A Novel Algorithm for Optimizing Multiple Services Resource Allocation

Gawanmeh, Amjad; April, Alain

doi:10.14569/ijacsa.2016.070655

Cited by 4 publications

(2 citation statements)

References 31 publications

(33 reference statements)

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“…With the development of the Internet, resource provider could provide nearly all the resources that users need, such as cloud computing provider provides on-demand resources including storage, memory, bandwidth and so on. Multiresource pricing schemes [24], such as the cost-optimized scheme considering multiple resources [25], have emerged. Meanwhile, since user requests are becoming necessary while providing service, some research has focused on user-demanddriven resource allocation [26], [27], [28].…”

Section: Related Workmentioning

confidence: 99%

Allocating Limited Resources to Protect a Massive Number of Targets Using a Game Theoretic Model

Liu

et al. 2019

Mathematical Problems in Engineering

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Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a tradeoff between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource, is considered in the proposed model. Additionally, a bounded rational behavior model (Quantal Response, QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness.

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Section: Related Workmentioning

confidence: 99%

Allocating Limited Resources to Protect a Massive Number of Targets Using a Game Theoretic Model

Liu

et al. 2019

Mathematical Problems in Engineering

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“…As result, we work on satisfying users' needs by providing resources allocation with lower cost. This cost is computed on the basis of smart solutions in datacenters (best host) according the resource constraints [8]. We provide a distributed resource allocation approach based on two technics: multi agent system (MAS) [17] and distributed constraint satisfaction problems (DCSP) [5], [10], [11], [23].…”

Section: Introductionmentioning

confidence: 99%

Smart Design for Resources Allocation in IoT Application Service based on multi-agent system and CSP

Bali

Tari

Almutawakel

et al. 2020

IJCAI

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In the present paper, we aim at solving two problems; the first problem occurring in the transformation of the IoT devices (sensors, actuators, …) to cloud service. Therefore, we work on maintaining a smooth and efficient data transmission for the cloud and support customer applications like: data sharing, storage and processing. The second problem has two dimensions. In the first dimension, the problem is arisen in the submission of cloudlets (customer requested jobs) to Virtual Machines (VMs) in the hosts. To solve this problem, we propose scheduling algorithm for resource allocation according to the lowest cost and load. In the second dimension, the problem lies in the hosting of new VMs in the hosts. To overcome this problem, we need take into account the loads when housing new VMs in different datacenters. In this work, we suggest a resource allocation approach for services oriented IoT applications. The architecture of this approach is based on two technics: Multi Agent System (MAS) and Distributed Constraint Satisfaction Problems (DCSP). The MAS manages the physical resources, making decision and the communication between datacenters, while DCSP used to simplify the policy of the resources provisioning in Datacenters. Variables and constraints are distributed among multiple agents in different layers. The experimental results show that the efficiency of our approach is manifested in: Average System Load, Cost augmentation Rate and Available Mips. Povzetek: Predlagan je način dodeljevanja virov za storitve v IoT aplikacijah na osnovi večagentnih sistemov (MAS) in zadovoljevanja porazdeljenih omejitev (DCSP).

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Green Evolutionary-Based Algorithm for Multiple Services Scheduling in Cloud Computing

Gawanmeh

Alomari

April

et al. 2018

Advances in Business Information Systems and Analytics

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The era of cloud computing allowed the instant scale up of provided services into massive capacities without the need for investing in any new on site infrastructure. Hence, the interest of this type of services has been increased, in particular, by medium scale entities who can afford to completely outsource their data-center and their infrastructure. In addition, large companies may wish to provide support for wide range of load capacities, including peak ones, however, this will require very higher costs in order to build larger data centers internally. Cloud services can provide services for these companies according to their need whether in peak load capacity of low ones. Therefore, resource sharing and provisioning is considered one of the most challenging problems in cloud based services since these services have become more numerous and dynamic. As a result, assigning tasks and services requests into available resources has become a persistent problem in cloud computing, given the large number of variables, and the increasing types of services, demand, and requirement. Scheduling services using a limited number of resources is problem that has been under study since the evolution of cloud computing. However, there are several open areas for improvements due to the large number of optimization variables. In general, the scheduling of services on available resources is considered NP complete. As a result, several heuristic based methods were proposed in order to enhance the efficiency of cloud systems. Since the problem has several optimization parameters, there are still several improvements that can be done in this area. This chapter discusses the formalization of the problem of scheduling multiple tasks by single user and multiple users, and then presents a proposed solution for each individual case. First, an algorithm is presented and evaluated for optimum schedule that allocates a number of subtasks on a given number of resources; the algorithm was shown to be linear vs. number of users. Then, an algorithm is presented to address the problem of multiple users allocations, each, with multiple subtasks. The algorithm was design using the single user allocation algorithm as a selection function. Since, this problem is known to be NP complete, heuristic based methods are usually used in order to provide better solutions. Therefore, a green evolutionary based algorithm is proposed in order to address the problem of resource allocation with large number of users. In addition, the algorithm presents allocation schedule with better utility, while the execution time is linear vs. different parameters. The results obtained in this work show that it overcomes the outcome of one of the most efficient algorithms presented in this regard that was based on game theory. Further, this method works with no restrictions on the problem parameters as opposed to game theory methods that require certain parameters restrictions on cost vector or compaction time matrix. On the other hand, the main limitation of the proposed algorithm is that it is only applicable to the scheduling problem of multiple tasks that has one price vector and one execution time vector. However, scheduling multiple users, each with subtasks that have their own price and execution time vector, is very complex problem and beyond the scope of this work, hence it will be addressed in future work.

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A Novel Algorithm for Optimizing Multiple Services Resource Allocation

Cited by 4 publications

References 31 publications

Allocating Limited Resources to Protect a Massive Number of Targets Using a Game Theoretic Model

Allocating Limited Resources to Protect a Massive Number of Targets Using a Game Theoretic Model

Smart Design for Resources Allocation in IoT Application Service based on multi-agent system and CSP

Green Evolutionary-Based Algorithm for Multiple Services Scheduling in Cloud Computing

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