain network. In the proposed model, the minimisation of costs and detrimental environmental effects are discussed. LP metric method is used to solve the bi-objective model and the proposed model is run by using simulated data in small and medium sizes in GAMS software. Finally, the model sensitivity analysis is measured in order to evaluate the validity and performance of the proposed model by using demand-driven scenarios. The results of this model indicate the effectiveness of the proposed model.

Mixed Integer Linear Programming Model for Vehicle Routing Problem for Hazardous Materials Transportation

This paper presents a mixed integer linear programming (MILP) model to operate a dual source heat pump (DSHP) optimally. The DSHP draws environmental energy from an underground ice storage tank (IST) or wind infrared sensitive collectors (WISC). WISC are further used to regenerate the IST. Thermal energy storage (TES) stores the thermal output of the DSHP. A single-objective optimisation approach is applied to minimise the operational cost of the DSHP over a scheduling horizon of 24h. The developed framework is tested on various days, which are representative for each season of the year. Furthermore, it is investigated how the variable electricity price market data influence the dynamic behaviour of the DSHP.

In this paper, we investigate power-efficient directed preconfigured cycles (p-Cycles) for asymmetric traffic protection in elastic optical networks (EONs) against single link failure. Owing to the advantage of distinguishing traffic amount in two directions, directed p-cycles consume low power by allocating different spectrum slots and modulation formats for each direction. A mixed integer linear programming (MILP) model is formulated to minimize total power consumption, which takes into account directed cycle generation, spectrum allocation, modulation adaptation, and protection capacity. To increase the scalability, the MILP model is decomposed, and a two-step approach is proposed: improved cycle enumeration and a simplified integer linear programming model. Extensive simulations are performed to study the power consumption of p-cycles under different traffic patterns in terms of traffic asymmetry (TASY), anycast ratio (AR), and the number of data centers (DCs). The results strongly demonstrate that directed p-cycles obtain significant power savings for protecting asymmetric traffic in EONs. The power savings rise up to 46.91% and 36.38% compared with undirected p-cycles as the TASY and AR increase, respectively. Moreover, the directed p-cycles achieve valuable power savings (up to 46.1%) with the introduction of DCs while the amount of power savings does not depend on the number of DCs.

An improved MILP model for scheduling crude oil unloading, storage and processing