Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Introduction to stochastic programming

http://www.parkjonghyuk.net/lecture/2015-1st-lecture/networksecurity/Paper2.pdf

Security in cloud computing

In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer's future demand and providers' resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing environments.

Optimization of Resource Provisioning Cost in Cloud Computing

Cloud computing provides users an efficient way to dynamically allocate computing resources to meet demands. Cloud providers can offer users two payment plans, i.e., reservation and on-demand plans for resource provisioning. Price of resources in reservation plan is generally cheaper than that in on-demand plan. However, since the reservation plan has to be acquired in advance, it may not fully meet future demands in which the on-demand plan can be used to guarantee the availability to the user. In this paper, we propose an optimal virtual machine placement (OVMP) algorithm. This algorithm can minimize the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment under future demand and price uncertainty. OVMP algorithm makes a decision based on the optimal solution of stochastic integer programming (SIP) to rent resources from cloud providers. The performance of OVMP algorithm is evaluated by numerical studies and simulation. The results clearly show that the proposed OVMP algorithm can minimize users' budgets. This algorithm can be applied to provision resources in emerging cloud computing environments.

Optimal virtual machine placement across multiple cloud providers

Amazon Elastic Compute Cloud (EC2) provides a cloud computing service by renting out computational resources to customers (i.e., cloud users). The customers can dynamically provision virtual servers (i.e., computing instances) in EC2, and then the customers are charged by Amazon on a pay-per-use basis. EC2 offers three options to provision virtual servers, i.e., on-demand, reservation, and spot options. Each option has different price and yields different benefit to the customers. Spot price (i.e., price of spot option) could be the cheapest, however, the spot price is fluctuated and even more expensive than the prices of on-demand and reservation options due to supply-and-demand of available resources in EC2. Although the reservation and on-demand options have stable prices, their costs are mostly more expensive than that of spot option. The challenge is how the customers efficiently purchase the provisioning options under uncertainty of price and demand. To address this issue, two virtual server provisioning algorithms are proposed to minimize the provisioning cost for long- and short-term planning. Stochastic programming, robust optimization, and sample-average approximation are applied to obtain the optimal solutions of the algorithms. To evaluate the performance of the algorithms, numerical studies are extensively performed. The results show that the algorithms can significantly reduce the total provisioning cost incurred to customers.

Cost Minimization for Provisioning Virtual Servers in Amazon Elastic Compute Cloud

Green computing is a new paradigm of designing the computer system which considers not only the processing performance but also the energy efficiency. Power management is one of the approaches in green computing to reduce the power consumption in distributed computing system. In this paper, we first propose an optimal power management (OPM) used by a batch scheduler in a server farm. This OPM observes the state of a server farm and makes the decision to switch the operation mode (i.e., active or sleep) of the server to minimize the power consumption while the performance requirements are met. An optimization problem based on constrained Markov decision process (CMDP) is formulated and solved to obtain an optimal decision of OPM. Given that OPM is used in the server farm, then an assignment of users to the server farms by a job broker is considered. This assignment is to ensure that the cost due to power consumption and network transportation is minimized. The performance of the system is extensively evaluated. The result shows that with OPM the job waiting time can be maintained below the maximum threshold while the power consumption is much smaller than that without OPM.

Optimal Power Management for Server Farm to Support Green Computing

Cloud providers can offer cloud consumers two plans to provision resources, namely reservation and on-demand plans. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, this resource provisioning is challenging due to the uncertainty. For example, consumers' demand and providers' resource prices can be fluctuated. Moreover, inefficiency of resource provisioning leads to either overprovisioning or underprovisioning problem. In this paper, we propose a robust cloud resource provisioning (RCRP) algorithm to minimize the total resource provisioning cost (i.e., overprovisioning and underprovisioning costs). Various types of uncertainty are considered in the algorithm. To obtain the optimal solution, a robust optimization model is formulated and solved. Numerical studies are extensively performed in which the results show that the solution obtained from the RCRP algorithm achieves both solution-and model-robustness. That is, the total resource provisioning cost is close to the optimality (i.e., solution-robustness), and the overprovisioning and underprovisioning costs are significantly reduced (i.e., model-robustness).

Sivadon Chaisiri

Papers

Optimization of Resource Provisioning Cost in Cloud Computing

Optimal virtual machine placement across multiple cloud providers

Cost Minimization for Provisioning Virtual Servers in Amazon Elastic Compute Cloud

Optimal Power Management for Server Farm to Support Green Computing

Robust cloud resource provisioning for cloud computing environments