Multi-Capacity Bin Packing with Dependent Items and its Application to the Packing of Brokered Workloads in Virtualized Environments

• Published in: Future generation computer systems Vol. 72; pp. 129 - 144
• Main Authors: Bassem, Christine, Bestavros, Azer
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2017
• Subjects: Bin packing; Brokerage environment; Network-aware; Resource allocation; Bin packing; Brokerage environment; Network-aware; Resource allocation
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2016.08.017

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provide predictability guarantees in such settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Multi-Capacity Bin Packing with Dependent Items (MCBP-DI) problem to model the various resource allocation models adopted in such a brokered setting. The MCBP-DI problem represents a class of multi-dimensional bin packing problems, in which the amount of resources consumed by a subset of the items depends on the relationship between these items. Focusing on offering predictability guarantees to data-intensive applications, we define a sub-problem of the MCBP-DI problem, namely the Network-Constrained Packing (NCP) problem, in which the items to be packed form a connected component, and the resources consumed by any subset of these items are equivalent to the cost of the cut of that subset from the component. Our definition of the NCP problem is presented as part of our proposed cloud brokerage framework, in which the optimal mapping of brokered resources to applications is decided with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem, and we evaluate its efficiency using simulations on various application workloads, and network models. •Proposal of a novel brokerage model for providing execution predictability services to data-intensive applications.•Definition of a new class of multi-dimensional bin packing problems, namely Multi-Capacity Bin Packing with Dependent Items.•Definition of the Network-Constrained Packing problem model for resource allocation in our proposed brokerage model.•The definition of an exact polynomial algorithm to solve an instance of our proposed packing problem, which occurs frequently in reality.•The development of a greedy approximation algorithm to solve the NCP problem in the general model.