Resource Allocation Method for Cloud Computing Environments with Different Service Quality to Users at Multiple Access Points

Abstract

In a cloud computing environment with multiple data centers over a wide area, it is highly likely that each data center would provide the different service quality to users at different locations. It is also required to consider the nodes at the edge of the network (local cloud) which support applications such as IoTs that require low latency and location awareness. The authors proposed the joint multiple resource allocation method in a cloud computing environment that consists of multiple data centers and each data center provides the different network delay. However, the existing method does not take account of cases where requests that require a short network delay occur more than expected. Moreover, the existing method does not take account of service processing time in data centers and therefore cannot provide the optimal resource allocation when it is necessary to take the total processing time (both network delay and service processing time in a data center) into consideration in resource allocation. This paper proposes to enhance the existing joint multiple resource allocation method, so as to provide the following two functions: (1) a function to prevent the degradation in service quality of other request types when requests that require a short network delay occur more than expected, and (2) a function to take account of the total processing time of network delay and service processing time in allocating resources. It is demonstrated by simulation evaluations that the enhanced method can handle up to twice as many requests as the existing method with the same amount of resources, and can cope with the excessive generation of requests from the specific access point.

Evaluation of Congestion Control Methods for Joint Multiple Resource Allocation in Cloud Computing Environments

“…Moreover, the authors have proposed the control method to cope with the excessive generation of requests from a specific access point (which results in the degradation in service quality of requests from other access points) [20]. This paper clarifies the effective areas of the proposed method compared with other two possible control methods.…”

“…Finally, Section 5 gives the conclusions. This paper is an extension of the study presented in References [19] and [20].…”

“…One is to postpone the service completion time by delaying resource allocation, and the other is to reduce the size of required resource for congested resource type. This paper also tries to clarify the effective areas of three control methods, discussed in Reference [20], to prevent the degradation in service quality of requests from other access points when requests from a specific access point occur more than expected. Figure 1 illustrates the system model for cloud computing environments.…”

“…Resource allocation for a cloud computing environment has been studied very extensively in References [6]- [20]. References [6] and [7] have proposed the market-oriented allocation of resources including auction method.…”

“…It was demonstrated by simulation evaluations that the first method is suitable for services in which a minimum size of resource needs to be allocated or for services in which there are many requests that do not require a large extension of the duration of resource allocation. Reference [20] has proposed the cloud resource allocation which supposes multiple heterogeneous resource-attributes for each resourcetype. For example, resource-attributes of bandwidth are network delay time, packet loss probability, required electric power capacity, etc.…”