A decomposition approach for multidimensional knapsacks with family-split penalties

The optimization of Multidimensional Knapsacks with Family-Split Penalties has been introduced in the literature as a variant of the more classical Multidimensional Knapsack and Multi-Knapsack problems. This problem deals with a set of items partitioned in families, and when a single item is picked to maximize the utility, then all items in its family must be picked. Items from the same family can be assigned to different knapsacks, and in this situation split penalties are paid. This problem arises in real applications in various fields. This paper proposes a new exact and fast algorithm based on a specific Combinatorial Benders Cuts scheme. An extensive experimental campaign computationally shows the validity of the proposed method and its superior performance compared to both commercial solvers and state-of-the-art approaches. The paper also addresses algorithmic flexibility and scalability issues, investigates challenging cases, and analyzes the impact of problem parameters on the algorithm behavior. Moreover, it shows the applicability of the proposed approach to a wider class of realistic problems, including fixed costs related to each knapsack utilization. Finally, further possible research directions are considered

Optimal Map Reduce Job Capacity Allocation in Cloud Systems.

We are entering a Big Data world. Many sectors of our economy are now guided by data-driven decision processes. Big Data and business intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions for allocating on demand large clusters. Capacity allocation in such systems is a key challenge to provide performance for MapReduce jobs and minimize cloud resource costs. The contribution of this paper is twofold: (i) we provide new upper and lower bounds for MapReduce job execution time in shared Hadoop clusters, (ii) we formulate a linear programming model able to minimize cloud resources costs and job rejection penalties for the execution of jobs of multiple classes with (soft) deadline guarantees. Simulation results show how the execution time of MapReduce jobs falls within 14% of our upper bound on average. Moreover, numerical analyses demonstrate that our method is able to determine the global optimal solution of the linear problem for systems including up to 1,000 user classes in less than 0.5 seconds

An optimization framework for the capacity allocation and admission control of MapReduce jobs in cloud systems

Nowadays, we live in a Big Data world and many sectors of our economy are guided by data-driven decision processes. Big Data and Business Intelligence applications are facilitated by the MapReduce programming model, while, at infrastructural layer, cloud computing provides flexible and cost-effective solutions to provide on-demand large clusters. Capacity allocation in such systems, meant as the problem of providing computational power to support concurrent MapReduce applications in a cost-effective fashion, represents a challenge of paramount importance. In this paper we lay the foundation for a solution implementing admission control and capacity allocation for MapReduce jobs with a priori deadline guarantees. In particular, shared Hadoop 2.x clusters supporting batch and/or interactive jobs are targeted. We formulate a linear programming model able to minimize cloud resources costs and rejection penalties for the execution of jobs belonging to multiple classes with deadline guarantees. Scalability analyses demonstrated that the proposed method is able to determine the global optimal solution of the linear problem for systems including up to 10,000 classes in less than 1 s

Performance Degradation and Cost Impact Evaluation of Privacy Preserving Mechanisms in Big Data Systems

Big Data is an emerging area and concerns managing datasets whose size is beyond commonly used software tools ability to capture, process, and perform analyses in a timely way. The Big Data software market is growing at 32% compound annual rate, almost four times more than the whole ICT market, and the quantity of data to be analyzed is expected to double every two years. Security and privacy are becoming very urgent Big Data aspects that need to be tackled. Indeed, users share more and more personal data and user-generated content through their mobile devices and computers to social networks and cloud services, losing data and content control with a serious impact on their own privacy. Privacy is one area that had a serious debate recently, and many governments require data providers and companies to protect users’ sensitive data. To mitigate these problems, many solutions have been developed to provide data privacy but, unfortunately, they introduce some computational overhead when data is processed. The goal of this paper is to quantitatively evaluate the performance and cost impact of multiple privacy protection mechanisms. A real industry case study concerning tax fraud detection has been considered. Many experiments have been performed to analyze the performance degradation and additional cost (required to provide a given service level) for running applications in a cloud system

Quality-of-service in cloud computing: modeling techniques and their applications

© 2014, Ardagna et al.; licensee Springer.Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management

Minimising general setup costs in a two-stage production system

This paper addresses a problem arising in the coordination between two consecutive stages of a production system. Production is organised in batches of identical jobs. Each job is characterised by two distinct attributes, and all jobs sharing the same attributes are processed together as a single batch. Due to the structural and organisational characteristics of the production system, the two stages have to process the same batch sequence. When two consecutive batches with different attributes are processed, at least one stage must pay a setup, in order to reconfigure its own devices. Each stage incurs a setup cost that is a general non-decreasing function of the number of its own setups, and the problem consists of finding a batch sequence minimising the total setup costs of the production system. We present an original solution approach for the considered problem that is shown to be very effective using an extensive experimental campaign

The Multiple Multidimensional Knapsack with Family-Split Penalties

The Multiple Multidimensional Knapsack Problem with Family-Split Penalties (MMdKFSP) is introduced as a new variant of both the more classical Multi-Knapsack and Multidimensional Knapsack Problems. It reckons with items categorized into families and where if an individual item is selected to maximize the profit, all the items of the same family must be selected as well. Items belonging to the same family can be assigned to different knapsacks; however, in this case, split penalties are incurred. This problem arises in resource management of distributed computing contexts and Service Oriented Architecture environments. An exact algorithm based on the exploitation of a specific combinatorial Benders’ cuts approach is proposed. Computational experiments on different sets of benchmark test problems show the effectiveness of the proposed algorithm. The comparison against a state-of-the-art commercial solver confirms the validity of the proposed approach considering also the scalability issue

Minimizing general setup costs in a two-stage production system

This paper addresses a problem arising in the coordination between two consecutive stages of a production system. Production is organized in batches of identical jobs. Each job is characterized by two distinct attributes, and all jobs sharing the same attributes are processed together as a single batch. Due to the structural and organizational characteristics of the production system, the two stages have to process the same batch sequence. When two consecutive batches with different attributes are processed, at least one stage must pay a setup, in order to reconfigure its own devices. Each stage incurs a setup cost which is a general non-decreasing function of the number of its own setups, and the problem consists of finding a batch sequence minimizing the total setup costs of the production system. We present an original solution approach for the considered problem which is shown to be very effective by an extensive experimental campaign