BGSA: Broker Guided Service Allocation in Federated Cloud

2021 ◽  
Vol 32 ◽  
pp. 100609
Author(s):  
Sameer Singh Chauhan ◽  
Emmanuel S. Pilli ◽  
R.C. Joshi
Keyword(s):  
Service Allocation

Efficient Multi-Service Allocation for Digital Terrestrial Broadcasting Systems

2014 ◽  
Vol E97.B (9) ◽  
pp. 1977-1983 ◽  
Author(s):  
Bo HAO ◽  
Jun WANG ◽  
Zhaocheng WANG
Keyword(s):  
Service Allocation

SUPPLY CHAIN FORMATION IN OPEN, MARKET-BASED MULTI-AGENT SYSTEMS

2002 ◽  
Vol 02 (03) ◽  
pp. 349-363 ◽  
Author(s):  
ANEURIN M. EASWARAN ◽  
JEREMY PITT
Keyword(s):  
Supply Chain ◽  
Service Providers ◽  
Search Algorithm ◽  
Multiple Criteria ◽  
Simultaneous Optimization ◽  
Open Market ◽  
Multi Agent Systems ◽  
Complex Tasks ◽  
Hierarchical Task Network ◽  
Service Allocation

Efficient allocation of services to form a supply chain to solve complex tasks is a crucial problem. Optimal service allocation based on a single criterion is NP-Complete. Furthermore, complex tasks in general have multiple criteria that may be conflicting and non-commensurable. This paper presents a two-stage brokering algorithm for optimal anytime service allocation based on multiple criteria. In the first stage, a hierarchical task network planner is used to identify the services required to solve a task. In the second stage, a genetic algorithm (GA) determines service providers based on multiple criteria to provide the services identified by the planner. We present our algorithm and results from various experiments conducted to analyze the effect of various parameters that influence the complexity of the problem. In general, the results show the GA finds optimal solutions much quicker than a standard search algorithm. The empirical results also indicate the performance of the algorithm is sub-linear or polynomial time for various parameters. The algorithm has the ability to deal with any number of criteria. By addressing this problem, we expand the range of problems being addressed to any that require simultaneous optimization of multiple criteria and/or planning.

Budget-Constrained Service Allocation Optimization for Mobile Edge Computing

2021 ◽  
pp. 1-1
Author(s):  
Yan Ding ◽  
Kenli Li ◽  
Chubo Liu ◽  
Zhuo Tang ◽  
Keqin Li
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Service Allocation

A Heuristic Approach for Service Allocation in Cloud Computing

2021 ◽  
pp. 904-919
Author(s):  
Ahmad Sharieh ◽  
Layla Albdour
Keyword(s):  
Cloud Computing ◽  
Fuzzy Sets ◽  
Supply And Demand ◽  
Critical Issue ◽  
Allocation Model ◽  
Computing Systems ◽  
Service Allocation ◽  
Fuzzy Input ◽  
Cloud Computing Service ◽  
Cloud User

Cloud computing systems are considered complex systems, because of the various classes of services offered for users and the big challenges for providers to meet the increasing demands. Thus, service allocation is a critical issue in cloud computing. Fuzzy modeling is one choice to deal with such complexity. In this paper, the authors introduce a new heuristic service allocation model for cloud computing service allocation. Fuzzy sets are used to determine a candidate cloud for providing a service and crisp sets are used to serve requests from a cloud. Supply and demand are used as the fuzzy input to provide the desired heuristic allocation model for the candidate cloud, and a set of parameters are used to determine a cloud user needs.

Metaheuristic Approaches to Task Consolidation Problem in the Cloud

2017 ◽  
pp. 168-189 ◽  
Author(s):  
Sambit Kumar Mishra ◽  
Bibhudatta Sahoo ◽  
Kshira Sagar Sahoo ◽  
Sanjay Kumar Jena
Keyword(s):  
Combinatorial Optimization ◽  
Distributed Computing ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Allocation Problem ◽  
Service Allocation ◽  
Multidimensional Knapsack ◽  
Np Hard Problem ◽  
Metaheuristic Techniques

The service (task) allocation problem in the distributed computing is one form of multidimensional knapsack problem which is one of the best examples of the combinatorial optimization problem. Nature-inspired techniques represent powerful mechanisms for addressing a large number of combinatorial optimization problems. Computation of getting an optimal solution for various industrial and scientific problems is usually intractable. The service request allocation problem in distributed computing belongs to a particular group of problems, i.e., NP-hard problem. The major portion of this chapter constitutes a survey of various mechanisms for service allocation problem with the availability of different cloud computing architecture. Here, there is a brief discussion towards the implementation issues of various metaheuristic techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), BAT algorithm, etc. with various environments for the service allocation problem in the cloud.

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