Heterogeneous Resource Allocation under Degree Constraints

• Published in: IEEE transactions on parallel and distributed systems Vol. 24; no. 5; pp. 926 - 937
• Main Authors: Beaumont, O., Eyraud-Dubois, L., Thraves Caro, C., Rejeb, H.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-05-2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Allocations; Approximation algorithms; bin packing; Clients; cloud computing; Computer Science; Context; Demand; Distributed, Parallel, and Cluster Computing; divisible scheduling; Marketing; On-line systems; Online; Online computation; Processor scheduling; resource augmentation; Resource management; Servers; Studies; Throughput; Virtual machining; Divisible Scheduling; Bin Packing; Approximation algorithms; Cloud Computing; Online computation; Resource Augmentation
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2012.175

In this paper, we consider the problem of assigning a set of clients with demands to a set of servers with capacities and degree constraints. The goal is to find an allocation such that the number of clients assigned to a server is smaller than the server's degree and their overall demand is smaller than the server's capacity, while maximizing the overall throughput. This problem has several natural applications in the context of independent tasks scheduling or virtual machines allocation. We consider both the offline (when clients are known beforehand) and the online (when clients can join and leave the system at any time) versions of the problem. We first show that the degree constraint on the maximal number of clients that a server can handle is realistic in many contexts. Then, our main contribution is to prove that even if it makes the allocation problem more difficult (NP-Complete), a very small additive resource augmentation on the servers degree is enough to find in polynomial time a solution that achieves at least the optimal throughput. After a set of theoretical results on the complexity of the offline and online versions of the problem, we propose several other greedy heuristics to solve the online problem and we compare the performance (in terms of throughput) and the cost (in terms of disconnections and reconnections) of all proposed algorithms through a set of extensive simulation results.