Greedy Placement Algorithm Research Articles

Optimal Placement of Virtual Machines for Supporting Multiple Applications in Mobile Edge Networks

Although mobile edge computing (MEC), as an extension of the cloud computing paradigm to edge networks, overcomes some obstacles of traditional mobile cloud computing, i.e., the reduced response time particularly, it is a nontrivial task to efficiently deploy virtual machine replica copies (VRCs) supporting multiple applications among numerous MEC servers in edge networks. To combat this issue, we are motivated to investigate in detail the optimal placement of VRCs to minimize the average response time (MART) in the MEC architecture with various requests demand among multiple applications and capacity constraints of MEC servers in edge networks. Besides optimal enumeration placement algorithm (OEPA) as benchmark, we design latency aware heuristic placement algorithm (LAHPA) with much lower computation complexity than that of OEPA. To enhance the performance of LAHPA on MART, clustering enhanced heuristic placement algorithm (CEHPA) is proposed, focusing on the optimal VRC placement in each cluster. We also develop substitution enhanced heuristic placement algorithm (SEHPA) to avoid falling into local optimal solutions. As corroborated by extensive simulation results, the performance of SEHPA on MART is very close to that of OEPA compared with LAHPA and CEHPA. Note that CEHPA also outperforms LAHPA, and both are better than a general greedy placement algorithm. Furthermore, we evaluate the normalized total cost for services provision in edge networks, where SEHPA can also get more outstanding results than other algorithms.

Design and analysis of collaborative EPC and RAN caching for LTE mobile networks

The explosive growth of mobile video traffic brings a great pressure on LTE mobile networks. Deploying caches at the evolved packet core (EPC) and radio access network (RAN) in LTE mobile networks can efficiently relieve this pressure by reducing duplicate content transmissions. In this paper, we propose a collaborative caching framework for the LTE mobile network with caches deployed at the EPC and RAN to reduce the bandwidth cost of Internet access and improve the end-user experience in terms of delay and congestion. Based on this framework, we decompose the caching problem into a content placement subproblem and a request routing subproblem. We solve the content placement subproblem with a greedy placement algorithm by utilizing the matroid and submodular properties. As for the request routing subproblem, we solve it by transforming it into the problem of maximizing a submodular function subject to a matroid constraint and present a greedy algorithm that schedules request routing with short time slot. The proposed CollaCache scheme can achieve at least 1/2 of the optimal solution in terms of bandwidth cost saving. Experimental results show that CollaCache can save more costs and support more requests compared with other schemes.

Hybrid Placement of Internet Gateways and Rechargeable Routers with Guaranteed QoS for Green Wireless Mesh Networks

To solve the challenges of ever-increasing energy consumption, wireless mesh networks (WMNs) consisting of routers using renewable energy sources have been emerging as a promising solution. In this paper, we jointly consider the node placement and energy management in a hybrid WMN consisting of electricity-powered Internet gateways and solar-powered rechargeable routers. We introduce a new metric of failure rate to evaluate network performance and formulate the problem as a constrained optimization problem with the objective of minimizing both capital expenses on installation and operational expenses related to energy consumption. The network Quality of Service (QoS) requirements include meeting the failure rate requirement, the users' dynamic traffic demand requirements in both downlink and uplink, the gateways' and routers' capacity constraints, the maximal transmission power constraints, and the rechargeable routers' energy consumption constraints. Furthermore, we propose a minimum cost association algorithm to connect mesh clients to the Internet in different slots and a greedy placement algorithm to find approximate solutions for hybrid node placement. Simulation results show that the hybrid placement with rechargeable routers reveals its advantage for large scale networks in terms of more energy saving. Compared with the optimal placement achieved by the exhaustive search, our proposed algorithm can achieve good performance with the significantly reduced computation complexity.

Backtracking Greedy Algorithm for Cutting Stock Problems

This paper presents a greedy search placement algorithm which incorporates backtracking for the leather stock cutting problem. In the leather manufacturing industry the efficient cutting of component parts (stencils) form a hide is of prime importance to maintain profitability. Consequently, the development of new approaches for generating cut-plans that minimise material waste and which can handle problem constraints have practical value. The unique feature of the greedy placement algorithm method presented in this paper is that it incorporates backtracking which allows previous placement steps to be retraced in situations where no placement solution can be found. The underlying encoding method is based on the use of the no-fit polygon (NFP) which describes the boundary around a stencil shape such that a second stencil shape can be placed while just touching the first but without overlapping. A material coverage of 64% can be achieved when taking placement constraints into account.

A variant of the permutation flow shop model with variable processing times

We consider a modification of the classical flow shop problem with infinite buffer capacities. The processing times on various machines are variable: the more one delays the beginning of an operation, the longer is its processing time. There are many industrial applications for this model, for instance in the planning of machine maintenance or service and in steel production where the material will cool during the waiting periods and has to be reheated for the subsequent process. The problem turns out to be already NP-hard in the strong sense for two processors, and in general the optimal schedule is not a permutation schedule. In the literature, a restricted problem is considered where one wants to determine the optimal release times for the jobs, once their order is given. We shall solve this m-machine flow shop problem by a greedy placement algorithm. For the two-machine case, where also the optimal order of the jobs has to be determined, several approximate solution methods are analyzed.