# European Institute for Statistics, Probability, Stochastic Operations Research and their Applications

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June 1-5, 2015

SCHEDULING UNDER UNCERTAINTY

 SUMMARY REGISTRATION SPEAKERS PROGRAMME ABSTRACTS

SUMMARY

The workshop's goal is to bring together and promote the dialogue and collaboration between researchers in two different communities:
the stochastic scheduling and networking community, and the worst-case approximation scheduling community.
Both these communities consider mathematical and algorithmic problems motivated by resource allocation, scheduling shared resources and load balancing, routing and speed scaling in large-scale systems.
However, the specific problems studied and the techniques used to address them are quite different.
The workshop will bring together experts in both these areas, and especially researchers who span both these communities, and foster links by exploring problem areas of mutual interest and encouraging lively interaction and discussions.
In addition to the invited participants, we also expect a broad attendance of local Dutch researchers and students from Europe.

The workshop will take place at EURANDOM, Eindhoven University of Technology on June 1-5, followed back-to-back by the 12th Workshop on Models and Algorithms for Planning and Scheduling Problems
(MAPSP 2015), which will be held in La Roche-en-Ardenne during the week of June 8-12.

## ORGANISERS

 Nikhil Bansal TU Eindhoven Sem Borst TU Eindhoven Leen Stougie VU Amsterdam & CWI Gerhard Woeginger TU Eindhoven

SPEAKERS

Keynote/tutorial speakers:

 Kamesh Munagala Duke University Kirk Pruhs University of Pittsburgh R. Srikant UIUC Jean Walrand UC Berkeley

 Matthew Andrews Alcatel-Lucent (Bell Labs) Urtzi Ayesta CNRS-LAAS Yossi Azar Tel-Aviv Marko Boon TU Eindhoven Christoph Dürr LIP6, Paris Leah Epstein University of Haifa Asaf Levin Technion Nicole Megow TU Berlin Rolf Möhring TU Berlin Mike Pinedo NYU Ramandeep Randhawa USC Sebastian Stiller TU Berlin Sasha Stolyar LeHigh Andreas Wiese Max Planck Institute Informatik Kuang Xu Microsoft Research-Inria / Stanford Yuan Zhong Columbia

PROGRAMME

MONDAY June 1

 10.00 - 10.35 Registration 10.35 - 10.45 Opening Remco van der Hofstad Welcome 10.45 - 11.30 Regular lecture 1 Rolf Möhring Stochastic Scheduling: History and Challenges 11.30 - 11.45 Break 11.45 - 12.30 Regular lecture 2 Mike Pinedo Scheduling Customer Arrivals with Overbooking in Service Systems with No-Shows 12.30 - 14.00 Lunch 14.00 - 14.45 Tutorial 1a Kirk Pruhs An Introduction to Competitive Analysis in Online Scheduling 14.45 - 15.00 Break 15.00 - 15.45 Regular lecture 3 Yossi Azar Recent results on scheduling with deadlines 15.45 - 16.00 Break 16.00 - 16.45 Regular lecture 4 Ramandeep Randhawa Scheduling Homogeneous Impatient Customers

TUESDAY June 2

 09.45 - 10.30 Tutorial 1b Kirk Pruhs Competitive Analysis in Online Scheduling using Potential Functions and Dual Fitting 10.30 - 10.45 Break 10.45 - 11.30 Regular lecture 5 Nicole Megow Online Resource Minimization 11.30 - 11.45 Break 11.45 - 12.30 Regular lecture 6 Alexander Stolyar Pull-based load distribution in large-scale heterogeneous service systems 12.30 - 14.00 Lunch 14.00 - 14.45 Tutorial 2a R. Srikant Performance Analysis of Scheduling Algorithms for Switches and Data Centers (Part I) 14.45 - 15.00 Break 15.00 - 15.45 Regular lecture 7 Asaf Levin On Adaptivity in variants of the stochastic knapsack problem 15.45 - 16.00 Break 16.00 - 16.45 Regular lecture 8 Urtzi Ayesta Non-Intrusive Scheduling of TCP Flows 18.30 - Conference dinner

WEDNESDAY June 3

 10.30 - 11.15 Tutorial 2b R. Srikant Performance Analysis of Scheduling Algorithms for Switches and Data Centers (Part II) 11.15 - 11.30 Break 11.30 - 12.15 Regular lecture 9 Sebastian Stiller How to order a waiting list? 12.15 - 14.00 Lunch 14.00 - 14.45 Tutorial 3a Kamesh Munagala Prophet Inequalities and Stochastic Optimization (Part I) 14.45 - 15.00 Break 15.00 - 15.45 Regular lecture 10 Gregor Brandt (ORTEC) Why methods for handling uncertainty are often not used 15.45 - 16.00 Break 16.00 - 16.45 5-minute Presentations

THURSDAY June 4

 09.45 - 10.30 Tutorial 3b Kamesh Munagala Prophet Inequalities and Stochastic Optimization (Part II) 10.30 - 10.45 Break 10.45 - 11.30 Regular lecture 11 Andreas Wiese Approximating Storage Allocation Problems as Good as Their Siblings 11.30 - 11.45 Break 11.45 - 12.30 Regular lecture 12 Matthew Andrews Wireless Scheduling: What Difference Do Walls Make? 12.30 - 14.00 Lunch 14.00 - 14.45 Tutorial 4 Jean Walrand Maximum Throughput Scheduling 14.45 - 15.00 Break 15.00 - 15.45 Regular lecture 13 Marko Boon A Design Heuristic for Skill Based Parallel Service Systems 15.45 - 16.00 Break 16.00 - 16.45 Regular lecture 14 Christoph Dürr Online Problems with advice

FRIDAY June 5

 09.45 - 10.30 Regular Lecture 15 Kuang Xu Capacity of Information Processing Systems 10.30 - 10.45 Break 10.45 - 11.30 Regular lecture 16 Leah Epstein Rent or Buy problems with a Fixed Time Horizon and Other Online Scheduling Problems with Rejection 11.30 - 11.45 Break 11.45 - 12.30 Regular lecture 17 Zhong Yuan Efficient coflow scheduling in data center networks 12.30 - 13.30 Lunch

Tip for the Friday evening......see: Amsterdam ArenA

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ABSTRACTS

Matthew Andrews

Wireless Scheduling: What Difference Do Walls Make?

In this talk we shall pose a number of questions connected to scheduling in high-capacity wireless networks. For example, what is the capacity loss if small cells have to "self-backhaul" their traffic to macro cells? hat is the most effective optimization paradigm for allocating radio resources among small cells? For each question we shall look at how the results will change if the propagation conditions have many discontinuities due to walls.

Urtzi Ayesta

Non-Intrusive Scheduling of TCP Flows

We investigate how to build a non-intrusive scheduled TCP. For the flows of a given  origin-destination pair, the objective is to schedule their TCP segments (according to some desired criteria) without modifying the network bandwidth-share used by these flows, which in turn ensures friendliness with respect to the rest of the network. We show that in order for a scheduling algorithm to be strictly non-intrusive, a sufficient and necessary condition is that the sender's and receiver's buffers are infinite. We then show that, under the additional condition that segments are neither lost or reordered, the number of active TCP flows can be minimized by size-based schedulers, and we propose a new scheduler FAIR, which  guarantees that the transfer time of every TCP flow for the origin-destination pair is reduced. We develop  SCHED_TCP, a user space implementation of our scheme in order to evaluate its performance on the Internet. Our experiments illustrate the non-intrusive property of  SCHED_TCP, and also illustrate that the performance gain with SCHED_TCP can be considerable. Our scheme is scalable, and it could be incrementally deployed on the Internet improving the user experience on every origin-destination pair. The main application domain of our approach correspond to situations in which there are many concurrent TCP connections within the same origin-destination pair, this might happen as a consequence of HTTP 1.1, Web 2.0 applications using AJAX (Google Maps etc.), Split TCP, Parallel Sockets, and also with the use of ChromeBook's where the user accesses to all services through the same back-end server infrastructure.
(joint work with L. Bertaux and D. Carvin).

PRESENTATION

Yossi Azar

Recent results on scheduling with deadlines

We discuss recent results for scheduling with deadlines of jobs released over time (with processing time and value). First we show a logarithmic lower bound for the competitive ratio of any randomized preemptive online algorithm for maximizing the value of completed jobs. This improves quadratically the Canetti and Irani (STOC 95) previous lower bound and matches the simple known upper bounds. The proof is surprisingly straightforward and and closes a gap which was supposedly open for 20 years. The lower bound can be circumvented by assuming deadline slackness. We consider a committed model, in which accepted jobs must be fully completed before their deadline. We show an interesting phase transition for a single server at slackness $s=4$. For any $s>4$ we develop a constant competitive committed algorithm and for any $s<4$ we prove that the competitive ratio of any such algorithm is unbounded. The algorithm can be made truthful.
(The first part is based on joint work with Oren Gilon and the second part on joint work with Inna Kalp Shaltiel, Brendan Lucier, Ishai Menache, Joseph Naor and Jonathan Yaniv)

Marko Boon

A Design Heuristic for Skill Based Parallel Service Systems

We study a queueing model of parallel servers of types S = {s1, . . . , sJ }, serving customers of types C = {c1, . . . , cI} under the policy FCFS-ALIS. Customers arrive in stationary streams, join the queue and then abandon or get served. Service is skill based, which is described by a compatibility graph G, where arc (ci, sj ) indicates that server type sj can serve customer type ci. Service times depend on both server and customer type. This generic model is motivated by novel modes of service seen in areas as diverse as manufacturing, call centers, health care, data server farms and online retailers. The design in terms of workforce, skills and service level decisions is an extremely challenging problem. In this paper we propose a heuristic design algorithm to determine, for given data and desired service mode, which can be quality or efficiency driven or both, the required workforce levels to meet target levels of service quality and work division. The algorithm is validated through detailed simulation of three representative examples, indicating that it is remarkably robust and effective.
(joint work with Ivo Adan, Gidoen Weiss)

PRESENTATION

Gregor Brandt (ORTEC)

Why methods for handling uncertainty are often not used

Although there is a lot of research being done for stochastic or other methods that should be able to handle uncertainties in scheduling, companies are often not too keen on using these methods or even considering them. In this workshop I will discuss several examples of problems where more advanced methods would have been an improvement but where they are not used. We will go through some cases, which are interesting by themselves, and go into more depth of why companies in these cases were not willing or hesitant to use methods that can handle uncertainties.

Christoph Dürr

Different computational models have been proposed that fit between the online computational model, where no information about futur requests is known, and the offline computational model, where the whole input is known in advance.  One of these models is online computation with advice, where some amount of general information is given about future requests.  Clearly the competitive ratio is a monotone function of the amount of given advice. For some problems improving the competitive below some threshold the necessary advice increases from a constant to a logarithm. We illustrate this model with recent results on the bin packing problem, which is joint work with Spyros Angelopoulos, Shahin Kamali, Marc Renault and Adi Rosén.

PRESENTATION

Leah Epstein

Rent or Buy problems with a Fixed Time Horizon and Other Online Scheduling Problems with Rejection

We study several variants of a fixed length ski rental problem and related scheduling problems with rejection. A ski season consists of m days, and an equipment of cost 1 is to be used during these days. The equipment can be bought on any day, in which case it can be used without any additional cost starting that day and until the vacation ends. On each day, the algorithm is informed with the current non-negative cost of renting the equipment. As long as the algorithm did not buy the equipment, it must rent it every day of the vacation, paying the rental cost of each day of rental. We consider the case of arbitrary, non-increasing, and non-decreasing rental costs. We consider the case where the season cannot end before the m-th day, and the case that it can end without prior notice. We propose optimal online algorithms for all values of m for all variants. The optimal competitive ratios are either defined by solutions of equations (closed formulas or finite recurrences) or sets of mathematical programs, and tend to 2 as m grows. We will also discuss related scheduling problems with rejection.
Based on joint work with Hanan Zebedat-Haider.

Asaf Levin

Adaptivity in variants of the stochastic knapsack problem.

We study stochastic variants of the knapsack problem in which item values are deterministic and item sizes are independent random variables with known, arbitrary distributions. Items are placed in the knapsack sequentially, and the act of placing an item in the knapsack instantiates its size. The goal is to compute a policy for insertion of the items, that maximizes the expected total value of items placed in the knapsack. The variants of the problem differ in the formula for computing the total value of the solution obtained by the policy.
We consider both adaptive policies (that can make dynamic decisions based on the instantiated sizes of the items placed in the knapsack thus far) and non-adaptive policies (that cannot make such dynamic decisions).
Our work characterizes the benefit of adaptivity. For this purpose we use a measure called the adaptivity gap: the supremum over instances of the ratio between the expected value obtained by an optimal adaptive policy and the expected value obtained by an optimal non-adaptive policy. We show that for some variants this quantity is bounded by a constant while for others it is unbounded.
(joint work with Aleksander Vainer)

(Presentation not available)

Nicole Megow

Online Resource Minimization

We consider the fundamental resource minimization problem in which we ask for the minimum number of machines that is necessary for feasibly scheduling preemptive jobs with release dates and hard deadlines. We study the online variant of this problem in which the job set is unknown in advance. Every job becomes known to an algorithm only at its release date. We discuss two complementary special cases of the problem, namely, laminar instances and agreeable instances, for which we provide an O(log m)-competitive and an O(1)-competitive algorithm, respectively. As a main result we present a general O(m^2 log m)-competitive algorithm, where m is the optimal number of machines used in an offline solution. This is particularly interesting as it is the first result that does not directly depend on the job sizes or number of jobs.
(joint work with Lin Chen and Kevin Schewior)

PRESENTATION

Rolf Möhring

Stochastic Scheduling: History and Challenges

Stochastic scheduling problems have been investigated since the 50ies. Most results address the case that processing times are random but precedence and resource constraints are deterministic. Scheduling is then done by policies which consist of a process of decisions over time that are based on the observed past and the a priori knowledge of the distribution of processing times. The objective is to find a policy that minimizes a regular performance measure such as makespan or weighted sum of completion times in expectation. Policies may be classified according to their way of resolving the resource conflicts. Suitable combinatorial properties of such policies have led to optimality and stability results, to computational methods for constructing policies, and also to approximation algorithms. I will give a survey on important results for such problems and highlight some challenging open questions. At the end, I will also address the problem of how to calculate risk measures when a scheduling project is carried out by a particular policy. Such risk measures have been applied successfully to shutdown and turnaround scheduling in chemical manufacturing.

PRESENTATION

Kamesh Munagala

Prophet Inequalities and Stochastic Optimization

Many recent advances in designing approximately optimal policies for stochastic optimization problems use variants of prophet inequalities.

In this tutorial, we will begin by defining these inequalities, and presenting several different proofs using linear programming and duality. We will then consider widely studied stochastic optimization problems such as multi-unit auctions, stochastic knapsack, and multi-armed bandits, and show how prophet-inequality like ideas provide both linear programming relaxations, as well as techniques to convert these relaxations to approximately optimal policies. We will show the connection between these policies and the classic index policies. Time permitting, we will describe the limitations of these inequalities, and some cases where stronger linear programs are needed.

PRESENTATION

Mike Pinedo

Scheduling Customer Arrivals with Overbooking in Service Systems with No-Shows

We analyze a discrete multi-server queueing model for scheduling customer arrivals under no-shows. Customers have different waiting cost coefficients and different no-show rates, reflecting their type and their history in attending scheduled appointments respectively. The challenge is to assign customers to time slots so that the service system utilizes its resources efficiently, and customers enjoy short waiting times. Theoretical and heuristic guidelines are provided for the effective practice of appointment overbooking to offset no-shows. For the case of heterogeneous customers, we provide structural properties of an optimal schedule and we introduce a new sequencing rule. When customers come from a homogeneous pool, recursive expressions for the performance measures of interest are derived and we provide an upper bound for the optimal overbooking level. Our extensive numerical experiments reveal further properties and patterns
that appear in the optimal solution, and motivate the development of two very well performing and computationally inexpensive heuristic solutions. Moreover, our analysis demonstrates the benefits of resource-pooling in containing operational costs and increasing customer throughput.
(joint work with Christos Zacharias (University of Miami))

PRESENTATION

Kirk Pruhs (Tutorial)

An Introduction to Competitive Analysis in Online Scheduling

There is a large body of literature studying algorithms for scheduling/prioritizing jobs that arrive at the server over time.  A competitive algorithm is one that guarantees that the provided quality of service is not too much worse than the best possible quality of service that could have been provided. I will introduce the most important basic definitions, concepts, and some basic algorithm analysis techniques.

Competitive Analysis in Online Scheduling using Potential Functions and Dual Fitting

There is a large body of literature studying algorithms for scheduling/prioritizing jobs that arrive at the server over time.  A competitive algorithm is one that guarantees that the provided quality of service is not too much worse than the best possible quality of service that could have been provided. I will cover the two state of the art algorithm analysis techniques in this area: potential functions and dual fitting analysis.

Ramandeep Randhawa

Scheduling Homogeneous Impatient Customers

Customer impatience has become an integral component of analyzing services, especially in the context of call centers. Typically, when customers arrive to such systems, they seem identical or homogeneous, however, from the system's perspective, as they wait in the queue, their residual willingness to wait changes. For instance, a customer who has already waited for 10 minutes may have a different residual willingness to wait as compared with a customer who has only waited for 1 minute. In this manner, as time progresses, customers become differentiated on their estimated patience levels. We exploit this dimension of customer heterogeneity to construct scheduling policies in overloaded systems that dynamically prioritize customers based on their time in queue in order to optimize any given system performance metric. Interestingly, the optimal policy has a very simple structure, and we find that implementing it can lead to significant improvements over the first come first serve policy.

R. Srikant

Performance Analysis of Scheduling Algorithms for Switches and Data Centers

We will consider scheduling algorithms that are widely used in high-speed switches in the Internet and data centers. In the first part of the talk, we will review the connection between these algorithms and maximum weighted matchings. In particular, we will show how these algorithms naturally arise from throughput maximization considerations in both deterministic and stochastic settings. We will then move beyond throughput maximization and formulate questions of interest in studying the delay or queue length performance of these algorithms. In the second part of the talk, we will present a new technique for analyzing the delay performance using drift techniques. The basic idea is to exploit the following simple fact: the expected drift of any reasonable function of the queue length must be equal to zero in steady-state. Using this simple fact. we will establish bounds on the total queue length in the network, and show that these bounds are tight in some regimes.
(joint work with Atilla Eryilmaz and Siva Theja Maguluri)

PRESENTATION 1

PRESENTATION 2

Sebastian Stiller

How to order a waiting list?

How to order the waiting list for an overbooked flight? This amounts to the problem of packing a knapsack without knowing its capacity. Whenever we attempt to pack an item that does not fit, the item is discarded; if the item fits, we have to include it in the packing. We show that there is always a policy that packs a value within factor 2 of the optimum packing, irrespective of the actual capacity. If all items have unit density, we achieve a factor equal to the golden ratio R = 1.618. Both factors are shown to be best possible. In fact, we obtain the above factors using packing policies that are universal in the sense that they fix a particular order of the items and try to pack the items in this order, independent of the observations made while packing. We give efficient algorithms computing these policies. On the other hand, we show that, for any R > 1, the problem of deciding whether a given universal policy achieves a factor of R is coNP-complete. If R is part of the input, the same problem is shown to be coNP-complete for items with unit densities. Finally, we show that it is coNP-hard to decide, for given R, whether a set of items admits a universal policy with factor R, even if all items have unit densities. This is joint work with Yann Disser, Max Klimm, and Nicole Megow.

Alexander Stolyar

Pull-based load distribution in large-scale heterogeneous service systems

We consider a heterogeneous service system, consisting of several (different) large server pools, and study an asymptotic regime in which the customer arrival rate and pool sizes scale to infinity simultaneously.

We introduce a 'pull-based' scheme (called PULL), for routing arriving customers to servers and prove that, under subcritical load, both waiting times and blocking probabilities asymptotically vanish. In particular, the performance of PULL is vastly superior to that of the celebrtated 'power-of-d-choices' (JSQ(d)) routing algorithm.

PRESENTATION

Jean Walrand

Maximum Throughput Scheduling

This talk reviews recent results on maximum throughput scheduling.  In particular, we review the stability of longest queue first policies and the design of robust policies that do not assume knowledge of service times.  We also discuss the stability of maximum weighted deficit for processing networks.
(joint work with A. Dimakis, L. Jiang, R. Pedarsani and Y. Zhong)

PRESENTATION

Andreas Wiese

On Approximating Storage Allocation Problems as Good as Their Siblings

Packing problems are a fundamental class of problems studied in combinatorial optimization. Three particularly important and well-studied questions in this domain are the Unsplittable Flow on a Path problem
(UFP), the Maximum Weight Independent Set of Rectangles problem (MWISR), and the 2-dimensional geometric knapsack problem. We study the storage allocation problem (SAP) which is a natural combination
of those three questions. While for MWISR and UFP there are quasi-polynomial time approximation schemes known and for 2D-knapsack and UFP also polynomial time $(2+\epsilon)$-approximation algorithms, the best known approximation factor for SAP is $9+\epsilon$.
We almost level the understanding of SAP and the other three problems above. Specifically, we present a $(2+\epsilon)$-approximation algorithm for SAP and additionally a quasi-PTAS if the edge capacities can be increased by an arbitrarily small factor. Building on our new insights for SAP and some additional ideas, we construct a PTAS for the dynamic storage allocation problem (DSA) in an resource-augmentation setting where we are allowed to reject an $\epsilon$-fraction of the input task, according to an arbitrary weight function.
(joint work with Jatin Batra and Tobias Mömke)

(Presentation not available)

Kuang Xu

Capacity of Information Processing Systems

We study an information processing system where a stream of jobs, each associated with a hidden label, is to be inspected by a group of experts. Each inspection produces a noisy result which depends on the job's hidden label and the type of the corresponding expert. Meanwhile, an inspection also occupies the expert for one unit of time, during which she cannot process other jobs. The decision maker's objective is to dynamically assign inspections so as to accurately uncover the jobs' hidden labels, while using only a minimum number of experts to ensure system stability. We are motivated by applications from areas such as crowdsourcing, machine learning, and experimental design, where information for a large number of items is to be gathered, using a limited set of experts or computational resources. Our main result is an asymptotically optimal inspection policy, under which the required number of experts matches a lower bound, as the probability of labeling error tends to zero.
This is joint work with Laurent Massoulié.

(Presentation not available)

Zhong Yuan

Efficient coflow scheduling in data center networks

Communication in data center jobs (such as the shuffle operations in MapReduce applications) often involve many parallel data flows, which may be processed simultaneously. This highly parallel structure presents new scheduling challenges in optimizing job-level performance objectives in data centers.
In this talk, we consider the efficient scheduling of coflows - an abstraction introduced in [Chowdhury and Stoica 2012] to capture communication patterns of large-scale data center jobs. We introduce the problem of minimizing the total weighted coflow completion times, show that it is strongly NP-hard, and develop the first polynomial-time approximation algorithms for this problem. We also evaluate the practical performances of a variety of algorithms through numerical experiments on a Facebook trace.
(joint works with Mosharaf Chowdhury, Rhea Qiu, Ion Stoica and Cliff Stein)

PRESENTATION

PRACTICAL INFORMATION

### ●      Venue

Eurandom, Mathematics and Computer Science Dept, TU Eindhoven,

Den Dolech 2, 5612 AZ  EINDHOVEN,  The Netherlands

Eurandom is located on the campus of Eindhoven University of Technology, in the (4th floor) (about the building). The university is located at 10 minutes walking distance from Eindhoven main railway station (take the exit north side and walk towards the tall building on the right with the sign TU/e).
Accessibility TU/e campus and map.

### ●      Accommodation

For invited participants, we will take care of accommodation. Other attendees will have to make their own arrangements.

We have a preferred hotel, which can be booked at special rates. Please email Patty Koorn for instructions on how to make use of this special offer.

For other hotels around the university, please see: Hotels (please note: prices listed are "best available").

More hotel options can be found on the webpages of the , Postbus 7, 5600 AA Eindhoven.

### ●      Travel

For those arriving by plane, there is a convenient direct train connection between Amsterdam Schiphol airport and Eindhoven. This trip will take about one and a half hour. For more detailed information, please consult the NS travel information pages or see Eurandom web page location.

Many low cost carriers also fly to Eindhoven Airport. There is a bus connection to the Eindhoven central railway station from the airport. (Bus route number 401) For details on departure times consult http://www.9292ov.nl

The University  can be reached easily by car from the highways leading to Eindhoven (for details, see our route descriptions or consult our map with highway connections.

### ●      Conference facilities : Conference room, Metaforum Building  MF11&12

The meeting-room is equipped with a data projector, an overhead projector, a projection screen and a blackboard. Please note that speakers and participants making an oral presentation are kindly requested to bring their own laptop or their presentation on a memory stick.

### ●      Conference Secretariat

Upon arrival, participants should register with the workshop officer, and collect their name badges. The workshop officer will be present for the duration of the conference, taking care of the administrative aspects and the day-to-day running of the conference: registration, issuing certificates and receipts, etc.

### ●      Cancellation

There is no registration fee, but should you need to cancel your participation after January 2, 2014, we will be obliged to charge a no-show fee of 30 euro.

### ●      Friday evening

On Friday evening (June 5) there is a friendly football (soccer) match between the Netherlands and USA, held in the Amsterdam ArenA. Some of the participants are going to the match, and making it a very sociable end to the workshop week! Should you want to join this sportive activity, tickets are on sale from: VIAGOGO .

### ●      Contact

Mrs. Patty Koorn, Workshop Officer, Eurandom/TU Eindhoven, koorn@eurandom.tue.nl

The organisers acknowledge the financial support/sponsorship of:

#### Last updated 02-07-15, by PK

P.O. Box 513, 5600 MB Eindhoven, The Netherlands
tel. +31 40 2478100
e-mail: info@eurandom.tue.nl