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Emerging applications with low-latency requirements such as real-time analytics, immersive media applications, and intelligent virtual assistants have rendered Edge Computing as a critical computing infrastructure. Existing studies have explored the cloudlet placement problem in a homogeneous scenario with different goals such as latency minimization, load balancing, energy efficiency, and placement cost minimization. However, placing cloudlets in a highly heterogeneous deployment scenario considering the next-generation 5G networks and IoT applications is still an open challenge. The novel requirements of these applications indicate that there is still a gap in ensuring low-latency service guarantees when deploying cloudlets. Furthermore, deploying cloudlets in a cost-effective manner and ensuring full coverage for all users in edge computing are other critical conflicting issues. In this article, we address these issues by designing a bifactor approximation algorithm to solve the heterogeneous cloudlet placement problem to guarantee a bounded latency and placement cost, while fully mapping user applications to appropriate cloudlets. We first formulate the problem as a multi-objective integer programming model and show that it is a computationally NP-hard problem. We then propose a bifactor approximation algorithm, ACP, to tackle its intractability. We investigate the effectiveness of ACP by performing extensive theoretical analysis and experiments on multiple deployment scenarios based on New York City OpenData. We prove that ACP provides a (2,4)-approximation ratio for the latency and the placement cost. The experimental results show that ACP obtains near-optimal results in a polynomial running time making it suitable for both short-term and long-term cloudlet placement in heterogeneous deployment scenarios. [ABSTRACT FROM AUTHOR]

Cloud providers provision their heterogeneous resources such as CPUs, memory, and storage in the form of virtual machine (VM) instances which are then allocated to the users. One of the major challenges faced by the cloud providers is to allocate and provision these resources such that their profit is maximized, and the resources are utilized efficiently. Recently, cloud providers have introduced auction-based models which allow users to submit bids for their requested VMs. We address the problem of autonomic VM provisioning and allocation for the auction-based model considering multiple types of resources by designing an approximation mechanism. In addition, the mechanism determines the payment the users have to pay for using the allocated resources. This problem is computationally intractable, and our proposed mechanism is by far the strongest approximation result that can be achieved for this problem. We show that the proposed approximation mechanism is a polynomial-time approximation scheme (PTAS). Furthermore, our proposed mechanism drives the system into an equilibrium in which the users do not have incentives to manipulate the system by untruthfully reporting their VM bundle requests and valuations. We perform extensive experiments using real workload traces in order to investigate the performance of the proposed mechanism. [ABSTRACT FROM AUTHOR]