PSBS: Practical Size-Based Scheduling

Size-based schedulers have very desirable performance properties: optimal or near-optimal response time can be coupled with strong fairness guarantees. Despite this, such systems are very rarely implemented in practical settings, because they require knowing a priori the amount of work needed to complete jobs: this assumption is very difficult to satisfy in concrete systems. It is definitely more likely to inform the system with an estimate of the job sizes, but existing studies point to somewhat pessimistic results if existing scheduler policies are used based on imprecise job size estimations. We take the goal of designing scheduling policies that are explicitly designed to deal with inexact job sizes: first, we show that existing size-based schedulers can have bad performance with inexact job size information when job sizes are heavily skewed; we show that this issue, and the pessimistic results shown in the literature, are due to problematic behavior when large jobs are underestimated. Once the problem is identified, it is possible to amend existing size-based schedulers to solve the issue. We generalize FSP -- a fair and efficient size-based scheduling policy -- in order to solve the problem highlighted above; in addition, our solution deals with different job weights (that can be assigned to a job independently from its size). We provide an efficient implementation of the resulting protocol, which we call Practical Size-Based Scheduler (PSBS). Through simulations evaluated on synthetic and real workloads, we show that PSBS has near-optimal performance in a large variety of cases with inaccurate size information, that it performs fairly and it handles correctly job weights. We believe that this work shows that PSBS is indeed pratical, and we maintain that it could inspire the design of schedulers in a wide array of real-world use cases.Comment: arXiv admin note: substantial text overlap with arXiv:1403.599

OS-Assisted Task Preemption for Hadoop

This work introduces a new task preemption primitive for Hadoop, that allows tasks to be suspended and resumed exploiting existing memory management mechanisms readily available in modern operating systems. Our technique fills the gap that exists between the two extremes cases of killing tasks (which waste work) or waiting for their completion (which introduces latency): experimental results indicate superior performance and very small overheads when compared to existing alternatives

Revisiting Size-Based Scheduling with Estimated Job Sizes

We study size-based schedulers, and focus on the impact of inaccurate job size information on response time and fairness. Our intent is to revisit previous results, which allude to performance degradation for even small errors on job size estimates, thus limiting the applicability of size-based schedulers. We show that scheduling performance is tightly connected to workload characteristics: in the absence of large skew in the job size distribution, even extremely imprecise estimates suffice to outperform size-oblivious disciplines. Instead, when job sizes are heavily skewed, known size-based disciplines suffer. In this context, we show -- for the first time -- the dichotomy of over-estimation versus under-estimation. The former is, in general, less problematic than the latter, as its effects are localized to individual jobs. Instead, under-estimation leads to severe problems that may affect a large number of jobs. We present an approach to mitigate these problems: our technique requires no complex modifications to original scheduling policies and performs very well. To support our claim, we proceed with a simulation-based evaluation that covers an unprecedented large parameter space, which takes into account a variety of synthetic and real workloads. As a consequence, we show that size-based scheduling is practical and outperforms alternatives in a wide array of use-cases, even in presence of inaccurate size information.Comment: To be published in the proceedings of IEEE MASCOTS 201

Solving a Real-Life Distributor's Pallet Loading Problem

We consider the distributor's pallet loading problem where a set of different boxes are packed on the smallest number of pallets by satisfying a given set of constraints. In particular, we refer to a real-life environment where each pallet is loaded with a set of layers made of boxes, and both a stability constraint and a compression constraint must be respected. The stability requirement imposes the following: (a) to load at level k+1 a layer with total area (i.e., the sum of the bottom faces' area of the boxes present in the layer) not exceeding α times the area of the layer of level k (where α≥1), and (b) to limit with a given threshold the difference between the highest and the lowest box of a layer. The compression constraint defines the maximum weight that each layer k can sustain; hence, the total weight of the layers loaded over k must not exceed that value. Some stability and compression constraints are considered in other works, but to our knowledge, none are defined as faced in a real-life problem. We present a matheuristic approach which works in two phases. In the first, a number of layers are defined using classical 2D bin packing algorithms, applied to a smart selection of boxes. In the second phase, the layers are packed on the minimum number of pallets by means of a specialized MILP model solved with Gurobi. Computational experiments on real-life instances are used to assess the effectiveness of the algorithm

Measuring Password Strength: An Empirical Analysis

We present an in-depth analysis on the strength of the almost 10,000 passwords from users of an instant messaging server in Italy. We estimate the strength of those passwords, and compare the effectiveness of state-of-the-art attack methods such as dictionaries and Markov chain-based techniques. We show that the strength of passwords chosen by users varies enormously, and that the cost of attacks based on password strength grows very quickly when the attacker wants to obtain a higher success percentage. In accordance with existing studies we observe that, in the absence of measures for enforcing password strength, weak passwords are common. On the other hand we discover that there will always be a subset of users with extremely strong passwords that are very unlikely to be broken. The results of our study will help in evaluating the security of password-based authentication means, and they provide important insights for inspiring new and better proactive password checkers and password recovery tools.Comment: 15 pages, 9 figure