Vector Bin Packing with Multiple-Choice

We consider a variant of bin packing called multiple-choice vector bin packing. In this problem we are given a set of items, where each item can be selected in one of several $D$-dimensional incarnations. We are also given $T$ bin types, each with its own cost and $D$-dimensional size. Our goal is to pack the items in a set of bins of minimum overall cost. The problem is motivated by scheduling in networks with guaranteed quality of service (QoS), but due to its general formulation it has many other applications as well. We present an approximation algorithm that is guaranteed to produce a solution whose cost is about $\ln D$ times the optimum. For the running time to be polynomial we require $D=O(1)$ and $T=O(\log n)$. This extends previous results for vector bin packing, in which each item has a single incarnation and there is only one bin type. To obtain our result we also present a PTAS for the multiple-choice version of multidimensional knapsack, where we are given only one bin and the goal is to pack a maximum weight set of (incarnations of) items in that bin

Optimal Placement Algorithms for Virtual Machines

Cloud computing provides a computing platform for the users to meet their demands in an efficient, cost-effective way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines (VMs) are utilized to satisfy the user needs and are placed on physical machines (PMs) of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of PMs used helps in cutting down the power consumption by a substantial amount. In this paper, we present an optimal technique to map virtual machines to physical machines (nodes) such that the number of required nodes is minimized. We provide two approaches based on linear programming and quadratic programming techniques that significantly improve over the existing theoretical bounds and efficiently solve the problem of virtual machine (VM) placement in data centers

Safety-Level Aware Bin-Packing Approach for Control Functions Assignment

International audienceThe assignment of functions to controllers is a crucial step when building an operational control system architecture. We identified this problem as a Multiple Choice Vector Bin-Packing with Conflicts that is a generalization of the one-dimensional Bin-Packing problem. Such problems are known to be strongly NP-Hard and exact techniques to solve them are too time and/or space consuming because of the combinatorial explosion. Therefore, in this paper, we propose a fast First-Fit Decreasing based heuristic that derives an optimized number of controllers in polynomial time. The objective is to minimize the global cost of controllers while satisfying safety constraints. We show through experiments that the adopted approach allows us to obtain values that are in average very close to the optimum

A multi-resource load balancing algorithm for cloud cache systems

With the advent of cloud computing model, distributed caches have become the cornerstone for building scalable applications. Popular systems like Facebook [1] or Twitter use Memcached [5], a highly scalable distributed object cache, to speed up applications by avoiding database accesses. Distributed object caches assign objects to cache instances based on a hashing function, and objects are not moved from a cache instance to another unless more instances are added to the cache and objects are redistributed. This may lead to situations where some cache instances are overloaded when some of the objects they store are frequently accessed, while other cache instances are less frequently used. In this paper we propose a multi-resource load balancing algorithm for distributed cache systems. The algorithm aims at balancing both CPU and Memory resources among cache instances by redistributing stored data. Considering the possible conflict of balancing multiple resources at the same time, we give CPU and Memory resources weighted priorities based on the runtime load distributions. A scarcer resource is given a higher weight than a less scarce resource when load balancing. The system imbalance degree is evaluated based on monitoring information, and the utility load of a node, a unit for resource consumption. Besides, since continuous rebalance of the system may affect the QoS of applications utilizing the cache system, our data selection policy ensures that each data migration minimizes the system imbalance degree and hence, the total reconfiguration cost can be minimized. An extensive simulation is conducted to compare our policy with other policies. Our policy shows a significant improvement in time efficiency and decrease in reconfiguration cost

Demo Proposal - Distrinet: a Mininet implementation for the Cloud

International audienceNetworks became so complex and technical that it is now hard if not impossible to model or simulate them. Consequently, more and more researchers rely on prototypes emulated in controlled environments and Mininet is by far the most popular tool. Mininet implements a simple, yet powerful API to define and run network experiments on a single machine. In most cases, running experiments on one machine is adequate but for resource intensive applications one machine may not be sufficient. For that reason we propose Distrinet, a way to distribute Mininet over multiple hosts. Distrinet uses the same API than Mininet, granting full compatibility with Mininet programs. Distrinet is generic and can optimally deploy experiments in Linux clusters or in public clouds and automatically minimizes the resource consumed in the experimental infrastructure

Optimal Fair Scheduling in S-TDMA Sensor Networks for Monitoring River Plumes

Underwater wireless sensor networks (UWSNs) are a promising technology to provide oceanographers with environmental data in real time. Suitable network topologies to monitor estuaries are formed by strings coming together to a sink node. This network may be understood as an oriented graph. A number of MAC techniques can be used in UWSNs, but Spatial-TDMA is preferred for fixed networks. In this paper, a scheduling procedure to obtain the optimal fair frame is presented, under ideal conditions of synchronization and transmission errors. The main objective is to find the theoretical maximum throughput by overlapping the transmissions of the nodes while keeping a balanced received data rate from each sensor, regardless of its location in the network. The procedure searches for all cliques of the compatibility matrix of the network graph and solves a Multiple-Vector Bin Packing (MVBP) problem. This work addresses the optimization problem and provides analytical and numerical results for both the minimum frame length and the maximum achievable throughput

Harmonic Algorithms for Packing d-Dimensional Cuboids into Bins

We explore approximation algorithms for the d-dimensional geometric bin packing problem (dBP). Caprara [Caprara, 2008] gave a harmonic-based algorithm for dBP having an asymptotic approximation ratio (AAR) of (T_?)^{d-1} (where T_? ? 1.691). However, their algorithm doesn\u27t allow items to be rotated. This is in contrast to some common applications of dBP, like packing boxes into shipping containers. We give approximation algorithms for dBP when items can be orthogonally rotated about all or a subset of axes. We first give a fast and simple harmonic-based algorithm having AAR T_?^d. We next give a more sophisticated harmonic-based algorithm, which we call HGaP_k, having AAR (T_?)^{d-1}(1+?). This gives an AAR of roughly 2.860 + ? for 3BP with rotations, which improves upon the best-known AAR of 4.5. In addition, we study the multiple-choice bin packing problem that generalizes the rotational case. Here we are given n sets of d-dimensional cuboidal items and we have to choose exactly one item from each set and then pack the chosen items. Our algorithms also work for the multiple-choice bin packing problem. We also give fast and simple approximation algorithms for the multiple-choice versions of dD strip packing and dD geometric knapsack

A Cost-Effective Cloud-Based System for Analyzing Big Real-Time Visual Data From Thousands of Network Cameras

Thousands of network cameras stream public real-time visual data from different environments, such as streets, shopping malls, and natural scenes. The big visual data from these cameras can be useful for many applications, but analyzing this data presents many challenges, such as (i) retrieving data from heterogeneous cameras (e.g. different brands and data formats), (ii) providing a software environment for users to simultaneously analyze the large amounts of data from the cameras, (iii) allocating and managing significant amount of computing resources. This dissertation presents a web-based system designed to address these challenges. The system enables users to execute analysis programs on the data from more than 120,000 cameras. The system handles the heterogeneity of the cameras and provides an Application Programming Interface (API) that requires slight changes to the existing analysis programs reading data from files. The system includes a resource manager that allocates cloud resources in order to meet the analysis requirements. Cloud vendors offer different cloud instance types with different capabilities and hourly costs. The manager reduces the overall cost of the allocated instances while meeting the performance requirements. The resource manager monitors the allocated instances; it allocates more instances if needed and deallocates existing instances to reduce the cost if possible. The manager makes decisions based on many factors, such as analysis programs, frame rates, cameras, and instance types