As tenants take networked virtual machines (VMs) as their requirements, effective placement of VMs is needed to reduce the network cost in cloud data centers. The cost is one of the major concerns for the cloud providers. In addition to the cost caused by network traffics (N-cost), the cost caused by the utilization of physical machines (PM-cost) is also non-negligible. In this paper, we focus on the optimized placement of VMs to minimize the cost, the combination of N-cost and PM-cost. We define N-cost by various functions, according to different communication models. We formulate the placement problem, and prove it to be NP-hard. We investigate the problem from two aspects. Firstly, we put a special emphasis on minimizing the N-cost with fixed PM-cost. For the case that tenants request the same amount of VMs, we present optimal algorithms under various definitions of N-cost. For the case that tenants require different numbers of VMs, we propose an approximation algorithm. Also, a greedy algorithm is implemented as the baseline to evaluate the performance. Secondly, we study the general case of the VM placement problem, in which both N-cost and PM-cost are taken into account. We present an effective binary-search-based algorithm to determine how many PMs should be used, which makes a tradeoff between PM-cost and N-cost. For all of the algorithms, we conduct theoretical analysis and extensive simulations to evaluate their performance and efficiency.

Loosely coupled and highly cohesived microservices running in containers are becoming the new paradigm for application development. Compared with monolithic applications, applications built on microservices architecture can be deployed and scaled independently, which promises to simplify software development and operation. However, the dramatic increase in the scale of microservices and east-west network traffic in the data center have made the cluster management more complex. Not only does the scale of microservices cause a great deal of pressure on cluster management, but also cascading QoS violations present a substantial risk for SLOs (Service Level Objectives). In this paper, we propose a Microservice-Oriented Topology-Aware Scheduling Framework (MOTAS), which effectively utilizes the topologies of microservices and clusters to optimize the network overhead of microservice applications through a heuristic graph mapping algorithm. The proposed framework can also guarantee the cluster resource utilization. To deal with the dynamic environment of microservice, we propose a mechanism based on distributed trace analysis to detect and handle QoS violations in microservice applications. Through real-world experiments, the framework has been proved to be effective in ensuring cluster resource utilization, reducing application end-to-end latency, improving throughput, and handling QoS violations.

Intelligent terminal devices (TDs) usually request delay-sensitive and resource-demanding jobs, which are consisted of many sequential tasks. Mobile edge computing (MEC) offloads tasks to edge networks closer to TDs, making up for the lack of long delay response in the cloud, but it has a limited energy supply. Thanks to low-energy TDs also having processing capacity, it is a critical and challenging issue to offload sequential tasks for sustainable computing and reducing carbon emission in a terminal-edge-cloud (TEC) architecture. Existing research on offloading is limited to MEC or cloud-edge coordination environment, and ignores the impact of sequential task ( S-Task ) constraint and service constraint. To bridge the gap, our paper first formulates the jointly optimal S-Task offloading and service deployment ( JOTOSD ) problems objected to maximize the energy utility related to response delay, which is NP-hard and is divided into deployment and offloading sub-problems. Then, we propose a comprehensive offloading and deployment ( COD ) method, including the Break-Point ( BP ) algorithm and the convex programming-based edge offloading ( CVEO ) algorithm under a service deployment strategy provided by an iterative service deployment ( ISD ) algorithm. Simulate results prove that the proposed method can improve by about 20% of energy utility by compared with other heuristic algorithms.

The placement of virtual machines (VMs) on a cluster of physical machines (PMs) is a primary task in clouds. We can benefit a lot from appropriate placement policy, e.g. cost saving. In this paper, we raise a continuous virtual machine placement problem and propose an on-line algorithm to reduce the power consumption of clouds. Generic methods which deal with this problem with greedy manner will result in resource leak, a phenomenon of resource wasting. The direct consequence of this phenomenon is larger number of running PMs, which will cause higher power consumption. We prove it is an NP-complete problem to minimize the power consumption during the placement of VMs while the VM requests reach the clouds continuously. We propose a balanced algorithm with the principle of avoiding resource leak. The balanced algorithm handles the VM requests in real time and guarantees high resource utilization. The number of PMs and power consumption can be cut down as a result of high resource utilization. We evaluate the balanced algorithm via experiments with various simulations. Experimental results show that the balanced algorithm can reduce the number of running PMs effectively, so that, it has a significant efficiency on power saving.

Virtualization is one of the most promising ways to enhance the scalability of data centers. Individual physical machines(PMs) can be carved into multiple virtual machines(VMs) for consolidation. In fact, most data centers are still using static greedy allocation of resources to VMs, regardless of the resource characteristics of both VMs and PMs. In this paper, we address that by optimizing the placement of VMs, we can save the cost of resource usage and improve the performance of applications at the same time. The objective is to minimize the total traffic in a data center. We should consolidate VMs with high inter traffic on the same PM, because VMs on the same PM can communicate using only memory copy. We also need to reduce the number of active PMs to save the power cost. This paper first formulates the VM placement as an optimization problem. Then, we propose a heuristic algorithm based on clustering to deploy VMs on PMs. A greedy algorithm is used for the online scenario. In the end, we experiment on data sets collected from a data center. Results show that our algorithm can efficiently reduce the overall traffic and power cost in the data center.