An Optimal Solution to a Resource Allocation Problem under COVID-19 Financial Constraints

2015 ◽

pp. 238-260 ◽

Cited By ~ 1

Author(s):

Dilip Kumar ◽

Bibhudatta Sahoo ◽

Tarni Mandal

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Data Centers ◽

Optimal Solution ◽

Solution Space ◽

Allocation Problem ◽

Resource Allocation Problem ◽

Cloud Infrastructure ◽

Two Phase ◽

Phase Selection

The energy consumption in the cloud is proportional to the resource utilization and data centers are almost the world's highest consumers of electricity. The complexity of the resource allocation problem increases with the size of cloud infrastructure and becomes difficult to solve effectively. The exponential solution space for the resource allocation problem can be searched using heuristic techniques to obtain a sub-optimal solution at the acceptable time. This chapter presents the resource allocation problem in cloud computing as a linear programming problem, with the objective to minimize energy consumed in computation. This resource allocation problem has been treated using heuristic approaches. In particular, we have used two phase selection algorithm ‘FcfsRand', ‘FcfsRr', ‘FcfsMin', ‘FcfsMax', ‘MinMin', ‘MedianMin', ‘MaxMin', ‘MinMax', ‘MedianMax', and ‘MaxMax'. The simulation results indicate in the favor of MaxMax.

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Adaptive OFDMA Resource Allocation using Modified Multi-Dimension Genetic Algorithm

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.12.1.11 ◽

2016 ◽

Vol 12 (1) ◽

pp. 103-113 ◽

Cited By ~ 1

Author(s):

Mohammed Ibrahim ◽

Haider AlSabbagh

Keyword(s):

Genetic Algorithm ◽

Resource Allocation ◽

Power Allocation ◽

Communication Systems ◽

Optimal Solution ◽

Solution Space ◽

Allocation Problem ◽

Base Stations ◽

Resource Allocation Problem ◽

Effective Parameters

A considerable work has been conducted to cope with orthogonal frequency division multiple access (OFDMA) resource allocation with using different algorithms and methods. However, most of the available studies deal with optimizing the system for one or two parameters with simple practical condition/constraints. This paper presents analyses and simulation of dynamic OFDMA resource allocation implementation with Modified Multi-Dimension Genetic Algorithm (MDGA) which is an extension for the standard algorithm. MDGA models the resource allocation problem to find the optimal or near optimal solution for both subcarrier and power allocation for OFDMA. It takes into account the power and subcarrier constrains, channel and noise distributions, distance between user's equipment (UE) and base stations (BS), user priority weight – to approximate the most effective parameters that encounter in OFDMA systems. In the same time multi dimension genetic algorithm is used to allow exploring the solution space of resource allocation problem effectively with its different evolutionary operators: multi dimension crossover, multi dimension mutation. Four important cases are addressed and analyzed for resource allocation of OFDMA system under specific operation scenarios to meet the standard specifications for different advanced communication systems. The obtained results demonstrate that MDGA is an effective algorithm in finding the optimal or near optimal solution for both of subcarrier and power allocation of OFDMA resource allocation.

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Heuristic Task Consolidation Techniques for Energy Efficient Cloud Computing

Web-Based Services ◽

10.4018/978-1-4666-9466-8.ch034 ◽

2016 ◽

pp. 760-782 ◽

Cited By ~ 1

Author(s):

Dilip Kumar ◽

Bibhudatta Sahoo ◽

Tarni Mandal

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Data Centers ◽

Optimal Solution ◽

Solution Space ◽

Allocation Problem ◽

Resource Allocation Problem ◽

Cloud Infrastructure ◽

Two Phase ◽

Phase Selection

The energy consumption in the cloud is proportional to the resource utilization and data centers are almost the world's highest consumers of electricity. The complexity of the resource allocation problem increases with the size of cloud infrastructure and becomes difficult to solve effectively. The exponential solution space for the resource allocation problem can be searched using heuristic techniques to obtain a sub-optimal solution at the acceptable time. This chapter presents the resource allocation problem in cloud computing as a linear programming problem, with the objective to minimize energy consumed in computation. This resource allocation problem has been treated using heuristic approaches. In particular, we have used two phase selection algorithm ‘FcfsRand', ‘FcfsRr', ‘FcfsMin', ‘FcfsMax', ‘MinMin', ‘MedianMin', ‘MaxMin', ‘MinMax', ‘MedianMax', and ‘MaxMax'. The simulation results indicate in the favor of MaxMax.

Download Full-text